bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated Virtual Machine Migration with Load Shifting Technique Avoiding Latency in Cloud Environment
Authors: Srinithya Kandukuri
Published: November 2024

Efficient management of Virtual Machines (VMs) in cloud computing is one key aspect to achieve the best utilization of resources and performance. The study is about improving Automated Virtual Machine Migration with a Load Shifting Technique in order to reduce latency through the cloud domain. This methodology dynamically distribute the load across multiple VMs by migrating their workloads to other physical hosts determined according certain resource utilization metrics available in real-time. The dynamic and unpredictable nature of cloud workloads requires a solution that is more responsive in the way it manages VMs. Live migration and pre-copy migration offer suboptimal performance in terms of time to migrate, downtime due to the prolonged copying period as heaps swell/collapse during live migrations. Utilizing sophisticated algorithms and real-time monitoring to avoid these pitfalls, our methodology allows us for proper load balancing with minimal latency. The primary elements of the proposed framework are a detailed monitoring module, an intelligent load shifting algorithm, high-performance migration method and feedback loop for continuous optimization. The monitoring module is responsible for collecting on-the-fly information about CPU, memory, network traffic and disk I/O such that can be exploited by the load shifting algorithm. The algorithm processes this data to decide which VMs should be migrated with the purpose of ensuring load balancing and decreasing latency achieved using these migrations. In order to test the proposed approach, comprehensive simulations and comparatives analyses utilizing other models were performed. This results in better latency reduction, increased resource utilization and improved overall system performance. In fact, the proposed method can handle not only current challenges in cloud computing such as load balancing and latency issued but also a scalable and effective approach for general cloud infrastructure usage. Finally, the Automated Virtual Machine Migration with Load Shifting Technique proves to be a reliable and successful solution for assisting in managing dynamic cloud workloads. Through real-time monitoring, advanced algorithms and continuous optimization this method has now been able to improve cloud services performance and reliability thereby allowing it to better serve service level agreements as well meet up with user satisfaction. We hope to adapt the algorithm and generalize it for even more advanced predictive models in future iterations, as well as evaluation of practical implementation on cloud-based deployment environments.
Optimizing Computation: A Biomimicry Design Spiral Approach to Algorithm Development
Authors: Vaishali B. Langote
Published: November 2024

The increasing focus on biomimicry in the built environment shows that designers are becoming more conscious of the opportunities that nature presents for enhancing human and systemic functions. The field of advanced material technology has extensively used biomimicry. Nevertheless, there has been little in-depth discussion of its possibilities in environmentally conscious building practices. Numerous branches of the social and natural sciences have had an impact on the field of architecture; at the present time, biological principles are finding their way into design processes. From the earliest days of bio-architectural movements to the most recent ones, bioinspiration has shaped architectural practices towards a wide range of new methods. Nevertheless, the line between architectural bioinspiration that just mimics natural forms and the genuine comprehension of biological principles—the bedrock of sustainable development—is blurry when it comes to biomimicry. One of the biggest obstacles is the lack of cross-disciplinary cooperation between architects and biologists, which is exacerbated by the vast knowledge gap that exists between the creative process of building design and the scientific disciplines that pertain to biology. All sorts of spiral pathways, their properties, and how to use them to build and enhance other optimisation methods are described in great detail.
Dynamic Lexicon-Based Sentiment Analysis Architecture Using Nonlinear Feature Optimization
Authors: V R N S S V Saileela P
Published: November 2024

Dynamic Lexicon-Based Sentiment Analysis (DLSA) improves sentiment detection by using adaptive, nonlinear feature optimization techniques. The model targets gaps in traditional methods, which often miss complex and subtle sentiment patterns. DLSA integrates modules for text preprocessing, lexicon and phrase extraction, and feature optimization with genetic algorithms. The system refines feature sets by selecting, combining, and adjusting features based on real-time performance data. Key findings show DLSA outperforms traditional models like SLPCF in accuracy, precision, and recall, especially in analyzing nuanced text data. This approach provides a flexible and efficient solution, enhancing the accuracy of sentiment classification in varied applications.
Probabilistic Risk Assessment in Cybersecurity: Bayesian Methods for Quantifying and Mitigating Cyber Risks
Authors: Fulsundar Amita Purushottam
Published: November 2024

In today's digital world, where everything is linked, privacy dangers are a big problem for businesses, states, and people. When it comes to cybersecurity, traditional ways of assessing risk don't always work because online threats are always changing. By combining statistical models with existing data and expert knowledge, Bayesian methods, on the other hand, look like a potential way to measure and reduce online risks.  This essay looks at how Bayesian methods can be used in probabilistic risk assessment (PRA) in the field of hacking. Bayesian reasoning is used by PRA to get a fuller picture of cyber risks by taking into account doubt, variation, and personal opinions during the risk assessment process. By using likelihood functions, posterior distributions, and prior probabilities, Bayesian methods give us a sensible way to update risk predictions as new information comes in. One of the best things about Bayesian PRA is that it can take into account how different online risks and weaknesses are connected and depend on each other. By using statistical modeling to look at these connections, businesses can find possible chain reactions and decide how to best stop them. Additionally, Bayesian methods let you use expert opinions and personal data, which makes it possible to look at online risks in a more complete way than just using numbers.  One of Bayesian PRA's strengths is that it can work with little data and unknown input values. When there isn't enough or a lot of good actual data, Bayesian methods let analysts combine information from different sources, like past data, threat intelligence, and expert opinions, in a planned way to get more accurate risk predictions.  Bayesian methods can be used to make decisions about how to reduce security risks as well as how to measure those risks. In a probabilistic framework, organizations can figure out the best ways to lower their general cyber risk exposure by modeling the possible results of different mitigating measures.  Overall, this study shows how Bayesian methods could help the area of computer risk assessment grow. By accepting doubt and using a variety of information sources, Bayesian PRA provides a powerful set of tools for measuring cyber risks and helping risk managers make decisions in a danger situation that is becoming more complicated.
A Novel Data Analysis Architectural Framework for Data Reduction on Edges for Smart City Healthcare
Authors: Swapnaja Rajesh Hiray
Published: November 2024

The most difficult task is managing and digesting the massive amount of heterogeneous data created by IoT. It questioned the efficiency and computational storage capacity of today's infrastructure resources. Furthermore, because there is so much data, it becomes difficult to give the user enough information to make a decision. A technology known as Complex Event Processing (CEP) has been created to glean important insights from massive real-time data streams. The goal of this research is to create a system that can extract complicated events from large amounts of sensor data and only transmit those events to a cloud server. Global healthcare services have been expanding at an exponential rate because of the vast amount of data that clinical and medical organizations generate daily. Healthcare systems can use e-health services to meet the medical and assistive requirements of people. Edge technology is proven in this direction. Mixing the old classic concepts with the innovative we have proposed the Novel data analysis architecture for smart heart disease prediction. We have created the fuzzy-based inference system at the edges for the data analysis and used the resulting Complex Events at the end predictions.
Unveiling Sarcasm in Hindi: Cutting-Edge Deep Learning Framework for Sarcasm Detection
Authors: Madhuri Thorat
Published: November 2024

Opinion mining, also known as sentiment analysis (SA), is a pivotal computer-based approach for discerning and categorizing sentiments within textual data. With the burgeoning popularity of social media platforms and the proliferation of user-generated content in Hindi, there is an escalating need for proficient sentiment analysis techniques tailored specifically for this language. The internet's accessibility in regional languages has become imperative to accommodate a diverse user base irrespective of age or linguistic inclination. While a significant portion of SA research has been conducted in English, there exists a dearth of comprehensive studies focusing on Indian languages, notably Hindi. This paper addresses this gap by investigating sentiment analysis within the context of Hindi, delving into various sentiment categories including positive, negative, neutral, and the notably complex sentiment of sarcasm. Sarcasm, characterized by its subtle irony and ridicule, poses a formidable challenge in sentiment analysis, particularly in languages like Hindi, which boast intricate grammatical structures and nuanced contextual cues. This study aims to enhance sentiment analysis methodologies by unraveling the intricacies of sarcasm detection in Hindi textual data.
Enhancing Big Data Clustering: The Improved K-Means - Artificial Bee Colony Algorithm with MapReduce
Authors: Satish S. Banait
Published: November 2024

The volume and diversity of data produced by scientific applications and the corporate world have increased drastically in the modern era. Large data is challenging to collect, store, transform, and analyse. Processing vast amounts of data is a challenging task, and one major problem with big data is that it takes longer to run typical algorithms. One of the most common data mining jobs is clustering. It is applied in several fields. The K-Means clustering algorithm is widely recognized as one of the prominent unsupervised learning techniques in machine learning. The advantages involve fundamental clarity, good effect, and ease of execution. As the online world grew quickly, the rise in data collection locations occurred simultaneously, marking the era of big data and an explosion of information. This research introduces the IK-ABC Algorithm (Improved K Means - Artificial Bee Colony) to tackle various challenges encountered in k- means clustering algorithms. These issues encompass limitations in global search capabilities, the sensitivity of cluster center selection, randomness in initialization, early-stage development, and sluggish convergence observed in the original artificial bee colony algorithm. To expedite computation and enhance the effectiveness of the iterative optimization process, a custom fitness function tailored for the K-means clustering technique and a position update formula relying on global guidance was created through the utilization of MapReduce.
Mathematical Evaluation and Performance Analysis of Renewable Cooling System by using Earth Heat Exchanger
Authors: Sumit Kumar Rai
Published: November 2024

The need for sustainable and energy-efficient cooling solutions grows as the globe struggles with the effects of climate change and the rising need for cooling in a variety of industries. However, sustaining a comfortable temperature in our buildings requires a lot of energy use, nearly 25% of global energy production is needed for the purpose of heating and cooling homes and commercial buildings. Therefore, utilization of earth’s underground unchanged temperature using Earth Heat Exchanger (EHE) system is a better option to eliminate the wastage of energy and emission of greenhouse gases. By experimenting on an actual demonstration small scale model, we can get the performance of the actual EHE system by taking some assumptions and calculations. This paper investigates two performances of the EHE system, the first one by the keeping the constant velocity of the inlet air throughout the day and next one is by varying the velocity to analyse the system performance.
Mathematical Analysis of Solid Particles Internal Support to Enhancing Pipe Bending
Authors: Amit S. Chaudhary
Published: November 2024

Airspace, aerospace launch vehicles, and a variety of electrical devices employ low thickness bent pipes with short bending radius as one of their essential components. Producing technology for seamless bends has become essential, as the traditional method of producing such bent pipes involves welding two stamped half bends, which is no longer able to meet the high dependability requirements. This work describes a novel approach of push bending low thickness pipes with modest relative bending radiuses using solid particles internal support. Using the DEM, the pressure distribution of the solid particles is examined (DEM). The main benefit of the solid particles in reducing pipe forming faults, particularly wrinkling, is the strengthening of bending deformation. Furthermore, the study explores how diameter of the particles, solid particles friction, & counter pressure affect distribution of pressure within the system. Solid particles plays a beneficial role in enhancing wrinkle resistance during bending, as demonstrated by the formation of pipes with a high diameter, low thickness, and short bending radius. All of these findings suggest that using solid particles internal support opens up new possibilities for the relative bending radius of low thickness pipes to be bent.
Enhancing Resource Utilization and Load Distribution with ACO and Reinforcement Learning in Dynamic Computing Infrastructures
Authors: Minal Shahakar
Published: November 2024

In the rapidly evolving landscape of dynamic computing infrastructures, efficient resource utilization and adaptive load balancing are critical for maintaining system performance and sustainability. This research introduces a novel framework that integrates Ant Colony Optimization (ACO) and Reinforcement Learning (RL) to enhance resource utilization and load distribution in such environments. In this research, we present a comprehensive framework for enhancing resource utilization and load distribution in dynamic computing infrastructures using a hybrid approach that integrates Ant Colony Optimization (ACO) and Reinforcement Learning (RL) algorithms. The proposed framework aims to address the challenges of adaptive load balancing and efficient resource management in highly variable and resource-intensive computing environments. Our approach leverages the strengths of ACO in discovering optimal paths and RL in learning from the environment to make informed decisions. We evaluate the performance of our framework using key parameters: Total Energy Consumption (kWh), Average Energy Consumption per Node (kWh), Peak Energy Consumption (kW), Energy Efficiency (Tasks/kWh), and Dynamic Energy Consumption (kWh/hour). The evaluation compares three methods: Least Load Balancing (LLB), ACO, and RL, with RL demonstrating the best results. Experimental results indicate that the RL-based approach significantly reduces Total Energy Consumption and Average Energy Consumption per Node while maintaining a lower Peak Energy Consumption. Furthermore, the RL method shows improved Energy Efficiency and optimal Dynamic Energy Consumption, highlighting its potential for sustainable and efficient resource management in dynamic computing infrastructures. This study underscores the importance of intelligent load balancing and resource optimization strategies in modern computing environments and demonstrates the effectiveness of combining ACO and RL techniques to achieve these goals. Our findings provide valuable insights for future research and development of advanced load balancing frameworks that can adapt to the ever-evolving demands of dynamic computing systems.
Enhancing Vehicle Tracking and Recognition Across Multiple Cameras with Multimodal Contrastive Domain Sharing GAN and Topological Embeddings
Authors: Rakhi Madhukararao Joshi
Published: November 2024

Using Multimodal Contrastive Domain Sharing Generative Adversarial Networks (GAN) and topological embeddings, this study shows a new way to improve car tracking and classification across multiple camera feeds. Different camera angles and lighting conditions can make it hard for current car tracking systems to work correctly. This study tries to solve these problems. Common Objects in Context (COCO) and ImageNet are two datasets that are used in this method for training. Multimodal Contrastive Domain Sharing GAN is used for detection and tracking. It makes cross-modal learning easier by letting you see things from different camera angles. This framework lets the model learn shared representations, which makes it better at recognizing vehicles in a wider range of visual domains. The Topological Information Embedded Convolutional Neural Network (TIE-CNN) is used to re-identify the car after it has been found and tracked. This network embeds the paths of vehicles into a continuous latent space, keeping the important spatial connections needed for accurate tracking. Real-world multi-camera datasets used for experimental confirmation show that tracking accuracy and recognition performance are much better than with standard methods. The suggested framework works great in tough situations like blocked views and sudden changes in lighting, showing that it are reliable in complicated surveillance settings. This study adds to the progress in multi-camera car tracking and identification by combining geometric data analysis with deep learning methods. This method uses Multimodal Contrastive Domain Sharing GAN and topological embeddings to improve the timing and spatial coherence of tracking results. It also sets the stage for future improvements in monitoring and self-driving systems.
Behaviour Analysis of Two Wheeler Driver ADAS for reduction of Road Fatality
Authors: Nupur Giri
Published: November 2024

India has seen a dramatic increase in traffic accidents, particularly those involving two-wheelers, which account for a significant per- centage of road-related fatalities and injuries. Current Advanced Driver Assistance Systems (ADAS) available in the Indian market are limited, primarily offering basic functionalities like GPS naviga- tion and simple vehicle diagnostics lacking in real-time, dynamic data integration, such as weather and road conditions or personal- ized driver behavior insights, which are crucial for enhancing road safety. This paper presents a novel Advanced Driver Assistance Systems (ADAS) tailored for two-wheelers in the context of Indian road conditions, utilizing Artificial Intelligence (AI) . The proposed system leverages AI algorithms and real-time data processing to en- hance rider safety, navigation, and communication. The core of the system is an AI-powered system that integrates with the vehicle’s sensors and navigation data. By analyzing this information, it pro- vides real-time alerts to the rider, effectively preventing potential accidents and alerting about possible vehicle failures. The approach involves developing and testing various AI models to detect and classify driving behaviors accurately. The performance of these models was evaluated to identify the most effective configuration for real-time driver assistance.
Comprehensive Analysis of Hybrid Machine Learning Models using Optimization Techniques for Improving Twitter Sentiment
Authors: Snehal Sarangi
Published: November 2024

Sentiment analysis (SA), an important part of natural language processing, is a key part of getting useful information from content generated by users, especially on social media sites like Twitter. This paper considers different techniques for analysing and improving sentiments, with a focus on the hybrid SVM_CNN model used with Twitter datasets. Support Vector Machines (SVMs) are used for classification, and Convolutional Neural Networks (CNNs) are used for feature extraction. The SVM_CNN model is proved to be an effective framework for emotion classification tasks. The study looks into different optimization methods, such as GridSearchCV, Genetic Algorithm, Particle Swarm Optimization (PSO), and Bayesian Optimization, to make the SVM_CNN model more accurate and reliable. The success of each optimization method is assessed by how successfully it determines the optimal hyper parameters that are specific to Twitter text data. The results show that Differential Evolution is the most accurate method, over other existing ones. This study helps to improve methods for sentiment analysis by showing how machine learning techniques and advanced optimization algorithms can work together to make them more useful.
An Experimental Investigation of Surface Roughness And Cutting Forces On GFRP
Authors: Asmita A. Bagade
Published: November 2024

In the current study, the emphasis was on finding experimental results on machinability of Glass fibre reinforced plastic composite with milling machine. Study is carried out with objectives of examination of cutting parameter on GFRP materials. Speed , feed, depth of cut & number of flute are consider for analyzing effect on surface finish & cutting forces of composites. Design of experiment is carry out by Taguchi’s L18 orthogonal array. To find out more about the machining parameters on GFRP composite using end mill  of cemented carbide, an analysis of variances (ANOVA) utilizing Minitab 15 software was finally carried out.
Leveraging Incentive Learning in BiLSTM Architectures for Enhanced Network Traffic Congestion Forecasting
Authors: Tejas Prashantrao Adhau
Published: November 2024

Within the past few a long time, organize activity has developed exceptionally rapidly, which has made it difficult to anticipate and handle bottlenecks. This paper looks at a better approach to make strides the precision of foreseeing arrange activity blockage that employments Motivation Learning (IL) in Bidirectional Long Short-Term Memory (BiLSTM) frameworks. IL may be a sort of fortification learning that changes the learning handle based on installment signs. This lets the demonstrate center on characteristics and designs that are more critical. The objective is to form a more dependable and precise estimate framework by combining IL with BiLSTM, which normally takes into consideration time connections from both past and future information. As portion of our strategy, we prepare the show on an expansive dataset that incorporates a wide extend of activity designs. This makes beyond any doubt that it can work in a assortment of organize circumstances. The results of the tests appear that this strategy is much way better at making forecasts than standard LSTM models and other cutting-edge methods. Our proposed strategy could be a great way to bargain with the issues caused by changing and non-stationary organize activity. It might be utilized to assist handle clog some time recently it gets out of hand. This consider adds to the field of arrange activity examination and appears how profound learning systems and support learning strategies can be utilized together to illuminate difficult issues in the genuine world.
Developing Secure and Interoperable Health Information Systems Using Blockchain Technology to Enhance Data Privacy, Security, and Accessibility in Healthcare
Authors: Reda. M. Radwan
Published: November 2024

Healthcare information systems are going through a huge change that will make them more efficient, safe, and easy to use. As more persistent records are digitized and private therapeutic information is shared, it is more critical than ever to create beyond any doubt that security, security, and availability are well secured. Blockchain innovation can be a great way to bargain with these issues since it makes a decentralized, unchangeable record that can be utilized to securely store and send healthcare information whereas securing its security and security. This exposition looks at how blockchain innovation might offer assistance make wellbeing data frameworks more secure and more congruous with each other. Healthcare companies can set up a trusted and unchangeable way to oversee understanding information over different frameworks and parties by utilizing blockchain independent plan, cryptography, and voting instruments. One of the leading things approximately blockchain innovation is that it can make data more private. Cryptographic instruments, like encryption and computerized marks, make it conceivable to store and share private wellbeing information securely with individuals who are permitted to see it, without compromising persistent security. Moreover, since blockchain is unchangeable, once information is recorded, it can't be changed or eradicated. This makes the history of who gotten to and changed information clear and simple to check. In conjunction with ensuring protection, blockchain innovation moreover makes things more secure by bringing down the chances of information spills and unlawful get to. Blockchain-based wellbeing data frameworks make information keeping less centralized and use solid encryption strategies to lower the chances of single focuses of failure and unlawful changes. This makes information more secure and dependable. Keen contracts on blockchain stages can too mechanize and execute get to control rules, making beyond any doubt that as it were individuals who are permitted to can see certain restorative data or do certain activities that have as of now been set up. Interoperability is another important part of modern healthcare information systems that lets different healthcare companies and systems work together and share data easily.
Wave Propagation Models for Optimization in Wireless Communication
Authors: Manoj Tarambale
Published: November 2024

To understand and build radio transmission systems, you need to know how waves travel. These models show how electromagnetic waves move through different settings, considering things like distance, obstacles, and the material the waves move through. A lot of what makes radio transmission work is being able to correctly guess how waves will spread in different situations. Wave propagation models come in many forms, such as open space, ground reflection (two-ray), and more complicated models such as the Hata, Okumura, and Rayleigh models. Each model is used for a different thing. For example, free space models are good at showing simple line-of-sight (LOS) situations, while empirical models like Hata are better at showing more complicated urban and residential settings. For example, the free space model believes that there are no hurdles between the sender and listener. This makes it perfect for situations like open fields or satellite communications. The Hata and Okumura models, on the other hand, are made for cities and suburbs, taking into account how buildings and geography affect signal power. Rayleigh and Rician models are scientific and are often used to describe situations where multipath transmission happens, like in crowded cities where signals bounce off many objects. These models help us understand things like fading, where the signal strength changes because of interference from different routes. Wave propagation models are important for making sure that wireless communication systems work well, whether they are simple field sets or complicated networks in cities. They help experts guess how signals will behave, find the best places to put antennas, and make the whole network work better. These models make sure that communication systems can stay connected and provide high-quality service in a wide range of settings by considering different transmission scenarios.
Web3 Subscriptions: A Catalyst for a More Inclusive and User-Centric Financial System
Authors: Greeshma Arya
Published: November 2024

This paper explores the burgeoning landscape of Web3 subscriptions, highlighting their potential to revolutionize recurring payments within the decentralized space. It contrasts traditional, centralized subscription models with Web3 alternatives, emphasizing the latter's advantages such as user control, fee reduction, and financial inclusion for the unbanked. The paper then delves into a pioneering project that leverages smart contracts and L2 scaling solutions to offer a user-centric and automated subscription management system. By analyzing this project's features, the paper underscores the transformative potential of Web3 subscriptions to reshape the future of recurring payments within the digital economy.
Data-Driven Prediction of Nitrogen Stress and Crop Yield in a Maize -Wheat Cropping System
Authors: Halley Okasa
Published: November 2024

The study has attempted to determine leaf-N content in a cropping system consisting of Wheat and Maize using plant sensors like GreenSeeker and SPAD meter (chlorophyll meter) and use machine learning models so that crop yields based on inputs can be predicted of data on nitrogen doses, NDVI values, SPAD values, and leaf N content. The data recorded from field experiments on wheat and maize crop variable fertilized by N (wheat: 0, 30, 60, 90, 120, 150, 180 and 240 kg N/ha; maize: 40, 80, 120, 160, 200, 240 and 300 kg N/ha) were used. A pair plot analysis showed a positive association between leaf N and NDVI, between leaf N and SPAD values, and between NDVI and SPAD values; all these parameters had a positive correlation with N- application rates. For yield prediction, the model calibrated for wheat could validate yield prediction for maize as well. Random Forest and Support Vector Machines showed reproducible performance (consistent)  on both datasets (maize and wheat), with accuracies of 85.71% and 100%, respectively, on the original dataset.
Human in Loop Machine Learning: A Paradigm Shift
Authors: Varsha Rahul Dange
Published: November 2024

This paper explores the transformative concept of Human-in-Loop (HIL) Machine Learning, which integrates human expertise into the machine learning process to enhance data quality, model accuracy, and ethical decision-making. Human interaction is added to traditional machine learning steps such data collection, preprocessing, model training, evaluation, and deployment through continuous feedback loops, data annotation, and model change. This integration makes use of human intuition and experience to enhance algorithm performance and model interpretability, hence mitigating the drawbacks of entirely automated approaches. HIL Machine Learning allows AI systems to adjust to changing obstacles and makes more accurate forecasts by promoting human-machine interaction. This study emphasizes HIL Machine Learning as a reliable method for successfully handling dynamic, complicated problems across a range of areas.
Differential Game Theory in Electric Vehicle Charging Optimization
Authors: Rajesh Kedarnath Navandar
Published: November 2024

Differential Game Theory is a strong way to solve the hard optimization problems that come up when charging electric vehicles (EVs) in smart grids. This method takes into account the strategic exchanges between many parties, such as EV users, charging station operators, and grid operators, each of whom has their own goals. In this study, we look at how differential game theory can be used to find the best charging plans for electric vehicles. Our goal is to lower total energy costs, make the grid less crowded, and get more energy from green sources. We take into account how power prices change, the different types of green energy that are available, and how EVs move around by modeling the charging process as a difference game. The game-theoretic method lets you come up with plans where each player maximizes their own reward while taking into account what other players do. To find the best price methods for different situations, such as peak and off-peak hours, different amounts of green energy usage, and different pricing plans, we use Nash equilibrium solutions. Our findings show that differential game theory can successfully balance the different needs of parties, which can lead to EV charging systems that work better and last longer. Furthermore, the suggested way not only makes smart grids work better, but it also helps lower carbon emissions by supporting the use of clean energy. In addition, this method helps us understand how to create reward systems that match individuals' goals with world improvement goals. This makes it possible for EV charging strategies to work together better. This study shows that differential game theory could be a very useful tool in the development of smart and environmentally friendly transportation systems.
Time Series Analysis in Electrical Load Forecasting
Authors: Meena Chavan
Published: November 2024

Time series analysis in electricity load projection is a key part of the energy sector because it makes it possible to control and distribute energy more efficiently. It is important to have accurate load forecasts in order to balance supply and demand, make power systems run more efficiently, and lower operating costs. This essay covers a wide range of ways that time series analysis can be used to guess how much electricity will be used. The study looks at a number of different models, such as the autoregressive integrated moving average (ARIMA), exponential smoothing, and machine learning methods like Long Short-Term Memory (LSTM) networks. These models are judged by how well they can show the patterns and time relationships that are common in electricity load data. A lot of attention is paid to the problems that come up because of non-stationarity, timing, and outside factors like weather that cause load changes. The study talks about how feature selection, data pre-processing, and model evaluation can help make forecasts more accurate. The study shows the trade-offs between model interpretability and forecasting power by comparing how well standard statistical methods and new machine learning techniques work. The study also talks about how mixed models, which take the best parts of more than one method and combine them, might help make forecasts more accurate. The results make it clear how important time series analysis is for building accurate load predicting models, which are needed to keep modern power systems reliable and efficient.
Design and Development of a Quad mode Circular Millimeter-Wave Patch Antenna for Breast Tumour Size Detection and Localization
Authors: Nishant Madhukar Borkar
Published: November 2024

In this study, we present the development of an antenna based on millimeter-wave specifically designed for performance in breast tumour detection and localization. A quad – mode circular microstrip antenna is engineered to function at millimeter-wave frequencies of 30 GHz and 40 GHz, focusing on accurate tumor size & location. At 30 GHz and 40 GHz, facilitating effective penetration through breast tissue and enabling precise tumor localization. The differential dielectric characteristics of malignant and normal breast tissues provide the basis for the detection of breast cancers using mm-wave imaging. Using quarter mode circular patch antennas, this method sends and receives pulses that travel through the breast at various points. The tumor's location and size are then determined by receiving the waves from the breast. Due to their strong scattering properties, malignant tumors can be identified by their transfer characteristics in the reflected signals they receive. The study also looks at how the tumor's size and location in relation to the antenna affect detection efficiency. These results suggest that mm wave planar antennas are useful for locating and detecting tiny breast cancers, offering a viable method for breast cancer detection.
Xác định nồng độ Granulysin trong huyết thanh của bệnh nhân hồng ban cố định nhiễm sắc
Authors: Hương Bùi Thị Thu , Huyền Trần Thị
Published: November 2024

Hồng ban cố định nhiễm sắc (fixed drug eruption, FDE) là một loại phản ứng thuốc có biểu hiện ở da, niêm mạc, đặc biệt tái phát ở cùng một vị trí (cố định) hoặc thêm vị trí mới khi tiếp xúc lại với thuốc. Cơ chế bệnh sinh của FDE có thể liên quan tới các tế bào TCD8+ và các protein gây độc của nó như granulysin. Nghiên cứu mô tả cắt ngang này nhằm xác định nồng độ huyết thanh granulysin bằng phương pháp ELISA và phân tích mối tương quan với một số đặc điểm lâm sàng. Sử dụng Mann-Whitney U test để so sánh nồng độ granulysin huyết thanh của hai nhóm FDE và nhóm người khỏe mạnh, Spearman rank correlation test để đánh giá sự tương quan. Sự khác biệt có ý nghĩa thống kê khi p < 0,05. Kết quả có 41 bệnh nhân (24 nam, 17 nữ), trung vị tuổi là 49 (khoảng tứ phân vị: 28 - 61) tuổi và 41 người khỏe mạnh tham gia nghiên cứu. Trung vị diện tích thương tổn da là 3% diện tích cơ thể (khoảng tứ phân vị: 3 - 5%).Trung vị số thương tổn da là 4 thương tổn (khoảng tứ phân vị: 3 - 6). Nồng độ granulysin huyết thanh ở nhóm FDE có trung vị là 334,1 pg/ml (khoảng tứ phân vị: 208,6 - 530,2 pg/ml), không cao hơn so với nhóm khỏe mạnh (trung vị là 331,9 pg/ml; khoảng tứ phân vị: 157,9 - 470,7 pg/ml). Không có mối tương quan giữa nồng độ granulysin huyết thanh với diện tích và số lượng thương tổn.
Tổng quan luận điểm sử dụng thuốc lợi tiểu điều trị bệnh Ménière
Authors: Thái Nguyễn Quang , Thuỷ Nguyễn Thị , Hưng Đỗ Bá
Published: November 2024

Đánh giá hiệu quả của phương pháp sử dụng thuốc lợi tiểu điều trị bệnh Ménière. Nghiên cứu tổng quan bao gồm các nghiên cứu sử dụng thuốc lợi tiểu điều trị bệnh Ménière trên thế giới từ cơ sở dữ liệu trang thông tin điện tử Pubmed, Cochrane Library, Google scholar. Chúng tôi đã tìm được 14 bài báo phù hợp với các tiêu chuẩn lựa chọn liên quan đến 736 bệnh nhân với 3 nhóm thuốc chính: Thiazid, Nhóm lợi tiểu ức chế carbonic anhydrase (C.A), Nhóm lợi tiểu thẩm thấu và 2 nhóm thuốc được phối hợp. Chúng tôi đã tiến hành phân tích về các đặc điểm và hiệu quả trong điều trị của các loại thuốc trên. Mặc dù còn nhiều tranh luận bài tổng quan chỉ ra rằng phương pháp sử dụng thuốc lợi tiểu trong điều trị bệnh Ménière là phương pháp điều trị hiệu quả để kiểm soát tình trạng chóng mặt và ù tai nhưng cải thiện thính lực vẫn còn hạn chế. Bài nghiên cứu cũng cho thấy thuốc lợi tiểu đường uống rất an toàn và thuận tiện để điều trị lâu dài bệnh Ménière.
Tổng kết phẫu thuật cấy ốc tai điện tử ở các trường hợp bất thường giải phẫu bẩm sinh tại Bệnh viện tai mũi họng Thành phố Hồ Chí Minh
Authors: Minh Lê Trần Quang , Huy Phạm Thành
Published: November 2024

Nghiên cứu mô tả hàng loạt ca với 49 bệnh nhân được phẫu thuật cấy ốc tai điện tử có bất thường giải phẫu bẩm sinh tại Bệnh viện Tai Mũi Họng TP. Hồ Chí Minh từ năm 2010 đến 2024. Độ tuổi trung bình khi phẫu thuật là 5,1 ± 4,6. Có 30,6% trường hợp dị dạng tai trong đơn thuần, 30,6% trường hợp khiếm khuyết thần kinh ốc tai đơn thuần và 38,8% các trường hợp vừa có dị dạng tai trong vừa có khiếm khuyết thần kinh ốc tai. Ốc tai phân chia không hoàn toàn loại 2 là dị dạng tai trong thường gặp nhất chiếm tỷ lệ 26,5%, tiếp theo là ốc tai kém phát triển loại 3: 16,4% và dãn rộng cống tiền đình: 10,3%. Về khiếm khuyết thần kinh ốc tai, thiểu sản chiếm tỷ lệ 42,9%, bất sản thần kinh ốc tai: 26,5%. Đa số trường hợp điện cực được đặt qua màng cửa sổ tròn: 79,6%. Kết quả phát triển ngôn ngữ đánh giá theo thang điểm CAP trung bình đạt 4,9 ± 1,5 thời điểm 1 năm sau phẫu thuật. Kết luận: với việc nghiên cứu kỹ các bất thường giải phẫu trên hình ảnh học trước phẫu thuật, lựa chọn điện cực và đường tiếp cận phù hợp, phẫu thuật cấy ốc tai điện tử ở các trường hợp bất thường giải phẫu bẩm sinh có thể thực hiện tương đối an toàn, với những kết quả bước đầu về phát triển ngôn ngữ khả quan.
Kết quả hóa chất dẫn đầu phác đồ TCF theo sau bởi hóa xạ trị ung thư vòm mũi họng giai đoạn III - IVA tại Bệnh viện K
Authors: Đăng Nguyễn Văn , Nhung Nguyễn Thị Thu
Published: November 2024

Ung thư vòm mũi họng là bệnh lý ung thư vùng đầu cổ thường gặp tại Việt Nam. Hóa xạ đồng thời triệt căn theo sau hóa chất dẫn đầu phác đồ TCF đang là một hướng điều trị mới giúp làm tăng tỷ lệ đáp ứng cũng như giảm tỷ lệ tử vong do di căn xa ở giai đoạn xâm lấn tại chỗ, tại vùng. Đây là nghiên cứu trên 40 bệnh nhân ung thư vòm mũi họng giai đoạn III - IVA điều trị hóa chất dẫn đầu TCF theo sau bởi hóa xạ trị tại Bệnh viện K từ 01/2019 đến 12/2020. Nghiên cứu ghi nhận tuổi trung bình là 45 ± 13, tỷ lệ nam/nữ = 3/1, giai đoạn III, IV chiếm tỷ lệ lần lượt là 60% và 40%. Về kết quả điều trị, tỷ lệ đáp ứng sau hóa chất dẫn đầu đạt 87,5%. Tỷ lệ sống thêm bệnh không tiến triển và sống thêm toàn bộ tại thời điểm 3 năm lần lượt là 77,5% và 87,5%. Ít gặp các tác dụng phụ cấp tính độ 3 trở lên. Nghiên cứu cho thấy đây là phác đồ điều trị mang lại kết quả khả quan và khả năng dung nạp tương đối tốt. Tuy nhiên, vẫn cần thêm các nghiên cứu để đánh giá về hiệu quả và tác dụng phụ lâu dài của điều trị.
Kiến thức về chăm sóc sốt của cha mẹ có con dưới 5 tuổi tại khoa nhi Bệnh viện Đại học Y Hà Nội năm 2024
Authors: Linh Lê Khánh , Sơn Nguyễn Thị , Dự Nguyễn Hữu , Nhung Nguyễn Thị Hồng
Published: November 2024

Nghiên cứu được thực hiện nhằm khảo sát thực trạng kiến thức chăm sóc sốt của cha mẹ có con dưới 5 tuổi đến khám và điều trị tại Khoa Nhi, Bệnh viện Đại học Y Hà Nội năm 2024. Nghiên cứu mô tả cắt ngang trên 207 cha/mẹ có con dưới 5 tuổi đến và điều trị tại khoa. Kết quả nghiên cứu cho thấy 52,2% cha mẹ cho rằng sốt có hại cho sức khỏe của trẻ. 60,9% cha mẹ trả lời đúng nhiệt độ của sốt. Nhiệt độ trung bình được coi là “sốt cao” là 38,61°C (độ lệch chuẩn: 0,56). 83,6% cha mẹ lau người bằng khăn ấm và 50,2% tắm nước ấm để hạ sốt cho trẻ. 33,8% cha mẹ sử dụng thuốc hạ sốt luân phiên với 47,1% theo chỉ dẫn của bác sĩ nhi khoa. Lo sợ chủ yếu về hậu quả của sốt là co giật (92,8%) và mất nước (47,8%). Ngoài ra, 57,0% đã đánh thức trẻ dậy để cho trẻ uống thuốc hạ sốt. Từ đó, nghiên cứu cho thấy còn tồn tại nhiều hiểu lầm trong cộng đồng về sốt và cách chăm sóc trẻ khi bị sốt.
ESG risk analysis and preparedness of companies in the Czech Republic
Authors: Macek Daniel , Vitásek Stanislav
Published: November 2024

This study examines the integration of Environmental, Social, and Governance (ESG) principles into the strategies of companies in the Czech Republic, a region where ESG considerations are increasingly influencing investment decisions and corporate governance. As global sustainability concerns gain momentum, Czech companies face growing scrutiny from investors, regulators, and stakeholders. This research aims to assess the current state of ESG risk analysis and evaluate the readiness of these companies to address the challenges posed by environmental stewardship, social responsibility, and governance effectiveness. To achieve this, the study employs a mixed-methods approach, combining qualitative data from semi-structured interviews with industry experts, corporate leaders, and regulatory authorities, and quantitative data from a survey distributed to a representative sample of companies across various sectors in the Czech Republic. The qualitative interviews provide in-depth insights into the challenges and strategies surrounding ESG integration, while the quantitative survey assesses the extent of ESG adoption, identifies key risk areas, and evaluates companies’ preparedness to tackle these issues. Data analysis includes thematic analysis of interview transcripts and statistical analysis of survey responses, offering a comprehensive view of ESG practices in the Czech business landscape. The findings reveal both best practices and areas needing improvement, providing valuable insights for stakeholders committed to enhancing corporate sustainability. This study contributes to the broader discourse on ESG by highlighting the evolving role of these factors in shaping corporate resilience, innovation, and long-term value creation in the Czech Republic, ultimately promoting a more sustainable and responsible business environment.
The Evaluation and Learning of Web Crawler Technology in the Field of Digital Media Art
Authors: Guo Jia-wei
Published: November 2024

With the rapid advancement of digital technology, digital media art has progressively emerged as a novel darling in the contemporary art market, primarily due to its distinctive interactivity, generativity, and digital distribution methods.The appraisal of market value is crucial in the transaction of digital art, and web crawling technology presents an innovative approach for data acquisition and processing. This study is dedicated to exploring the convergence of digital media art and web crawling technology, conducting an in-depth investigation on the application of web crawlers in the determination of market value for digital art.In the context of developing a theoretical framework, a novel value assessment model is proposed by integrating the features of the digital art market with established value assessment theories. On this basis, a value assessment tool targeted at the digital media art market was developed.This tool integrates multiple factors such as historical transaction data of artworks, artist backgrounds, work style, and media exposure, and employs machine learning algorithms to conduct market value assessment of artworks.Case analyses have also confirmed the accuracy of the assessment results and the practicality of the assessment model.Results show that this assessment tool not only provides powerful market guidance for art investors and collectors but also offers a pricing decision basis for digital art trading platforms.This research not only provides new perspectives and practical tools for the assessment of digital art but also makes a valuable exploration into the application of web crawling technology in the acquisition of art data information.It is of positive significance for the value discovery of digital media art and the long-term healthy development of the art market.
A Study on the Evolution of Public Sentiment and Emotional Communication Mechanisms in Women's Issues on microblog: Case Study of "Quan XiXi' s Dialogue with Chizuko Ueno"
Authors: Jiang D. Ling , BihongGao M.
Published: November 2024

The research utilizes Sina microblog as its platform, analyzing textual data associated with trending topics featuring "Peking University Dormitory and Ueno Chat", to explore patterns of sentiment evolution and emotional expression. Findings reveal that public sentiment follows a four-phase trajectory: inception, peak, fluctuation, and decline. Negative sentiments dominate among users discussing the topic. This analysis elucidates microblog's role in disseminating women's issues, identifies key characteristics and challenges of such discussions on the platform, delineates major disagreements and focal points in user discourse, and endeavors to uncover underlying societal symptoms. The insights contribute valuable data to studies on the forms and impacts of women's issue dissemination on microblog.