bluuebunny/crossref_metadata_2025 huggingface dataset:
Possible effects of treatments durations with Ruxolitinib on inhibiting jak phosphorylation of STAT1 in A549 lung cancer cells
Authors: Si Junhui
Published: July 2024

This study explores the effects of different treatment durations of Ruxolitinib on inhibiting JAK phosphorylation of STAT1 in A549 lung cancer cells. The JAK/STAT signaling pathway is critical in lung cancer development and progression. By utilizing western blotting, a STAT luciferase reporter assay, and an MTT viability assay, the study evaluates the impact of Ruxolitinib treatment on STAT1 phosphorylation, STAT activation, and cell viability. Understanding how treatment duration influences JAK/STAT pathway inhibition provides valuable insights for developing targeted therapies in lung cancer. This research contributes to addressing the challenges of lung cancer treatment by exploring the potential of Ruxolitinib as a therapeutic agent.
Elucidating the mechanism of action of benzodiazepine drugs: The role of hippocampal damage in the development of anterograde amnesia
Authors: Gao Xing
Published: July 2024

Benzodiazepines are often prescribed medications that are widely utilized for a variety of medical issues. The usage of benzodiazepines comes with a number of adverse effects, including anterograde amnesia, which can have a significant negative impact on a patients life. This paper analyzes current amnesia cases and the mechanism of benzodiazepine-induced anterograde amnesia in order to determine how benzodiazepines cause anterograde amnesia. This paper focuses on the mechanism by which benzodiazepines impede long-term potentiation (LTP) by reducing glutamate release and enhancing the inhibitory effect of gamma-aminobutyric acid (GABA) on n-methyl-D-aspartate (NMDA) receptors. The end outcome of this interference is antegrade amnesia. Additionally, this paper gives some suggestions on the use of benzodiazepines and the future research direction. It highlights the necessity of more study to comprehend the underlying mechanisms in greater detail and to develop alternate therapies that lessen the negative consequences on cognition.
Advances in tacrine for Alzheimers disease
Authors: Li Wenting
Published: July 2024

Alzheimers disease is a kind of neurodegenerative disease with insidious and progressive onset, which will affect the normal life of patients to varying degrees. At present, there is no specific drug that can completely cure Alzheimers disease, and there is still a long way to go in the treatment of the disease. In order to provide better ideas for the development of drugs to treat Alzheimers disease, people have speculated and verified a variety of pathogenesis of the disease as far as possible and developed targeted drugs on this basis. In addition to the need to develop drugs from multiple pathogenetic perspectives, it is also necessary to conduct longitudinal studies on one drug. Since tachrine is one of the drugs with great development potential, this paper will take Tachrine, a representative of cholinesterase inhibitors, as the main drug research object, analyze the structure and efficacy of various existing tachrine drugs, and try to provide the future development direction of Tachrine drug transformation.
The total synthesis of Wulfenioidin F
Authors: Sun Ruoshui , Wang Yicheng
Published: July 2024

Zika virus, an RNA virus that can cause severe damage to the human body, has long lacked an effective drug. Scientists have discovered recently that Wulfenioidin, a plant distributed in southern China, contains a compound in its root that holds the potential to become an anti-Zika drug. Through extraction, multiple kinds of Wulfenioidin were discovered and their structures were characterized through various spectroscopic methods. However, there isnt a total synthesis pathway for Wulfenioidin. This essay focuses on Wulfenioidin F and provides a full synthesis pathway for the molecule. The route begins with the synthesis of two fragments 6-(2-bromo-6-methylphenyl)-6-methoxy-2-methylhexan-2-ol and 5-(propan-2-yl)-3-(I^5-diazynylidene)-3,4-dihydro-2H-pyran-2,4-dione, which then undergo a convergent coupling to form an eight-carbon ring. An intermolecular Diels-Alder reaction combines the benzyne and pyrone together, forming the final product and completing the total synthesis.
The inquiry of association between intellectual disabilities and physical disabilities
Authors: Park Kyubin , Eo Eunseo , Lee Jihwan
Published: July 2024

This paper shows the associations between intellectual and physical disabilities. The lack of data concerning the individuals with disabilities population has motivated us to research further upon this topic. The topic deals with people who have physical disabilities and people who have intellectual disabilities in specific states in the U.S. Data about individuals grappling with both physical and intellectual disabilities within California, Texas, and Pennsylvania, spanning the years from 1980 to 2018, was assembled. R-squared values, indicative of the strength of the relationship between occurrences of physical disabilities and intellectual disabilities in these states, were employed to uncover these interconnections. The R-squared values consistently point to a pronounced correlation between these two categories of disabilities. These discoveries imply that individuals with physical disabilities have a heightened likelihood of experiencing intellectual disabilities, and conversely. This study and its associated results serve as a valuable resource for numerous institutions engaged with the people with disabilities community. Furthermore, it equips them with a potent tool for enhanced comprehension of their needs, facilitating the development of more effective and successful contributions.
Up-regulation of IL-1 signaling pathway enhances gene expression of transcription factors (Jun, Junb, Jund, and Fos)
Authors: Li Xiang
Published: July 2024

Preventing he accumulation of Beta-amyloid (A) plaques has been explored as main treatment of Alzheimers disease. Microglial deletion of the gene encoding -site APP cleaving enzyme (BACE-1), an important enzyme facilitating the formation of A, leads to the transition from homeostatic microglia to stage 1 disease-associated microglia (DAM-1) while preventing DAM-1 from further converting into stage 2 disease-associated microglia (DAM-2). Therefore, investigating the pathway of this process can be insight of targeted drug development. In the review part of this paper, the Interleukin-1 (IL-1) pathway is discussed. Based on previous research, a proposal hypothesizing that the activation of the IL-1 signaling pathway enhances the activity of TF and leads to the up-regulation of TF gene expression is designed. The goal is to prove that the up-regulation of IL-1 Signaling pathway can enhance the gene expression of Transcription factors including Jun, Junb, Jund, and Fos, therefore stabilizing the microglia at the stage of DAM-1. Based on the expected experimental results of this study, targeted drugs such as Soluble Type II receptor of Interleukin-1 (sIL-1R2) inhibitors can be developed for AD treatment.
Exploring the expression of CG18269 under Zelda's regulation in early embryonic development of Drosophila melanogaster
Authors: Chen Yiwi
Published: July 2024

As a master regulatory protein, Zelda (Zld) plays a significant role in gene expression of Drosophilas embryonic development. It targets both genes, including sna, that are studied in previous researches, and genes that even their molecular function is still unknown, such as CG18269. Through examining both qualitative and quantitative data in this research, the absence of Zelda has proved to significantly down regulate the genes expression. The gene is also observed to have multiple Zelda-binding sites, which may or may not have correlation with its neighboring genes. Future experiments, such as CRISPR-Cas9, may be conducted to further decipher the gene.
Investigating the role of metallothionein-3 (MT-3) in snxiety-like bBehaviors: An in-depth research proposal
Authors: Wang Sijin
Published: July 2024

Anxiety is a common mental health disorder that profoundly impacts individuals' daily lives. Metallothionein-3 (MT-3), a protein found in the central nervous system, is hypothesized to play a role in anxiety-like behaviors. This research proposal aims to investigate the induction of anxiety-like behaviors in the presence of MT-3 using an experimental approach. Previous studies have shown that MT-3 deficiency is associated with abnormalities in psychological behaviors. The proposed study will build upon this research to further explore the cellular and molecular mechanisms underlying MT-3 involvement in anxiety. The study will utilize a mouse preclinical model and behavioral assays to assess anxiety-like behaviors in the presence or absence of MT-3. The findings from this research may contribute to the development of novel therapeutic strategies for managing anxiety.
Research on novel antibiotics: Bacterial division inhibitors
Authors: Shi Yaling
Published: July 2024

We all know that bacterial infections can cause many diseases, which can affect the normal life of people and even seriously threaten life safety. But now, many antibiotics in use have developed resistance, and the form of antibiotic resistance is becoming more and more serious. Although several treatment methods have been found that can not rely on antibiotics, there are certain shortcomings in terms of cost and treatment effect. So, the hardest but most effective way is to develop new antibiotics. Bacterial division is an important process of bacterial growth and reproduction, and its inhibitors have great potential to become new antibiotics. Therefore, this paper introduces the inhibitors of bacterial division.
Comparison of Business Investment Value Between Traditional and New Logistics Companies
Authors: Zhang Qingjiang
Published: July 2024

The logistics industry has experienced significant transformations as a result of the rapid evolution of globalization and e-commerce, leading to notable shifts in business models, technology applications, and service quality. This paper aims to delve into the differences in business investment value between traditional logistics companies and new logistics companies, while also exploring the impact of these disparities on enterprise competitiveness and future development. And as a goal to provide some high-quality suggestions to the new enterprises currently in development and the traditional enterprises in transformation, to promote the upgrading and development of the logistics industry. To achieve this objective, the study utilizes two primary research methodologies: case analysis and comparative analysis., By conducting a comparative analysis of the financial development and service advantages and disadvantages of two representative traditional logistics companies, FedEX and Amazon logistics, as well as modern logistics companies, this study aims to clarify the significance of digital transformation in the development of traditional logistics enterprises. Furthermore, it provides optimization suggestions for digital logistics transportation information security and other risks, ultimately contributing to the high-quality development of logistics enterprises.
An Investigation of Machine Learning Applications in the Financial Fraud Detection
Authors: Han Yuge
Published: July 2024

Financial fraud presents itself in various forms, and it often involves intricate financial transaction networks, making it challenging to detect the perpetrators and identify the characteristics of the fraud. In recent years, machine learning has gained widespread application within the financial sector. Therefore, various financial fraud detection models have been developed based on diverse machine learning methodologies. In the method part, this paper provides an overview of the machine learning process and then discusses the application of machine learning models in various financial fraud scenarios. Financial fraud in the insurance field has been further refined into automobile and medical insurance fraud. In automobile insurance fraud detection, some studies applied implicit Naive Bayes Model to analyze observable features and estimate hidden variables, and some studies used resampler to solve data imbalance and adopted 7 kinds of machine learning models for analysis. In health insurance fraud detection, many studies train medical data with multiple models, including AdaBoost, Logistic Regression and Support Vector Machine. In credit card fraud detection, studies use five algorithms, including random forest and decision tree et al., and some construct a model combining Decision Tree (DT) and Logistic Regression (LR). In bank fraud detection, some studies introduce Value-at-Risk to combine it with machine learning algorithms, and some studies propose a decentralized model training method based on federated learning. In the discussion section, this paper addresses the current limitations of the research, such as poor interpretability, uneven distribution of data sets, and issues related to customer privacy, and proposes corresponding solutions.
Medical image intelligent diagnosis system based on facial emotion recognition and convolutional neural network
Authors: Leng Qian , Peng Lai
Published: July 2024

The facial emotional information of patients is one of the important indicators of their health status. By combining facial emotion recognition technology, medical imaging intelligent diagnostic systems can more comprehensively understand the psychological and physiological status of patients. This study first selected medical imaging datasets from the publicly available Medical Machine Learning Image Standard database and performed necessary preprocessing, including data cleaning, enhancement, and standardization. Next, a CNN model is constructed to extract key features from medical images and facial expression images through structures such as convolutional layers, activation functions, pooling layers, and fully connected layers. In addition, the system also integrates facial emotion recognition module and medical image analysis module, improving the comprehensiveness of diagnosis through feature fusion. Finally, the diagnostic results are displayed through a user interaction interface. Through comparative experiments, this study verified that the proposed CNN model outperforms traditional LSTM and SVM models in terms of accuracy, recall, and precision. The CNN model has shown significant advantages in medical imaging intelligent diagnosis systems, especially achieving a recall rate of 99%, demonstrating extremely high disease recognition ability. The results of this study not only provide new research directions for the field of intelligent diagnosis in medical imaging, but also lay a solid foundation for future clinical applications.
Research on automated rapid diagnosis of distal radius fractures using X-ray images
Authors: Liu Yunpeng , Zhuo Yupeng , Wang Ying , Gan Kaifeng , Liu Dongquan
Published: July 2024

The primary diagnostic method for distal radius fractures remains conventional X-ray imaging. In emergency situations, rapid assessment and decision-making are often required from non-orthopedic physicians or junior radiologists in the emergency room. Therefore, an accurate and efficient auxiliary diagnostic technology for distal radius fractures is of great significance. This paper combines deep learning with image analysis techniques to propose an effective classification method for types of distal radius fractures. Firstly, an extended U-Net three-level cascade segmentation network is used for precise segmentation of the most critical joint surface and non-joint surface areas for fracture identification. The edge is further optimized using the central pixel image block classification method. Then, fracture identification is performed separately on the joint surface and non-joint surface areas. Finally, based on the classification results of the two types of images, a comprehensive judgment is made to determine normal or ABC fracture types. The experimental X-ray data comes from three tertiary hospitals, with a total of 12,000 images in the training set, 3,000 of each type, and 1,200 images in the test set, 300 of each type. To further demonstrate the generalization performance of the classification experiment, an additional 500 images were added for fracture classification testing, coming from collaborative medical institutions in the United States and Germany. All images were annotated by orthopedic medical experts with more than ten years of experience. The accuracy rates for normal, type A, type B, and type C fractures in the test set were 0.99, 0.92, 0.91, and 0.82, respectively. For orthopedic medical experts, the average recognition accuracy rates were 0.98, 0.90, 0.87, and 0.81. The proposed automatic recognition method overall performs better than experts and can be used for preliminary auxiliary diagnosis of distal radius fractures without expert participation.
Research on the application of deep learning-based BERT model in sentiment analysis
Authors: Wu Yichao , Jin Zhengyu , Shi Chenxi , Liang Penghao , Zhan Tong
Published: July 2024

With the rapid expansion of the Internet and social media, there has been an explosion of text data, containing valuable emotional insights. Sentiment analysis, aimed at discerning and processing people's emotional inclinations, opinions, and perspectives, has emerged as a pivotal area within natural language processing (NLP). This research explores the application of Deep Learning-based BERT models, particularly DistilBERT, in sentiment analysis. It highlights the importance of sentiment analysis in understanding emotional insights from vast text data and introduces BERT models as revolutionary methodologies for enhancing accuracy and efficiency. The study conducts experiments using the SST2 dataset, showcasing the effectiveness of DistilBERT in sentiment classification tasks. Overall, it underscores the transformative potential of BERT models in revolutionizing sentiment analysis methodologies and driving advancements in natural language processing research.
Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models
Authors: Zhan Xiaoan , Shi Chenxi , Li Lianwei , Xu Kangming , Zheng Haotian
Published: July 2024

The rapid rise of e-commerce platforms has changed people's shopping habits, driving the popularity of online shopping. Users express their opinions on products and services by purchasing products on platforms and posting comments. These comment data contain rich user experiences, which are crucial for enterprises to understand user needs and improve product quality. Sentiment analysis of comment text is an important research direction in text mining, focusing on how to extract user evaluations of products from comment data to provide comprehensive, authentic, and accurate product feedback. This paper mainly investigates aspect-based fine-grained sentiment analysis. Because users express multiple sentiments towards different aspects in comments, coarse-grained sentiment analysis cannot accurately capture users' sentiment tendencies. This study utilizes artificial intelligence technology based on deep learning, firstly, it constructs auxiliary training samples to transform aspect sentiment tasks into machine reading comprehension or language inference tasks, using BERT model to extract text features and sentence features from comment data. To guide the model to focus on the features most relevant to the given aspect, a cross-attention mechanism is utilized to cross-focus the features of text with aspect category features. Finally, the sentiment polarity of given aspect category text can be predicted through a forward network. Experimental results on multiple datasets demonstrate that this method outperforms other deep learning models.
Enhancing capabilities of generative models through VAE-GAN integration: A review
Authors: Cai Dongting
Published: July 2024

Our review explores the integration of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), which are pivotal in the realm of generative models. VAEs are renowned for their robust probabilistic foundations and capacity for complex data representation learning, while GANs are celebrated for generating high-fidelity images. Despite their strengths, both models have limitations: VAEs often produce less sharp outputs, and GANs face challenges with training stability. The hybrid VAE-GAN models harness the strengths of both architectures to overcome these limitations, enhancing output quality and diversity. We provide a comprehensive overview of VAEs and GANs technology developments, their integration strategies, and resultant performance improvements. Applications across various fields, such as artistic creation, medical imaging, e-commerce, and video gaming, highlight the transformative potential of these models. However, challenges in model robustness, ethical concerns, and computational demands persist, posing significant hurdles. Future research directions are poised to transform the VAE-GAN landscape significantly. Enhancing training stability remains a priority, with new approaches such as incorporating self-correcting mechanisms into GANs training being tested. Addressing ethical issues is also critical, as policymakers and technologists work together to develop standards that prevent misuse. Moreover, reducing computational costs is fundamental to democratizing access to these technologies. Projects such as the development of MobileNetV2 have made strides in creating more efficient neural network architectures that maintain performance while being less resource-intensive. Further, the exploration of VAE-GAN applications in fields like augmented reality and personalized medicine offers exciting opportunities for growth, as evidenced by recent pilot studies.
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Authors: withdraw withdraw
Published: July 2024

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Problems and prospects of rural energy transformation in China
Authors: Fan Xinyue
Published: July 2024

In recent years, Chinas rural energy supply security capacity has been enhanced, and the adjustment of energy structure has achieved remarkable results, but there are still some problems: The low utilization rate of waste energy in rural areas restricts the comprehensive utilization of resources and environmental protection, the level of rural energy consumption is always in the stage of development, and the lagging construction of rural energy socialized service system restricts the development of rural energy. In view of the problems and challenges faced by Chinas rural energy development, focus on rural energy consumption. This paper analyzes the basic situation of energy development in Chinas rural areas (taking Lantian County of Shaanxi Province as an example). Under the special premise of Chinas national conditions, the specific characteristics of different development in different regions are integrated, and the traditional fossil energy is gradually replaced by the storage and utilization of rural waste and new energy. The key issues such as the construction of rural energy service system and policy guidance are studied, and the measures and suggestions for promoting the scientific development of rural energy in China are put forward.
An in-depth study of the principles and technologies of wind-solar complementary systems: Optimization strategies and future development trends
Authors: Liu Xianzhi
Published: July 2024

In the face of the global energy crisis and the challenges of climate change in the 21st century, there is an urgent need to shift to sustainable energy solutions. Wind-solar hybrid systems, renewable energy technologies that combine wind and solar energy, are particularly important because they improve the stability and efficiency of energy supply. Through the analysis of technological innovation and system optimization strategies, this study explores ways to enhance system performance and economy by relying on the latest research on wind and solar energy technology development as well as empirical studies of wind-solar complementary systems in different application scenarios. The results of the study show that wind-solar hybrid systems can effectively reduce the dependence on fossil fuels and reduce environmental pollution, and they play an increasingly important role in the transformation of the global energy structure. Continued technological innovation and policy support are key to advancing the widespread deployment of the system.
Convolutional neural network model compression method
Authors: Chu Likai
Published: July 2024

In the field of deep learning, Convolutional Neural Networks (CNN) has become a focal point due to its multi-layered structure and wide application. The success of deep learning is due to the model has more layers and more parameters, which gives it a stronger nonlinear fitting ability. Traditionally, CNNs are primarily run on Central Processing Units (CPUs) and Graphics Processing Units (GPUs). However, CPUs have lower computational power, and GPUs consume a lot of energy. In contrast, Field-Programmable Gate Arrays (FPGAs) offer high parallelism, low power consumption, flexible programming, and rapid development cycles. These combined advantages make FPGAs more suitable for the forward inference processes of deep learning compared to other platforms. However, CNNs has the characteristics of parameter redundancy, the storage cost of deploying it on FPGA is too high. In order to apply CNNs to FPGA, we need to optimize CNNs for compression. Because after compression, This paper analyzes several specific cases of model compression for convolutional neural networks, and summarizes and compares the efficient methods of model compression. The results show that the model compression methods are mainly divided into the structure change of convolutional neural network and the quantization of parameters. The two compression methods can be cascaded at the same time to achieve a better optimization effect.
Flexible nanomaterial sensors for non-invasive health monitoring
Authors: Kopp Maximilian B.
Published: July 2024

According to the Center for Disease Control (CDC), diabetes is the eighth leading cause of death in the United States, while about 1 in 5 patients are unaware that they have been affected. Current detection methods are crude, using rudimentary pricking methods that require needles and being unconventional for continuous tracking, this strongly discourages people from early detection and management. Therefore, developing a non-invasive glucose monitoring technique is of utmost importance. The primary objective of this research is to address the limitations of existing glucose monitoring techniques through the development of nanomaterial sensors seamlessly integrated into wearable devices. A novel two-dimensional material, GeSe, is chosen for its flexibility and optical responsivity. Flexible polarimetric sensors are developed using a facile direct-transfer method. These sensors aim to provide accurate and non-invasive monitoring of blood glucose levels through measuring the light polarization after interaction with glucose molecules. To ensure reliability, the sensors are calibrated using glucose solutions with known concentrations. A machine-learning algorithm is developed to improve measurement accuracy. Rigorous testing of the nanomaterial sensors is conducted in comparison to traditional blood glucose monitoring devices, employing controlled studies that evaluated their effectiveness across different objects. This approach not only establishes the viability of nanomaterials in transforming health monitoring but also highlights their potential to significantly improve the accuracy and convenience of blood glucose monitoring for individuals managing diabetes. The integration of nanomaterial-based sensors into wearable devices represents a noteworthy stride towards enhancing the efficiency and accessibility of healthcare technologies.
Application prospects of graphene in environmental science
Authors: Song Chenglin
Published: July 2024

Nanomaterials and graphene, as cutting-edge materials, have play a crucial role in environmental governance. The introduction of nanomaterials brings new hope to environmental governance. Graphene not only improves the efficiency and cost-effectiveness of water treatment but also drives innovation and development in environmental technologies. This article provides a summary and analysis review starting from the characteristics of nanomaterials and Graphene, comprehensively reviewing Graphenes advantage in environmental technologies and discovered its application in this area. The research summarized the conclusion that Graphenes characteristics make it suitable for addressing environmental issues such as water pollution, its composites can play an important role in water treatment process including water purification, heavy metal adsorption, organic compound adsorption and removal through different preparation and reaction mechanisms. By introducing Nanopore structures and artificially designed stacking structures on graphene membranes, the permeation performance could be modulated and improved. By the way of physical and chemical adsorption, ion exchange, electrochemical and photocatalysis, Graphene and its oxide could be applied to remove heavy metal ion and organic substances from polluted water. The article also introduced the main preparation methods of Graphene and its potential secondary pollution problem. This article provides valuable insights for scientific research and engineering practices in environmental protection and sustainable development.
Enhancing radio signal classification under low SNR conditions using deep residual networks with channel attention mechanisms
Authors: Zhang Bingxu
Published: July 2024

This paper delves into the advancement of deep residual networks (ResNets) integrated with channel attention mechanisms for the classification of radio signals under conditions of low Signal-to-Noise Ratio (SNR). Utilizing an expansive dataset of radio signals, this paper introduces a novel architecture, MyResNet1, that combines residual learning with channel-wise attention, allowing the model to concentrate on essential features for precise classification. My, investigations exhibit notable improvements in classification accuracy, especially in challenging low SNR scenarios, highlighting the potential of attention-augmented deep residual networks in radio signal processing. Furthermore, this studyexplores various optimization strategies, including data augmentation and regularization techniques, to enhance the models performance and robustness. My findings contribute significantly to cognitive radio technologies and illuminate the potential of deep learning in sophisticated signal classification tasks, aligned with recent explorations in automatic modulation recognition (AMR) through deep learning and autoencoder-based methodologies for enhancing I/Q channel interactions.
Modal analysis of Shanghai world financial center based on using ANSYS
Authors: You Bowen , Shen Zhijie , Wang Yizhao , Li Enze , Yu Zhe
Published: July 2024

This study delves into the resonance phenomena within the complex structure of the Shanghai Global Financial Center. By utilizing ANSYS simulations, we can accurately predict the vibration patterns and magnitudes exhibited by the building under various conditions. These resonance phenomena have the potential to significantly impact the buildings stability, occupant comfort, and overall structural integrity. This research conducts a thorough analysis of the self-resonance conditions of the Shanghai Global Financial Center through the application of ANSYS software. The study successfully identifies resonance frequencies and provides a deeper understanding of resonance mechanisms. These findings offer valuable insights for comprehending and mitigating resonance-related issues. Moreover, they hold great significance for the design and engineering of super-tall skyscrapers, offering essential guidance to ensure their safety and performance. AMSS_0403rchitects and engineers can use this knowledge to optimize the design and construction of such impressive structures, ultimately contributing to the advancement of tall building technology. Enze Li and Zhe Yu are dedicated to data collection, Bowen You and Yizhao Wang specialize in modeling and data recording and analysis, while Zhijie Shen and Enze Li are responsible for carefully crafting and refining the paper.
Solution to SRAM static power consumption with MTCMOS
Authors: Yuan Jiyao , Xue Xiaochuan
Published: July 2024

The rapid growth of mobile devices has led to an increasing demand for battery life and energy efficiency in recent years, the reduction of circuit power consumption has become extremely crucial. SRAM has become an indispensable component of modern System-on-Chip (SoC) designs, and reducing its power consumption holds significant importance in minimizing overall chip power consumption. On the other hand, as manufacturing processes advance, static power consumption resulting from leakage currents has gradually emerged as a primary source of power consumption. This paper analyzes the power composition of SRAM, provides a detailed explanation of the principles and influencing factors of MTCMOS design technology, and conducts modeling analysis on 6T SRAM based on MTCMOS. In the modeling analysis, we compare the leakage current and static power reduction effects of 6T SRAM using four different process technologies: 28nm, 40nm, 65nm, and 90nm. From the data, it can be observed that MTCMOS has a notable effect in reducing leakage current for 6T SRAM across various process technologies. Comparing 6T SRAM of different process technologies, we can roughly see that the power reduction effect reaches a peak and gradually decreases. However, due to the lack of more advanced process libraries, we cannot further validate whether this inference is accurate.
Pelatihan Peningkatan Kualitas Pembelajaran Di Madrasah Melalui Manajemen Mutu Terpadu
Authors: Solechan Solechan , Afif Zahrotun Ni’mah , Sintasari Beny , Azizah Mar’atul , Kartiko Ari
Published: July 2024

This research aims to explore and analyze the impact of implementing integrated quality management (TQM) in improving the quality of learning at MTS Al Hidayah. The community service method is used as the main approach, with a focus on designing and implementing comprehensive training programs for teachers and school staff. This approach includes an initial evaluation to identify madrasah needs, designing training programs that are appropriate to the local context, as well as ongoing implementation and monitoring. The research results show that TQM training is effective in improving teacher teaching competence, encouraging the use of formative evaluation, and strengthening transformational leadership in madrasas. Ongoing evaluations show improvements in the physical infrastructure and collaborative school culture. This research contributes to the literature on the implementation of TQM in the context of madrasah education, with practical implications for the development of sustainable educational policies and strategies
NASOPHARYNGEAL ACE2, CD147, AND TMPRSS2 EXPRESSIONS IN MIS-C PATIENTS
Authors: Alpaslan Medine , Karadag Sefika , Can Cansu , Erdeniz Emine , Ugurtay Eda , Yildiran Alisan
Published: July 2024

Objective: Multisystem Inflammatory Syndrome in Children (MIS-C) is a disease developed after about eight weeks of the new coronavirus disease (COVID-19) infection in children. The underlying reason for MIS-C is still unknown. This study aims to assess the nasopharyngeal expressions of Angiotensin-converting enzyme 2 (ACE2), the differentiation 147 receptor cluster (CD147), and the transmembrane serine protease 2 (TMPRSS2) receptors in the MIS-C population. Since these receptors are related to COVID-19 infection, children with previous COVID-19 history were included as a control group. This study is important for future studies to see if there is any significant expression difference between these receptors in control and MIS-C populations.
Methods: The prospective cohort study took place at XXXXX University. The participants of this study consisted of patients aged 0-18 years who were diagnosed with MIS-C due to COVID-19 infection or patients with only previous COVID-19 pneumonia but without MIS-C development. A total of 79 cases were registered in this study.
Nasopharyngeal samples of children were collected. Total RNA was isolated from all samples. ACE2, CD147, and TMPRSS2 genes’ expression was accessed by real-time quantitative PCR (RT-qPCR).
Results: Nasopharyngeal expression of CD147 and TMPRSS2 were not changed in COVID-19 (n=42) and MIS-C (n=37) cases. Expression of ACE2 was increased in the under 10-year-old MIS-C group (n=23) (p=0.004381).
Conclusion: Future studies may exclude the nasopharyngeal expression of CD147 and TMPRSS2 to develop MIS-C. Further investigations are required to learn how ACE2 expression contributes to MIS-C development.
Analysis of computerized tomography characteristics in gastrointestinal subepithelial lesions diagnosed through endoscopy or endovascular ultrasound
Authors: kap G
Published: July 2024

Aim: Subepithelial lesions (SEL) of the gastrointestinal tract are lesions arising from the mucosa, submucosa or muscularis propria. This study is aimed to asses diagnostic characteristics of SELs on computerized tomography (CT) and to present the localization, size, CT attenuation and contrast enhancement patterns of the lesions that may aid in making correct diagnosis.
Materials and Methods: Between December 2022 and March 2024, patients with a preliminary diagnosis of SEL on endoscopy or EUS and who underwent abdominal CT were retrospectively screened from the hospital database. Localization, size, pre-contrast and post-contrast Hounsfield Unit (HU) values of the lesions, and pathologic diagnoses were recorded and compared in patients with SEL.
Results: Pathologic diagnosis was available in 25 (23.4%) patients with SEL detected on CT. Among these, the most common lesion was gastrointestinal stromal tumors (GIST, 52%). In 22 (20.6%) patients, the lesion was not detected on CT. The lesions that could not be seen on CT were less than 2 cm in size. Gastrointestinal distension was insufficient in the majority of patients in whom the lesion could not be visualized on CT. While the HU values increased by approximately 3 times in patients diagnosed with neuroendocrine tumors (median [interquartile ranges] 36.5 [32 – 40] vs. 101.5 [81.5 – 127.5]; p = 0.068), the increase was approximately 2 times in those with GIST (30 [29 – 38] vs. 70 [57 – 77]; p=0.003).

Conclusion: This study presents several CT characteristics and pitfalls that may aid correct diagnosis of SELs with CT. SELs smaller than 2 cm in size and located in cardia are more likely to be missed with CT. Adequate gastric distention is crucial in correct diagnosis.
The causes of death-censored graft loss among kidney transplant recipients
Authors: Ciftci Felat , Simsek Arife , Piskin Turgut , Unal Bulent , Dogan Sait , Ulutas Ozkan , Tabel Yilmaz , Sevgi Serkan , Sahin Idris , Taskapan Hulya , Yilmaz Sezai
Published: July 2024

Objective: This study presents the causes of death-censored graft loss among kidney transplant recipients.
Materials and Methods: Medical records of the patients, who had undergone kidney transplantation at a tertiary center between November 2010 and December 2018, were retrospectively reviewed. Death-censored graft loss was described as an irreversible graft failure signified by return to long-term dialysis (or re-transplantation). Inclusion criteria were: patients who had undergone kidney transplantation, and subsequently lost their first graft, and a follow-up of more than one year after kidney transplantation.
Results: Of 269 kidney transplant recipients, 33 recipients with a mean age of 33.54 ± 15.37 years (17 male and 16 female) were included in the study. The rate of death-censored graft loss was 12.26%. Of graft failures, 3.03% occurred in the hyperacute phase, 18.18% in the acute phase, and 78.78% in the chronic phase. Chronic allograft nephropathy was the leading cause of graft failure (48.48%). Other causes were medical problems (18.18), immunological problems (18.18%) and surgical complications (15.15%).
Conclusion: Identification of the true causes of graft failure described under the heading chronic allograft nephropathy is noteworthy. Comprehensive biochemical, physiological, pathological, immunological, and genetic research should be implemented to remove the obstacles in kidney transplantations.
Installations de manutention des hydrocarbures (IMH) le long du Saint-Laurent : portrait des réglementations applicables, documentation des installations et identification des sources de risque
Authors: Trépanier Martin , Peignier Ingrid , Philippe Amaury
Published: July 2024

Le transport maritime joue un rôle crucial dans l’économie du Québec, notamment grâce au corridor maritime du Saint-Laurent, l’une des plus longues voies maritimes intérieures du monde. Mais, les risques associés au transport maritime de matières dangereuses, comme les hydrocarbures, sont significatifs. Avec l’augmentation prévue du trafic maritime et des matières dangereuses transportées, une gestion rigoureuse des risques est essentielle. En outre, les statistiques indiquent que la majorité des déversements causés par des navires surviennent lors du chargement et du déchargement d’hydrocarbures des bâtiments aux installations de manutention d’hydrocarbures (IMH). Dans ce contexte, le CEGRIM, composé de représentants de six ministères québécois, a mandaté le CIRANO pour réaliser une étude visant à mieux comprendre les risques associés aux IMH du Québec utilisant le mode maritime. À l’aide de trois sources de données (les données de la Régie du Bâtiment du Québec sur les réservoirs associés aux IMH, les données AIS sur les mouvements de navire et les données d’une enquête CIRANO exclusive auprès des IMH du Québec), cette étude apporte une connaissance approfondie des IMH, incluant les réglementations en vigueur, les activités et les mesures de gestion des risques mises en place. Elle propose également des indices relatifs aux sources de risque et une cartographie des IMH, permettant une meilleure gestion des risques dans le secteur maritime québécois.
Jiddu Krishnamurti and The Fear of Not Being a “Good” Teacher
Authors: Downey Adrian M.
Published: July 2024

Abstract: Fear is a ubiquitous but somewhat underexamined phenomenon in education. In this essay, the author draws on the holistic educational philosophy of Jiddu Krishnamurti in order to reflect on the role of fear in his experiences as a classroom teacher. The author recounts experiences of difficult classroom management in which his fear was piqued. The author then draws on Krishnamurti’s writing to suggest that there is a deep seeded fear of not being a good teacher at the root of the desire to control students’ behaviour. The author concludes the paper by pointing to the need for serious and ongoing self-inquiry within education and society more broadly—a central tenant of Krishnamurti’s educational thought.
Stock Market Predictions Using Machine Learning Techniques
Authors: D N Nagapoojitha ,   Department of Computer Science and Business Systems, Dayananda Sagar College of Engineering.
Published: July 2024

Accurately predicting stock market prices is vital in today’s economy, leading researchers to explore novel approaches for forecasting. Recent studies have shown that historical stock data, search engine queries, and social mood from platforms like Twitter and news websites can predict future stock prices. Previous research often lacked comprehensive data, especially concerning social mood. This study presents an effective method to integrate multiple information sources to address this gap and enhance prediction accuracy. We utilized Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) models to analyse individual data sources. To further improve prediction accuracy, we employed an ensemble method combining Weighted Average and Differential Evolution techniques. The results yielded precise forecasts for one-day, seven-day, 15-day, and 30- day intervals, providing valuable insights for investors and helping companies gauge their future market performance. Keywords-- Stock market prediction; Sentiment Analysis; Neural Networks; Long-short Term Memory Neural Networks, DJIA, Ensemble Method, Weighted Average
Ancestor Worship in Contemporary Japan
Authors: Avdiushenkova I. V.
Published: July 2024

This article examines the transformation of ancestor worship in the context of socio-political and religious conditions and identifies the characteristics of worship in contemporary Japanese society and the changes in the form of practices and functions of this worship occurring today.Ancestor worship is not a phenomenon unique to traditional societies: in the 21st century Japan, ancestor worship rituals are practiced by a large part of the population. After World War II, Japanese veneration practices underwent significant changes. The post-war modernization and urbanization of Japanese society played a major role in these changes, leading to the breakdown of the traditional family system. The concept of ancestor itself changes: the concept of ancestor tends to expand and begins to extend bilaterally (to both the husband and wife lines). There has been a transition from the “obligatory” concept of an ancestor, which includes all deceased ancestors in the direct line of succession regardless of personal preferences, to an “optional” one, which limits the concept of “ancestor” to close relatives whose memories are dear to the descendant. With the change in the concept of the ancestor, the functions of ancestor veneration also undergo a transformation: the former functions of veneration rituals contributed to the stability of the ie system, while the new ones consist in relieving psychological tensions between the living and the dead and bringing comfort to particular people. There has been a “privatization” of ancestor veneration, i.e., a growing dominance of personal functions in veneration. Diversification of family types, especially pronounced in the first decades of the 21st century, is also reflected in the rites of ancestor veneration: alternatives to traditional funerals and new forms of burial and storage of remains are appearing.The article concludes that, despite changes in the functions and forms of ancestor worship, the place given to the dead in their lives by the living remains invariably important. And the individualization of veneration practices and the undying belief of a large part of the Japanese population in the power of ancestor spirits indicate that the ancestor cult in contemporary Japan is apparently at the next stage of its unfolding, but by no means of extinction.