I. Introduction

Hypothese

II. Literature Review

III. Methodology

IV. Results

V. Discussion

IV. Conclusion

I. Introduction

II. Hemoglobins

III. Sickle Cell Anemia

i. Sickle cell anemia history

ii. Pathophysiology of SCD

IV. Complications

i-insight

A study conducted on 8521 SCD patients (through a follow-up interval of 2.7 years) showed that Vaso — occlusive crises (VOC) are the primary reason behind SCD care utilization and medical contact like ER visits and inpatient admissions where the study recorded an average of 2.79 VOCs per patients in the first follow up year, 0.90 of which were handled in an ER setting. In addition to 0.51 VOCs handled in an outpatient setting, 0.24 VOCs were handled in an office setting, and 0.09 VOCs were handled in other settings such as a pharmacy. necrosis (AVN), acute chest syndromes (ACS), and kidney failure. Further complications include priapism, strokes, leg ulcers, asthma, chronic pulmonary hypertension, and sudden death as well (all of which are "hemolysis-endothelial dysfunction" sub-phenotype-associated complications) [20] . Current medications depend on managing the disease symptoms. However, researchers are working on a lentiviral gene therapy. SCD medication will be covered in a later section. Before that, the most important complications need to be addressed in more detail.

In the first year of follow-up, 3,493 ER visits and 1,705 hospital visits related to VOC episodes and SCD complications were identified. For about 85% of those ER visits and hospitalizations, VOCs were the primary reason for admission, while the rest 15% had SCD complications as the primary reason. Considering the four SCD genotypes, HbSS and HbSβ^0^-thalassemia are known to be the most severe ones and are termed as sickle cell anemia (SCA), while HbSC and HbSβ^+^ are more benign (severity variation from one case to another must be considered) [7]. Even patients with milder SCD forms typically suffer from painful crises in addition to other severe complications including avascular

ii-Cardiovascular complications

SCD is a vascular disorder, mainly driven by interactions between endothelium and the blood formed elements including sickled erythrocytes, leukocytes, inflammatory proteins, ...... etc. This leads to VOC episodes and severe organ damage. Cardiovascular and endothelial changes driven by SCD differ a bit from those of non-hemolytic anemia. In patients suffering from anemia, the cardiac output (CO) increases to compensate for the low oxygen-carrying capacity of blood, and the cardiac index rises proportionally according to the severity of anemia. This higher output is sustained by higher stroke volume rather than elevating the heart rate or the preload. The decrease in vascular resistance accounts for this increase in stroke volume. Vasodilation of resistance arterioles, exploiting previously dormant cutaneous and muscular vessels besides new vessel growth initiated by hypoxia, and reduced blood viscosity are mainly responsible for this resistance drop. Elevated stroke volume and dropped vascular resistance give rise to other physiological changes including the renin- angiotensin-aldosterone system stimulation, salt and water retention by the kidney, and a volume overload state. Consequently, chronic volume overload triggers the dilation of all cardiac chambers, developing eccentric hypertrophy as a reaction to increased wall stress [20] . In SCD, a significant drop in vascular resistance associated with an obvious rise in cardiac index (CI = CO / body surface area) are noticed compared to those of anemia, suggesting additional factors contributing to this, potentially ischemia-induced new vessel growth along with hemolysis induced inflammation. CI gives an insight into the function of the right ventricle and dropped CIs < 2.0 L/min/m2 are associated with a high risk (10%) of death within a year [15]. Moreover, SCD patients generally suffer from cardiomegaly along with increased left ventricular end-diastolic diameter and left ventricular mass index.

Mainly, SCD patients developing pulmonary arterial hypertension (PAH (precapillary disease)) have progressive elevation in pulmonary vascular resistance, smooth muscle, and intimal proliferation, and in situ thrombosis as a common cause, eradicating pulmonary arterioles and resulting in a progressive heart failure in addition to reduced exercise capacity [3]. PAH is defined according to an average pulmonary artery pressure ≥ 25 mm Hg (with 15 mm Hg as the normal value), along with a left ventricular end-diastolic pressure ≤ 15 mm Hg and a pulmonary vascular resistance value ≥ 3 Wood Units. On the other hand, pulmonary venous hypertension is a result of an increase in the pressure downstream of the pulmonary arterioles and capillaries, usually associated with elevated left heart filling pressures because of diastolic or systolic heart failure. These two hemodynamic forms of PH are among the most common complications encountered by SCD patients.

iii- Acute Splenic Sequestration Crisis (ASSC)

V. Medications

i-Hydroxyurea

Unfortunately, some SCD complications don't show improvement and may even deteriorate more during HU therapy, including childhood avascular necrosis and priapism. Moreover, 25-30% of patients suffering from SCD have suboptimal or no response at all to HU treatment [14]. Furthermore, the HUSTLE trial reported long-term toxicities in children. Also, myelosuppression is a common HU adverse effect among patients intaking higher doses and older patients. To reduce its probability of occurrence, the minimum effective dose is recommended. HU also has negative effects on spermatogenesis as it reduces sperm count and motility which normally don't return to normal even after giving off HU intake. Also, azoospermia was reported in patients treated with HU [4].

ii- Lentiviral Gene therapy

VIII. Conclusion

I. Introduction

II. Quantum Physics

A. Schrödinger's Equation

B. The Hamiltonian Approximation

C. The Hubbard Model

III. Quantum Computing

A. Jordan Wigner-Mapping

B. Tensor Network Optimization

Now that we can convert our Hamiltonian into quantum computing language, we must face the fundamental problem of optimizing a quantum circuit: efficiently exploiting parameters. We can solve this through differentiable programming given hardware constraints by minimizing the loss function that encodes the problem we want to solve, $$ \alpha_{t+1} = \alpha_t \; - \eta \nabla_\alpha \mathcal{L} (\alpha, t) $$ (3.2.1) where $\alpha$ are the parameters, $\eta$ the learning rate, and $\nabla \mathcal{L}$ the gradient operator on the loss function. This loss function can be largely responsible for the feedback on how well a quantum circuit is performing given a set of parameters, updating it each time for optimization, and is commonly used in machine learning. We can use tensor networks (TN), useful mathematical, graphical representations of multi-dimensional arrays that can store information through tensor nodes in a computation graph. We can significantly enhance optimization by exploiting automatic differentiation in the tensor computation graph. This technique of backpropagation exploits the chain rule of a partial differential to propagate the gradient back from the network output and calculate the gradient of the weight respectively [19] . With the forward pass being $$ |\psi\rangle=U(\alpha)|\psi_0\rangle \newline \langle O \rangle = \langle \psi |O|\psi \rangle $$ (3.2.2) where $U(\alpha)$ representing a quantum circuit with parameters $\alpha$ acting on initial state $| \psi_0 \rangle$ to produce output state $| \psi \rangle$ , followed by the expectation value of $O$ , a physical observable that could be represented by a Hermitian operator. We can begin to see the similarities with the original system quantum evolution representation for application, $$ \psi(t)=e^{-iHt} \psi(0). $$ (3.2.3) From this, the implications of applying backpropagation through the expectation value computation to get gradients with respect to circuit parameters tying to the simulation of quantum systems are obvious. After this forward pass, we can backpropagate (backwards pass) this through the chain rule and see the loss function $L$ depending on the expectation value $\langle O \rangle$, $$ \frac{\partial L}{\partial \alpha} = \frac{\partial L}{\partial \langle O \rangle} = \frac{\partial \langle O \rangle}{\partial \alpha} $$ (3.2.4) Representing this in tensor networks through the traversing of data flow as in nodes, we can visually see the chain rule and reverse-mode automatic differentiation [20] , $$ \displaylines{ \alpha \rightarrow W^1 \rightarrow W^2 \ldots W^n \rightarrow L \newline \frac{\partial L}{\partial \alpha} = \frac{\partial L}{\partial W^n} \frac{\partial W}{\partial W^{n-1}} \ldots \frac{\partial W^1}{\partial \alpha}. \newline \leftarrow \leftarrow \leftarrow \leftarrow \leftarrow \leftarrow \leftarrow } $$ (3.2.5)

C. Automatically Differentiable Quantum Circuits Tensor Networks (ADQC-TN)

IV. Applications

A. Variational Quantum Eigensolver (VQE)

B. Modeling

V. Future Research

VI. Discussion

VII. Conclusion

I. Introduction

i. Inflammatory Diet

ii. Neurotransmitters

iii. Alzheimer's disease

II. Methods

(I) Role of Amyloid- β in Alzheimer's disease pathogenesis

Amyloid β (Aβ) is considered a critical reason for the development of Alzheimer's disease (AD) the senile plaques (SPs) and intracellular neurofibrillary tangles (NFTs), which results in the loss of neurons and synapses. The SPs are formed by aggregation of (Aβ) as some studies propose that it is a small protein composite of 39-43 amino acids [18] . At (B) phase in Figure 1: represents the two cleavage processes of Amyloid precursor protein (APP), it is a transmembrane glycoprotein with a large luminal domain and a short cytoplasmic domain. In processing of cleaving the (APP) and Aβ formation, APP could go through either an amyloidogenic or non-amyloidogenic pathway [18] .

i. Biochemistry of Amyloid β-Protein and Amyloid Deposits in Alzheimer's Disease

Amyloid (A) is a 39-43 residue amyloidogenic peptide that is deposited in the extracellular amyloid plaques [6] , whose 1-letter code sequence is shown in Figure 2. That defines the brain of people with Alzheimer's disease (AD). On chromosome 21, there is the gene for amyloid beta precursor protein. Moreover, it is formed by the enzymatic cleavage of APP, the Amyloid Precursor Protein, a type-1 transmembrane protein produced in many organs, particularly the central nervous system (CNS) [6] .

According to some studies on modern research on the fundamental mechanism of Alzheimer's disease (AD). The meningovascular amyloid subunit in Down's syndrome brains was the same "β-protein," as they had dubbed it. Glenner drew attention to this evidence of a crucial biochemical link between Down's syndrome and AD, a theory he had championed in a foresighted paper in Medical Hypotheses as early as 1979 (Glenner 1979) [15] . Glenner reasoned that since trisomy 21 caused an accumulation of Alzheimer-type A in arteries and plaques, familial AD may well be caused by a deficiency in the -protein precursor on this chromosome. Glenner omitted to mention the variability of familial types of AD and the possibility that many cases may not be genetically determined, at least at the time. Figure 3 represents a Confocal microscopy image of a neurotic (senile) plaque in the cortex of an Alzheimer's patient that has been three- dimensionally rebuilt. Amyloid -protein is colored red by an antibody to identify extracellular amyloid, and tau is colored green by an antibody to reveal closely related dystrophic neurites. The plaque core, it should be noted, is not a single, porous mass of amyloid, but rather is broken up and contains aberrant cell processes intercalated therein [8] .

ii. Amyloid beta and acetylcholine

Figure 4 shows the Targets of Aβ that modulate cholinergic transmission: (1) Aβ reduces the activity of pyruvate dehydrogenase, an enzyme that generates acetyl coenzyme A (CoA) from pyruvate, (2) Aβ reduces high-affinity uptake of choline; (3) long-term or high-dose exposure to Aβ reduces activity of the choline acetyltransferase (ChAT) enzyme; (4) Aβ reduces acetylcholine (ACh) content; (5) Aβ reduces ACh release from synaptic vesicles (SV); (6) Aβ impairs muscarinic M1-like signaling. AChE = acetylcholinesterase, Ch U = site of choline uptake, M2 = presynaptic muscarinic M2 receptor, N = presynaptic nicotinic receptor, PtdCho = phosphatidylcholine.[16] The studies of APP and Aβ [17] , suggest the notion that overexpression levels of Aβ peptide, and the aggregation of Aβ that cause subcellular alterations or neuronal loss in selected brain regions. The great levels of Aβ peptide may incentivize the formation of neurofibrillary tangles in tau in some experiments on mice[18] . Furthermore, these results suggest that Aβ peptides have a role in the neurodegenerative process in which the nerve cell loses its function and dies.

III. Results

i. Vegetarian diet lack of vitamins associated with AD.

IV. Discussion

ii. Oxidative stress and the amyloid beta peptide in Alzheimer's disease

Oxidative stress is known to play an important role in the pathogenesis of a number of diseases [7] . It is specifically associated with the etiology of Alzheimer's disease (AD), an age-related neurodegenerative illness that is the leading cause of dementia in the elderly. Furthermore, AD is characterized by intracellular neurofibrillary tangles and extracellular production of senile plaques formed of aggregated amyloid-beta peptide (A) and metal ions such as copper, iron, or zinc [5] [6] . Active metal ions, for example, copper, can speed up the production of Reactive Oxygen Species (ROS) when bound to the amyloid-β (Aβ). Thus, ROS can be produced in the hydroxyl radical, which is the most reactive one, which may lead to oxidative damage on both the Aβ peptide and surrounding molecules (proteins, lipids, etc. ) [7] .

iii. Oxidative stress and diet

V. Conclusion

I. Introduction

II. The Physical Processes of Formation and

i. Gravity

ii. Hydrodynamics and Thermal Evolution

iii. Star Formation

iv. Star Formation Feedback

v. Black Hole Formation and Growth

vi. Active Galactic Nuclei (AGN) Feedback

vii. Stellar Populations and Chemical Evolution

viii. Radiative Transfer

III. Current Simulation Frameworks

i. The Semi-analytic Framework

ii. The N-body/Numerical Hydrodynamics Framework

iii. The Lambda Cold Dark Matter (ΛCDM) Framework

IV. Semi-analytic Models

V. Numerical Hydrodynamic Models

VI. methods

VII. Conclusion

VIII. Acknowledgment

I. Introduction

II. AI and Electrocardiograms

i. The potential of utilizing AI in CVD diagnosis

Wear-able devices

Risk Prediction Models

ii. Electrocardiograms: properties and advantages.

Before delving into Electrocardiograms and their properties, a basic understanding of the heart must be achieved. The human heart operates mostly by intrinsic electric impulses. These impulses first arise in the sinoatrial (SA) node, located at the top of the heart's upper-right chamber (the right atrium), which is also known as the heart's "natural pacemaker." These impulses flow through the heart through a process known as conduction where the impulses travel to the ventricles causing their contraction in a phenomenon known as ventricular contraction. This contraction is considered a representation of one heartbeat. In normal cases with no abnormalities, this rhythm is recorded as a sinus rhythm and is considered the basic rhythm of the heart. This previously mentioned electrical activity can be recorded via a device known as an electrocardiogram. This device mainly records the starting point of these impulses and their conduction through the heart. An ECG is mainly administered to patients suffering from symptoms of CVDs such as blackouts or strokes as they're usually caused by an irregular heart rhythm. Electrocardiograms have various types depending on the condition that's being checked for. The most important types are the stress test, which monitors the heart during exercise to detect CVDs such as coronary artery disease; the Holter monitor, which monitors for longer periods; and the resting 12-lead ECG, which is used in a resting state and is considered the optimal type of ECGs. To record an ECG, electrodes must be inserted in the limps and chest to record different views of the heart. These views are called leads and the number of leads is not equal to the number of electrodes. For a full picture of the heart, a 12-lead ECG is optimal which is why 12-lead ECG tests are preferred. To interpret the reading of an ECG, some basics must be understood. The ECG visualizes each ventricular contraction (heartbeat) by one ECG complex as shown in figure 1.

There are 5 main points in an ECG complex those being "P", "Q", "R", "S", and "T". The "P" wave shown here is a representation of the electrical activation of arterial muscle. The PR interval is the amount of time needed for the impulse to travel from the artery to the ventricle. The QRS complex symbolizes the spread of the impulse causing ventricular contraction. The ST interval showcases the full activation of the ventricles, while the "T" wave shows the return of the ventricles to a resting electrical state. Without any abnormalities, a normal beat should be a succession of one P wave, a QRS complex, and finally a T wave. The way those waves and intervals are displayed can tell a lot about the condition of the heart. For example, if the QRS complexes are compressed together, this indicates a higher heart rate. They can also indicate the rhythm of the heart based on how consistent QRS complexes are. Ideally, the reading of a healthy heart via ECG should look like figure 2.

iii. Types of deep-learning models used in ECG analysis.

• Convolutional Neural Network (CNN):
CNNs represent a class of deep neural networks (DNNs) that are widely applied for image classification, natural language processing, and signal analysis. A standard CNN is composed of several convolutional layers followed by a batch normalization layer, nonlinear activation layer, dropout layer, pooling layer, and classification layer [19] . Section Ⅲ will focus on CNNs in detail.
• Recurrent Neural Network (RNN):
It has been widely used to solve tasks of processing time series data, speech recognition, and image generation, and recently, ECG signal denoising and ECG classification. RNNs, including variants like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are suitable for sequence data like ECGs. They can capture temporal dependencies and patterns in ECG waveforms, making them useful for tasks such as heart rate prediction, rhythm classification, and anomaly detection. A typical RNN includes an input layer, a hidden layer, and an output layer, where at each time step, the RNN receives an input, updates its hidden state, and makes a prediction. While RNN is highly suitable for short-term dependent problems, it is ineffective in dealing with long-term dependent problems. That's why the types mentioned long short-term memory (LSTM) and gated recurrent unit (GRU) were introduced to overcome the shortcomings of RNN [20] [21].
• Multilayer Perceptron (MLP):
The most popular supervised neural network, MLP, is successful in learning complex systems. Despite being variable, the MLP architecture consists of numerous layers of neurons coupled to one another in a feed-forward manner [20] .
• Generative Adversarial Networks (GANs):



This type of model consists of two sub- models: a generative model G that captures the data distribution of a training dataset in a latent representation and a discriminative model D that calculates the probability that a sample generated by the generator comes from the true data distribution [21] . It can be useful in data augmentation or simulating abnormal conditions for training and testing classifiers. figure 5 illustrates the architecture of the GAN.
• Deep Belief Network (DBN):
DBN is a powerful learning model used to model evolving random variables over time. It is composed of multiple Restricted Boltzmann Machine (RBM) layers. The function of each RBM in a layer is to receive the inputs of the previous layer and feed the RBM in the next layer [20] . Figure 6 shows a brief comparison between DL models:

III. Convolutional AI in ECG Analysis:

i. Convolutional neural networks (CNNs) and their applications.

• Convolutional layer:
The convolutional layer plays a vital role in the operation of CNNs. It is the main building block that determines the output from the given input. This output is achieved through a feature detector, which is known as a kernel. Before understanding what a kernel does, it should be taken into consideration that any digital image consists of a matrix of pixel values from 0 to 255 (channel), where zero corresponds to black color and 255 to white color. In a typical digital camera, an image consists of 3 of these channels, each one corresponding to one of the RGB colors (red, green, blue). A kernel is a matrix with initial values. When the data hits the convolutional layer, the layer convolves the filters over the height and width of the information data, and while that it computes the dot product between the input and filter values of each matrix (which are the initial values of the kernel), therefore building a 2-D activation map of that filter [22] [23]. Figure 8 visualizes this process. "From this, the network will learn kernels that 'fire' when they see a specific feature at a given spatial position of the input, which is known commonly as activations" [23] . Each kernel will have an associated activation map, which will be stacked along the depth dimension to create the convolutional layer's whole output volume [23] .



• Pooling layer:
The main aim of this layer is to reduce the dimensionality of the maps, by keeping the most important parts and discarding the rest, therefore decreasing the parameters, the time complexity of the model, and the probability of overfitting [23] . In this stage, each activation map in the input undergoes scaling its dimensionality using the "MAX" function by the pooling layer. Max-pooling layers are the most common pooling layers, where they have kernels of a dimensionality of 2 × 2. "This scales the activation map down to 25% of the original size - whilst maintaining the depth volume to its standard size" [23] .
• Fully connected layer (FC):
The FC layer is a typical deep NN, where it consists of directly connected layers of neurons, with no other layers connected in between them [23] . In other words, each neuron in each layer is connected directly to each neuron in the two adjacent layers to this layer. The aim of this layer is to build predictions from the activations to be classified into categories and to associate features to each particular label. Figure 9 shows a simple CNN architecture.

ii. Discuss how CNNs are adapted for ECG analysis.

Detecting Myocardial Infarction (or heart attacks) using CNNs:

iii. Benefits of CNNs in ECG analysis

IV. Autocardiogram necessity

i. Integration with new technologies: body sensors, MRI, echo, and more

ii. AI-analyzed ECGs: Accurate decision-making and complications prediction

V. Conclusion

I. Introduction

II. Early studies

i. Pre-2000 studies on the presence of exoplanetary auroras

ii. Post-2000 studies on the presence of exoplanetary auroras

III. Auroras

i. Auroral formation mechanisms

With the assistance of oxygen and nitrogen atoms 20 to 200 miles above the surface, the Sun's particles ultimately generate their stunning light display after traveling 93 million miles across the galaxy. The atomic constituents get activated and radiate light when they make contact with the atmospheric composition at the atomic level. Hence, this process is known to be the "Excitation of Atmospheric Gases" [16] . "Relaxation and Emission of Light" occurs when the hues of the sky are influenced by the photon's wavelength from an atom. The tones of sapphire and scarlet are a byproduct of the excitation of nitrogen atoms, while the shades of lime and crimson are yielded through the excitation of oxygen atoms [17] .

A magnetic field significantly impacts the interaction between a planet and the solar wind, affecting the global distribution of energy dissipated into atmospheric gases. A planet without an intrinsic magnetic field may appear more exposed to atmospheric ablation, but the net loss may be comparable with or without a magnetic field. The presence of a magnetic field concentrates electro- mechanical energy dissipation at the planetary end of flux tubes, which link with the boundary layer between the solar wind and its magnetic field and the planetary magnetic field and its enclosed plasma. This region accelerates charged particles, generating the aurora [19] . The high-latitude ionosphere-magnetosphere region is a rich and interesting area in the space plasma universe, accessible for direct measurements. Its energetic processes contribute to the expansion and escape of the ionosphere beyond geospace's outer regions. Figure 2 illustrates the range of processes driving these outflows upward and outward [19] .

Ionospheric outflows from high-latitude ionosphere- lower magnetosphere generation regions flow on magnetic field lines into magnetospheric regions like the tail lobes and plasma sheet. If these outflows remained on polar lobe field lines, they would be lost from the magnetosphere. However, they join the convective circulation of plasma in the magnetosphere as they expand out of the ionosphere. These emerging ionospheric plasmas flow across the magnetic field into the closed field line region of the plasma sheet, carrying the current responsible for the stretched magnetotail. Ionospheric flows from nightside auroral regions are injected directly into the plasma sheet, which is the primary source of plasmas for the quasi-trapped hot plasmas of the inner magnetosphere. These ionospheric plasmas populate the ring current in considerable quantities. (Figure 3) [19] .

ii. Types of Auroras

• A diffuse aurora is a widespread and faint type of auroral display resulting from the scattering of charged particles along the Earth's magnetic field lines, creating a gentle and uniform glow in the night sky. They lack distinct shapes and patterns. Typically, they are seen during periods of low geomagnetic activity. The term "diffuse" describes the spread-out nature of the light emitted by this type of aurora [20] .
• Discrete auroras are well-defined and localized bright regions within the auroral display, and they are characterized by distinct arcs or bands of concentrated light in the sky. Some researchers even reported occasional mysterious sounds. Unlike other types of auroras, discrete ones have ribbon-like structure which shapes them into intricate shapes. This term describes the specific, clearly visible features of auroras that stand out from the overall auroral glow [20] .
• An "Alfvén Aurora" occurs when Alfvén waves, which are plasma waves with a component parallel to the magnetic field, interact with charged particles, particularly electrons. This interaction leads to the acceleration of electrons and contributes to the formation of auroral emissions. Alfvén auroras were first proposed as a theoretical concept in the late 20^th^ century and were several years later observed and confirmed through satellite and ground-based observations. They have unique features such as vertical stripes and dynamic movements. The term "Alfvénic" is derived from Hannes Alfvén, a Swedish physicist who made a significant contribution to the studying the formation of auroral emissions [20] .

iii. Aurora properties

• Shape: Auroras often appear as waves with smooth arcs or chaotic patterns due to three factors which are convergence of magnetic field lines towards magnetic poles, collision of charged particles with gas molecules, and atmospheric density [21] .
• Wavelength color relation: The different colors of aurora are produced when different atoms and molecules are excited to various energy levels. For instance, the most common color pale green can be seen at the wavelength of approximately 5500 nm as a result of oxygen having been excited to the Singlet S state, whereas another less common color deep red is visible at the wavelength of around 6250 nm as a result of oxygen having been excited to the Single D state [22] .
• Activity: During periods of high solar activity, such as solar flares and coronal mass ejections, auroras can become more vivid and occur at lower latitudes than usual [23] [24] .
• Sounds: People have occasionally described hearing tremulous sounds or crackling noises connected to auroras. These noises are thought to be caused by electromagnetic waves from the auroras interacting with the magnetic field and atmosphere of the Earth. Further, the experiments should be repeated to make sure the sounds come from auroras [18] .

IV. Aurora simulations

The Japanese satellite Hinode conducted an experiment to establish initial and boundary conditions for solar flare onset and CME propagation from the sun to Earth. The large-scale structure of the quiet auroral arc is believed to be formed due to the interaction between magnetosphere and ionosphere, with ionospheric feedback instability being a candidate. Auroral energetic electrons, which excite emission lines in aurora, need to be accelerated by an electrostatic double layer created by kinetic processes in micro-scale instability (Figure 4). The results of the interlocked space weather simulations of the solar active region model (top- left), the solar coronal model (top-right), and the heliospheric model (bottom) are shown in Fig.4 [25] .

V. Auroras on other planets and moons

IV. Magnetic field

Neptune: Neptune's magnetic field is off-center and tilted away from its axis of rotation, similar to Uranus. Scientists believe magnetic or electrical interactions occur closer to the planet's surface, similar to Earth [30] . The existence of magnetic fields is an important aspect of the habitability of the planet. The magnetic field protects us from harmful radiation from the Sun and helps keep our atmosphere from leaking into space (Figure 6) [33] .

i. Planetary Magnetism Development

ii. Magnetic dynamo theory's general concepts

The dynamo theory explains Earth's main magnetic field as a self-sustaining dynamo. Fluid motion in Earth's outer core generates an electric current, which interacts with fluid motion to create a secondary magnetic field. This secondary magnetic field is stronger than the original and lies along Earth's rotation axis. The heat from radioactive decay in the core is thought to induce convective motion (Figure 7) [30] . Electrons move through a liquid, creating electric currents and converting energy into a magnetic field. Planetary bodies like Earth, Moon, Mars, and asteroids have magnetic fields, which reveal the formation of a metallic core. This field is unique in remotely sensing a core buried deep beneath a body's surface. This process can tell a lot about the origin of the planetary body and even the history of climate change for that body. To fully grasp planetary magnetism, it is important to comprehend the physics of dynamos and how they affect the body in which they are found [42] [43] [30] .

iii. What then generates the magnetic field?

iv. Properties of Planetary Magnetic Fields

v. Earth-like exoplanets' magnetic fields

V. Influence of auroras on the habitability

VI. Impact on Proxima b's habitability

VII. Previous research on detecting habitability using the aurora and magnetic field

VIII. Discussion

IX. Conclusion

I. Introduction

i. Genetic Editing and Its Relevance:

ii. DNA Transcription and Transposition:

iii. Nexus Between Mental Health and DNA Processes:

iv. The Objectives:

II. Literature review

i. Uncontrolled Gene Editing: Implications and Risks:

ii. The Genetic Contribution to Mental Health Issues:

iii. Our Contribution:

• Concentrating our investigation on a specific demographic: adults aged 18 and above.
• Establishing a comprehensive understanding of the nexus between gene editing and mental health concerns, with a particular focus on the linkage provided by DNA transposition and transcription processes.
• Utilizing computational biology methodologies to model and discern how gene editing processes impact mental health, with the ultimate goal of proposing potential avenues for intervention.

III. Genetic Editing and Mental Healt

i. CRISPR-Cas9 System:

ii. Influence of TALENs on Mental Health:

• TALEN Structure

TALENs consist of TALE repeats, represented as colored cylinders, and a carboxy-terminal truncated "half" repeat. Each TALE repeat contains two hypervariable residues represented by letters. The TALE-derived amino- and carboxy-terminal domains, essential for DNA binding, are depicted as blue and grey cylinders, respectively. The non-specific nuclease domain from the FokI endonuclease is illustrated as a larger orange cylinder.

• TALEN Binding and Cleavage

TALENs function as dimers, binding to the target DNA site. The TALE-derived amino- and carboxy-terminal domains flanking the repeats may interact with the DNA. Cleavage by the FokI domains occurs within the "spacer" sequence, located between the two regions of DNA bound by the two TALEN monomers.

• TALE-Derived DNA-Binding Domain:

A schematic diagram illustrates the structure of a TALE- derived DNA-binding domain. The amino acid sequence of a single TALE repeat is expanded below, with the two hypervariable residues highlighted in orange and bold text.

• TALE-Derived DNA-Binding Domain Aligned with Target DNA

The TALE-derived DNA-binding domain is aligned with its target DNA sequence. The alignment shows how the repeat domains of TALEs correspond to single bases in the target DNA site according to the TALE code. A 5' thymine preceding the first base bound by a TALE repeat is indicated

iii. The Errors Have Invaded Everything:

First pathway is Non-Homologous End Joining (NHEJ): This pathway, occurring without template, attempts to directly join the broken ends of the DNA. Always it goes to this pathway. It can introduce small insertions or deletions (indels) in the process, often resulting in gene disruptions. As shown in Figure 2, NHEJ begins with the binding of the Ku80-Ku70 heterodimer to the broken DNA ends. This complex then recruits DNA-PKcs. Notably, DNA-PK is not present in yeast. Several proteins, including Artemis, polynucleotide kinase (PNK), and members of the polymerase X family, process the DNA ends in preparation for the next steps. In the final step, ligase IV, working in conjunction with its co-factors Xrcc4 and Cernunos/Xlf, joins the DNA ends together [37] The error in the non-homologous end joining (NHEJ) typically happens when the DNA's broken ends are region. Because NHEJ does not rely on a template to direct the repair process, it is an error-prone repair mechanism. Instead, the two ends of the broken DNA strands are directly joined or ligated [37] . In some case the survivable gene can be only 0.1% as the DNA cleavages again [38] .

Many genes that are disrupted can have repercussions for neural function, brain development, and mental health conditions. However, this can occur due to decreased or altered key gene responses for neurotransmitter regulation. This, in turn, can cause transcription deficiencies or translocation of DNA sequences. For instance, error-prone NGEJ can lead to deficiencies in the DLG3 (Discs, large homolog 3) gene, which is responsible for memory and learning. Mutations in this gene can result in Intellectual Disability, Speech and Language Delays, and other neural disorders. The second pathway is Homology-Directed Repair (HDR): In this pathway, a template DNA molecule is used to repair the break, allowing for precise DNA sequence changes to be introduced during repair. These repairs occurred by ordered steps as shown is Figure 5: In the intricate process of HDR (Homology-Directed Repair) following gene editing, a highly coordinated sequence of events unfolds to facilitate the precise and faithful repair of double-strand breaks (DSBs) in the DNA. Initially, the occurrence of DSBs activates the ATM checkpoint, a critical regulator of DNA repair processes and cell cycle progression. This activation is a crucial trigger for subsequent repair steps. During the S- phase of the cell cycle, CDK-mediated phosphorylation of CtIP at S372 plays a pivotal role in priming CtIP for action, ultimately leading to the formation of the BRCA1- CtIP complex. This complex is responsible for modifying the chromatin environment around the DSB site, making it conducive to DNA end resection.

IV. Computational methodology

Code Description

1. Importing Libraries

   

   • Bio.Seq and Bio.SeqUtils are modules from the BioPython library used for manipulating DNA sequences and calculating properties such as melting temperature (Tm).
   • Bio.Restriction is another module from BioPython that is utilized to simulate the recognition site of the Cas9 protein.
2. Defining Protein and DNA Classes

   

   In this section, we define Python classes to represent biological entities. Protein objects have attributes such as name and phosphorylated to model the phosphorylation status. DNA objects encapsulate DNA sequences.
3. Defining Protein and DNA Entities:

   

   We instantiate specific biological entities using the defined classes. For example, ATM, CDK, CtIP, and BRCA1 represent proteins, while dsb_site represents a DNA sequence. These entities model key molecules and the DNA site of gene editing. Each protein has a name and a phosphorylated attribute, which can be set to True or False to simulate phosphorylation status. DNA sequences are represented as strings in the DNA class.
4. Designing gRNA

   

   The design_gRNA function is used to design a guide RNA (gRNA) for the target DNA sequence. The gRNA is designed based on specific criteria, including melting temperature and the absence of Cas9 recognition sites.
5. Designing Conditions X and Y:

   

   Two conditions, X and Y, are defined based on the presence or absence of a suitable repair template. condition_X is True if a gRNA is designed, indicating the activation of the Homology-Directed Repair (HDR) pathway. condition_Y is the inverse of condition_X, representing the activation of the Non-Homologous End Joining (NHEJ) pathway if no repair template found.
6. Simulation of HDR Pathway

   

   The perform_HDR function simulates the sequential steps of the Homology-Directed Repair (HDR) pathway following gene editing. It includes events such as the activation of the ATM checkpoint, phosphorylation events, formation of protein complexes, and additional HDR-specific interactions.
7. Simulation of NHEJ Pathway

   

   The perform_NHEJ function simulates the stages of the Non-Homologous End Joining (NHEJ) pathway following gene editing. It encompasses events such as the binding of the Ku80-Ku70 heterodimer, recruitment of DNA-PKcs, DNA end processing, and DNA end ligation.
8. Mutation in IDH1 Gene

   

   Within the NHEJ pathway simulation, we introduce a step to simulate mutations in the IDH1 gene caused by NHEJ. IDH1_mutated is set to True to indicate the occurrence of mutations.
9. Effects of IDH1 Mutations on Cellular Processes:

   

   Following the simulation of IDH1 mutations, we provide a descriptive account of the effects of these mutations on cellular processes. This includes the production of abnormal isocitrate dehydrogenase (IDH) enzymes, generation of oncometabolite 2-hydroxyglutarate (2-HG), disruption of cellular pathways, competition with isocitrate, loss of function in wild-type IDH enzymes, and disruption of epigenetic regulation.
10. Mental Health Impact

    

    We briefly outline the potential effects of the cellular changes resulting from IDH1 mutations on mental health. This encompasses concepts such as abnormal metabolites (e.g., 2-HG), neuroinflammation, neurotransmitter imbalances, neurological effects, mood alterations, and cognitive processes.

V. Ethical Consideration and Study Constrain

i. Ethical Consideration:

ii. Study Constrains:

VI. Discussion

i. The Findings:

ii Thermotical Potential Cure:

iii. DNA Ligation:

As the error occurs in the final steps of DNA ligation, as shown in figure 6 , in NHEJ, involving a ligase enzyme encoding ligase enzymes into cells to correct genetic defects will be beneficial. This correction process can be monitored by BLI, allowing for the detection of the time of ligation The process of introducing specific genes, including those encoding ligase enzymes, into cells to correct genetic defects or enhance cellular functions involves a series of well-defined steps. This approach holds great promise for addressing genetic abnormalities like the IDH1 gene, as mentioned in the research paper. To begin, DNA ligases play a pivotal role in this process by catalyzing the formation of phosphodiester bonds, which are essential for sealing nicks in DNA strands. In humans, three genes encode different DNA ligase enzymes: DNA ligase I, III, and IV. These enzymes function in various cellular processes, including DNA replication, repair, and maintenance.

VII. Conclusion and Recommendation

I. Introduction

II. Dark Matter Candidates and Interactions

Dark matter is a hypothetical form of matter that is thought to account for about 27% of the matter in the universe. [2] It is invisible to telescopes because it does not emit or absorb any form of electromagnetic radiation. However, its existence is inferred from its various effects on visible matter.

i. Evidence that supports the existence of dark matter

1. The rotation curves of galaxies: This curve is a plot describing the rotation rate in a galaxy as a function of distance from the galaxy's center. It shows that the stars in the outer parts of galaxies are moving much faster than they would be if they were only affected by the gravity of the visible matter in the galaxy. This suggests that there is a lot of unseen matter in the outer parts of galaxies, which is holding the stars in place.[4]
2. The gravitational lensing of galaxies: Gravitational lensing is the bending of light by the gravity of massive objects. When light from a distant galaxy passes by a nearby galaxy, the gravity of the nearby galaxy bends the light, causing the distant galaxy to appear distorted. This distortion can be used to measure the amount of dark matter in the nearby galaxy. [6]

ii. Dark matter candidates

III. Redshift: its relationship with dark matter

i. Factors causing redshift:

A. Doppler effect

B. Gravitational redshift

ii. Dark matter effect on the gravitational redshift of cosmological bodies

IV. Neutron Stars: Composition and Properties

i. Formation and Composition

ii. Properties

Neutron stars are incredibly dense, with a mass of about 1.4 times that of the sun, but a radius of only about 10 kilometers. Their density is about 10¹⁷ grams per cubic centimeter, which is about 100 million times the density of lead. It is often said that "A tablespoon of a neutron star material would weigh more than 1 billion U.S. tons (900 billion kg). That's more than the weight of Mount Everest, Earth's highest mountain." [9] Neutron stars' temperatures can reach up to 10 million degrees Celsius. This heat is produced by the collapse of the star's core and the friction of the neutrons as they collide with each other. They are also extremely magnetic, with magnetic fields that are billions of times stronger than the Earth's magnetic field. These strong magnetic fields are thought to be generated by the rotation of the star.

iii. Types of Neutron stars

1. Pulsars are neutron stars that emit beams of radiation, usually radio waves. The beams are emitted from the poles of the neutron star as the star rotates. This causes the pulsar to appear as if it is blinking as it rotates. "Because of the rotation of the pulsar, the pulses thus appear much as a distant observer sees a lighthouse appear to blink as its beam rotates. The pulses come at the same rate as the rotation of the neutron star, and, thus, appear periodic." [11]
2. Magnetars are an exotic type of neutron stars with extremely strong magnetic fields reaching up to a trillion times stronger than the magnetic field of Earth. Magnetars release enormous amounts of x-rays and gamma-rays making them responsible for some of the most powerful explosions in the universe, such as gamma-ray bursts. [12] . It is believed that "its magnetic field would destroy your body, tearing away electrons from your atoms and converting you into a cloud of monatomic ions, that is, single atoms without electrons." [13]
3. X-ray binary Systems are systems that contain a neutron star and a companion star. The companion star can be a main sequence star, a white dwarf, or another neutron star. The neutron star in an X-ray binary accretes matter from the companion star, which causes it to emit X-rays.

iv. Advantages of Binary Neutron Star Systems for Research:

v. Mass limit of Neutron stars

V. Potential interactions between dark matter and neutron stars

i. The density of neutron stars

ii. Scattering off the neutrons in the star.

VI. Neutron Star: Structure and Equation of State

VII. Detection Methods of Dark Matter in Neutron Stars

i. Indirect Detection Methods

A. Neutrino Emissions

B. Gamma Ray Emissions

ii. Hybrid Detection Methods

A. Star Spectroscopy

According to a 2019 study [26] "neutron star spectroscopy could constitute the best probe for dark matter particles over a wide masses and interactions strength". By studying the heat caused by dark matter-neutron interactions based on the current population of neutron stars scientists were able to draw in the region in which black matter particles could be detected using star spectroscopy in the luminosity vs. age plane. See Fig (3) [26] for an illustrative drawing of the process. The reasoning behind this technique relies on the simple conversion from recoil energy to thermal energy [27] , in which we compute the energy transferred to a neutron inside a neutron star is the dark matter-nucleon scattering process in a relativist setting. "This energy corresponds to the same recoil energy inferred in direct detection experiments on Earth but considering relativistic effects. For this reason, neutron stars indeed constitute an orthogonal search for dark matter."

ii. Direct Detection Methods

A. Scattering Experiments

VIII. Constraints and Applicability to Neutron Stars

i. Neutrino Emissions

ii. Gamma-ray Emissions

iii. Scattering Experiments

iv. Gravitational Red Shift Measurements

IX. New Suggested Detection Approach

i. Gravitational Redshift Measurements

X. Discussion

XI. Conclusion

I. Introduction

II. Literature Review

The first prior project is MyVox—-Device for the communication between people: blind, deaf, deaf- blind and unimpaired. People with visual impairments face challenges in utilizing smartphone technologies for communication due to their limited visual capabilities, and to address this issue, a voice- interaction-based messaging application was developed, enabling individuals with visual impairments to communicate using smartphones. This application offers touch and voice command controls as shown in figure(1), providing speech output as feedback for each command. The application's voice-interaction service received a Mean Opinion Score (MOS) of 3.7, indicating that users found it to be of good quality [2] .

The second prior project is a communication aid for deaf-blind people using vibration motors. This project proposes the use of a Braille type input/output device as a communication aid for individuals who are both deaf and blind. The device allows for both input and output operations in 6- point Braille format as shown in figure(2). It features a combined structure of a vibration motor and a push-button switch for input/output functions. Braille information can be easily inputted or presented using specific hands. positions. An experiment was conducted to determine the optimal position for presenting Braille information, resulting in visually impaired subjects achieving an 84% recognition rate after ten minutes of training. Inputting Braille information by a sighted subject yielded a rate of approximately 36 characters per minute after five hours of training [3] .

The third prior research is a study of the field of assistive technology for visually impaired and blind individuals. While subjective accounts have been written in the past, we conducted an objective statistical survey using information analysis and network-theory techniques. By analyzing a comprehensive database of scientific research publications from the past two decades, we identified key research areas, growth patterns, leading journals and conferences, and active research communities as shown in figure(3). Our findings indicate sustained growth in the field, with an increase from fewer than 50 publications per year in the mid-1990s to nearly 400 publications per year in 2014. This growth suggests that assistive technology for visually impaired individuals will continue to advance and positively impact their lives as well as the elderly population [1] .

The fourth project states that the inability of blind people to see makes it difficult for them to obtain the most recent knowledge and technologies that could give them alternate communication skills. Due to their increased price and limited portability, modern technological advancements are not easily accessible to persons who are visually impaired. Because of this, it has become increasingly important to provide a quick, cheap, and portable Braille system for persons who are blind. In this study, a novel communication channel for deaf, blind, and visually impaired individuals is introduced. It consists of three distinct subsystems that offer various facilities to enhance the communication abilities of those who are visually impaired. The following three modules make up the system: a portable low-cost refreshable device Using six tiny vibrators, the Body-Braille device displays Braille characters. ii) a straightforward Braille writer for writing Braille characters, and iii) an SMS-based remote communication system as shown in figure(4). This new communication method is affordable, transportable, quick, and precise [4]

III. Proposed Model

i. approach and tools/techniques:

1. The first system is to understand what people say. The smartphone application will be connected to the device to send the letters that will be expressed by vibration motors.
2. The project aims to send words from the blind- deaf to the normal one through six push buttons. The words will be shown on an LCD.
3. The third system is an ultrasonic sensor that will help the blind-deaf to avoid obstacles.
4. The last one will be about object detection. The application on the smartphone will send objects detected through the camera to the Arduino.

ii. overview of system modules

Keypad: It is a set of buttons arranged in a grid or a matrix used to enter numerical or alphabetical data as shown in figure(5). It's a common input device used in various electronic devices like calculators, security systems, and mobile phones. In this project, a keypad is used as an input device to write for normal people. Each number in figure (2) represents a circle in braille language.

Vibration motors: small-sized electric motors that produce mechanical vibrations when a voltage is applied to them as shown in figure(6). vibration motors are used to transform text messages through vibrations for the deaf-blind.

Ultrasonic module: An ultrasonic module shown in figure(7) is a sensor that emits ultrasonic waves and measures the time it takes for the waves to bounce back from an object. It is commonly used in distance measurement, obstacle detection, and navigation systems. In this project, an ultrasonic module is used to detect obstacles and prevent collisions.

Bluetooth module: The bluetooth module shown in figure(8) is a small electronic circuit board that enables wireless communication between devices over short distances. It is used in connecting the device to the smartphone application. It works by sending letter by letter to the serial which is convenient.

LCD display: It is a flat panel display that uses liquid crystals to produce images as shown in figure(9). It is commonly used in digital watches, calculators, and electronic devices that require low power consumption. In this project, an LCD display is used to display the text messages written on the keypad.

Android Mega pro It is a device to connect and program all the device's components as shown in figure (10). It is preferable to use because of containing many pins options. It helps to connect all the components together.

IV. Methods

i. the prototype method.

The method to make the device in figure (11): To make the writing part: The first step is to connect the keyboard to the breadboard and then connect each button of the keyboard to the Arduino. The next step is to write the code as follows: define two variables, a null variable, and an empty string variable., Make if statement, so if each button of the six button is pressed and represent 1 and not is equal to zero. At the end the null string will make a unique number and each number represents a unique letter. The letter will be added to the empty string. There are two push buttons: 1- to start and end writing 2- to move to a new letter.

ii. Research Methodology:

V. Results

i. Survery

1. Experience with using assistive technology.
2. The most comfortable shape for long-standing usage.
3. The time length of the survey from 1 to 10 4- The satisfaction of the survey from 1 to 10

1. if all the vibration motors are on, so write avoid
2. If some vibration motors are turned, someone is writing you a word

ii. quantitive test:

1. Object detection:

   Object name	Model prediction	Model effictiveness
   mouse	mouse	50%
   pen	Toothpaste
   chair	chair
   lamp	Refrigerator
   person	person
   calculator	cellphone
   Laptop	Laptop
   couch
   bag	person
   window	oven

2. Writing and reading

   sentence length (in letters)	Time to receive	Time to send another
   7	42 sec	20 sec
   20	2 min	1 min 39 sec
   12	1 min 12 sec	44 sec
   Average per letter	6 sec	4.87 sec

3. Obstacle avoidance

   Distance (cm)	Measured distance
   20	23 (15% increase)
   40	40 (0% decrease)
   52	56 (9.6% increase)
   35	36 (2.9% increase)
   50	53 (6% increase)
   Result/real	106.7%

VI. Discussion and Future plan

i. reading section of the prototype:

The braille language is expressed by vibration motors. 90.5% of all answers were right, so that proves the technique is good. The average estimate of the length of the experience was 6 out of 10, so a little long time was need. It was chosen for 6 seconds for each letter. The letters were sent by an application as shown in figure (14).

ii. writing section of the prototype shown in figure (15):

iii. obstacle avoidance system:

v. obstacle detection system:

1. The gloves shape will be better as a smartphone.
2. Using a new object detection model.
3. Separating the ultrasonic sensor from the original one
4. Using a PCB board instead of cables.

VII. Conclusion

IX. Acknowledgement

I. Introduction

II. Literature review

III. Methods

i. Quantitative methods

ii. Qualitative methods

IV. Results

In Malaysia, a country characterized by a tropical climate, previous scholars conducted a case study. They employed a qualitative analysis through interviews (conducted by Razlin Mansor, Low Sheau-Ting) to ascertain occupants' preferences for well-being within office buildings [8] . Among the various findings, one discovery that resonates with our research question pertaining to energy-related matters involves occupants' preferences concerning their health and comfort. This aspect strongly influences the ecological considerations within office buildings, specifically addressing factors like thermal comfort, indoor air quality, and indoor lighting (Figure 1).

Moving on to energy consumption again, in Malaysia, other scholars noted the ecological consequences from inefficient energy consumption regarding over-specification in cooling systems [10] . They employed experts' interviews (conducted by Nurul Zahirah, Nazirah Zainul and other scholars) to clarify the causes of over-specification issues (Figure 2). It can reasonably be inferred that those issues can be classified into 2 categories of drawbacks: imprudence during building (non- calculated resistance, searching for quick solutions, size-dependent mentality) and careless maintenance (stingy fee structures, inaccurate testing). Therefore, the research later will propose a specific metric that is responsible for building and controlling new systems in office buildings.

Over a 24-hour time interval, the performance metric was established by integrating values of HVAC Consumption (in Kilowatts per Hour), Outdoor Temperature, and Number of Users in the building (Figure 3). The objective of performance metrics is to detect elevated average power usage during periods of low outdoor temperatures and reduced occupancy (Table 2).

V. Discussion

VI. Conclusion

VII. Acknowledgement

VIII. Tables & Figures

I. Introduction

i. The causes of Genocide Problem in the world.

ii. The importance of research.

II. Habitation of the Uyghur Nation now

i. Consequences of pressure on the Uyghur nation

ii. China's governmental system

iii. Organizations fighting for Uyghur Nation's rights

IV. Other countries' reaction on that issue

i. International countries' policy

ii. Methods of exploring research

iii. Actions of China, which divert the attention of other countries

iv. China's contribution to the Uighur nation

V. Conclusion

I. Introduction

II. Quantum Bits

Another significant concept underlying the quantum system is Schrödinger's equation, which measures changes in a function over time. It's represented as $i \hslash \frac{d}{dt} | \psi (t) \rangle = H|\psi (t)\rangle$ A notable algorithm that capitalizes on the concept of superposition is Grover's algorithm for searching unsorted lists. It employs multiple Hadamard gates to bring the qubit into an even superposition state. Further, Pauli-Z gates amplify the wave function's amplitudes to move closer to the desired answer, achieved through a process called the Oracle. [4] Quantum entanglement, a captivating aspect of qubits, allows them to establish an intangible connection—resembling an imaginary string— regardless of the physical distance between them. This entanglement profoundly influences the states of the connected qubits, opening new vistas to transcend classical computing. Quantum teleportation exemplifies this phenomenon by transmitting the state of one qubit from a source to a distant location. This concept holds potential applications in distributed quantum computing, enabling the efficient transfer of qubit states to different processors for enhanced problem- solving capabilities. The process of quantum teleportation involves manipulating an entangled pair of qubits, with one serving as the sender qubit and the other as an additional qubit. [5, 6] In essence, operations are executed on the sender qubit, along with the qubit intended for teleportation and the additional qubit. These $$\displaylines{ |\Phi^+ \rangle = \frac{|00 \rangle + |11 \rangle}{\sqrt{2}} \;| \Psi^+ \rangle = \frac{|01 \rangle \; + |10 \rangle}{\sqrt{2}} \\\\ |\Phi^- \rangle = \frac{|00 \rangle - |11 \rangle}{\sqrt{2}} \;| \Psi^- \rangle = \frac{|01 \rangle \; - |10 \rangle}{\sqrt{2}} } $$ Equation 3: The 4 bell states. operations, represented as classical bits, are transmitted to the receiving end, where specific actions are taken to transform the receiving qubit's state into the desired state. These actions correspond to what is known as bell operations or bell states. These states are encapsulated in four equations—Phi Plus $(|\Phi^+ \rangle)$, Phi Minus $(|\Phi^- \rangle)$, Psi Plus $(|\Psi^+ \rangle)$, and Psi Minus $(|\Psi^0 \rangle)$. Each equation describes the entanglement nature between two qubits. For instance, $|\Phi^+ \rangle = \frac{1}{\sqrt{2}} (|00 \rangle + |11 \rangle), |\Phi^- \rangle = \frac{1}{\sqrt{2}} (|00 \rangle - |11 \rangle), |\Psi^+ \rangle = \frac{1}{\sqrt{2}} (|01 \rangle + |10 \rangle)$, and $(|\Psi^- \rangle = \frac{1}{\sqrt{2}} (|01 \rangle - |10 \rangle)$. These states also offer insights into the relationships between entangled qubits and find utility in quantum error correction and addressing the challenges posed by decoherence. [7]

III. Types of Quantum Computers

Gate-based quantum computers

An inherent property of quantum gates is their ability to create entanglement between qubits. When two or more qubits are entangled, the state of one qubit $|\psi \rangle$ is inherently dependent on the state of another, even when physically separated. This phenomenon of entanglement constitutes a bedrock for various quantum algorithms and quantum error correction schemes [11][12]. One quantum gate of remarkable significance is the Toffoli gate, colloquially known as the Controlled-Controlled-Not (CCNOT) gate.

Quantum circuits, serving as visual blueprints of quantum algorithms, assume a pivotal role in quantum computing. These circuits are an amalgamation of quantum gates and operations, meticulously sequenced to delineate the step-by- step computational journey. The Quantum Fourier Transform (QFT), often represented by a unitary operator (U), stands as a quintessential example, facilitating a quantum rendition of the discrete Fourier transform—a critical operation in the quantum domain. QFT transmutes an input quantum state |a⟩ into another quantum state |b⟩ nestled within the frequency or Fourier basis.

This transformative feat finds application in a spectrum of quantum algorithms, including Shor's algorithm for integer factorization and quantum phase estimation [21-25]. The Quantum Phase Estimation (QPE) algorithm, a cornerstone of quantum computing, emerges as a potent tool for the estimation of phase (ϕ) or eigenvalues of a unitary operator. QPE's importance reverberates through various quantum algorithms, including the trailblazing Shor's algorithm and quantum simulations. This algorithm plays a pivotal role in deciphering intricate problems in cryptography and quantum system simulations [26-28].

Annealing-based quantum computers

Superconducting Quantum Computers

A distinctive part of superconducting quantum computers that sets them apart from other quantum computer types is the Josephson junction. This integral element plays a crucial role in establishing the necessary energy level structure for various aspects of qubit manipulation and coherent control over quantum states, as well as facilitating the creation process of superconducting qubits. The Josephson junction primarily consists of two superconducting electrodes, separated by a thin oxide insulating barrier that enables the flow of supercurrent across the junction. [38, 39]

IV. Cooling Process of QC

An indispensable pillar of quantum computing's success lies in the process of cooling, an essential process that reduces thermal noise, preserving the intricate quantum states indispensable for precise computation. At the core of quantum computing setups stands dilution refrigeration, a cooling method hinging on the properties of isotopes, mainly helium-3 and helium-4. Through the mixing of the He isotopes, remarkable transformations transpire—a transition to a superfluid state. This transition results in a dramatic reduction in temperature [45] .

One of the key principles governing the behavior of helium isotopes in this cooling process is described by the Debye Model expressed in this equation: $C_V = 9N\mathcal{k} (\frac{T}{\theta_D})^3 \int_{0}^{\frac{\theta_D}{T}} \frac{x^4 e^x}{(e^x - 1)^2} \: dx.$ Where $C_V$ represents the heat capacity, $T$ is the absolute temperature, and $\theta_D$ is the Debye temperature, which characterizes the crystal lattice vibrations in the solid, $N$ represents the total number of oscillators, and $\mathcal{k}$ represents the Boltzmann constant, which is approximately $1.38 \times 10^{−23}$. Adiabatic demagnetization refrigeration (ADR), an alternate cooling approach deployed in quantum computing, operates at the intersection of magnetic entropy and temperature dynamics. This process relies on the Adiabatic Demagnetization Equation: $\Delta T = \frac{\mu_{0 \: M}}{C} \Delta B$ . Where $\Delta T$ represents the change in temperature, $\mu_0$ is the magnetic constant (permeability of free space), $M$ is the magnetization of the material—The ratio of magnetic moment to the volume of the material—, $C$ is the heat capacity of the material, $\Delta B$ represents the change in magnetic field. The meticulous orchestration of magnetization and demagnetization cycles, as dictated by this equation, culminates in a marked reduction in temperature, crucial for quantum computing [46] .

Pulse tube refrigeration ushers in an innovative era of quantum cooling methodologies. This approach relies on cyclic compression and gas expansion, facilitating heat extraction from the refrigeration stage. This heat is subsequently transported to the cold head, initiating the cooling process. In the realm of pulse tube refrigeration, the concept of Carnot Efficiency comes into play. The Carnot Efficiency equation is a fundamental expression in thermodynamics, and it provides an upper limit on the efficiency of any heat engine or refrigerator. For a heat engine, the Carnot Efficiency $\eta_c$ is given by: . Where $\eta_c = 1 - \frac{T_C}{T_H}.$ Where $T_C$ is the absolute temperature of the cold reservoir and $T_H$ is the absolute temperature of the hot reservoir. Efficiency is a critical factor in pulse tube refrigeration, as it determines how effectively heat can be extracted from the system to achieve the desired cooling effect [47-49].

Cryogenic cooling stands as a primary method for maintaining the exceedingly low temperatures required for the operation of quantum computers. Cryogenic coolers represent specialized refrigeration systems meticulously engineered to reach and sustain temperatures nearing or even touching absolute zero (0 Kelvin or -273.15°C), a critical threshold for achieving superconductivity. The cornerstone of these cryogenic coolers is the "Compression- Expansion cycle for cooling." Within this cycle, a working gas, in this context, $He^3$, undergoes a series of compressions and expansions, resulting in a cooling effect for various systems, most notably superconducting quantum computers.

V. Superconductors

Zero electrical resistance stems from the formation of Cooper pairs—a phenomenon described earlier. Unlike conventional conductors where electrons traverse the lattice structure of the material, colliding with lattice ions and inducing resistance and heat, superconductors exhibit a distinct behavior [53] . Cooper pairs glide through the lattice without scattering or collisions, behaving as a single entity due to their unique quantum properties and wave function overlap. This unimpeded flow negates the generation of heat and resistance, thus preserving the quantum states of qubits and reducing decoherence.

Mercury Barium Calcium Copper Oxide $(HgBa_2 Ca_2 Cu_3 O_8 + \delta)$ represents yet another example of a high-temperature superconductor. It becomes superconducting at temperatures surpassing -150°C (123.15 K) and is distinguished for its robust coupling of superconductivity with magnetic properties. This distinctive attribute renders $HgBa_2 Ca_2 Cu_3 O_8 + \delta$ of significant interest both in fundamental research and prospective applications [62] . The quest for achieving room-temperature superconductivity stands as a monumental aspiration within the realm of quantum computing. If realized, this breakthrough holds immense potential to revolutionize numerous technological processes. Recent strides have brought this aspiration within closer reach through the introduction of the superconductor known as "LK-99." Comprising a composition of $Cu Pb_9 (PO_4)_6 O$ and possessing an apatite-like structure, LK-99 has generated considerable attention due to its purported ability to exhibit superconductivity attemperatures exceeding 400K without necessitating external pressure. This development holds promise for substantially enhancing cost efficiencies in quantum computing and reshaping the quantum computational landscape.

VI. LK-99

Cryogenic Superconductors

LK-99 (Room-Temperature Superconductor)

Prospective Financial Advantages of Adopting LK-99

VII. Conclusion

I. Introduction

1. The main aim of the research is to identify Kazakhstan's role in Turkic integration.
2. How will Kazakhstan's level of participation in Turkic integration change in the future?
3. Are there tangible benefits of Turkic unity for Kazakhstan?

II. Context

III. Methods

IV. Results

Figure 1 shows that 57 adults who live in Astana were surveyed. The first question was about how strong do they think the relations between Kazakhstan and Turkic nations. Only 4 answered as low (1-3), 24 as average (4-7), and the majority 25 as high (8-10). The medium was 7 and mean 7.088.

The figure 2 shows that the far majority 73.7% want Kazakhstan to improve their relationships with these countries, and no one is against. The figure 3 shows that the majority answered that the cooperation must be in the economy overall and tourism, one sector of economy. Another major choices was the sphere of culture and science (42.1 and 38.6 percent respectively).

V. Discussion

VI. Conclusion

VII. Evaluation

I. Introduction

II. Literature Review

i. Formation of Black Holes

ii. The Fate of Neutron Stars

Despite the enormous density and heat of neutron stars. During this time, neutron stars cool and become dark, cold and lifeless. Conversely, when neutron stars in binary star systems accumulate enough mass to exceed the Tolman-Oppenheimer- Volkoff limit, they instead form black holes in an event known as a neutron star black hole merger. This event is fundamental to understanding the properties of neutron stars and the evolution of black holes. There are several ways a neutron star can accumulate a mass of. A process called mass transfer can occur, in which a nearby star transfers mass to another star through gravitational interaction. Another common way is the collapse of two stars. In this case, two neutron stars could collapse and instantly form a black hole. Another common way is the collapsing between two stars, here the two neutron stars may collapse to immediately form a black hole [12] .

iii. The History of Neutron Stars

In the late 18th century, John Michell and Pierre Simon Laplace independently hypothesized that a star could be so massive that not even light could escape its surface. However, research into these phenomena did not continue until the 20th century, when Albert Einstein published his general theory of relativity. The theory described the influence of mass on the structure of space-time. In 1916, Karl Schwarzschild calculated the general relativity equation in the extreme case and discovered that the structure of space-time would collapse on itself, creating a region of zero volume and infinite density from which neither matter nor light could escape black holes [13] .

iv. Detecting Neutron Stars

v. Discovering Black Holes

vi. Usage of Machine Learning in Astrophysics

vii. Gravitational Waves Detectors

viii. The Usage of Interferometers

The absolute purpose of the Laser Interferometric Gravitational Wave Observatory (LIGO) is to allow the detection of astrophysical gravitational waves and use this information for research in physics and astronomy. In this research, the main goal of studying LIGO devices is to support research on the structure and equation of the state of black holes. In addition, it measures the birth rates, masses, collisions, and distribution of black holes and neutron stars [20] .

ix. Latest Updates

x. Measurement Methods of Neutron Stars' masses

III. Methods

The final step in the selection process was to measure the gravitational waves emitted by each star, with the highest values indicating the presence of gravitational collapse caused by a neutron star- neutron star merger or a neutron star-black hole merger, which refers to the conversion of the pulsar star into a black hole. The model was trained and evaluated using an 80%:20% split. Finally, the accuracy was determined by importing the Sklearn library. It's important to mention that these are the absolute clear, right steps to construct the model; however, due to the insufficient datasets found and the roadblocks faced, the model couldn't follow these steps, and the actual methods were much simpler. The proposed machine learning model was based on the decision tree algorithm, which has the function of classifying and deciding which stars will turn into black holes and which will not, based on multiple datasets that were gathered in order to make clear decisions. Data-cleaning was the first step done in the practical work, removing empty rows and incomplete data to run the decision process smoothly and have fewer errors while coding. According to the fact that the TOV limit was unsuitable for the datasets we gathered, another mass limit was needed to be dealt with in the model; however, it is important to mention that both the TOV limit and the used mass limit here are correct in the sense that the new estimations of the neutron stars' mass limit are starting from the TOV limit. The mass limit that is used here is 2.17 [27] . most of the updated estimations are about this limit. The way the model was trained was by measuring the difference between the star's actual mass and the maximum mass, then building and training the "if" condition that the model would base its decision on. If the mass exceeds the difference, the model will make a 'YES' decision, which means that the neutron star will end up as a black hole. On the other hand, if the mass doesn't exceed the limit, the model will make a 'NO' decision.

IV. Results

V. Discussion

i. Contribution to Space discovery

ii. Scientific theories and equations

1. The mass density is non-negative, i.e., gravity is attractive.
2. Neutron matter should be fluid where the pressure at zero temperature is a function of density.
3. $d\:/\:d\:\rho\:\geq\:0$, so that the zero-frequency sound speed of neutron matter $(dp\:/d\rho)^{\frac{1}{2}}$ is real and matter is stable.
4. The sound speed does not exceed the speed of light, $dp\:/\:d\rho\:\leq c^2$, hence signals cannot be superluminal, and causality is satisfied.

The speed of sound is equal to the speed of light $c^2$.

5. Gravitational Waves: Gravitational waves (GWs) are ripples that travel as waves to other places in the universe. The mass of an object to be identified as a black hole is not enough to only be estimated, but also the gravitational waves of the neutron star for stability against collapse into a black hole should be taken into consideration [45] . Most of the neutron stars, after accreting huge masses enough for the conversion turn into a type of black hole classified as a "Schwarzschild Black Hole". They have neither charge nor rotation with a Singularity in the middle, also called a "static black hole" or "ideal black hole [46] ." At the Schwarzschild radius, if more mass, greater than M, was accreted, the gravitational waves will oppose and overwhelm any other force converting the neutron star into a dense mass of matter which is the black hole.

iii. Achievements

iv. Limitations and roadblocks

v. Future Plans

VI. Conclusion

VII. Acknowledgements

I. Introduction

II. Groundwork: Algebraic Concepts

i. Elementary Definitions:

ii. Abstract Definitions:

III. Historical Exploration Through Higher- dimensional Complex Numbers

i. The History of Complex Numbers

ii. The History of Quaternions

iii. The History of Octonions

IV. Constructing The Hyper — Complex Numbers

i. Quaternions:

V. Algebraic Operations, Multiplications Diagrams, and Mathematical Definitions

i. Quaternions

The quaternions have a basic addition rule, similar to the dual system of complex numbers [9, 42, 44]: $$ \displaylines{ (a_1 + b_1i + c_1j + d_1k) + (a_2 + b_2i + c_2j + d_2k) \\ = (a_1 + a_2) + (b_1 + b_2)i + (c_1 + c_2)j + (d_1 + d_2)k + } $$ (5.1) Despite that the quaternions have a basic addition rule, they have a unique multiplication rule. To determine the multiplication algorithm, the way to multiply $i, j,$ and $k$ needs to be known a way by using the following diagram 1 represents the multiplication diagram for quaternions roots [7] [9] : (figure 1)
Given this time that [7] [9] : $$ i^2 = -1, j^2 = -1, k^2 = -1, ijk = -1 $$ (5.2) $$ ij = k, jk = i, ki = j $$ (5.3) $$ ji = -k, ik = -j, ki = -i $$ (5.4) The previous diagram is the same as a multiplication table, where the three components of the number are arranged clockwise around the circle. The product of two components results in the third component or the negative component according to the direction of the multiplication [7, 9, 46].

The rules of multiplication are called Hamilton's Rules. The set of quaternions was denoted by ℍ in honor of Hamilton's numbers discovery. The following figure 2, represents the orthogonal state of a quaternions and it can be illustrated mathematically through the Hamilton's Rules [44] [46] : After introducing the multiplication diagram for the quaternions, we can multiply two arbitrary quaternions. Thus, Let $$ q_1 = (a_1 + b_1i + c_1j + d_1k) $$ (5.5) $$ q_2 = (a_2 + b_2i + c_2j + d_2k) $$ (5.6) By using the multiplication diagram [9][44] : $$ \displaylines{ q_1 q_2 = a_1 a_2 + a_1 (b_1i) + a_1 (c_2j) + a_1 (d_2k) + \\ (b_1i)a_2 + (b_1i)(b_2i) + (b_i)(c_2j) + (b_1i)(d_2k)+ \\ (c_1j)a_2 + (c_1j)(b_2i) + (c_1j)(c_2j) + (c_1j)(d_2k) + \\ (d_1k)a_2 + (d_1k)(b_2i) + (d_1k)(c_2j) + (d_1k)(d_2k) } $$ (5.7) In spite the non-commutative nature of the quaternions, dealing with them is still possible since they are considered associative [9][44] . $$ (q_1 q_2)q_3 = q_1 (q_2 q_3) $$ (5.8) After clearing the definitions, operations, and properties of quaternions, we are ready to learn more about new forms of a quaternion that most of us know. The conjugate of a quaternion is denoted as $\bar{q}$, while the absolute of a quaternion or the magnitude is denoted as $\mid q \mid$ [9] . $$ \bar{q} = a - bi - cj - dk $$ (5.9) $$ \mid q \mid = \sqrt{a^2 + b^2 + c^2 + d^2} $$ (5.10) From these equalities, we can conclude the following product [9] : $$ q \cdot \bar{q} = \mid q \mid $$ (5.11)

ii. Octonions

The addition operation of two octonions is identical to the complex numbers and the quaternions [7, 44, 46]. Thus, let $a, a' \in \mathbb{O}$ $$ \displaylines{ a + a' = (a + a') + (b + b')e_1 + (c + c')e_2 + (d + d')e_3 + \\ (e + e')e_4 + (f + f')e_5 + (g + g')e_6 + (h + h')e_7 } $$ (5.12)

The octonions are ridiculously huge to deal with. Similar to the quaternions, the octonions had a multiplication table that can be translated into a diagram. In order to conduct the process of multiplying octonions, we need to calculate all the possible products out of any permutation out of the $\{ e_1, e_2, e_3, e_4, e_5, e_6, e_7 \}$ [7] [44] . We are going to rely on a well-known structure in the graph theory called the Fano plane [7, 9, 44]. The Fano plan is an apparatus with 7 points and 7 lines. The "lines" are the sides of the triangle, its altitudes, and the circle containing all the midpoints of the sides. Each pair of distinct points lies on a unique line. Each line contains three points. The following figure 3 illustrates the Fano plane used to multiple octonion factors [7] :

If $e_i, e_j$ and $e_k$ are cyclically ordered in this way, then: $$ e_i e_j = e_k , e_j e_i = -e_k $$ (5.13) According to the previous statement, these rules hold:

• 1 is the multiplicative identity.
• $e_1, ..., e_7$ are square roots of -1

The Fano plane explains the algebraic structure of the octad system of the octonions. Nevertheless, the Fano plane of octonions multiplication is not the full story. The octonions are projective structures over the 2-element field $\mathbb{Z}_2$ . Precisely, they consist of lines passing through the origin in the vector space $\mathbb{Z}_2^3$ [7] . In conclusion, by assuming that $1 \in \mathbb{I}$ (the octonions multiplicative identity), then the Fano plane can be thought of as the following figure 4 which shows the visualization of the "Fano plane" by assuming that $1 \in \mathbb{I}$ [7] :

After accessing the required algorithm for multiplying octonions components, we can examine the process of multiplication through an example. Let $u, v \in \mathbb{O}$, our multiplication operation can be conducted by using the vector form of the octonions and multiplying using a matrix [44] $$ \displaylines{ u \cdot v = (u_0, u_1, u_2, u_3, u_4, u_5, u_6, u_7) \\ \cdot \; (v_0, v_1, v_2, v_3, v_4, v_5, v_6, v_7) = \\ \begin{bmatrix} u_0 v_0 & -u_1 v_1 & -u_2 v_2& -u_3 v_3 & -u_4 v_5& -u_5 v_5 & -u_6 v_6& -u_7 v_7 \\ u_1 v_0 & u_0 v_1 & u_2 v_4 & -u_4 v_2 & u_5 v_6 & -u_6 v_5 & u_3 v_7 & -u_7 v_3 \\ u_2 v_0 & u_0 v_2 & u_3 v_5 & -u_5 v_3 & u_6 v_7 & -u_7 v_6 & u_4 v_1 & -u_1 v_4 \\ u_3 v_0 & u_0 v_3 & u_4 v_6 & -u_6 v_4 & u_7 v_1 & -u_1 v_7 & u_5 v_2 & -u_2 v_5 \\ u_4 v_0 & u_0 v_4 & u_1 v_2 & -u_2 v_1 & u_5 v_7 & -u_7 v_5 & u_6 v_3 & -u_3 v_6 \\ u_5 v_0 & u_0 v_5 & u_2 v_3 & -u_2 v_3 & u_6 v_1 & -u_1 v_6 & u_7 v_4 & -u_4 v_7 \\ u_6 v_0 & u_0 v_6 & u_3 v_4 & -u_4 v_3 & u_7 v_2 & -u_2 v_7 & u_1 v_5 & -u_5 v_1 \\ u_7 v_0 & u_0 v_7 & u_4 v_5 & -u_5 v_4 & u_1 v_3 & -u_3 v_1 & u_2 v_6 & -u_6 v_2 \\ \end{bmatrix} } $$

Note: The octonions multiplication is a non-commutative operation. Moreover, the octonions multiplication is also a non-associative operation [7, 18, 44, 46]. These properties can be verified through the following example [44] : $$ (u_1 \cdot v_2) \cdot w_3 = (uv)_4 \cdot w_3 = -(uvw)_6 $$ $$ u_1 \cdot (v_2 \cdot w_3) = u_1 \cdot (vw)_5 = (uvw)_6 $$ Where we use the notation, $u_1 = (0, u, 0, 0, 0, 0, 0, 0), (uv)_2 = (0, 0, uv, 0, 0, 0, 0, 0)$ [and so on…] for the octonions containing only one non-zero element [44]

VI. Cayley — Dickson Construction

VII. QQM (Quaternion Quantum Mechanics)

VIII. Three-Dimensional Rotation

i. Euler Angles

To ensure the uniqueness of each orientation using Euler angles, the heading angle $\theta$ and the bank angle $\varphi$ are restricted to a domain of [-180°,180°], whereas the pitch angle $\Psi$ (The second rotation) is restricted to a domain of [-90°,90°]. As much as it seems easy to perform rotations with Euler angles, an irritating problem might be encountered in certain cases. If we set the patch angle to ±90°, it may force the first and the third rotations (heading and bank) to be performed about the same axis or to be aligned. This phenomenon is known as the Gimbal Lock, illustrated in figure 5.

ii. The Axis-Angle Representation

iii. Quaternion 3-D Rotation

IX. Conclusion

I. Introduction

II. GALAXY CLUSTER FORMATION AND EVOLUTION

A) Hierarchical structures formation

B) Role of dark matter and dark energy in the universe

III. OBSERVATIONAL METHODS AND DATA

A) Optical observations

Redshift surveys measure the previous redshifts in a section of the sky to identify astronomical objects. By combining redshift surveys and the angular positions of the objects with the observed redshifts, a 3D distribution of matter in the universe can be mapped, helping in identifying large- scale structures of the universe. Generally, spectroscopy is used to measure the apparent wavelengths of the selected section of the sky, a field of science that studies visible light and electromagnetic radiations, analyzing the radiations to their spectra. Figure 1 shows a map of galaxies plotted by the data received from the 2MASS redshift survey. [7]

B) X-ray observations

C) Gravitational lensing

Both types of gravitational lensing occur due to predictions made by the general theory of relativity in which massive objects can bend light passing nearby. Strong lensing occurs when massive systems, such as galaxy clusters, have high gravitational potential to bend light rays passing through them, forming arcs and multiple images as in Figure 2. These arcs give information about the mass distribution in galaxies. [9] Weak gravitational lensing is the subtle distortion of the shapes of distant galaxies caused by the gravitational influence of foreground mass distributions. Unlike strong lensing, weak lensing does not produce multiple images but instead induces coherent, systematic shape distortions in the observed galaxy population. These distortions, known as shear, contain valuable information about the underlying mass distribution along the line of sight. [10]

D) Surveys and data sources

Planck satellite, named after Max Planck, is sent to measure the cosmic microwave background over the whole sky. The satellite is composed of three main parts. The telescope focuses light onto the detectors which is done by 2 mirrors. It also protects the detectors from the glare of objects in the sky such as the moon or bright planets as shown in figure 3. The satellite has two instruments. The low-frequency instrument, LFI, and the high-frequency instrument, HFI. Those are in a box under the mirrors with horns that collect the radiation. Both instruments are cooled to 20K and 0.1K, preventing them from detecting their thermal glow. Both instruments look at different wavelengths. LFI looks at light with a wavelength of 10, 7, and 4 mm (corresponding to frequencies of 30, 45, and 75 GHz), while HFI looks at light with wavelengths between 3 and 0.3 mm (corresponding to frequencies between 100 and 900 GHz). [13]

IV. MORPHOLOGICAL CHARACTERISTICS

A) Shapes of galaxy clusters

B) Sizes and mass distribution

C) Substructures within galaxy clusters

D) Spatial distribution and clustering properties

V. CONSTRAINTS ON COSMOLOGICAL PARAMETERS

A) Maximum likelihood estimation and Bayesian analysis

B) Systematic uncertainties

C) Constraints on cosmological parameters

D) Friedmann's equations

VI. DISCUSSION AND IMPLICATIONS

A) Implications for the ΛCDM model

B) Comparison with other cosmological probes

VII. CONCLUSION

I. Introduction

It is no secret that the internet plays a significant role in people's lives. According to some studies, 90% of adults own a smartphone [1] , and more than 71% of American adolescents, ages 13—17, regularly use Facebook [2] . So, it is impossible to deny the space social media occupies, especially in adolescents' routines, and how much it is responsible for imprinting behavioral values and mediating relationships [3] . One of these types of relationships—-which became more common with social media—-are the parasocial relationships, defined as one-sided connections with celebrities, media figures, or characters (which can be real or fictional) [4] . As shown by Figure 1, these connections are primarily built in through the media platforms [5] .

The audience usually imagines these relationships since they do not have contact with the person they are following. Still, these relationships can affect viewers' personalities, emotions, and behaviors [6] . According to Giles [7] and Lana [8] , these interactions started to be established more frequently in the twentieth century with the rise of TV and cinema, allowing celebrities and artists to gain the public's admiration. Now, with these public figures on social media, the audience can follow their routines more closely and get to know them, creating a sense of familiarity [9] . As studies show, these parasocial relationships can satisfy emotional, behavioral, and cognitive needs [10] . Just like friends and family do, these connections help viewers feel less lonely [11] , impact buying decisions [12] , and affect people's view of their bodies [9] and [13] . As shown by the chart below (Figure 2), these relationships depend on several factors, such as attraction to media figures, dependency on media, etc. [14] .

II. Literature Review

Considering the current generation was born in the digital era, the social media content they consume plays a tremendously important role in their lives [31] and [32] . According to a Brazilian study, data collected from teenagers in 2019 indicated they spent almost 5,8 hours connected on their phones on weekdays and 8,8 hours on weekends [33] . These numbers call the scientific community's attention to analyze the interactions these teenagers build in the digital environment, especially when considering how their characters and personalities are shaped based on whom they interact.

III. Methodology

IV. Results

As illustrated below by image four, when asked how many hours they spend on their cell phone daily, 39.8% answered from 3 to 5 hours; 31.3% from 5 to 7 hours; 21.7% from 1 to 3 hours. 7.2% declared that they spend 10 hours or more. No one answered "Less than 1 hour". Next, participants were asked how much of their screen time was spent checking and interacting with the celebrities/fictional characters they followed. As shown by the graph of figure five, the majority — which covers over 61.4% of participants— said that they spent less than one hour checking; 26.5% spent 1 to 3 hours; 7.2% spent 3 to 5 hours; 2.4% spent 5 to 7 hours and only 2.4% spent 10 hours or more.

63.9% declared that they do not usually interact on the social media of their favorite celebrities and fictional characters through commentaries on lives, publications, or private messages, while 36.1% declared that they do. 75.9% of the 83 participants also stated that they have never created a fan club or page dedicated to their favorite celebrities or fictional characters, while 24.1% stated that they have. When asked if they know a lot about the lives of these celebrities or fictional characters, 48.2% of the 83 participants declared somewhat; 43.4% declared yes, and 8.4% no.

Next, the participants were asked if the celebrity or fictional character they liked had positive or negative effects on developing the mental diseases they suffered from. 50.6% answered that it does not apply (do not suffer from any mental disease), 32.5% declared yes to positive effects, 12% had no changes in their psychological picture, and 4.8% experienced positive and negative effects. No one stated having negative effects. All of this data is represented by the chart below. 78.3% answered that they are not familiar with the concept of parasocial relationships, 21.7% declared that they are.

V. Discussion

VI. Conclusion

VII. Acknowledgments

I. Introduction

II. Abbreviation Table

III. Application programming interfaces (APIs)

1. API

API is a set of rules and protocols that allows different software applications to communicate and interact with each other as Figure (1) illustrates. It defines the methods and data structures that developers can use to build and integrate various software components without needing to understand the inner workings of each component. APIs enable developers to leverage the functionality of other software systems, services, or platforms, making it easier to create complex applications by using pre- built building blocks. [7] APIs play a critical role in modern software development by enabling developers to access services, retrieve data, perform actions, and integrate with external systems seamlessly. They can be used for various purposes, such as retrieving data from databases, interacting with web services, controlling hardware devices, and more. APIs can be designed for different levels of abstraction, from low-level system APIs that interact with hardware components to high-level APIs that provide specific functionalities like payment processing, social media integration, or cloud services. [7]

2. Cloudflare

Cloudflare is a content delivery network (CDN) and internet security company that offers services such as content delivery, Distributed denial of service (DDoS) protection, security enhancements, and optimization tools. It operates by routing website traffic through its globally distributed network of servers as Figure (2) shows [8] .

3. DNS

4. Content Delivery

5. Security and load balancing

6. Akamai

7. Edge Server

8. Security Solutions

Security Solutions: Akamai offers security solutions like DDoS mitigation and bot protection. Integrate these solutions to enhance security alongside Cloudflare's offerings [9] as Figure (4) shows.

IV. XGBoost:

V. Methods

i. Integration Between Akamai's and Cloudflare's API:

import requests
import openai
import pandas as pd
from sklearn.model_selection import train_test_split, 
GridSearchCV, StratifiedKFold
from sklearn.datasets import make_classification
from sklearn.pipeline import Pipeline
from xgboost import XGBClassifier
from sklearn.feature_selection import SelectKBest, chi2
from imblearn.over_sampling import SMOTE
                      

# Akamai API configuration
akamai_api_url =
"https://api.example.com/akamai/data_endpoint"
akamai_api_key = "your_akamai_api_key"
# Cloudflare API configuration
cloudflare_api_url = 
"https://api.cloudflare.com/client/v4/"
cloudflare_api_key = "your_cloudflare_api_key"

 # Function to retrieve data from 
Akamai API
def get_akamai_data():
  headers = {"Authorization": 
f"Bearer {akamai_api_key}"}
  response = 
requests.get(akamai_api_url, 
headers=headers)
  data = response.json()
  return data

 # Function to apply insights to 
Cloudflare
def 
apply_insights_to_cloudflare(insights):
  # Implement Cloudflare API requests 
based on insights
  Pass 

                    

# Function to generate AI insights 
 (you need to implement this)
def generate_ai_insights(data):
# Use AI (GPT-3 or your choice) for 
insights
# Implement your AI logic here and 
return insights
  Pass

# main function
def main():
  # Retrieve data from Akamai API
  akamai_data = get_akamai_data()
  # Generate AI insights
  ai_insights = 
generate_ai_insights(akamai_data)
  # Apply AI insights to Cloudflare
  
apply_insights_to_cloudflare(ai_insight
s)
if __name__ == "__main__":
  main()

ii. Machine learning implementation:

for i in range(len(df)):

  if "*" not in str(df.iloc[i,0]):
    x = str(df.iloc[i,0]).split(".")
    b=[]
    if len(x)==4:
      for i in range(len(x)): 
        b.append(x[i])

      out_csv.append(b)
      b.append(df.iloc[i,1])
                      

import pandas as pd
df = pd.read_csv('save.csv')
x = df.drop("case", axis=1)
y = df["case"]
print(df.isnull().sum())
print(df.info())
                      

# Handle class imbalance with SMOTE 
rus = SMOTE(sampling_strategy={0:8974 , 1:8974 })
X_res, y_res = rus.fit_resample(x, y)
# Split data 
X_train, X_test, y_train, y_test = train_test_split(X_res, 
y_res, test_size=0.1, random_state=1000)
# Model selection pipeline 
clf = Pipeline([('feature_selection', fea_sel),
            ('classification', 
XGBClassifier(objective='binary:logistic', 
                  n_estimators=500, max_depth=8))])
# Hyperparameter tuning
parameters = { 'feature_selection__k': list(range(1,4)),
          'classification__max_depth': [3,5,7],
          'classification__n_estimators': [100,300,500]}
cv = StratifiedKFold(n_splits=5)
grid = GridSearchCV(clf, parameters, cv=cv, n_jobs=-1, 
verbose=1)
# Fit the model 
grid.fit(X_train, y_train) 
                      

iii. Monitoring and Analytics:

iv. Redundancy:

v. Optimization:

VI. Results

i. Negative results:

One of the greatest obstacles that faced the project was achieving a high accuracy of the ML model. For example, the data was imbalanced, so many of the tested ML algorithms had biases in their prediction. Graph (1) shows the accuracy of each model. So, it may be eccentric that although logistic regression (LR) has the highest accuracy, it was not used. That is because the data had blacklist IPs much greater than safe IPs, and LR can't deal with imbalanced data, so there were biases in the data. For instance, when the RL printed its predictions, none of them was safe. On the other hand, by examining the accuracy report of the XG boost classifier as shown in Figure (14), it can be easily determined from the precision that the model predicts both categories according to what it learned from the training data. in other words, no bias exists.

ii. Positive results:

The three APIs were tested using the Cloudflare tester to test it on a real website. Firstly, Each API is tested individually. Cloudflare -individually- started with 20% accuracy and ended with 43%. Akamai's API's highest accuracy was 73% even though it was running for 2 hours- usually when the runtime increases, the accuracy increases. When it comes to the paper's project, the first attempt was 85.7%. However, it ended with 97.5%. Graph (2) includes all results, showing how much the paper's project's accuracy is greater than other APIs.

VII. Discussion

Limitations:

Recommendations:

VIII. Conclusion

Appendix

I. Introduction

II. Understanding Schizophrenia

III. Neural Basis of Schizophrenia

Schizophrenia is associated with abnormalities in various brain regions, including the prefrontal cortex, hippocampus, thalamus, and striatum. These regions are critically involved in cognitive processes, emotion regulation, and sensory perception, all of which are disrupted in individuals with schizophrenia. One prominent hypothesis suggests that individuals with schizophrenia may have altered levels of certain neurotransmitters in their brains, particularly dopamine. Dysregulation of dopamine signalling has been implicated in the manifestation of psychotic symptoms. Medications that target dopamine receptors have shown effectiveness in alleviating symptoms, providing further support for the involvement of dopamine in the pathophysiology of schizophrenia.

IV. Neuroimaging studies on Schizophrenia

Neuroimaging techniques, such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and magnetic resonance imaging (MRI), have been instrumental in investigating the structural and functional abnormalities associated with schizophrenia. Structural neuroimaging studies have revealed significant differences in brain morphology between individuals with schizophrenia and healthy controls. These differences include reduced grey matter volume, abnormal cortical thickness, and altered white matter integrity. Such structural abnormalities may underlie the cognitive and functional deficits observed in schizophrenia.[5] Functional neuroimaging studies have provided insights into the neural mechanisms underlying schizophrenia. These studies have shown that individuals with schizophrenia exhibit distinct patterns of brain activity during cognitive tasks. Abnormalities in the neural circuits involved in learning and memory, processing speed, and attention have been observed, providing further evidence of disrupted cognitive functioning in schizophrenia.[6]

I. Introduction

II. Literature review

III. Methods

VI. Results

Responses to the questionnaire of people in the tissue engineering sphere

The scope of the participants was small since there should be only experienced people. Therefore, there were 5 participants in the survey, and all of them were from the biology and healthcare spheres. For the first question, "What is your occupation?" 80% answered healthcare workers, whereas only 20% of all respondents reported a biology student. (Figure 1)

For the next question, "Do you consider 3D bioprinting ethical?", all of the participants responded positively. (Figure 2) The third item asked about "How old are you?", and answers varied from 19 to 70. However, most of them are middle-aged people. The mean of the participants age is 45.8. The third question was "Do you think there is a future of 3D bioprinting?". As it is presented in Figure 3, all of them (100%) believe that there is definitely a future of 3D bioprinting.

Healthcare worker's responses to the interview

V. Discussion

VI. Conclusion

VII. Appendix