Authors - Thomas K P, Sherly K K Abstract - Permanent Magnet Synchronous Motors (PMSMs) are commonly utilized in electric vehicle (EV) traction systems because of its high efficiency, power density, and reliability. Conventional field-oriented control (FOC) schemes require accurate rotor position and speed information, typically obtained from mechanical sensors, which increase cost and reduce system reliability. Sensor less control techniques based on observer theory have therefore gained significant attention. Among them, sliding mode observers (SMOs) offer strong robustness against parameter variations and external disturbances but suffer from chattering and noise sensitivity. This paper presents an advanced sensor less FOC strategy for PMSM drives using a super-twisting SMO (ST-SMO) for rotor position sensing and estimation of speed. The proposed approach employs a ST-SMO algorithm to achieve the convergence in finite-time while significantly reducing chattering effects. The observer is integrated into a standard FOC framework and evaluated under EV-relevant operating conditions, including low-speed operation and load transients. Comparative performance discussion demonstrates the suitability and the effectiveness of the proposed method for high-efficiency EV traction.
Authors - N. V. Naik, Raga Madhuri Dhulipudi, Marisetti Sandhya, Jadda Anjan Kumar Abstract - Distributed systems rely on data replication to ensure availability, fault tolerance, and scalability across multiple nodes in modern cloud environments. Replication enables systems to maintain continuity even when individual nodes fail or experience network disruptions. However, replication often introduces synchronization delays between primary and replica nodes, known as replication delay. These delays can cause temporary data inconsistency, stale reads, and increased response latency, degrading application performance and user experience. As infrastructures scale to larger clusters, communication overhead, network latency, and workload variability further amplify replication delays, making efficient synchronization increasingly challenging. Traditional replication mechanisms typically rely on static synchronization intervals or sequential update propagation strategies. These approaches fail to adapt to dynamic network conditions and fluctuating workloads, resulting in inefficient data propagation and delayed consistency across nodes. In large scale systems, such limitations may cause bottlenecks, reduced reliability, and inconsistent states during high workload periods or network congestion. Addressing replication delay is critical for maintaining reliability and consistency in distributed environments. Recent research emphasizes intelligent synchronization mechanisms capable of adapting to changing conditions. Adaptive synchronization strategies that monitor network latency, workload intensity, and node communication patterns offer improvements in replication efficiency. By enabling replication decisions that respond dynamically to system behavior, such approaches reduce synchronization delays and improve data consistency across clusters. Enhanced replication efficiency ultimately strengthens reliability, scalability, and operational performance in modern distributed computing platforms operating under variable workload conditions.
Authors - Shaik Shafi, C Santhoshi Abstract - In the recent past, vehicle theft in India has increasing nearly 2.5 times, with more than 2 lakh vehicles stolen annually. The Delhi NCR region alone accounts for over 30% of reported cases, and in Delhi, a vehicle is reportedly stolen approximately every 14 minutes. These alarming trends highlight the ur-gent need for stronger and smarter vehicle security mechanisms. Traditionally, vehicle anti-theft technologies have relied largely on non-biometric approaches such as GPS–GSM tracking modules. Thus, biometric authentication is an emerging security approach that limits vehicle access to authorized individuals by verifying unique biological traits such as fingerprints, facial features, iris pat-terns, or voice. Although this technology significantly strengthens vehicle security, its widespread deployment still faces certain technical and social constraints. Thus in this paper, an IoT enabled biometric ignition system with security alerts is proposed. The proposed model makes use of an ESP32 micro controller and fingerprint sensor to replace traditional keys. The system operates in two stages: first secure door access and secondly engine ignition authorization. Any unauthorized attempts trigger real-time alerts with GPS location via IoT protocols like MQTT or HTTP. Further, cloud integration enables remote monitoring, data storage, and scalability, making suitable for modern intelligent transport systems. In the same way, the fingerprint-based vehicle starter grants the privilege of starting the vehicle only to the registered users, thus deterring theft and ensuring safety. Over all, biometric vehicle ignition is a dependable, economical, and hassle-free solution to access control as well as theft prevention.
Authors - A.Sree Rama Chandra Murthy, T.Gamya Sri, B.Harshitha, G.Vincent Paul Abstract - Accurate forecasting of drug demand is one of the challenging areas in the healthcare service to reduce waste as well as shortages. Some recent studies focused only on predicting drug use demand for regions and hospitals, missing an overall way to combine these forecasts. In this study, a multilevel machine learning framework is presented that merges regional tender demand predictions with monthly and seasonal order forecasting in hospitals and pharmacies. With historical drug usage, the system captures time-based changes, seasonal demands, and also location specific behaviors . Models for regional tenders predict yearly procurement, but models at hospitals and pharmacies try to tell the need of each month, allowing better resource distribution. The rigorous experimental process showed better estimates and forecasting with less error than just making a single-level prediction. This framework helps to make better purchasing decisions and ensures a stable drug supply across healthcare systems. Health departments, hospital chains, and pharmacy groups can benefit from using a model.
Authors - Seamus Lyons Abstract - Methane (CH4) emission from rice paddies is a significant source of greenhouse gas emissions from agriculture. Currently, most models for methane prediction from rice paddies depend on collecting field data and sending it to a server. In this new paradigm, several privacy concerns arise, model scalability is restricted, and a large number of data points are exposed to the attacker. This paper addresses all privacy con cerns by providing an edge-based solution for modeling methane emis sions from rice paddies that leverages data from edge sensors at respec tive locations, while keeping individual sensor data private. The method employs different machine learning (ML) algorithms, including Linear Regression, Random Forest, XGBoost, and a Feedforward Neural Net work (FNN), implemented using TensorFlow Federated (TFF) in both centralized and federated learning (FL) frameworks. The FL-based FNN achieved an R2 score of 0.91, which was superior to both centralized classical and centralized FL models, especially for highly non-IID client side data distributions in sensor datasets. In summary, this paper extends the current literature on modeling methane emissions from rice paddies and provides a comprehensive evaluation of our proposed FL system ar chitecture, an in-depth discussion of the communication resources re quired for FL implementation, and an examination of the effects of abla tion studies on clients’ data heterogeneity. Therefore, the proposed FL approach is efficient and scalable, enabling safe, privacy-preserving modeling of methane emissions from rice paddies to effectively imple ment Climate Smart Agriculture (CSA) and mitigate global warming while supporting sustainable rice cultivation.
Authors - Gia Nghi Thoi, My An Tran, Tram Thi Tuyet Le, Nhat Van Hoang Nguyen, Long Hong Buu Nguyen, Dien Dinh Abstract - Medical diagnosis using Small Language Models (SLMs) of ten suffers from hallucinations and knowledge inconsistency. While re inforcement learning (RL) from knowledge graph feedback offers a po tential solution, pure reinforcement learning strategies often encounter challenges related to sample inefficiency and poor exploration. To address this, a hybrid training pipeline that combines supervised alignment with structural reinforcement is proposed. The method applies knowledge guided supervised fine-tuning (SFT) with hard negatives to refine deci sion boundaries and employs a bipartite-specific reward model to capture interactions between symptoms and diseases. Experiments on multiple medical datasets, including DXY, GMD, and MED-D, demonstrate that this hybrid approach outperforms pure RL methods. By incorporating knowledge graph (KG) information as a structural regularizer, the model achieves improved accuracy, stronger cross-dataset generalization, and reduced overfitting while maintaining strict adherence to diagnostic out put constraints
Authors - Mustafa Icel, Ochilbek Rakhmanov, Ergul Gunerhan, Muhammad Qasim Abstract - Artificial intelligence driven adaptive learning systems progressively operate as knowledge management platforms by collecting, refining, and using learner knowledge to personalize instruction. However, empirical evidence demonstrating how managed knowledge translates into measurable student achievement remains as a question to answer. This study examines the effective ness of AI driven adaptive learning as a knowledge management system in a high school setting. Using de-identified archival data from 182 students across three academic years, the study explores relationships among AI-managed knowledge mastery, engagement, course performance, and standardized assessment out comes. Learning analytics techniques, including descriptive statistics and Pear son correlation analysis, were employed to examine knowledge–performance re lationships. Predictive modeling using multivariable linear regression and Ran dom Forest classification was performed to assess the extent to which knowledge management indicators predict end-of-course achievement and performance lev els. Results indicate that final knowledge mastery is moderately associated with standardized assessment outcomes and is a stronger predictor of achievement than time-on-task alone. While predictive models demonstrate modest accuracy, findings suggest that AI driven knowledge management supports student achievement when integrated within instructional contexts.
Authors - Akshay Kumar, Reena Satpute, Kumar Gaurav, Sanjit Kumar, Edidiong Akpabio, Sudhir Agarmore Abstract - Recent literature has posed LLMs as nonlinear dynamical systems. LLM safety, in these modern LLMs is about the systematic and critical monitoring of logit based oscillations, hidden state rotations and entropy fluctuations. Many of these important factors are spectral proxies for the generation of imaginary eigenvalues. These imaginary eigenvalues are, in a way, determinants of the latent oscillation energy. Though the system in its original state space is inherently nonlinear, through the Koopman operator, we can linearize the evolution in the lifted space of observables. We design a spectral jailbreak detector that has a Sparsely regularized koopman autoencoder as its backbone. We obtain the koopman operator through this SR-KAE, and also obtain the imaginary component of the eigenvalues of that spectral operator, A new risk score metric is proposed that is used to classify prompts as either jailbreak or safe. This becomes a physics-style stability classifier on prompts. We present several test cases, while we discuss the strengths and limitations of this new system.
Authors - Kamala L, Mohan K G Abstract - This paper presents the error performance of digital commu nication systems operating over α-Beaulieu-Xie (α-BX) and its extreme variant, the α-BXe fading channel. A generalized noise model, additive white generalized Gaussian noise (AWGGN), is adopted to account for various practical scenarios including impulsive and Laplacian environ ments. We derive closed-form average bit error rate (ABER) expressions utilizing the Fox-H function. The mathematical expressions derived are validated through numerical integration for binary phase shift keying (BPSK) and binary frequency shift keying (BFSK) modulation schemes. Our results demonstrate the degradation caused by Laplacian noise and characterize the irreducible error floors inherent in the α-BXe model, providing a robust tool for system designers in complex fading environ ments.
Authors - Akshay Kumar, Deepa Thilak Abstract - Smart city apps are growing quickly, which means that there are more real-time, latency-sensitive, and privacy-critical workloads that are hard for traditional single-cloud computing models to handle. In particular, smart mobility and traffic management systems generate large volumes of geographically distributed data that require efficient processing with minimal delay and high reliability. This project proposes a multi-cloud task scheduling framework that protects privacy and uses federated learning to solve these problems. The suggested system turns real-time smart mobility traffic data into abstract scheduling tasks and sends them to different cloud regions using a lightweight, decision-free task broker. Each cloud region has its own local federated scheduler that uses only data that is available in that region to schedule tasks based on latency and congestion. Federated learning is used to work together to improve scheduling policies by safely combining local model updates without sharing raw data. This keeps data private and meets data sovereignty requirements. The system enables improved scalability, reduced response time, fault tolerance, and avoidance of vendor lock-in compared to centralized scheduling approaches. Using a smart mobility dataset to test the proposed method shows that it works well for scheduling tasks quickly and with privacy in mind in multi-cloud settings.
