bluuebunny/crossref_metadata_2025 huggingface dataset:
A Hybrid Machine learning Techniques for Detection of Chronic Kidney Disease
Authors: Santushti Betgeri
Published: November 2024

Chronic kidney disease (CKD) is a prevalent medical condition that is distinguished by the progressive deterioration of renal function over time. The diagnosis, management, and treatment of this condition are exceedingly difficult due to its multifactorial etiology and extensive array of manifestations. The objective of this review paper is to provide a comprehensive examination of chronic kidney disease, including epidemiology, pathophysiology, clinical presentation, diagnostic criteria, treatment modalities, and preventive strategies. Health professionals must address the intricacies of the condition in order to manage or reduce the complications associated with CKD. Diabetes, hypertension, advanced age, and gender are among the numerous risk factors that increase the likelihood of developing CKD. The predictors of CKD were described in this investigation using machine learning algorithms. Our results indicate that ML can assist in the early identification and management of individuals who are at risk of developing CKD. The application of ML models to electronic health record data can reduce the prevalence of undiagnosed CKD, which is particularly beneficial in progressive diseases such as CKD, where the treatment burden and cost increase as the disease progresses.
Enhancing Breast Cancer Collagen Segmentation with Improved Swim Transformation Algorithm
Authors: Swati Bula Patil
Published: November 2024

The precise segmentation of collagen fibres in breast tissue is essential for an efficient diagnostic and treatment strategy because breast cancer is still a major worldwide health concern. By better separating fibers from the background, the Swim Transformation (SWT) algorithm has demonstrated potential to improve collagen fiber segmentation. Unfortunately, the resilience and accuracy of the original SWT method are limited because to its susceptibility to noise and distortions. This work suggests an updated version of the SWT algorithm that includes multiple improvements to maximize collagen segmentation in breast cancer tissues, thereby addressing these constraints. The novel method improves the detection of collagen fibers by introducing a noise reduction pre-processing step and adaptive thresholding. It also incorporates an extra post-processing phase to eliminate segmentation mistakes and improve the overall outcomes. Using a dataset of microscopic pictures of breast cancer tissue, the suggested method's effectiveness was assessed and contrasted with that of the original SWT algorithm and other cutting-edge segmentation techniques. The enhanced SWT algorithm achieves greater segmentation accuracy, sensitivity, and precision, according to experimental data. In addition, the study expands on previous research by using a deep learning framework that classifies breast cancer cells based on segmented collagen properties. This framework uses Long Short-Term Memory (LSTM) and Bidirectional LSTM models. The promise of deep learning for automated cancer detection was demonstrated when the model was trained on segmented collagen pictures to identify the presence of malignant cells. The suggested method provides a notable enhancement in the segmentation of collagen, yielding more dependable outcomes that aid medical professionals in diagnosing breast cancer and developing treatment plans.
Mathematical Analysis of Novel Method to Solve Protection Issues Pertaining To Solar PV Integration
Authors: P. S. Patil
Published: November 2024

The integration of Solar Photovoltaic (PV) systems into modern power grids presents protection challenges, such as voltage fluctuations, fault detection complexities, and bidirectional power flow issues. These challenges compromise grid reliability, requiring advanced methods to address them. This study introduces a novel mathematical analysis approach to solve protection issues in solar PV integration, focusing on developing an adaptive protection scheme using advanced mathematical models. The proposed approach utilizes differential equations, optimization techniques, and machine learning algorithms to create dynamic protection settings responsive to varying grid conditions. The method integrates fault detection and classification algorithms based on wavelet transform and support vector machines (SVM), allowing for rapid and accurate identification of fault types and locations. The findings demonstrate that the proposed adaptive protection scheme significantly enhances fault detection accuracy, reducing false trip rates by over 30% compared to conventional protection systems. Moreover, the method efficiently distinguishes between transient and permanent faults, ensuring swift isolation and minimizing disruption to solar PV operations. The developed model also exhibits robustness in handling variations in solar irradiance and load fluctuations, making it suitable for real-world grid applications. This research provides a substantial contribution to the field of solar PV integration by offering a mathematically grounded, adaptive protection solution that ensures improved reliability and resilience in power systems. The proposed method paves the way for the development of more advanced protection schemes, ensuring the seamless integration of renewable energy sources into modern grids while maintaining system stability and security.
Mathematical Techniques for Autonomous Vehicle Navigation Systems
Authors: Manoj Tarambale
Published: November 2024

Advanced mathematical methods are used a lot in autonomous car guidance systems to make sure they work correctly, reliably, and efficiently. This abstract talks about some of the most important mathematics methods used to create and improve these systems. One important method is probabilistic robots, which uses Bayesian filters, such as the Kalman filter and its nonlinear versions (Extended Kalman Filter and Unscented Kalman Filter), to estimate a vehicle's state and make sense of sensor data that isn't always clear or loud. Path planning algorithms, like A and Dijkstra's algorithm, are needed to find the best routes. Sampling-based methods, like Rapidly-exploring Random Trees (RRT), can help with problems in high-dimensional space. Control theory is a very important part of keeping a car stable and following the direction you want it to take. Model Predictive Control (MPC), for example, is used a lot because it can handle control jobs with multiple variables while considering the system's behavior. To model how a vehicle moves, differential equations and dynamical systems theory are used to show how control inputs affect how the vehicle acts over time. Also, methods that combine data from different sources like LiDAR, cameras, and GPS are very important for making an accurate and complete picture of the world. Optimization methods improve tracking even more by adjusting the path of the car, cutting down on energy use, and shortening trip times. Besides these methods, machine learning and deep learning are being added to guidance systems more and more to help them make better decisions and be more flexible in settings that are changing quickly and are very complicated. These models can learn from very large datasets by finding trends and making predictions that are important for tasks like finding objects, understanding scenes, and making decisions on their own. Autonomous car tracking systems are getting smarter, more capable, and more reliable by using both old-fashioned math methods and new, cutting-edge machine learning methods together. This makes it possible for them to be widely used in real life.
An Analytical Review on Predictive Fault Tolerance for Industrial IoT Using Advanced Learning Techniques
Authors: Arti Gawade
Published: November 2024

This article presents a thorough examination of hybrid machine learning methodologies employed to improve fault tolerance in Industrial Internet of Things (IIoT) systems. These hybrid models enhance fault detection precision and resilience by integrating various machine learning techniques, surpassing conventional methods. The research commences with a comprehensive literature review of contemporary fault tolerance techniques, subsequently presenting a comparison of various datasets and a proposal for hybrid predictive algorithms. The efficacy of these algorithms is subsequently assessed in comparison to cutting-edge techniques. Principal findings underscore the enhanced precision of hybrid models in real-time applications, their diminished computing complexity, and their scalability in dynamic industrial settings. Nonetheless, issues pertaining to integration, data reliance, and real-time implementation are also addressed. The article concluded by outlining prospective research methods, encompassing sophisticated integration techniques, automated model optimisation, adaptive self-learning frameworks, and methodologies to safeguard data privacy. This paper outlines a framework for future advancements in fault tolerance, with considerable implications for both academia and industry.
Developing Advanced Navigation Algorithms for Autonomous Vehicles to Enhance Safety and Efficiency in Urban Environments Using Real-Time Sensor Data and Machine Learning
Authors: Harsha Avinash Bhute
Published: November 2024

Autonomous vehicles (AVs) have the ability to completely change how people get around cities. They could make transportation safer and more efficient while also reducing pollution and traffic. But their broad utilize depends on the creation of progressed direction frameworks that can dependably discover their way around complicated urban settings. An inventive way to illuminate this issue is portrayed in this think about. It employments real-time sensor information and machine learning to form and utilize cutting-edge direction strategies that work well in cities. There are five primary steps within the study process. To begin with, exhaustive information collection strategies are utilized to urge valuable sensor information from numerous places, such as GPS, LiDAR, webcams, and more. Another, progressed machine learning models are made and instructed with this information so that AVs can make smart choices and find their way around. These models are made to require into consideration things just like the environment, activity, how individuals walk, and other changing factors to form beyond any doubt they work well in complicated urban circumstances. A number of distinctive measures are utilized to judge the suggested algorithms, such as their security, adequacy, steadfastness, and capacity to alter. Re-enactment circumstances are utilized to test the strategies in a run of urban settings, from swarmed city zones to calm roads within the rural areas. When compared to conventional strategies, the comes about appear enormous picks up in following execution. The equations make it more secure and more productive to utilize them in cities. The modern strategies are too compared to current direction frameworks to appear how much way better they are at being precise, adaptable, and fast to reply. The research also finds possible problems and restrictions, like the need for real-time processing and complicated computations, and suggests ways to fix them.
Potential of Emerging Sensor Technologies like LiDar for Enhanced Spatial Computing: Unlocking the Future of Technology
Authors: Greeshma Arya
Published: November 2024

In recent years, the synergistic integration of Light Detection and Ranging (LiDAR) technology with the burgeoning field of spatial computing has ignited a transformative spark across diverse industries. This groundbreaking fusion has revolutionized how we perceive and interact with the digital world, unlocking unprecedented possibilities in augmented reality (AR), virtual reality (VR), autonomous driving, and environmental mapping. These advancements transcend mere technological enhancements; they represent a paradigm shift, reshaping entire ecosystems and fostering a new era of innovation.
This research paper delves deeper into the immense potential of LiDAR as an emerging sensor technology, specifically exploring its profound impact on the landscape of spatial computing. We will meticulously examine its diverse applications, dissect the inherent benefits it offers, and analyze its far-reaching consequences on the digital sphere. Additionally, we will utilize Apple Vision Pro as a compelling case study, meticulously dissecting how this innovative AR/MR headset leverages the power of LiDAR technology to achieve superior spatial awareness and deliver unparalleled user experiences. Through this multifaceted examination, we aim to illuminate the transformative potential of LiDAR and its role in shaping the center of spatial computing.
Novel IDS Framework: Integrating GWO Feature Selection with KAN Classification
Authors: Thamraj Narendra Ghorsad
Published: November 2024

The past few decades have seen significant lifestyle improvements through cutting-edge innovations like AI and fog computing enabled by the World Wide Web, which are crucial for practical applications involving both symmetrical and asymmetrical information distributions. However, these advancements also come with the risk off requent and dangerous cyber-attacks. To mitigate these unwanted malwares, researchers must develop and implement novel intrusion detection systems (IDSs). Challenges such as inadequate accuracy scores due to numerous unnecessary and ineffective features, poor identification of attacks through specific deep learning classification methods, the costly and inefficient use of labeled training databases, and the excessive time required for system development and evaluation hinder there liability and efficiency of IDSs.This paper proposes a hybrid approach that combines the Grey Wolf Optimizer (GWO) for feature selection and the Kolmogorov-Arnold Network(KAN) algorithm for classification to address these challenges.The GWO is utilized to extract important features through meta-heuristic methods, demonstrating its effectiveness in feature selection. The proposed model achieves a high accuracy score, precision, recall, and F1-score of 92.09%, indicating its capability to accurately identify both normal and intrusive activities. The Matthews Correlation Coefficient (MCC) of 0.9019 further validates the model's robustness.
Retracted
Authors: Retracted
Published: November 2024

Retracted
Trends in Industry Sectors and Geographic Locations Attracting Maximum Outward FDI from India
Authors: Vrujesh Jalindar Salunkhe
Published: November 2024

In recent years, India has focused on foreign direct investment (FDI) actively participating in internal as well as outward investment. While outward FDI (OFDI) plays a crucial role in the country's economic growth and export expansion, it remains relatively underexplored in terms of guiding FDI decisions in foreign markets. This study investigates various industries to empirically examine OFDI trends and their relationship with geographic locations that attract FDI from India. The research evaluates different industrial sectors involved in OFDI to identify which sectors and regions are most attractive to Indian investors. Using data from the Reserve Bank of India (RBI) spanning 2011 to 2024, the analysis employs advanced tools like EViews, Tableau, and Python. The findings provide deeper insights into OFDI trends, with a time series analysis of Joint Ventures as well as Wholly Owned Subsidiaries highlighting the investments made by different Indian sectors. The results demonstrate that Indian companies have a wide scope of investment with Wholly Owned Subsidiaries specifically in financial and business services dominantly in developed countries.
A Hybrid Approach for Intrusion Detection by Dragonfly Optimization and Bayesian Neural Network Methodologies
Authors: Satish Kumar Garapati
Published: November 2024

The technique of keeping an eye out for malicious activity or policy violations on a computer network or system is known as intrusion detection. It entails examining system logs, network traffic, and other data sources to spot potential security breaches, misuse, and illegal access. Network-based and host-based intrusion detection systems (IDS) are the two types available. The former analyzes network traffic and packets, while the latter concentrates on specific hosts or devices. This paper investigates how to improve intrusion detection systems in cyber security by combining dragonfly optimization (DFO) and Bayesian neural network (BNN) methodologies. The NSL-KDD dataset, which is tested and trained in the system, is the source of the dataset used in the study. By utilizing the optimization skills of DFO and the probabilistic reasoning powers of BNN, the hybrid technique improves the accuracy and flexibility of detecting and addressing network intrusions. This research illustrates the effectiveness of the suggested methodology in navigating dynamic network environments and identifying emerging dangers through a thorough examination.  In addition to improving detection performance, the integration of BNN and DFO gives enterprises insightful information about infiltration patterns and the ability to take proactive measures to thwart hostile activity. The created model's enhanced intrusion detection performance is demonstrated by the experimental findings. These results are validated in terms of accuracy, precision, recall, execution time, and F1 score by comparing them with models that are currently in use.
Breaking the Haze: Enhanced CycleGAN Models with Self-Attention for Single-Image Dehazing
Authors: Anant More
Published: November 2024

A common preprocessing step in vision-based applications is image dehazing, which has drawbacks such color distortion and detail loss. Conventional techniques frequently fall short of sufficiently retrieving sharp images from foggy situations. In order to improve single-image dehazing, this paper presents a unique method that combines Self-Attention Generative Adversarial Networks (SAGANs) with Cycle-Consistent Generative Adversarial Networks (CycleGAN). In order to capture long-range dependencies and enhance the perceptual quality of the dehazed images, our method includes self-attention processes from SAGANs and makes use of CycleGAN's strengths for efficient image-to-image translation without the need for paired training samples. We present a dual-framework design in which SAGANs improve textural and contextual details by focusing on relevant characteristics throughout the whole image space, while CycleGAN assures content fidelity through cycle consistency. We show through our studies that this integrated strategy achieves higher scores in common metrics like Peak Signal-to-Noise Ratio (PSNR) of 34.146 dB and Structural Similarity Index (SSIM) of 0.964, greatly outperforming both qualitative and quantitative assessments of previous methods. Together with establishing a new standard for single-image dehazing, this work shows how effective hybrid generative models can be when handling challenging image processing problems.
Investigations into Biomass Utilization: A Renewable Alternative Fuel for Sustainable Energy
Authors: Ashutosh Umre
Published: November 2024

Indian municipal solid waste production exceeds 55 million tonnes per year, with a five percent yearly growth. Municipal solid waste has several environmental benefits, making it a promising feedstock for large-scale energy production. For energy utilisation, consider pelletising municipal solid waste with or without a binder. The volume of municipal solid trash created causes deposition, degradation, and maintenance concerns. India is increasing rapidly in population and urbanisation. This has consistently increased municipal solid trash production. The Planning Commission of India reports that 51% of Indian municipal solid trash is organic. Yard debris and food waste from restaurants, hotels and retail stores make up most of it. Since one-third of food is wasted, it's a major concern. One billion and three hundred thousand tonnes of food waste are produced worldwide.  Municipal trash is dumped at open dumps in India. Open dumping is the least recommended method of solid waste management because it emits greenhouse gases and produces foul odours, flies, and rats, which spread disease and cause complaints in nearby residential areas. The spilt leachate may also affect nearby ground and surface waterways. Open dumps also need land, which is scarce in densely populated India. This is a major issue in India because landfills are open dumping sites without engineering solutions. Compared to anaerobic digestion and biomass gasification, pelletisation is a better organic waste treatment. Pelletisation is easy if wastes are categorised. After separation, garbage is treated and turned into pellets for burning. This study examines sixteen samples for calorific value, moisture, ash, and CHNS ratio. Energy crops, agricultural trash, forestry waste, and municipal garbage are samples.
Use of Data Augmentation and Fine-tuned Transfer Learning Models for Classification of Cervical Cancer
Authors: Pratiksha D. Nandanwar
Published: November 2024

Innovative strategies for early and accurate diagnosis of cervical cancer continue to be a global health priority. In this research, we present a powerful framework for improving cervical cancer classification by combining data augmentation methods with tailored transfer learning models. The lack of properly labelled medical data is a major roadblock to developing reliable diagnosis models. The geometric transformations, contrast tweaks, and noise addition that we use to increase the dataset artificially help us overcome this difficulty. The model's robustness is improved as a result of the additional exposure to diverse real-world circumstances provided by this supplemented dataset. We use a transfer learning strategy based on pre-trained convolutional neural networks (CNNs) to harness the potential of deep learning. These networks are well-suited for medical image analysis due to their ability to quickly and accurately identify features in a wide variety of images. Our expanded cervical cancer dataset is used to fine-tune these algorithms for their diagnostic purpose. Our experiments show that utilising both data augmentation and fine-tuned transfer learning considerably raises the accuracy of cervical cancer categorization. In terms of sensitivity, specificity, and overall accuracy, the model performs at a state-of-the-art level. This suggests that it may be useful in the diagnosis and early identification of cervical cancer. Additionally, our method demonstrates the significance of efficient data utilisation and model adaptability in the field of medicine. Our method is scalable and affordable, and it can be easily implemented in healthcare settings since we handle the problem of data scarcity and take advantage of the features of pre-trained deep learning models.
Construction and Study of an Improved PP-Human Multi-Objective Tracking Method
Authors: Liang Ma
Published: November 2024

Multiple objective tracking represents a crucial research focus within the realm of computer vision. It has a very broad application prospect in video surveillance, intelligent transportation, robot navigation and positioning and other fields.However, multi-target visual perception is affected by light, weather, occlusion and other factors, and is vulnerable to noise interference, faced with problems such as unstable image enhancement effect, target correlation accuracy and robustness.In this study, the PP-Human framework is applied to multi-target tracking. Through training and refinement of the PP-Human model, integration of a high-accuracy detector, enhancement of pedestrian re-identification (ReID) techniques, and optimization of the data association approach, the model's multi-object detection performance is enhanced, achieving efficient and precise multi-target tracking. Using the extended Market 150 dataset to comprehensively evaluate the performance of the proposed multi-objective tracking method, this paper improves the accuracy of multi-objective tracking by 5.0% and reaches 95.0% of MOTA through a series of optimization measures.To validate the efficacy and robustness of this approach, experimental evaluations were conducted. The refined PP-Human framework demonstrated strong performance in multi-target tracking tasks, establishing a reliable basis for practical applications such as pedestrian analysis, behavior recognition, and flow statistics.
Mathematical Modeling and Statistical Analysis of Novel Swarm Computing Based Charging Management Framework for Electric Vehicles
Authors: Apoorva Sharma
Published: November 2024

This paper presents a novel swarm computing-based charging management framework for electric vehicles (EVs), designed to optimize charging operations and enhance the grid's capacity to handle EV load without compromising system reliability. We introduce a mathematical model that incorporates swarm intelligence algorithms, particularly Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), to efficiently manage the charging schedules of EVs across multiple charging stations. The framework aims to minimize energy costs, balance load during peak hours, and reduce charging time by distributing the charging load in a more organized manner.A statistical analysis is conducted to validate the effectiveness of the proposed model. Using real-time data and simulation, the study evaluates various scenarios to analyze the impact of the swarm-based approach on overall grid stability and EV charging efficiency. The results indicate significant improvements in grid management, with a reduction in peak load demands and enhanced utilization of renewable energy sources. The analysis also demonstrates the scalability and adaptability of the model to different urban settings and EV penetration rates.The paper concludes with a discussion on the potential integration of this model into existing grid systems and its implications for future smart grid and EV infrastructure developments. The proposed framework not only contributes to more sustainable energy management practices but also opens new avenues for research in the domain of intelligent transportation systems and smart grid integration.
Matheamatical Modeling of Multi-Objective Adaptive Neuro Fuzzy Inference Based Optimization for IOT Based Wireless Sensor Network
Authors: Tiab Ali
Published: November 2024

The proliferation of the Internet of Things (IoT) and its integration with wireless sensor networks (WSNs) necessitates advanced optimization techniques to enhance performance and resource allocation while ensuring reliability and energy efficiency. This study introduces a mathematical modeling approach utilizing a Multi-Objective Adaptive Neuro-Fuzzy Inference System (MO-ANFIS) designed for optimizing IoT-based WSNs. The proposed model synergistically combines the adaptive learning capabilities of neural networks with the reasoning prowess of fuzzy inference systems, encapsulated within a multi-objective framework to concurrently address key operational objectives such as minimizing energy consumption, maximizing network lifetime, and enhancing data accuracy and throughput. The mathematical model is formulated to dynamically adapt to changing network conditions and sensor inputs, enabling real-time tuning of fuzzy rules and membership functions through backpropagation neural training. This adaptability ensures optimal performance despite the variable nature of IoT environments. Simulation results demonstrate that the MO-ANFIS model significantly outperforms traditional optimization methods, offering a robust, scalable solution for complex, dynamic WSNs in the IoT landscape. The findings suggest promising applications in various domains, including smart cities, environmental monitoring, and healthcare, where IoT integration is pivotal. This research not only bridges the gap between theoretical fuzzy-neural frameworks and practical IoT applications but also sets a foundation for future explorations into intelligent, adaptive network management systems.
Industry 5.0 for Society 5.0 a new Transformation
Authors: Pankaj Chandre
Published: November 2024

Industry 5.0 represents a transformative approach within Society 5.0, leveraging advanced technologies like AI, IoT, Robotics, and Blockchain to enhance productivity, automation, and decision-making across various sectors. This abstract examines how privacy, security, and ethical issues are addressed by modern technologies to create a societal structure that is morally and humanely sound. IoT improves smart city infrastructure and environmental monitoring, while AI enables tailored education and predictive healthcare. Blockchain guarantees safe and transparent data transfers, while robotics helps with accurate healthcare operations. Collectively, these technologies support Society 5.0's sustainability, inclusivity, and resilience. They also hold out hope for a time when technological breakthroughs will preserve strict ethical standards and cybersecurity safeguards while prioritizing the needs of people and society.
Mathematical Optimization of Electric Motor Designs
Authors: Yogita D. Bhise
Published: November 2024

When designing and improving the performance of electric motors, mathematical optimization is very important. The main goals are usually to make the motors more efficient, lower their costs, and work within certain limits. The main topic of this study is how to improve electric motor designs using advanced optimization methods, such as multi-objective optimization. The study looks at how to use different types of computer algorithms together, like gradient-based methods, genetic algorithms, and particle swarm optimization, to solve difficult design problems. Some of these problems are reducing energy loss, making the best use of materials, and finding the right balance between different performance measures such as speed, power density, and temperature management. Building mathematical models of the motor's physical and functional features is the first step in this method. Then, optimization methods are applied to these models. Finite element analysis (FEA) is used to correctly model the motor's electric behavior. This makes sure that the optimization process considers physical limits and nonlinearities that happen in the real world. The study also looks into how different design factors, like the shape of the motor's core, the way the windings are set up, and the materials used, affect its total performance. The study also looks at the fact that motor design has more than one goal by using Pareto front analysis to find the best ways to balance different goals. This lets people come up with motor designs that are good for speed, efficiency, and cost-effectiveness. Case studies of the design of different kinds of electric motors, such as induction motors, permanent magnet synchronous motors (PMSMs), and brushless DC motors (BLDCs), show that the proposed optimization method works well. The results show that mathematical optimization has the ability to make the planning process a lot better. This could lead to motors that work better, cost less, and are better suited to certain uses. The study ends with a talk of how optimization methods could be used in the future to improve the design of electric motors, especially for new technologies like electric cars and green energy systems.
Predictive Analytics for Mental Health: Machine Learning Approaches in the Tech Industry
Authors: Mansi Bhonsle
Published: November 2024

Mental health concerns in the technology sector have become increasingly prominent, drawing attention due to the industry's high-pressure environment and intense workloads. This study investigates the application of machine learning techniques to predict mental health conditions among technology industry professionals. The research utilizes a comprehensive dataset, analyzing factors such as work-life balance, job satisfaction, workplace environment, and individual well-being. The study implements and evaluates several Machine Learning approaches, including Logistic Regression, Random Forest, Decision Tree Classifier, KNN, Gradient Boosting Classifier, XGB Classifier, to determine the most effective method for predicting mental health outcomes. The results highlight the potential of predictive analytics in early identification, enabling organizations to implement proactive strategies to enhance mental wellness and improve employee productivity. Additionally, this investigation emphasizes the necessity of incorporating mental health support mechanisms in technology-driven workplaces to foster a more positive work environment. The paper concludes with a discussion of the study's limitations and potential avenues for improving mental health prediction models in the future.
Sự sẵn sàng chăm sóc người cao tuổi của sinh viên khoa điều dưỡng Trường Đại học Đại Nam
Authors: Anh Vũ Hoàng , Trang Nguyễn Thị Thu
Published: November 2024

Sự gia tăng tỷ lệ người cao tuổi cùng với nhu cầu chăm sóc sức khỏe của người cao tuổi tại Việt Nam đòi hỏi nguồn nhân lực điều dưỡng có chất lượng và sẵn sàng làm việc trong lĩnh vực này. Tuy nhiên, số lượng nghiên cứu về sự sẵn sàng của sinh viên điều dưỡng trong lĩnh vực chăm sóc người cao tuổi còn hạn chế, dẫn đến thiếu hụt cái nhìn thực tế và cách thức hỗ trợ sinh viên điều dưỡng hiệu quả. Nghiên cứu cắt ngang sử dụng Thang đo Sẵn sàng làm việc chăm sóc người cao tuổi (CW) trên 252 sinh viên từ năm nhất đến năm thứ tư cho thấy, sự sẵn sàng của sinh viên ở mức độ trung bình cao, giá trị trung bình 3,63 ± 0,77. Một số yếu tố liên quan đến sự sẵn sàng chăm sóc người cao tuổi của sinh viên khoa Điều dưỡng Trường Đại học Đại Nam gồm giới tính, kiến thức đối với người cao tuổi, thái độ đối với người cao tuổi và mong muốn sống với bố mẹ đẻ hoặc bố mẹ chồng trong tương lai. Trong đó, thái độ tích cực đối với người cao tuổi và mong muốn sống với bố mẹ đẻ hoặc bố mẹ chồng trong tương lai là hai yếu tố có liên quan tích cực mạnh nhất đến tuổi sự sẵn sàng làm việc trong lĩnh vực chăm sóc sức khỏe người cao tuổi của sinh viên.
Tác dụng của Domuvar trên mô hình gây hội chứng ruột kích thích thực nghiệm
Authors: Dương Đậu Thuỳ , Thanh Nguyễn Phương , Hằng Đinh Thị Thu , Mai Đặng Thị Ngọc , Thuý Nguyễn Thị , Anh Phạm Thị Vân
Published: November 2024

Domuvar là sản phẩm chứa probiotic Bacillus subtilis với chỉ định hỗ trợ điều trị triệu chứng các bệnh lý đường tiêu hoá. Nghiên cứu này được thực hiện với mục tiêu đánh giá tác dụng bảo vệ và phục hồi của Domuvar trên mô hình gây hội chứng ruột kích thích trên thực nghiệm. Chuột nhắt trắng chủng Swiss được chia thành 6 lô, trong đó lô 1 - 3 được uống nước cất, lô 4 uống mebeverin, lô 5 - 6 uống Domuvar với hai mức liều 0,82 x 109 CFU/kg/ngày và 1,64 x 109 CFU/kg/ngày. Chuột ở các lô 3-6 được gây hội chứng ruột kích thích thể tiêu chảy bằng dầu mù tạt trước khi dùng thuốc thử (để đánh giá tác dụng phục hồi) và sau khi dùng thuốc thử (để đánh giá tác dụng bảo vệ). Kết quả cho thấy Domuvar liều 1,64 x 109 CFU/kg/ngày có tác dụng giảm nhu động ruột thể hiện bằng giảm độ di động than hoạt trong lòng ruột và cải thiện tình trạng phân. Như vậy, Domuvar có tác dụng bảo vệ và phục hồi trên mô hình gây hội chứng ruột kích thích thể tiêu chảy trên thực nghiệm.
Tình trạng suy dinh dưỡng và thiếu vi chất dinh dưỡng ở trẻ 5 - 10 tuổi tại phòng khám dinh dưỡng, Bệnh viện Nhi Trung ương
Authors: Thành Hoàng Tuấn , Nhung Nguyễn Thị Trang , Liên Nguyễn Thị Hương , Hồng Nguyễn Thị Thuý
Published: November 2024

Nghiên cứu mô tả cắt ngang đánh giá tình trạng suy dinh dưỡng và thiếu hụt vi chất dinh dưỡng trên 231 trẻ độ tuổi 5 - 10 tuổi đến khám tại Phòng khám Dinh dưỡng, Bệnh viện Nhi Trung ương từ tháng 7/2023 đến tháng 6/2024. Kết quả: Suy dinh dưỡng nhẹ cân chiếm tỷ lệ 9,1%, thấp còi 7,4% và gày còm 7,8%, thừa cân béo phì 6,1%. Thiếu vi chất dinh dưỡng: thiếu kẽm tỷ lệ cao nhất 55,0%, tiếp đến thiếu sắt 22,1%, thiếu canxi 16,0%, thiếu phospho 5,2%, thiếu vitamin D 3,0% và magie 1,3%. Như vậy, trẻ 5 - 10 tuổi là đối tượng có nguy cơ suy dinh dưỡng và thiếu vi chất dinh dưỡng.
Mối liên quan giữa tình trạng lệch lạc khớp cắn và vệ sinh răng miệng ở học sinh 15-16 tuổi tại trường Hermann Gmeiner, Hà Nội năm 2024
Authors: Tường Lưu Văn , Mai Vũ Ngọc , Khởi Trương Đình , Chiều Hà Ngọc , Long Dương Đức , Đại Phùng Hữu , Hồng Đinh Diệu
Published: November 2024

Nghiên cứu cắt ngang mô tả trên 119 trẻ em từ 15-16 tuổi tại trường THPT Hermann Gmeiner, Hà Nội để xác định mối liên quan giữa tỉ lệ sai lệch khớp cắn theo Angle và tình trạng vệ sinh răng miệng. Kết quả: Tỉ lệ vệ sinh răng miệng rất tốt và tốt giảm dần từ khớp cắn loại bình thường, loại I, II và III Angle, ngược lại, tỉ lệ trung bình và kém tăng dần từ khớp cắn loại bình thường, loại I, II và III Angle. Tương quan cắn hở và cắn ngược răng trước, tăng độ cắn chìa, cắn sâu, lệch đường giữa không có mối liên quan với tình trạng vệ sinh răng miệng với p > 0,05. Kết luận: Sai lệch khớp cắn theo phân loại của Angle có liên quan ở mức độ nhẹ đối với tình trạng vệ sinh răng miệng, trong đó, cắn chéo răng sau gây ra tình trạng vệ sinh răng miệng kém cao hơn 2,7 lần so với không có cắn chéo răng sau. Tuy nhiên, các yếu tố khớp cắn khác chưa phát hiện thấy có mối liên hệ với tình trạng vệ sinh răng miệng ở trẻ từ 15 - 16 tuổi.
Đặc điểm nội soi tai và cắt lớp vi tính Cholesteatoma bẩm sinh tai giữa ở trẻ em
Authors: Luật Nguyễn Hữu , Dũng Đào Trung
Published: November 2024

Nghiên cứu nhằm mô tả đặc điểm nội soi tai và cắt lớp vi tính cholesteatoma bẩm sinh tai giữa ở trẻ em. Phương pháp nghiên cứu mô tả loạt ca bệnh, 63 bệnh nhi được chẩn đoán cholesteatoma bẩm sinh tai giữa tại Bệnh viện Tai Mũi Họng Trung ương; Bệnh viện Bạch Mai, Bệnh viện Nhi Thanh Hóa từ tháng 01/2020 đến tháng 6/2024. Kết quả cho thấy tuổi trung bình là 4,5 ± 2,7 năm; 81% bệnh nhi ≤ 6 tuổi. Tỷ lệ nam/nữ là 2,7/1. 88,9% tình cờ phát hiện bệnh. Nội soi thấy khối cholesteatoma đều ở một bên: 55,6% ở góc phần tư trước trên, 33,3% ở góc phần tư sau trên, 11,1% ở ≥ 2 góc phần tư. Tỷ lệ giai đoạn I, II, III, IV lần lượt là 61,9%; 9,5%; 22,2%; 6,3%. Cắt lớp vi tính chẩn đoán đúng giai đoạn 90%, tổn thương xương con 66,7%. Như vậy, cholesteatoma bẩm sinh tai giữa thường không triệu chứng. Nội soi tai giúp phát hiện sớm bệnh, cần chụp cắt lớp vi tính xác định giai đoạn và lập kế hoạch phẫu thuật.
Đặc điểm lâm sàng, cận lâm sàng, tổn thương mạch vành và kết quả điều trị sớm hội chứng mạch vành cấp ở người bệnh ung thư
Authors: Đại Lê Quang , Đạt Phan Tuấn
Published: November 2024

Chúng tôi tiến hành nghiên cứu cắt ngang trên 40 người bệnh hội chứng mạch vành cấp có bệnh lý ung thư tại Viện Tim mạch quốc gia, Bệnh viện Bạch Mai từ tháng 8/2023 đến tháng 9/2024. Nhóm nghiên cứu có tuổi trung bình 72,3 ± 9,0. Ba bệnh lý ung thư hay gặp nhất là ung thư đại tràng (20,0%), ung thư phổi (17,5%), ung thư vú (17,5%). Đau ngực là triệu chứng hay gặp nhất, chiếm 95,0%. Số nhánh mạch vành tổn thương lần lượt: 1 nhánh (50,0%), 2 nhánh (27,5%), 3 nhánh (22,5%). Hai type tổn thương mạch vành nhiều nhất là type C (47,5%), type B1 (32,5%). Điểm Syntax trung bình 17,36 ± 12,35. Có 01 người bệnh được phẫu thuật bắc cầu chủ-vành (CABG), 04 người bệnh điều trị nội khoa. Có 35 người bệnh được can thiệp động mạch vành qua da (PCI), trong đó có 01 người bệnh có biến chứng chảy máu sau can thiệp. Tỷ lệ tử vong nội viện của cả nhóm nghiên cứu là 0%. Không có sự khác biệt có ý nghĩa thống kê về đặc điểm tổn thương mạch vành của các nhóm ung thư hay gặp (ung thư đường tiêu hoá, ung thư phổi, ung thư vú).
Kết quả bước đầu của kỹ thuật tạo nhịp tim vùng bó nhánh trái ở người bệnh Block nhĩ thất
Authors: Kiên Lê Võ , Nhơn Bùi Văn , Sơn Phạm Nguyên , Khánh Phạm Quốc , Sơn Phạm Trường , Giang Trần Song , Hải Đặng Minh , Việt Trần Tuấn , Linh Nguyễn Duy , Đạt Bùi Thành
Published: November 2024

Đánh giá kết quả của kỹ thuật tạo nhịp tim vùng bó nhánh trái ở người bệnh block nhĩ thất. Người bệnh block nhĩ thất có chỉ định cấy máy tạo nhịp vĩnh viễn được cấy điện cực thất vào vùng bó nhánh trái. Tiêu chí đánh giá: tỷ lệ thành công, kết quả thủ thuật, thông số điện tâm đồ và điện cực, biến chứng sớm. 60 người bệnh block nhĩ thất được cấy điện cực thất tiếp cận vùng bó nhánh trái (52/60 cấy 2 buồng, 8/60 cấy 1 buồng). Thời gian cấy điện cực thất trung vị là 16 (12,5; 25) phút; nhóm 2 buồng có thời gian thủ thuật trung vị 70,5 (60; 90) phút và thời gian chiếu tia trung vị 10,95 (8,65; 14,25) phút. QRS sau cấy 114,05 ± 14,10ms. Ngưỡng tạo nhịp 0,85 ± 0,26/0,4ms. Tỷ lệ thành công tạo nhịp vùng bó nhánh trái 95% (57/60 ca), còn lại 3 người bệnh đạt tạo nhịp vách sâu. QRS sau cấy của nhóm 57 bệnh nhân thành công là 113,16 ± 13,77ms.Không có biến chứng đáng kể. Tạo nhịp tim vùng bó nhánh trái có tỷ lệ thành công cao, khả thi và an toàn.
The Principle of Cost-Based Supervision in Practice
Authors: Dumlao Luis
Published: November 2024

A key response of financial regulators around the world to the financial and economic crises of ten years ago has been the formation of supervisory committees. Such committees now exist in several countries worldwide. Consequently, many regulators fund their supervisory function by charging their supervisees. The objective of this paper is to compare how these regulators charge fees, identify common practices, draw conclusion from observations, and provide relevant recommendations. This paper especially focuses on the Federal Reserve, the European Central Bank and the Bank of England that charge fees just enough to recover the cost of supervisory function, or those that follow what this paper refers to as the “principle of cost-based supervision.” A discussion on how selected examples of regulators practice the principle of cost-based supervision follows. Simulations of fees using selected procedures to supervisees in the Czech Republic and Greece follow. Then this paper introduces the asset elasticity of cost notated as n and simulates the hypothetical supervisory fee if n is constant.
The Evolution of On-Demand Platforms: Conceptual Framework, Regulatory Challenges, and Policy Implications in the Digital Economy
Authors: Popescu Alina I.
Published: November 2024

Digital platforms currently form the cornerstone of what is referred to as 'platform economics,' progressively assuming a central role in the city's economy, experiential landscape, and governance. On-demand platforms play a pivotal role in elevating the level of digital sophistication among citizens. This article extends the current research on on-demand service markets, aiming to cultivate an understanding of this nascent domain of digital platform business within the broader framework of the smart city. In the first section, we delve into various concepts related to on-demand service markets, outlining their distinctive features in comparison to traditional service markets, and elucidating the principal components of the on-demand digital platform economy. The second part of this paper employs bibliometric analysis to unveil, among other insights, emerging areas of interest, key works, and influential researchers in the field, as well as the countries and institutions where research on on-demand service markets is most advanced. This research endeavour may lay the foundation for the development of a theory pertaining to the provision of on-demand services within the context of the platform economy. Finally, the research extensively discusses regulatory challenges and public policy implications in the context of on-demand platforms, with a focus on worker protection, algorithmic transparency, and data security.
The Effect Government Recognition of Indigenous Communities Has on Climate Adaptation Funding in the United States
Authors: Cottrell Clifton
Published: November 2024

The global community already spends billions of dollars per year on projects and strategies intended to adapt to current and future challenges brought about by climate change. Indigenous communities, which are often place-based and culturally dependent on local resources, are especially vulnerable to the effects of climate change. Without adequate funding to build climate resilience, these communities face potential displacement and cultural extinction. The United States government maintains a complex and subjective system by which it acknowledges the Indigenous communities within its borders and enforces this recognition system to make determinations on which Indigenous Peoples to engage through numerous climate adaptation programs and grants. This study utilizes a comparative statutory review of dozens of federal climate funding opportunities and concludes that a lack acknowledgement of a Tribal nation’s sovereignty severely impacts access to climate adaptation financial resources. Efforts by some federal agencies to redefine what constitutes an “Indian tribe” in certain programs calls into question the bureaucratic structures that typically determine which Indigenous Peoples can and should be acknowledged by the U.S. government. Adjustments to federal recognition procedures for Indigenous Peoples may be necessary to ensure justice and equity in adapting to climate change.
CLASSIC-TPACK PDP for Teaching Composition: A Suggested Professional Developmental Program for Teaching L2 Writers
Authors: AlKadi Sundus Ziyad
Published: November 2024

This study explores Saudi foundation-year students' instructional challenges in developing academic writing skills. Many students enter university with limited experience in writing, often relying on memorization rather than original expression, which leads to increased writing anxiety, hinders critical thinking, and places undue focus on error correction over fluency. To address these issues, the current paper introduces the CLASSIC-TPACK Professional Development Program (PDP) designed to enhance English instructors' teaching methods. Rooted in The Critical Approach and Reid's Teacher Training Quadrants, this flexible and inclusive framework supports varied learner needs. The program uses the Cooperative/Collegial Development Model to foster a collaborative environment where educators engage in reflective practice and action research, incorporating technology to enrich their teaching. This initiative aims to support the growth of skilled L2 writers and highlights the importance of ongoing professional development to strengthen academic writing within Saudi education.
« Les grands manufacturiers qui font gagner le pain à tant de pauvres gens doivent trouver protection devant les tribunaux »
Authors: Nootens Thierry
Published: November 2024

La jurisprudence québécoise en matières civiles constitue une source précieuse pour l’histoire du travail et du salariat. Elle dévoile un vaste éventail de conflits vécus dans le monde ouvrier durant la transition au capitalisme industriel et la manière dont l’appareil judiciaire a procédé à la régulation de ces mêmes conflits. À partir de 128 causes rapportées, l’auteur examine le sort qu’ont connu les poursuites déposées par – ou contre – des ouvriers et des ouvrières des manufactures, des journaliers et des charretiers, travailleurs directement affectés par le prélèvement de la survaleur générée par le labeur physique et par le libre jeu des rapports de force dans la sphère économique. Six catégories de litiges marquent cette jurisprudence : les clivages institués par le droit parmi les travailleurs manuels (notamment en vertu de la législation sur les maîtres et serviteurs); les défauts de paiement des salaires; la mise en œuvre malaisée des privilèges ouvriers; les contestations de la discipline patronale; l’imposition de dommages à l’occasion du travail; et, enfin, les aléas des saisies exercées à l’encontre des travailleurs. Nombre d’indices trahissent l’incapacité du binôme réunissant le droit civil et la justice civile, en qualité d’instruments de régulation sociale, à obvier à la fragilité profonde de la condition salariale au 19e siècle et au début du 20e siècle.