bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on wildlife conservation strategy based on gray prediction and entropy weight method
Authors: Wang Weihan , Zheng Jianshu , Zhang Jinqiu
Published: August 2024

This study provides an in-depth examination of nature reserve programs and their feasibility, focusing on the challenges posed by illegal wildlife trade. Through an extensive literature review of scientific databases such as SCI and EI, the thesis illuminates the frontiers of current conservation efforts and assesses the compatibility of nature reserve programs with stakeholders. The research includes a comprehensive case study of wetlands in Patagonia, Brazil, analyzing the correlation between illegal wildlife trade crime indicators and selected socio-economic factors. Using advanced modeling techniques, including the gray predictive model GM (1,1) and the information entropy weighting method (EWM), the paper predicts trends in endangered species such as the jaguar and assesses the effectiveness of conservation measures. In addition, an assessment improvement framework is proposed that integrates a perspective through vector autoregression (VAR) analysis to enhance the assessment of conservation strategies and stakeholder empowerment.
Advancing Decision-Making in Dynamic Environments
Authors: Hu Xinyan
Published: August 2024

This paper introduces the Adaptive Thompson Sampling (AdaTS) algorithm, specifically designed for the challenges posed by opportunistic bandit problems. Unlike conventional multi-armed bandit scenarios where the optimality of actions remains relatively static, opportunistic bandit environments are significantly influenced by fluctuating external conditions. These variations necessitate a highly adaptive strategy for decision-making. The AdaTS algorithm meets this requirement through a novel integration of real-time system load assessments, which dynamically adjust the balance between exploration and exploitation. This method not only ensures more effective adaptation to changing conditions but also considerably enhances performance metrics. By incorporating system load into the decision-making process, AdaTS effectively reduces regret and maximizes reward, particularly under scenarios of variable load intensities. This is particularly evident in environments characterized by binary-valued loads and stochastic reward distributions. The results highlight AdaTS's robustness and efficiency, making it a promising approach for complex environments where adaptability is crucial. This research marks a significant advancement in the field of adaptive algorithms for opportunistic bandit problems.
Impact of Hyperparameters on the Quality of Image Translation Using CycleGAN
Authors: Hu Yichen
Published: August 2024

The study delves into the intricate relationship between hyperparameters and image translation quality in Cycle-Consistent Generative Adversarial Network (CycleGAN), which is one of the most widespread tools for unsupervised image-to-image translation. The hyperparameters investigated include learning rate, identity loss, and cycle consistency loss, which are aimed at enhancing the model's quality of producing realistic and good images. Furthermore, by employing horse2zebra dataset author was able to execute various experiments in order to understand what influence hyperparameters in CycleGAN have to its performance in style transfer problems. The analysis shows that changing the learning rate and identity loss weight have a drastic effect on whether the translation done yields detailed or correct images, tallied with the low-learning rate producing more explicit and accurate image translation. Loss weight transform manipulation of the identity is a pivotal element in the restoration of the original and the transformed features from the images. The study provides more insights into the understanding of how CycleGAN behaves in different training environments, and it stresses the significance of adequate parameters fine-tuning for the optimal image translation.
Research on the Law and Prediction of Traffic Accidents Based on Value at Risk in the Context of Intelligent Transportation
Authors: Cheng Zihang , Shao Zihan , Wang Xinyi
Published: August 2024

The research introduces traffic accident predictive models known as the Convolutional Long Short-Term Memory Model and a K-means cluster algorithm with Random Forest Network, which aims to provide precise forecasts of incident rates Through the amalgamation of the strengths inherent in Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) neural network, K-means cluster algorithm and Random Forest algorithms. These fusion models surpass the performance of the standalone LSTM model, particularly excelling in the prediction of incident rates during driving hours and the location. The study's outcomes reveal that the proposed model exhibits remarkable proficiency in specific transformation domains, underscoring its superior efficacy compared to single conventional models. These results underscore the advantages of integrating multiple algorithms within a unified framework to enhance predictive accuracy significantly. Consequently, the Convolutional Long Short-Term Memory and K-means cluster algorithm with a Random Forest Network emerge as promising solutions for advancing incident rate forecasting capabilities.
Overview of Federal Learning and Privacy Protection
Authors: Tian Ziyue
Published: August 2024

In recent years, federated learning technology has developed rapidly and been widely used in the field of data processing. This paper makes a comprehensive discussion on the privacy protection methods in federated learning,and makes a detailed analysis of three basic methods:data encryption,data perturbation and trusted hardware-based,introduces the principle of each method,and objectively analyzes their performance in practical applications. This paper gives a visual example to compare the principle of secure multi-party computing, which is easy for readers to understand.Aiming at homorphic, this paper first introduces the background of this method and explains the algorith with block diagram,and then compares and analyzes the advantages and disadvantages of somewhat homorphic encryption and fully homorphic encryption. The specific denoising mechanisms are divided into three types:Laplace mechanism,Gauss mechanism and exponential mechanism.This paper summarizes these three mechanisms according to the types of data that need to be denoised.Finally,the paper systematically expounds the privacy protection methods based on trusted hardware ,lists two typical schemes,TrustZone and SGX,and analyzes how they work. In this paper,the hot spots and development prospects are prospected.
Sentiment Analysis of Chinese R&B Music Album Short Reviews Using SKEP Topic Model
Authors: Xiao Sa
Published: August 2024

In the current online environment, music reviews can be a good reflection of the popularity of the music, which can attract others to listen and pay attention to the music. A sentimental analysis of these reviews and the identification of emotional attitudes are essential for the dissemination of music. Based on the classic Chinese R&B album "David Tao" and its related reviews, this paper aims to analyze sentiment tendencies, analyze word frequency, and extract comment topics based on the Baidu AI Cloud interface of the SKEP algorithm. Research has found that listeners have a relatively positive emotional attitude towards this album, with positive reviews accounting for about 85%, and the reviews cover a variety of hot topics. The album's popularity, word-of-mouth evaluation, influence and other indicators all show a high level, highlighting that this album indeed provides a certain direction and strategy for the development of Chinese R&B. The charm of the album's musical style has been fully reflected and attracted widespread attention and recognition. It not only injects new vitality into the development of Chinese R&B music, but also provides valuable reference and inspiration for those interested in this music style. The research in this article emphasizes the important role of music in cultural inheritance and innovation, and also provides useful reference and guidance for the sustainable development of the music industry.
Optimizing Reward and Minimizing Comprehensive Cost in Recommendation Systems
Authors: Gu Shuohan
Published: August 2024

An in-depth discussion will explore the advanced methodologies designed to minimize regret in recommendation systems, shedding light on the principal strategies and assessing their effectiveness. This analysis will also consider additional factors that could profoundly impact the decision-making mechanisms within these systems. Special focus will be placed on user-specific variables such as contextual nuances, temporal dynamics, and individual preferences that, although frequently overlooked, have the potential to significantly improve the personalization and efficacy of recommendations. By incorporating these elements, the goal is to enhance the adaptability and predictive accuracy of recommendation systems, ultimately leading to a more engaging and satisfying user experience. This progressive approach is set to redefine the standards for user satisfaction and system efficiency in the dynamic realm of digital interactions and personalized technology, marking a significant evolution in how these systems cater to individual needs and preferences.
Bitcoin prediction and parameter analysis based on LSTM
Authors: Wang Ding
Published: August 2024

Stock price prediction is currently a research focus in the financial field, especially in blockchain research. The central focus of this research is to forecast Bitcoin's closing price through the integration of deep learning techniques, specifically employing Long Short-Term Memory (LSTM). This study takes into account that Bitcoin is a mainstream virtual currency, and predicting its future price can help investors make better judgments in trading. The goal of this exploration is to identify the most favorable parameter combinations and function prediction applications, ultimately obtaining the most accurate prediction results. The research process includes dataset selection, data processing, model construction, and training. Then adjust and improve the parameters used in the model, and record the process. Finally, test the model and output the test results. And model testing and result output. At the end of the experiment, the effects of different optimizers and parameters on the training results were compared, and the optimal combination was found. The model's predictive accuracy was evaluated through the examination of test data. This study can provide valuable references for researchers and firms.
Systematic analysis of Intelligent retail system structure and its optimization
Authors: Du Qiyang
Published: August 2024

The article delves into the transformative realm of Smart Retail, exploring its evolution, technological foundations, and real-world applications. With a focus on cutting-edge technologies such as Smart Shelf Systems, Radio Frequency Identification (RFID) Tags, Near Field Communication (NFC) Technology, Smart Carts, and advanced algorithms like eXtreme Gradient Boosting (XGBoost) and Asynchronous Advantage Actor-Critic (A3C), this paper offers a comprehensive analysis of the role of these technologies in reshaping the retail landscape. The document presents in-depth discussions on the principles, working mechanisms, and operational processes of these Smart Retail components, drawing insights from case studies such as Xingbianli, Metro, and Wumart Group. Furthermore, the study explores the integration of IoT, data analytics, and artificial intelligence in retail operations, highlighting the potential for enhanced customer experiences, operational efficiency, and market competitiveness. The conclusion emphasizes the significance of Smart Retail in driving innovation, improving decision-making, and meeting the evolving demands of modern consumers. This research provides valuable insights for retailers, technology developers, and industry stakeholders seeking to navigate the dynamic landscape of Smart Retail and harness its transformative potential.
Research and Analysis of the Application of Machine Learning in Agricultural Development
Authors: Yuan Yimin
Published: August 2024

Agriculture is the most basic, fundamental and important industry. Now, amid global climate change and resource shortages, agriculture must deal with the challenges of growing demand as the world's population increases This article organizes three aspects of agriculture that need improvement: anticipatory preparation before production, improvement of production methods, and detection and classification of agricultural products, and analyzes how machine learning can help agricultural progress in these three aspects. Residual deep convolution and spatial pyramid pooling algorithms in machine learning can be used to help detect plant pests and diseases. The RF algorithm, XGBoost algorithm, LightGBM algorithm and CatBoos in machine learning can generate landslide susceptibility maps. Deep learning, convolutional neural networks, and support vector machines can identify hybrid wheat. Through this research, it can be determined that machine learning can be of great help to agricultural development, and this help and development is mutual. The significance of this study lies in how machine learning can help agricultural development and face these problems.
Performance Analysis of Intelligent Vehicle Interactive Chips
Authors: Xiang Yucheng
Published: August 2024

Today's automotive intelligent cabin gradually tends to multi-screen, multi-screen interconnection research, design and development, intelligent cabin is being gradually applied to more and more production models, the car has become including LCD dashboard, head-up display, streaming media rearview mirror, intelligent driving assistance, voice recognition control, gesture control and other complex electronic equipment collection. “One core and multiple screens” is the way most car companies build their smart cabins. The design concept’s intelligent cabins are being actively developed and applied by auto parts manufacturers at home and abroad. With the increase of the vehicle screen, the interaction of several screens is enhanced, and the customer's smooth and functional requirements for the vehicle system are improved, the theoretical performance of the vehicle is slowly becoming important. In order to realize the smooth interaction of various modes such as touch screen, voice, face recognition, and gesture recognition in the intelligent cabin, achieve multi-screen linkage, and produce vivid and smooth display pictures, the CPU and GPU computing requirements of the on-board chip will be increased. So, this article will discuss how much performance today's in-car chips need to support the smoothness of human-computer interaction.
Deep Learning-Based Techniques for Electroencephalogram (EEG) Signal Denoising
Authors: Zhang Xuanpeng
Published: August 2024

Electroencephalography (EEG) signals denoising is crucial for neural signal interpretation, particularly in complex noise conditions. Traditional methods often fail to address these conditions effectively. In contrast, deep learning-based techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Generative Adversarial Networks (GANs), and Transformers offer enhanced adaptability and robustness by tuning to diverse noise characteristics. This paper presents a systematic review and comparative analysis of updated EEG denoising models, utilizing the EEGdenoiseNet benchmark dataset for a consistent evaluation framework. Performance of each model is assessed using the Relative Root Mean Square Error (RRMSE) and Correlation Coefficient (CC) across time domains. The study aims to elucidate the strengths of each denoising model, providing insights for model selection. Furthermore, it outlines each model's practical implications and limitations, setting the stage for future innovations in EEG signal processing.
Real Estate Location Optimization Model Based on Linear Programming Model Mombined with Property Insurance Pricing
Authors: Qin Yuanyuan , Qin Yuanyuan , Cha Yunzhuo , Liu Hanhong
Published: August 2024

Real estate location selection is integral to real estate decision-making. Accurate real estate site selection is of great significance to ensure profits for real estate developers, increase real estate flexibility, and improve the serviceability of the real estate industry. To accurately locate real estate, this paper takes Dallas County, which is a high-risk area, as the research object, and establishes a real estate location optimization model combined with property insurance pricing model. This model selects nine indicators that affect housing prices and uses ArcGIS software to simulate the housing price score of the area based on these nine factors. Then, three factors, including property insurance premium, disaster resistance coefficient, and regional risk level, were introduced, and a real estate location optimization model with the highest profit as the objective function was established. Based on this model, the specific values of the disaster resistance coefficient, housing prices, and low-profit housing prices are calculated to achieve optimal real estate decisions in Dallas County. This model can provide various communities and real estate developers with profitable real estate decisions under frequent extreme weather conditions.
Spinal Cord Stimulation Modalities and Charging Systems for Optimizing Chronic Pain Management
Authors: Lin Zhihui
Published: August 2024

Chronic pain, notably in conditions like failed back surgery syndrome (FBSS), challenges healthcare systems, prompting a search for effective therapies. However, pharma treatment triggers human body’s resistance which disfunction in long-term treatment. Therefore, spinal cord stimulation (SCS) offers a promising avenue. According to the gate control theory of pain control, it is possible to control the pain signal by sending new electrical waves through spinal cord to the brain. In this paper, three SCS modalities (TCS, BURST, HF) will be introduced and evaluated according to previous studies, which mainly focus on user-friendly and nerve pain control. Besides the efficacy of SCS modalities (TCS, BURST, HF), wearable charging systems warrant scrutiny. Different charging strategies (magnetic induction, magnetic resonance coupling, radio reception) will be introduced and analyzed in detail. This paper offers a concise overview of these modalities and charging strategies, aiming to inform clinical decision-making and enhance patient outcomes in chronic pain management.
GUNet: Duplex Global Graph Relationship Mining for Polyp Segmentation
Authors: Xie Wumiao , Yang Kaicheng Frank
Published: August 2024

Colorectal Cancer is one of the most common cancers worldwide, and as the probability of cure decreases as the stages progress, the early-stage detection of possible indications of colorectal cancer (i.e., polyps) during colonoscopy exams can contribute greatly to its prevention and curation. Since some of the indications of colorectal cancer are hard to identify by the human eye, it becomes crucial to develop an algorithm that can assist in the early identiﬁcation of abnormal tissue walls that may be signs of early-stage cancer. This paper proposes the GUNet, where duplex global graph relationships are mined through graph convolution operations. The core of this framework is the extraction and utilization of both spatial and semantic information from diverse encoded feature maps to acquire contextual semantic information, done so to construct a fuller and more diverse global representation. We also train the GUNet on a combination of three public datasets to enhance its generalization abilities. Experiments show that the GUNet can serve as a competitive alternative to the U-Net, achieving a 64.47% IOU on the combined dataset.
Detection of road defects with weak small samples based on multiple deep learning models
Authors: Lu Huaizheng , Wu Xinyi , Zhang Dedong
Published: August 2024

Potholes on roads not only jeopardize traffic safety and driving comfort but also necessitate efficient detection and maintenance measures. Traditional manual detection methods are labor-intensive and time-consuming. Thus, developing automated solutions is imperative. This study addresses this challenge by constructing a dataset and evaluating various deep learning models, including GoogleNet, VGG16, ResNet50, AlexNet, YOLOV5, and YOLOV8, for pothole detection. YOLOV8 emerges as the optimal choice due to its superior accuracy. However, accurately estimating pothole areas proves challenging due to their irregular shapes. To mitigate this, an innovative algorithm is proposed, integrating YOLO with a pre-trained segmentation model. This enables precise pixel-level delineation of pothole areas. Additionally, the algorithm incorporates the intersection over union (IOU) metric to calculate the ratio of pothole area to the total image area. By enhancing both detection and area estimation, this approach holds promise for improving road safety and facilitating maintenance efforts. Automated detection and accurate area estimation not only save time and resources but also provide crucial data for prioritizing and planning road repair and maintenance tasks.
Stacked Block Analysis Based on LSTM for Stock Price Prediction
Authors: Shao Shuaijie
Published: August 2024

Long Short-Term Memory (LSTM) networks and their modified versions, Stacked LSTM, are often used for time series prediction due to their powerful ability to model long-range dependencies. However, few papers have delved into the relationship between the number of layers of LSTM and model performance. For this purpose, this study used LSTM and stacked LSTM at different levels to predict the univariate time series dataset of Apple Inc. stock prices. The goal is to study the relationship between stacking and performance in stock price prediction. The time span in the data is from 2000 to 2022, to discuss whether this relationship and overfitting will damage the model. The results indicate that as long as the model complexity is suitable for the complexity of the dataset, the number of layers is positively correlated with the model performance. It can provide basic knowledge of selecting layers when training other stock market prediction models, which will significantly save time and effort.
Research on the Relationship Between Sleep Quality and Health
Authors: Sun Tianwei
Published: August 2024

Sleep quality is an important factor affecting human health, and more and more factors in daily life can affect sleep quality. Although there have been numerous studies demonstrating the relationship between sleep quality and health, there are still research gaps in different populations and key influencing factors. This study discusses the relationship between factors that affect sleep quality and health and conducts in-depth research on their impact on human health development. It conducted a correlation analysis on the open-source dataset of sleep quality and health using visualization analysis and the Pearson correlation coefficient method. The paper discussed the key factors affecting sleep quality and how sleep quality affects physical and mental health. The research results indicate that poor sleep quality directly affects human health, and multiple factors can cause sleep disorders, which in turn have a negative impact on health. In summary, there is a close relationship between sleep quality and health, and it provides a reliable basis for improving human health and happiness in the future.
Study on the Main Causes of Depression in Contemporary College Students
Authors: Wu Xingyan
Published: August 2024

In today's fast-paced and rapid development era, people's pressure is also increasing rapidly, too much pressure also makes depression become the focus of public attention, and effective prevention has become an urgent problem to be solved. College students are the backbone of social progress, but a large number of college students suffer from depression, which makes it very important to find out the main causes of depression. Whether to effectively prevent depression among college students is not only whether the mental health problems of the new generation can be solved, but also whether the society can continue to efficiently produce high-quality talents and promote social development in the future. Therefore, this paper mainly focuses on the main causes of depression among college students. This paper mainly uses linear regression method to infer the sample data and find out the main causes of depression. In order to identify the most important causes, this paper selected the most likely causes of depression from the majority of academic studies, and compared the strength of the linear relationship to infer the more important causes. Results show that annual income performs a significant influence on depression in college students.
Research on the design of multibeam bathymetric survey line based on geometric derivation
Authors: Xu Rui , Sun Mengwen , Lv Qing
Published: August 2024

Based on the working principle of multibeam bathymetric system, this paper establishes a multibeam line design optimization model and coverage width geometric model by establishing a spatial right-angled coordinate system, dichotomy method, moving surface method and other methods. A two-dimensional geometric model of the coverage width and the overlap rate of adjacent strips is established in the two-dimensional plane. Then, based on the geometric model, the relationship equations of seawater depth at the centre of different lines are obtained by triangulation, and then the sine equations of seawater depth at the centre of the line, coverage width and coverage rate are obtained by the sine theorem. Then the equations of the line plane, the multibeam plane and the seabed slope are solved in 3D space, and finally the geometric model of the line perpendicular to the gradient and the optimisation model of the line parallel to the gradient are established respectively. Finally, comparing the total lengths of the lines in the two directions, it is determined that the line direction is east-west, the total number of lines is 34, the total length of the lines is 125936m, and the interval between the lines decreases with the increase of gradient.
Prevención del delito en materia de seguridad ciudadana para la corresponsabilidad social de la policía comunitaria
Authors: Cespedes Juan Jose
Published: August 2024

The solution to the problem of citizen security seen from the perspective of its multiple causes and the consequences it generates, has been a constant of the authorities that govern the Venezuelan state in matters of Public Security and is led by the efforts of President Nicolás Maduro and the different public security organizations in their municipal, state and national territorial spheres. Consequently, one of the aspects that is very important is that, once those structural and cyclical factors that promote crimes, violence or any violation of the law have been specified, it is undoubtedly the correction of the courses of action by the of the authorities in charge of enforcing the law and achieving the long-awaited social peace and sense of security that everyone deserves. In this sense, the communal police or community police have a purely preventive approach, approaching the very heart of the communities, a position that gives them a great advantage in preventing as it allows observing the social phenomenon of the criminal and other forms of violence. in the front row and then take measures to prevent these events from happening. Now, this article aims to first describe some cases about the experience of communal police at the Latin American level, and then make a small analysis where the positive aspects of these experiences will be highlighted. To achieve this, the approach is based on a qualitative paradigm similar to that of phenomenological hermeneutic paradigm, using a field design oriented to the descriptive level, taking into account the use of constant comparative and analytical methods.
Student’s Satisfaction and Academic Achievement Nexus Using Physical Facilities and Classroom Environment in Secondary Schools
Authors: Qadeer Amatul , Hussain Shahid Shahid , Ullah Naeem
Published: August 2024

This study explores the relationship between student satisfaction and academic achievement, focusing on the roles of physical facilities and classroom environments in secondary schools. Recognizing that a conducive learning environment is critical for student success, this research investigates how well-maintained facilities and supportive classroom atmospheres contribute to student satisfaction and academic performance. Utilizing a mixed-methods approach, data were collected through surveys and interviews with students, teachers, and school administrators from various secondary schools. Quantitative data were analyzed using statistical techniques, while qualitative insights were derived from thematic analysis. The findings indicate a strong positive correlation between the quality of physical facilities—such as classrooms, libraries, and sports facilities—and student satisfaction. The study reveals that classroom environments characterized by effective teaching practices, mutual respect, and a sense of safety significantly enhance students' academic outcomes. The research suggests that improving the physical infrastructure of schools and fostering a positive classroom climate can lead to higher levels of student satisfaction, which, in turn, positively impacts academic achievement. The study concludes with recommendations for policymakers, educators, and school administrators to prioritize investments in school infrastructure and cultivate a nurturing classroom environment. By doing so, educational institutions can create a holistic and supportive setting that promotes student well-being and academic excellence.
Google Gemini's Influence on Workplace Dynamics in Bangkok
Authors: Limna Pongsakorn , Kraiwanit Tanpat
Published: August 2024

This research study delved into the intricacies surrounding adoption of Google's Gemini and its ramifications on employee interactions, daily operational tasks, and overall job satisfaction within organizational contexts. Employing qualitative methodology, the study utilized in-depth interviews as the primary data collection method. Employing a purposive sampling technique, the participant pool comprised a diverse array of eight individuals from various organizational departments of firms in Bangkok, Thailand to ensure inclusion of different perspectives and roles. Through content analysis, the study scrutinized the multifaceted impact of Gemini on the dynamics of organizational operations. The findings revealed that the introduction of Google's Gemini significantly reverberated across three dimensions of workplace dynamics: employee interactions, execution of daily operational tasks, and the overarching sentiment of job satisfaction among employees. While integration of Gemini promises to substantially augment productivity levels and elevate workforce morale, a nuanced and balanced approach is imperative to optimize its benefits while mitigating potential drawbacks. This necessitates a deliberate focus on preserving the human elements indispensable for fostering a dynamic, innovative, and harmonious team environment amidst the technological advancements facilitated by Gemini.
Impact of Remote Work on Work-Life Balance in the Southeastern Asia Union Mission of Seventh-day Adventists
Authors: Prim Moniroth , Balraj Noah Anburaj
Published: August 2024

The COVID-19 pandemic led to an increase in remote work arrangements. While remote work has been praised for its flexibility, it is unclear how remote work affects work-life balance. This study examined the remote work practices and work-life balance of Southeastern Asia Union Mission (SEUM) Seventh-day Adventist employees, as well as those working in its subsidiary missions in Cambodia, Laos, Thailand, and Vietnam before and during the pandemic. A quantitative survey was administered to all 121 and mission office employees of SEUM. The online survey questionnaire explored demographics, pre-pandemic work experiences, during pandemic work-life balance experiences, and remote work satisfaction.
The findings revealed a significant negative correlation (-.392, p < .01) between remote work and work-life balance for SEUM employees. Employees generally reported a decrease in work-life balance when working remotely as compared to their pre-pandemic work experiences. Interestingly, strong organizational communication regarding remote work policies was associated with higher remote work satisfaction. This study offers valuable insights about the need to create a supportive work environment for remote employees. Recommendations include prioritizing clear communication, investing in employee well-being initiatives, and conducting further research to inform long-term remote work strategies.
Importance of strategic alliances for the achievement of profitability in the companies of the coal sector in the department of La Guajira
Authors: Mejia Reales Marlene Leonor , Barrios Angelica
Published: August 2024

This paper focused on identifying the importance of strategic alliances for the achievement of profitability in companies of the coal sector in the department of La Guajira. It used a descriptive approach with a non-experimental and cross-sectional field design. The population included thirteen managers, surveyed through a population census. A structured survey with Likert scale was used for data collection and descriptive statistics for analysis. The results highlighted the importance of technology, infrastructure and productivity for strategic alliances. It was concluded that there is a moderately favorable trend, suggesting that some managers do not adequately prioritize these alliances, which limits the potential success of the companies
Jual Beli Online Dalam Perspektif Maqashid Syariah Imam Haramain (Studi Kasus Marketplace Shopee)
Authors: Fery Priawan  , Amalia Shofia  , Luqman Luqman
Published: August 2024

Kemajuan teknologi telah memunculkan sejumlah strategi yang membantu bisnis menghasilkan lebih banyak uang dari produk mereka. Hal ini menunjukkan kemajuan layanan masyarakat. Oleh karena itu, penggunaan platform seperti Facebook, Instagram, dan marketplace yang tidak sepenuhnya mengesampingkan risiko aspek gharar (penipuan) tidak dapat dihindari oleh para pebisnis. Islam mengatur semua aspek kehidupan manusia, termasuk bidang ekonomi. Gagasan maqashid syariah, yang didasarkan pada prinsip maslahah, berfungsi sebagai panduan ketika mengkaji peraturan yang mempromosikan keadilan dan kesejahteraan. Kajian terhadap transaksi yang digunakan dan diedarkan oleh masyarakat tidak diragukan lagi diperlukan mengingat berbagai aplikasi marketplace yang muncul. Dengan menggunakan metodologi deskriptif, penelitian ini berfokus terutama pada kerangka umum untuk menerapkan maqashid syariah pada transaksi komersial melalui marketplace Shopee. Shopee, yang mencakup penjual, pembeli, dan penyedia pasar, memiliki volume penjualan terbesar di antara semua pasar. Meskipun didirikan di negara non-Muslim, Shopee bercita-cita untuk mencapai maslahat melalui prinsip-prinsip bisnis Islam.
HSP90AB1’s effect on hepatocellular carcinoma’s local immune response and prognosis
Authors: Yudong Wang
Published: August 2024

Background: HSP90AB1 (heat shock protein 90 kDa alpha, class B, member 1) helps collapsing proteins, and the restraint of HSP90AB1 is anticipated to successfully illuminate the issue of warm stun reaction of the restraint of HSP90. The fundamental component by which HSP90AB1 influences the neighbourhood-resistant reaction to tumours remains vague. Materials and strategies: This investigation analyzes the impact of HSP90AB1 on the composition and dispersion of safe cell invades inside hepatocellular carcinoma (HCC) tissues. At that point, by examining HSP90AB1-associated immunoregulatory qualities, KDR, MICB, and TNFRSF4 were distinguished as critical supporters. A prognostic show joining these qualities was built, and its exactness was approved by utilizing free datasets from the ICGC and TCGA databases. Results: Our discoveries illustrate that the restraint of HSP90AB1 expanded the penetration of CD8+ T cells and other resistant cell populaces inside HCC tissues. This improved resistant reaction was related to moving forward with a quiet prognosis, proposing a potential restorative procedure for HCC. Conclusion: We propose that focusing on HSP90AB1 may improve the survival results of HCC patients by encouraging safe cell penetration into the tumour microenvironment.
Akabane Test—Its Capabilities and Real Application in Traditional and Modern Medicine
Authors: Muzhikov Valery , Muzhikov Ruslan , Nikitin Kirill
Published: August 2024

Background: Currently, for high-quality monitoring of the body, it is necessary to have many different devices with you and transmit their data to the doctor. It's expensive, complicated and ineffective. To solve this problem, we used the thermoalgometry method in the form of the Akabane test, which gives an integral assessment of the functional activity of the entire organism, including at the level of individual organs and systems, down to biochemistry. Methods: Using this method, we examined several thousand patients with various pathologies over 30 years. This allowed us to draw well-founded and practice-tested conclusions. As a focus group in this article to evaluate the test's performance compared to traditional diagnostic methods, test results were analyzed in more than 700 of our patients with diabetes. Results: Thermoalgometry data have a significant correlation with the main indicators of the body, including a number of biochemical indicators. At the same time, using the test profile, you can diagnose various diseases, and using the test indicators in the dynamics of observation, you can evaluate the biorhythms of individual organs to predict the condition for several days. A system has been developed for assessing the individual effect of drugs on various organs, their effectiveness and side effects, with determination of their optimal dose and time of administration. Conclusion: The thermoalgometry method opens up a new principle and source of obtaining medical information, based on the assessment of symmetry in the body under normal conditions and in various diseases.
ROLE OF ACADEMIC ANXIETY IN ACADEMIC PROCRASTINATION: A THEORETICAL PERSPECTIVE
Authors:   Baba Mastnath University, Asthal Bohar, Rohtak, Haryana, India., Seema Seema
Published: August 2024

Academic procrastination is significantly influenced by academic anxiety, which also affects students' capacity to do assignments efficiently. Avoidance behaviours are frequently the result of high levels of concern among students about their academic achievement. This nervousness may be caused by a lack of trust in one's own skills, perfectionism, or a fear of failing. Students put off beginning or completing homework as a result, thinking that doing so may momentarily reduce their stress. But this anxiousness is sometimes made worse by the procrastination, leading to a vicious cycle. There's more pressure to meet deadlines and less time to do projects when they draw near, which causes stress and lower-quality work. Procrastination also causes guilt and frustration, which can impair one's self-esteem and exacerbate academic anxiety. Reducing procrastination requires effective handling of academic anxiety. Students can overcome anxiety and form better study habits by using techniques like time management, talking to teachers or counselors for support, and engaging in mindfulness exercises. Taking care of academic anxiety promotes general wellbeing, academic success, and task completion.
SECTORAL ANALYSIS OF PRINCIPLE 2 OF BRSR FILINGS
Authors:   Janki Devi Memorial College, University of Delhi, Delhi, India, Sinha Manisha , Arora Gurminder Kaur ,   Sri Guru Gobind Singh College of Commerce, University of Delhi, Delhi, India.
Published: August 2024

There is a growing global demand from investors for more rigorous regulations regarding non-financial disclosures. Internationally, several ESG reporting frameworks, such as the Global Reporting Initiative (GRI) , Carbon Disclosure Project (CDP) , Sustainability Accounting Standards Board (SASB) , and the Taskforce on Climate-related Financial Disclosures (TCFD) , have been established. These frameworks require companies to report on their sustainability performance, underlying principles, processes, and key performance indicators. The International Sustainability Standards Board (ISSB) has issued a global baseline for sustainability reporting in 2023 as IFRS S1(General Requirements for Disclosure of Sustainability-related Financial Information) and IFRS S2 (Climate-related Disclosures) .
AN EMPIRICAL STUDY ON IMPLICATIONS OF FINANCIAL LITERACY ON FINANCIAL WELL-BEING
Authors:   D.N.P.G. College, Fatehgarh (CSJM University), Uttar Pradesh, India., Tewari Vinod Kumar , Sharma Ayushi ,   D.N.P.G. College, Fatehgarh (CSJM University), Uttar Pradesh, India.
Published: August 2024

The study aimed to assess the level of financial literacy among individuals in Delhi. The implications of financial literacy on financial well-being were analysed. Financial literacy was measured using three dimensions: financial attitude, financial knowledge and financial behaviour. An attempt was also made to identify financial knowledge under/overconfidence. The relationship between various socio-demographic factors with financial literacy and financial well-being was also analysed after investigation. The primary data was collected and analysed using statistical tools such as percentage analysis, t-test, ANOVA, linear multiple regression analysis, and chi-square analysis and finally, a Structural equation model was constructed to understand the impact of financial literacy on financial well-being. The analysis uncovered some significant findings, which are summed up in this paper. Various areas of concern are also identified and suggestions are offered to improve financial literacy and enhance financial well-being.
Analysis of Employee Recruitment at ID Food (BUMN) 2020-2022
Authors: Rahayu Fransisca Tutii
Published: August 2024

This study analyzes the recruitment of ID FOOD (BUMN) in the period 2020-2022. The data used comes from the company's annual report. This analysis includes employee recruitment programs, and the impact of recruitment on the company. The method used is qualitative research with descriptive analysis technique of literature study. The object of this research is the recruitment process carried out by the ID Food (PERSERO) company. The data collection technique used in this research is to use secondary data by collecting relevant research articles. The results showed that ID Food (PERSERO) experienced a decrease in employees in 2020 and 2021 due to employees retiring and resigning. There is no data disclosure so that there are differences in the number of employees in the 2020 financial stAatements and 2021 financial statements.  The increase in 2022 was due to five companies being built. The quality of new employees at ID Food (PERSERO) is said to be lacking because many employees resigned in 2021 even though in 2022 the company opened a new subsidiary. If the employee is qualified, the employee will be transferred instead of resigning. However, researchers can find out that there are irregularities in the financial statements where there is no match in the number of employees in the 2020 financial statements and 2021 financial statements. In addition, the 2023 financial statements have not been published. Even though the ID Food company is categorized as a go public company which should have sent the latest financial statements.
Software Cost Estimation: Algorithmic and Non-Algorithmic Approaches
Authors: Khan Bilal , Khan Wahab , Arshad Muhammad , Jan Nazir
Published: August 2024

Software Development Organizations develop a huge number of projects on a yearly basis. One of the main issues is that which tool can be selected for an accurate estimation of the cost of software projects. Software cost estimation (SCE) is one of the main objectives of any project. SCE directly introduces almost all management events including resources allocation, project planning, and project bidding. In this study, some important SCE methods have been studied for comparative analysis and this has been concluded that none of the methods are essential inferior or superior to others, as there is no individual approach that is finest for every situation. The selection of the method is depending upon the nature of the project.
Cybersecurity Education Through Mobile Application to Prevent Cyber Attacks During Covid-19
Authors: Ying Chan Xin , Kasmin Intan Farahana , Amin Salmiah , Zainal Nur Khairunnisha
Published: August 2024

Since Covid-19 pandemic, population in all the world have change their lifestyle in order to suppress the virus transmission. The popularised of digital technology give a great opportunity to the cybercrimes to conduct their cyberattack activities. Since the pandemic, the cybercrimes have manipulated the victims as the source of financial income. The main aim of this research is to develop mobile application to educate people on cybersecurity and prevent them from being attacked. 400 of Malaysian who are not adequate the cybersecurity knowledge is involved in the questionnaire survey. The proposed system has implemented stratified sampling method which falls under probability sampling. In the questionnaire, clear instructions will be given with either ranking questions or rating questions. As a consequence, the proposed mobile application is important to the Malaysian in order to reduce the cyberattack occurred in any individuals. By using the mobile application, the public may increase the cybersecurity awareness as well as having precaution on getting cyberattacks. The future recommendations can more focus on the functionality of the mobile application and related topic on the cybersecurity education.
Minimizing Human Elements in Phishing Attacks by Integrating Administrative Controls to Phishing Detection Systems
Authors: Pang Jonathan Owen , Kasmin Intan Farahana , Amin Salmiah , Zainal Nur Khairunnisha
Published: August 2024

Phishing emails are a prevalent threat in the current cybersecurity landscape and industry- standard phishing detection system employed are considered insufficient in preventing phishing attacks as the detection system prioritizes on filtering and blocking phishing emails. This research aims to propose an enhanced phishing detection system addressing the limitation of the existing system deployed by integrating administrative countermeasures to reduce the number of successful phishing attacks. Regarding research methodology, this research will focus on gathering both quantitative and qualitative data by distributing surveys comprising of open-ended and close-ended questions and employing stratified sampling method to draw conclusions on the data collected. In conclusion, the proposed system highlights signs of phishing in an email which alleviates human error resulting in lower rate of successful phishing attacks. Future recommendation regarding the proposed system involves using an advanced Artificial Intelligence branch like Deep Learning and adding semantic analysis capability to the proposed system.