bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization design and performance improvement of ther-mal barrier coating (TBC) system
Authors: Lang Zijian , Makunza John
Published: July 2024

Thermal Barrier Coatings (TBC) technology has become one of the key technologies to improve the performance and prolong the service life of gas turbine and other high temperature equipment. In this study, the selection of materials, coating methods, the influence of environmental factors on the performance of TBC system and the thermal stress analysis and optimization strategy are discussed, improve the overall performance and stability of TBC system. TBC coatings were efficiently prepared by the use of Nikolay bonding layer and stabilized zirconia as top ceramic materials, combined with electron beam Physical vapor deposition (EB-PVD) and plasma spraying (APS) techniques. In addition, the thermal stress model was established by FEA, and the thermal stress distribution of TBC system under extreme operating conditions was analyzed in detail, to reduce thermal stress concentration and improve the thermal barrier effect of the coating. Finally, through the establishment of TBC system life prediction model and in-depth analysis of the failure mechanism, a series of preventive measures and performance improvement strategies are proposed, it provides theoretical basis and practical guidance for coating design of gas turbine and other high temperature equipment.
Addressing data imbalance in neural network spam detection with insights from SMS spam collection
Authors: He Siyi
Published: July 2024

In cybersecurity, the persistent challenge of spam detection remains paramount. Traditional methods reliant on human scrutiny or rule-based algorithms are proving inadequate against the constantly evolving tactics employed by spammers. Machine learning emerges as a promising solution, leveraging vast datasets to swiftly and objectively discern patterns and traits within spam messages. By uncovering subtle correlations among message elements, machine learning enhances the precision and efficacy of spam detection systems, offering a dependable and economical approach to combat spam. This study aims to investigate the impact of different strategies for addressing data imbalance on neural network-based spam detection performance. Using the SMS Spam Collection Dataset, four methods for mitigating data imbalance are evaluated against an untreated scenario. Notably, despite inherent data imbalance, the unprocessed scenario exhibits the highest overall performance. Stratified sampling emerges as the most effective technique for accurately identifying spam, while SMOTE excels in preserving legitimate messages (ham) while filtering out spam. These results contribute significantly to peoples understanding of the intricate dynamics in controlling data imbalance in spam detection and offer insightful information for future studies and real-world applications.
The number of Hamiltonian cycles in groups of Symmetry
Authors: Jiang Zixia
Published: July 2024

Groups are algebraic structures in Abstract Algebra comprised of a set of elements with a binary operation that satisfies closure, associativity, identity and invertibility. Cayley graphs serve as a visualization tool for groups, as they are capable of illustrating certain structures and properties of groups geometrically. In particular, each element in a group is assigned to a vertex in Cayley graphs. By the group action of left-multiplication, distinct elements in the generating sets can act on each element to create varied directed edges (Meier[1]). By contrast, the presence of a Hamiltonian cycles within a graph demonstrates its level of connectivity. In this research paper, utilizing directed Cayley graphs, we present a series of conjectures and theorems regarding the number and existence of Hamiltonian cycles within Dihedral groups, Symmetric groups of Platonic solids and Symmetric groups. By exploring the relationship between Abstract groups and Hamiltonian graphs, this work contributes to the broader field of research pertaining to Groups of Symmetry and Geometric Group Theory.
Study of charged particle multiplicity with centrality in Au+Au collisions at SNN = 200 GeV
Authors: He Chensu , Lai Chia-che , Jin Qixian , Lai Pengyun
Published: July 2024

We have carried out methods of study to find the correlation between charged particle multiplicity and collision centrality of Au+Au collisions at SNN = 200 GeV. We have studied the open data from the ALICE experiment and arrived at the conclusion that central collisions produced a greater number of charged particles than peripheral collisions. This is done by reconstructing the three silicon layers inside the inner tracking system of the detector and studying the change in two spatial variables between layers: pseudorapidity with range || < 1 and azimuthal angle with acceptance , which can then be input into the sideband method to find the final multiplicity of each event. The study provides an insight into the transient nature of the Quark Gluon Plasma (QGP) produced in collisions by exploring how collision aftermaths vary with different conditions of the partons, which hopefully adds to the understanding of matter under the strong force at extreme energy density.
Comparison between EAST and ITER tokamak
Authors: Zhou Chunhao
Published: July 2024

This paper provides an overview of nuclear fusion, which is considered as an ideal energy source for the future, with emphasis on its theoretical foundation and tokamak device. In nuclear fusion, the mass of products is always less than the mass of reactants, and this mass defect is converted into enormous released energy. Scientists have proposed several ideas to reach such extreme conditions for nuclear fusion. Among these concepts, the tokamak device which uses magnetic fields to generate heat and confine high temperature plasma is probably the most promising one. Over the last half century, with the unremitting endeavor of building and operation a series of international tokamak projects, magnetic confinement fusion has made a lot of satisfactory successes. In this paper, two representative tokamak devices are discussed, which are International Thermonuclear Experimental Reactor (ITER) and Experimental Advanced Superconducting Tokamak (EAST), about their major difference and similarities between them. ITER is constructed with the cooperation of many countries, and it is the largest tokamak project which targets to prove nuclear fusion is commercially feasible. EAST is another tokamak device which is located in China and made great achievements of magnetic confinement nuclear fusion, such as the record-breaking plasma temperature.
Eclipsing binaries and related photometry
Authors: Chen Yitang
Published: July 2024

This work mainly focuses on eclipsing binaries. It classifies binary stars based on the way they are discovered and chooses eclipsing binaries as the major topic. Firstly, the paper defines that the criteria for classifying eclipsing binaries in this work are Roche lobe by conductions, which divides eclipsing binaries into three kinds. To further explore the characteristics of three different types of eclipsing binaries, this paper applies light curve analysis, an extremely useful method for finding and analyzing binaries on them, and conducts some special features. Based on the analysis, this paper connects the conduction of the Roche lobe with observation data together. Nowadays, research on binary stars is prevalent because they are related to discovering exoplanets. Research on binary stars contributes to the development of discovering exoplanets as well. A couple of methods in finding binary stars can be applied in finding exoplanets, which have dramatic practical utilizations.
Fundamentals of the flatness problem
Authors: Zhang Zican
Published: July 2024

The Flatness Problem is a fine-tuning problem (defined as when a physical constant appears to fall within an arbitrarily precise range such that the current status of the Universe was able to be developed) in physical cosmology, related to the density of matter and energy in the universe. In this paper, I provide an overview of the problem, describing the problems history, beginning with its foundations by Albert Einstein and Alexander Friedmann in General Relativity and the discoveries of Edwin Hubble and Georges Lemaitre regarding the nature and expansion of the Universe. I also present the mathematical basis of the energy density of the Universe by completing a simple derivation from the first Friedmann Equation, before analyzing observational data regarding the apparent density of the Universe. This data comes from the Cosmic Microwave Background and distant Type 1a supernovae. Finally, I explore the possible solutions to the Flatness Problem, including Anthropism and Inflation, and recount each ones strengths and weaknesses.
Interdisciplinary applications of finite group theory - From epidemic modeling to financial market analysis
Authors: Liu Xuedong , Wu Yuhan , Yin Hao , Zhao Haoqian
Published: July 2024

Finite group theory has a long history. Many mathematicians have done researches on it. As time goes on, it becomes a useful tool for human as it has a lot of applications. Finite group theory becomes more than just mathematical. As a result, its applications on epidemic modeling and financial market analysis are introduced respectively. These focus on SIR (Susceptible-Infective-Recovered) model and stock markets. Whats more, there are some other applications in different areas. What we came to the conclusion is that wired groups have developed in many fields, not only in financial markets and infectious disease models, but also in many other fundamental disciplines, chemistry, physics, computer science. They are going to be showed in the following essay.
Study of stellar properties under TESS satellite observations
Authors: Tian Tian
Published: July 2024

This study employs data from the TESS satellite to investigate the properties of stars. TESS, the Transiting Exoplanet Survey Satellite, offers insights into star activity, periodic variations, and planetary transits by monitoring changes in stellar brightness. Our analysis of TESS data explores critical star attributes, including mass, luminosity, activity levels, and dynamic changes. By examining the relationship between these stellar properties and the presence of planets, we aim to uncover the intricate interplay between celestial bodies and unveil the universe's mysteries. This research encompasses data collection methods, processing techniques, and data analysis approaches, addressing star classifications, parameters, and periodic stellar activity. We also delve into flare energy, which reveals the energy released during stellar flares, and photoperiodic analysis, a tool for understanding periodic light changes in celestial bodies. The study concludes by highlighting the significance of newly discovered planets and the diversity of stars observed by TESS. It underscores the importance of comprehending the connections between stars and planets, shaping our understanding of celestial habitability. In summary, this study enriches our knowledge of star and planet properties, contributing to a broader comprehension of the universe. Through our analysis, we seek to illuminate the complex relationships between celestial entities and unveil the universe's enigmas.
Parkinsons disease detection based on image analysis of EEG signals
Authors: Zhang Yitao , Yu Han , Sun Chenyang , Jin Mingheng
Published: July 2024

Parkinsons disease (PD) is a common chronic neurological disease, that causes great disturbance to the patient's life and work, and when the disease develops seriously, it may even lead to the death of the patient. Until now, treating PD has been a tough nut to crack and a financial challenge for families and governments alike. In this paper, we propose to use the Resnet-50 Neural Network model to differentiate between 41 PD patients and 41 normal subjects by analyzing time-frequency domain maps of electroencephalography (EEG) signals. This method achieves classification accuracies ranging from 81% to 85% for six-channel detection and varying from 76% to 77% for single-channel detection, which opens up new avenues for the early diagnosis of Parkinson's disease, demonstrating the potential to combine EEG signals with image processing.
Verification of Taylor's theorem
Authors: Li Qiyu
Published: July 2024

Multivariate function calculus is an important part of mathematical analysis courses, and most conclusions can be found and generalized in univariate calculus. However, the biggest difficulty in teaching multivariate calculus lies in its abstraction, such as Taylors theorem, multiple integral regions drawing, and integral variable transformation. At the same time, ordinary differential equations are also one of the basic courses of the profession, and dynamic systems based on ordinary differential equations have extensive applications in mathematical models of continuity problems and optimal control problems. Software such as Mathematica, Python, Matlab, etc. can solve similar problems. Therefore, this article will use the visualization and computational capabilities of Mathematica to validate important definitions and conclusions in multivariate calculus, and compare the differences among the three software in solving approximate numerical solutions of dynamic systems of ordinary differential equations from different perspectives.
Overview of the habitability of exoplanets several factors of habitability
Authors: Zhang Boya , Wang Mengduo , Xu Xiaohang
Published: July 2024

By analyzing the data of the earth and the sun, we get some of the factors of a planets habitability, such as the distance from the star to the planet, the atmosphere, and the tidal locked effect. We use the equation of radiative equilibrium and get the orbital semi-major axis in terms of the stars radius and the planets temperature and albedo. Then, according to the data of the planet and the definition of habitat zone, we can get the range of the habitat zone. We find that the relationship between the radius of the star and the planets radius is the radius of the stars surface, the temperature of the planets surface, and the albedo. We also discussed how the albedo, the star mass, the composition of the atmosphere, and the tidal lock influence the planets habitability. In conclusion, the only factor of the habitability of a planet is not the distance from the sun to the planet, we need to consider more factors and re-examine the habitability of the planets.
Habitability of an exoplanet based on stellar wind
Authors: Lee Changseok
Published: July 2024

This paper aims to investigate the influence of stellar winds on the atmosphere of exoplanets. Stellar winds, which consist of charged particles produced by stars, can heavily alter and affect the evolution and composition of exoplanet atmospheres. This paper will provide an overview of stellar wind characteristics and their interactions with exoplanets. It will also investigate the consequences of such interactions, such as atmospheric erosion, atmospheric escape, and the exoplanets habitability. In order to develop a more sophisticated and accurate method for identifying habitable planets that may host life, it is necessary to better understand the mechanisms of stellar wind to build more accurate models of stellar wind.
Enhancement of CAR-T cell-targeted immune responses against solid tumors: role of lowered CARs affinity and addition of PD-1 chimeric antigen complexes
Authors: Lee Junyoung , Qin Jason , Qu Shihao , Li Zhaoyi
Published: July 2024

Cancers rising prevalence demands novel treatments. Chimeric Antigen Receptor T-cell (CAR-T) therapy emerges as a transformative option. Conventional cancer treatments of-ten lack efficacy and carry severe side effects. This study focuses on enhancing CAR-T therapy against solid tumors. Strategies include lowering CAR affinity for antigens and in-tegrating PD-1&CD-28 chimeric antigen complexes. While CAR-T therapy excels against hematological malignancies, challenges remain, such as immune checkpoint upregulation and potential toxicity. The research aims to optimize CAR-T therapys efficacy through innovative modifications. Alternative strategies like multi-targeted CARs and combina-tions with immune checkpoint inhibitors are explored. Overall, this study advances can-cer therapy by addressing CAR-T therapys limitations and proposing complementary strategies for improved personalized treatments.
Effect of smoking on lung cancer: A causal inference approach
Authors: Qiu Ziyang , Ge Tianyi , Zhang Zhaopeng , Lu Zhenchen , Cao Louis
Published: July 2024

Smoking is associated with an increased chance of developing lung cancer. Three causal inference methods, backdoor adjustment, front-door adjustment, and counterfactual are used to analyze observational data on smoking, lung cancer, and related risks factors. Backdoor adjustment fails to allow for possible presence of unobserved confounders, which is merited by front-door adjustment. Counterfactual harnesses individual patient statistics to establish causal relationships between smoking and cancer on the individual level, so as to evaluate lung cancer risks after changes in individual smoking habits. Results by different methodology are in good agreement and showcase a strong causation between smoking and lung cancer at both group and individual level.
DTI Tractography
Authors: Qu Yuxin
Published: July 2024

The DTI technology used to observe the Nerve fascicle - Nerve fascicle imaging was also discussed in detail. Two types of deterministic and probabilistic traction imaging were compared from effectiveness and computational efficiency perspectives. Then an experimental study was conducted using different software to compare the fiber bundles of the subjects brain and the normal brain and to evaluate the impact of different angle thresholds on the number of observable fiber bundles. Research has confirmed that the subjects whole brain fiber bundle imaging reconstruction was successful, and there was no significant difference compared to the normal brain fiber bundle. The study also found a specific quantitative relationship between the angle threshold and the number of fiber bundles. This experiment also explored the differences in connectivity strength between the brain regions of the left and right brains and their significance.
Quantitative structure-activity relationships modelling in antimicrobial peptides design
Authors: Shang Zhongrui
Published: July 2024

Antimicrobial peptides are antimicrobial agents with high bioactivity and low bacterial resistance, which inspire scientists to recognize AMPs as appealing candidates for microbial infection treatments. QSAR modeling is one of the most important techniques in computer-aided drug design that is typically beneficial to promoting the design of AMPs. This paper presents an overview about breakthroughs and achievements in AMPs designed by QSAR modeling in recent 10 years, and primarily talks about 6 successful AMPs discoveries covering different QSAR models and solving various antimicrobial problems such as biological activity, cytotoxicity and bacterial resistance, which is responsible for future developments in new generation of AMPs and optimization in QSAR modeling.
Exploring the interplay between Alzheimer's disease and COVID-19: Insights into cognitive impairment and drug interactions
Authors: Xu Jiachen
Published: July 2024

Alzheimers disease (AD) is a severe well-known cognitive disease, and it is also the most worldwide cause of dementia. Studies have shown that AD may have some interactions with the coronavirus disease 2019 (COVID-19) in some particular areas. This paper aims to figure out the potential associations how one causes the interplay of another, whether directly or indirectly. In addition, the paper provides a comprehensive mechanism underlying cognitive impairment also known as brain fog of COVID-19, including the effects on neurons and glial cells by persistent elevation of the chemokine CCL11. Then the paper provides drug-drug interaction with metabolized by same cytochrome enzymes lead to major changes in the pharmacology of treatments. Finally, the paper concludes the points mentioned about the associations between COVID-19 and AD then suggests the direction of the future studies.
Comparative analysis of antibiotics: Inhibition of protein synthesis versus DNA synthesis
Authors: Wang Xiaoci , Liu Daini , Li Yachun , Zhang Duohan
Published: July 2024

In this piece of writing, we will be addressing the following factors: properties of antibiotics, mechanism of action, clinical safety profile, as well as toxicity, adverse event profile, and case fatality rate. From our evaluation, in terms of properties of the two groups of antibiotics, protein synthesis-inhibiting antibiotics displays a like number of lipophilic and hydrophilic properties, with the majority of Anti-30S inhibitor drugs being predominantly hydrophilic, and a higher number of lipophilic drugs amongst Anti-50S inhibitors. Anti-50S inhibitors could therefore be preferred in oral intake drug development. DNA synthesis-inhibiting antibiotics show a limited trend in their properties. The mechanism of action for protein synthesis-inhibiting antibiotics generally follows four steps: activation of amino acids, initiation, extension, and termination of peptide chain synthesis. On the other hand, DNA synthesis-inhibiting antibiotics exhibits a range of different mechanisms, all of which results in the damage of DNA structure of the bacterium. In both clinical safety and toxicity studies, it is concluded that the two groups of antibiotics have previous cases of side effects, with the DNA synthesis-inhibiting drug, fluoroquinolones, more dangerous due to its potential to result in ventricular fibrillation on a patient. By assessing and comparing all the factors, this review aims to present a precise comparison of the similarities and differences between the protein and DNA synthesis inhibitors, and an evaluation of the effectiveness of the two groups against bacterial infections, along with their safety for regular medication use.
MRI phenotype mediates the association between APOE4 and AD: A cross-sectional study
Authors: Luo Jiayin
Published: July 2024

Background: Alzheimers disease (AD) is a prominent factor in mortality among older people. The precise biological mechanisms linking APOE4 and cognitive impairment in AD are still unclear, despite significant research efforts. Thus, our study aimed to explore the potential mediating role of MRI phenotypes in the relationship between APOE4 and cognitive impairment in AD. Methods: The white mass volume of MRI phenotypes was measured in 1721 older adults based on the ADNI. Linear regression was employed to explore the association of cognitive impairment and MRI phenotype with the APOE4 allele, respectively. Furthermore, the researchers performed mediation analyses to explore the potential mediating effect of the MRI phenotype on the relationship between the APOE4 allele and cognitive ability indicated by the MMSE score. The researchers also stratified the results by gender and by age to ensure comprehensiveness. Results: The research indicated that both the mean causal mediating effect (ACME) and the mean direct effect (ADE) of LH, RH, LA, and RA in relation to the APOE4 and MMSE score associations were negative. Besides, the white matter volume of the MRI phenotype mediated the relationship between APOE4 and MMSE scores, with mediation proportions of 47.5%, 43.7%, 33.1%, and 28.3%, respectively. Notably, the proportion mediated was higher in the hippocampus than in the amygdala, and it was also higher in the left hemisphere compared to the right hemisphere. All of these findings were statistically significant (p-value < 0.05). Additionally, when stratified by age, the older half of the participants exhibited higher ACME levels compared to the younger half, and when stratified by gender, females demonstrated higher ACME levels than males. Conclusions: The mediation analysis showed a good fit with previous studies. The model presents a promising target for further future studies on clinical diagnosis and potential therapeutic targets related to APOE4.
Emerging immune checkpoint and its combination therapy in cancer treatment
Authors: Yue Xiaozheng
Published: July 2024

In recent decades, cancer immunotherapy has become a cornerstone of cancer treatment, standing alongside traditional methods such as surgery, chemotherapy and radiation therapy. This article provides a comprehensive exploration of cancer immunotherapy, with a particular focus on the critical role of immune checkpoints in cancer treatment. It covers classic immune checkpoints such as CTLA-4 and PD-1, as well as emerging checkpoints such as TIGIT, TIM-3, LAG-3, VISTA, and IDO-1, elucidating their mechanisms and ongoing clinical trials. In addition, the promising areas of combination therapy involving immune checkpoint inhibitors (ICIs) are investigated. In this study, the combined action and synergistic effect of different ICIs such as CTLA-4 and PD-1 were investigated. In addition, it examines how ICIs can be strategically paired with other treatment strategies, including EGFR TKIs, anti-VEGF /VEGFR therapies, radiotherapy, and mRNA-based vaccines. These combinations are designed to enhance anti-tumor responses and overcome drug resistance. In conclusion, this article provides a comprehensive overview of cancer immunotherapy, highlighting its critical role in modern oncology. It points to the richness and complexity of immune checkpoints, highlighting their importance, challenges, and the development of combination therapies aimed at harnessing the potential of the immune system to fight cancer.
The potential inhibition effect of ellagic acid on liver cells in the treatment of hepatocellular carcinoma
Authors: Xu Yongxi
Published: July 2024

Hepatocellular Carcinoma (HCC) is the most common kind of liver cancer, which has a relatively high fatality rate. The main cause of this type of cancer is liver inflammation, which leads to disorder in the tumor microenvironment. There are some current treatments, including liver transplants, radiofrequency, microwave ablation, and transarterial chemoembolization. However, the treatments still have limited disadvantages, which cannot be ignored. Recently, a traditional Chinese medicine - Chebulae Fructus, has been proven to have an inhibition ability on HCC cells. One of the main constituents, ellipticine, is found to have therapeutic targets that can treat HCC: PTGS2, CYP1A2, CCNB1, and RASGRF1. Moreover, the essay also mentioned that another type of constituent ellagic acid could also have an inhibition effect on tumor progression by binding to Cyclin-Dependent Kinase 6 (CDK6). This study will further investigate the HCC inhibition effect of ellagic acid by providing an overview of the predicted results of the experiment. It will also provide the possible research direction of ellagic acid HCC treatment.
Phainanoids, a promising natural product from traditional Chinese medicine
Authors: Xie Chengmin , Luo Jie
Published: July 2024

This article presents a comprehensive introduction to the biological activities, structural characteristics, and synthetic research of Phainanoids. These compounds hold immense potential for medicinal applications. Notably, their unique structural features encompass two distinct subunits, 3H-spiro[benzofuran-2,1-cyclobutan]-3-one and 1,6-dioxaspiro[4.4]nonan-2-one, rendering their synthesis challenging due to the unconventional skeleton and functional group structures they possess. The structure-activity relationship analysis of Phainanoids reveals that their activity is intricately linked with their structure; notably, the most potent compound among them Phainanoid F exhibits efficacy levels surpassing cyclosporine by 7-fold or even up to 221-fold. Consequently, Phainanoids exhibit vast prospects for development as novel anti-tumor and immunosuppressive drugs. Despite significant progress made in this field thus far, several unresolved issues demand attention moving forward. Future endeavors should focus on further elucidating the mechanisms of action and in vivo activities exhibited by Phainanoids while also developing efficient synthetic methodologies for large-scale production purposes. Given their wide range of biological activities coupled with distinctive structures and pharmacological characteristics possessed by these compounds, there remains ample scope for exploration aimed at unraveling their mechanisms of action and enhancing their utilization potential.
The role of FGF2 in stress-induced cFos expression
Authors: Zhang Yifan
Published: July 2024

During periods of stress, organisms endocrine systems respond by releasing glucocorticoids which later get expressed through corticosterone, simplified as CORT. CORT is proved to express FGF2, proteins found in critical glial cells named astrocytes. Increased FGF2 expression leads to higher rate of cell proliferation - including neurons. Theres been discoveries that newborn neurons, specifically those who have been exposed to FGF2, led to early neuron activation and enhanced neuronal function. Whats unclear is whether FGF2 played a role in somehow newborn neurons having enhanced function and activating earlier than usual (by releasing the early activation gene cFos). An experiment has been outlined in order to test the hypothesis that FGF2 does indeed play a role in altering some neurons. Using nanoparticles containing the micro RNA of NUDT6 - a gene that suppresses FGF2 expression - to target FGF2 in adult male Sprague-Dawley rats would directly test if FGF2 played a role in enhanced neuron activation.
Exploring patterns and insights in a comprehensive diabetes dataset
Authors: Zhuang Eric , Chen Yiyu , Ran Xima , Yi Jinfei
Published: July 2024

Diabetes, a pressing global health concern, imposes significant economic and healthcare burdens on millions worldwide. Understanding the multifaceted factors contributing to diabetes is pivotal for effective prevention strategies. In this study, we leverage a comprehensive dataset (952 instances, 17 predictor variables) and employ a multifaceted statistical approach to explore the intricate interplay among stress levels, blood pressure (BP), body mass index (BMI), age, sleep quality, and physical activity in relation to diabetes, with a focus on classification and predictive implications. Our research begins by establishing fundamental relationships between discrete variables using crosstabs and chi-square tests. We uncover close associations between stress levels and BP, heightened diabetes risk with increased BMI values, and the influence of age on sleep quality. Subsequent analysis, based on descriptive data, reveals a robust correlation between physical activity and stress levels, with the paradoxical observation that excessive exercise may increase stress levels. Factor analysis further elucidates the pivotal roles of sound sleep and regular exercise in diabetes prevention, supported by asymptotic significance levels below 0.05. To culminate our study, we construct a logistic regression model with an impressive 89.3% accuracy rate for predicting diabetes risk. Notably, age, family history of diabetes, and regular medication usage emerge as the most influential factors, with regular medication demonstrating significant potential for reducing diabetes risk. Our research underscores the intricate web of factors shaping individual health and offers valuable insights for a comprehensive understanding of health and well-being in the context of diabetes prevention. Moreover, it highlights the importance of considering multiple factors in health-related research. Future research could delve into the long-term effects of interventions targeting the identified risk factors, explore the impact of socio-economic factors on diabetes risk, and investigate the potential role of emerging technologies in personalized diabetes prevention strategies.
Review of current anti-obesity medication
Authors: Shi Jiayi
Published: July 2024

Although it is most widely accepted to lose weight by purely exercising and modifying diet, such a strategy often takes a relatively long time to work and may be inadequate for patients with other conditions, such as knee problems. The anti-obesity medications (AOMs) approved in the market are generally divided into two groups with either acting sites on peripheral digestive organs or on central nervous systems (CNS). Two representatives of these categories are Orlistat and Semaglutide. Especially, the GLP-1 receptor agonists (GLP-1 RAs), represented by Semaglutide, have a more promising future and more medications undergoing development.
Beneficial effects of extra virgin olive oil on type 2 diabetes
Authors: Jiang Rixing
Published: July 2024

In contemporary society, chronic diseases become increasingly prevalent, among which is type 2 diabetes. Nowadays, daily diet is gaining growing attention and interest from the general public for its potential to prevent and improve these diseases. One of the ingredients highly recommended to be included in peoples diet is extra virgin olive oil (EVOO), since substantial evidence has shown that it is helpful in reducing body weight and the risk of obesity and type 2 diabetes. Consequently, this review paper is aimed at comparing EVOO with other types of oil or low-fat diets and summarizing its health benefits to type 2 diabetes by analyzing relevant parameters, including fasting blood glucose (FBG), insulin, glycated hemoglobin (HbA1c), body weight, Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), etc. A systematic literature search was conducted using Google Scholar with the year range from 2010 to 2023. After being considered against the exclusion criteria, 8 articles were eventually included. Results showed that EVOO is associated with improving some risk factors of diabetes, leading to decreased blood glucose and greater short-term weight loss compared to low-fat diets, and resulting in more body fat loss compared to soybean oil. Thus, EVOO is now expected to be clinically used in treating type 2 diabetes in the future.
Explore the impact of free sulfur dioxide on red and white wine
Authors: Tian Yuyang , Peng Jiahuizi , Li Xinyue , Wang Sirui
Published: July 2024

Today, red and white wine are essential symbols that motivate the worlds economy and are cultural symbols. Thus, the taste and process of alcohol during fermentation have become highly evocative to producers today. In this way, this article focuses on the effect of free sulfur dioxide on the concentration of fixed and volatile acids since fixed and volatile acids have a high impact on the taste of red and white wine, according to scientific research. This article also focuses on the PH value of both wines to investigate whether the free sulphate dioxide has a positive effect on the PH acidity of red and white wine. By paying attention to the acidity, the producers can further investigate the health impact of both wines and provide better choices and plans for the consumers. Using Cortezs data, this article matches the linear regression model to compare the fixed and volatile groups between red and white wine. PH values of both wines are also laid out in the final linear regression model group. The linear regression model tests the difference between free sulfur dioxides effects on the two wine categories. Lastly, the RMSE value has been used to test whether the result is reliable.
Research question: A comparison of Fluorouracil and Docetaxel in the treatment of gastric cancer
Authors: Pan Junteng
Published: July 2024

Cancer is a serious lethal disease that is prevalent worldwide. This disease is mainly due to the development of abnormal cells that divide without any control. These abnormal cells, also known as tumor cells, can destroy normal body tissue. Causing numerous deaths around the world. Gastric cancer, commonly known as stomach cancer, is the cancer to be discussed in depth in this essay. It is one of the most prevalent and lethal cancers worldwide. Its cases and mortality rates vary significantly across different regions, reflecting differences in diet, genetics, and medicine access. Gastric cancer presents substantial challenges in medical treatment. The disease's complexity also leads to the exploration of various treatment methods. Among many pharmacological options, Fluorouracil and Docetaxel are two significant medicines that are worth discussing in depth. Fluorouracil and Docetaxel are both used in chemotherapy. However, they have distinct mechanisms and properties. Fluorouracil is a pyrimidine analog used to inhibit DNA synthesis of cancerous cells, while Docetaxel is a taxane that targets microtubules. They all played a vital role in treating cancer. Nevertheless, their comparative effectiveness and safety remain in debate. This essay aims to provide a depth comparison of Fluorouracil and Docetaxel. Focusing on their chemical structure, mechanisms of action, delivery modes, resistance, and toxicity, in the treatment of gastric cancer. Offering a better comprehension of two drugs, which are used for their optimal use in patient care.
Modification of ibuprofen synthesis through the mechanism analysis
Authors: Hu Xinzhi , Song Yulai , Li Jincheng , Huang Yueying
Published: July 2024

Ibuprofen is a common anti-inflammatory drug (NSAID) that was first invented and patented by Boots UK back in the 1960s. Even though ibuprofen is made in huge amounts worldwide nowadays, researchers are still trying to improve how it's synthesized to make the process more efficient, sustainable, and less harmful to the environment. This paper looks at how ibuprofen synthesis methods have evolved over history, from Boots' original approach to more recent stuff like the BHC process, using electrochemistry, and continuous flow systems. It examines the mechanism, green chemistry measures, and the pros and cons of each technique. While it gave the first usable manufacturing method, Boots' synthesis wasn't so great with its atom economy. The BHC synthesis boosted yield and atom economy a lot by streamlining the process. Newer ways aim to make it even more selective and sustainable by using novel chemistries and tighter process control. Basically, ibuprofen synthesis has steadily progressed over time thanks to step-by-step innovation and a better understanding of the mechanisms. The paper suggests ways forward to produce this important drug in safer, more efficient, eco-friendly ways using modern green chemistry practices. Any pharmaceutical synthesis impacts people and the environment, so we gotta keep improving processes.
Research on the function of CG15382 by mutating Zelda binding region
Authors: Chang Cheng
Published: July 2024

Zelda protein (Zld) is a transcription factor in drosophila. It binds on specific sequence to activate the expression of genes involved in the early development of drosophila, which hinted a method to study the function of these genes. Since the expression of genes can be regulated by Zld, mutation of Zld binding site can be used to silence target genes. Three base pairs were substituted by using CRISPR-Cas9 editing. The function of target genes in the development process can be studied by comparing wild type drosophila and the Zld absent mutant (Zld-). This paper will focus on CG15382 and discuss the design of the mutant drosophila.
Multifunctional GelMA platforms with Hybrid hydrogel
Authors: Zhou Jingye , Wang Jinmoqian , Liu Minghao
Published: July 2024

Polymer hydrogels, 3D scaffolds for tissue adjustment and delivery systems for therapeutic molecules and cells, are a fascinating platform. For example, GelMA methacrylate has become a representative hydrogel preparation and has wide-ranging applications in biomedicine. Recent research on GelMA-based hydrogels has focused on combining them with other things is a helpful way to make GelMA better for some uses, such as carbon nanotubes (CNT), graphene oxide (GO), tiny particles from minerals, other natural materials, and man-made materials, aiming to provide GelMA with enhanced biological properties and physicochemical. The benefits of this approach are manifold: i) change the electrical conductivity and simulate the propagation of electrical signals, present electrical/magnetic properties to generate for repair of specific tissues, ii) promote bone cell growth without specific growth factors, and iii) can closely mimic the natural tissue environment. iv) Facilitates tissue culture and drug delivery. The method utilized in GelMA can also be applied to other existing polymer hydrogel systems.
A preliminary exploration of brain fiber bundle reconstruction and its influencing factors based on diffusion tensor imaging
Authors: Wang Pinqin
Published: July 2024

This paper focuses on the methods and progress of brain fiber bundle construction based on diffusion tensor imaging data, and conducts a preliminary exploration of the construction results. The study is based on 45 subjects in the public database, using different software to construct nerve fiber bundles for comparison, calculating the number of fiber bundle tracking under different parameters, and finally conducting a t-test on the fiber bundle connection strength of the left and right brain. The nerve fibers constructed by different software are relatively consistent and also consistent with human brain nerve fibers. A more relaxed length threshold will increase the number of fiber tracking, and as the angle threshold increases, the number of fiber tracking will first increase and then decrease. There are differences in the connection strength of the left and right brain. This preliminary study provides a foundation for further exploring brain connectivity and analyzing differences between individuals or patient groups. While the fiber bundles constructed matched expectations, more validation is needed against post-mortem dissections or other imaging techniques. Additionally, expanding the study to include more subjects and analyzing specific fiber bundles or regions could provide more detailed insights. The methods and parameters evaluated in this work help establish best practices for fiber tracking and analyzing connectivity strength metrics. With refinement, such fiber bundle analysis tools may help researchers better understand structural brain networks and differences related to development, aging or disease.
China's Digital Economy: Examining Its Impact on Inequality Through the Lens of Employment, Income Disparity, Innovation, and Competition
Authors: Huang Muzeng
Published: July 2024

This paper examines the impact of Chinas digital economy on inequality, focusing on employment, income inequality, innovation, and competition. Overall, the digital economy creates inequality in individuals and firms. It exacerbates the income disparity between high-skilled and low-skilled workers, and/or across different regions. It also creates an oligopolistic structure in the digital markets and new industries, where big firms use network effects and incumbency advantage to reduce competition. Therefore, challenges arise for antitrust enforcement due to the dynamic nature of the digital economy and attention platforms. Nevertheless, the digital economy optimizes employment structure, having a net effect of greater creation effect than substitution effect. It also increases innovation efficiency and opportunities for smaller players in the traditional sectors. Therefore, the digital economy has positive implications for economic growth. Overall, it is crucial to encourage equality and stimulate economic growth by implementing certain policies, including measures to enhance labor quality, reduce regional disparities, and strengthen antitrust regulations.
Investment Advice of Dialysis Companies: Whats the Trend of the Industry?
Authors: Song Jiayi
Published: July 2024

The global dialysis market is continuing expanding in the recent years and dialysis industry has expected investment prospects. This study presents a comprehensive investment analysis framework comparing four top companies in the kidney care and dialysis service industry. This article aims help investor choose the most suitable company among them with the highest investment value to invest in. The study uses official financial data from Nasdaq from 2023 and forecast data from estimates of 2024 of 4 dialysis companies on the waiting list. By analyzing and comparing the financial statements of four companies, the asset investment potential and growth rate of the four companies were determined. Results demonstrate that the company DaVita rank as the most appealing investment option out of four companies, followed by Medtronic, Baxter and Fresenius. Based on financial statements, DaVita is attractive with highest expected growth rates in EPS and gross revenue, PEG ratio, and relatively high annual and quarterly gross margin.
The Application of Differentiated Pricing Strategy in M&A Practice of Listed Companies
Authors: Xu Yuancong
Published: July 2024

In recent years, with the continuous active market of listed companies M&A and reorganization, the phenomenon of differentiated transaction pricing M&A is very common. In order to quickly shorten the transaction process, the initial design of the transaction plan needs to pay attention to the demands of the transaction parties, and the application of differentiated pricing strategy meets the diversified needs of the transaction parties. Based on the historical wave of M&A and the development status of Chinese listed companies, this paper analyzes the applicability and defects of M&A pricing of Chinese listed companies from the point of view of the core issue in the M&A process. This paper studies the pricing problem from two aspects. First, starting from the deterministic system, it analyzes the pricing of M&A enterprises under the perfect market system. The second is from the actual situation of China, the use of AMPS model for analysis. The research results show that the practice of differentiated pricing strategy of listed companies can not only deepen the understanding of the role of corporate governance in M&A, but also promote the effective risk control and profit maximization of listed companies. The research significance is to have a further understanding of the M&A pricing of China's joint-stock enterprises.
Chinese dragon modeling based on Openscad
Authors: Li Guanchen
Published: July 2024

Nowadays, 3D modeling technology has become a vital link between modern technology and traditional culture. The question of how to replicate and innovate traditional cultural elements using advanced computer graphics (CG) technology has become a topic worthy of exploration. Therefore, this paper aims to discuss how to construct a dragon model rich in Chinese traditional characteristics through the powerful 3D modeling tool OpenSCAD, to demonstrate the potential of integrating modern modeling technology with Chinese traditional cultural elements. The paper first introduces the basic features of OpenSCAD and its potential applications in constructing complex 3D models, followed by a detailed discussion on the design concept, modeling process, and technical details of the Chinese dragon model, including the dragon's posture, detail depiction, and color coordination. Ultimately, a digitally rendered Chinese dragon with profound traditional aesthetic sensibilities was successfully created. The experimental evaluation results garnered widespread recognition from evaluators, particularly in terms of appearance.