The growing emphasis on sustainable construction practices in the transportation sector has intensified interest in the reuse of Reclaimed Asphalt Pavement (RAP). However, due to the aged binder and degraded mechanical integrity, RAP requires stabilization before structural application. Among the various performance measures, the Unconfined Compressive Strength (UCS) test is widely used to evaluate the load-bearing capacity and durability of stabilized RAP mixtures. This review synthesizes findings from recent studies focusing on the UCS performance of RAP stabilized with lime, fly ash, cement and other chemical additives. The influence of mix composition, curing periods, and testing standards is discussed to provide insights into the design of high-performance base materials using RAP.

Monitoring student engagement in a class- room environment has become an area of active re- search in computer vision, affective computing and educational technology. Since the last decade ap- proaches have evolved from specialized hardware- based tools to lightweight and real-time pipelines run- ning on RGB video. In this paper, we review twenty- two studies from 2013 to 2026 that cover different aspects of automated classroom engagement moni- toring, including motion and depth-based attention assessment, skeleton-based action recognition, facial expression analysis, multi-object tracking, behaviour detection using YOLO-family architectures, multi- modal fusion of behaviour and emotion cues, dataset building and deployment readiness. Each study is an- alyzed according to modality, detection method, data set and limitations. We divide the literature into six streams: early sensor-based systems, pose and skele- ton approaches, facial expression approaches, be- haviour detection models, multi-modal fusion frame- works, datasets and benchmarks. A comparative ta- ble summarizes the important characteristics of all reviewed works. We identify some research gaps as being persistent -india-based emotion data is not available at all for the student level, longitudinal per- student tracking is not done as a whole, there is lim- ited on-premise deployment and there is no integra- tion of teacher-facing analytics -which are essential for future research in the field.

The global transition towards sustainable transportation has positioned the Electric Vehicle (EV) as a cornerstone technology. At the heart of every EV lies its battery pack, typically based on Lithium-ion chemistry owing to its high energy density and longevity. However, the performance, safety, and lifespan of these complex electrochemical systems are critically dependent on an intermediary electronic system—the Battery Management System (BMS). This paper offers a thorough analysis of the fundamental and advanced aspects of BMS technology for EVs. It delves into the core objectives of a BMS, including monitoring, protection, state estimation, thermal management, and communication. The review systematically analyzes the prevalent circuitry configurations, namely centralized, distributed, and modular, comparing their advantages, limitations, and suitability for different vehicle classes. Furthermore, the paper explores the critical algorithms that form the intelligence of the BMS, with a particular focus on State of Charge (SoC) and State of Health (SoH) estimation techniques, ranging from traditional Coulomb counting and Open-Circuit Voltage methods to advanced model-based approaches like Kalman Filters and machine learning algorithms. The synthesis of current literature indicates a clear trend towards modular, master-slave architectures for scalability and advanced, data-driven algorithms for improved accuracy and reliability.

Biometric authentication using Electroencephalo-gram (EEG) signals is getting more popular because it is hard to fake or steal as compared to things like fingerprints, iris or face scans. Electroencephalogram(EEG) is unique to each person and we cannot just sneak up and generate or grab those brain waves easily. But most of the systems nowadays use these handpicked features from the spectrum and they only test on a few people. So it does not really work well for everyday use. In this paper, we came up with EEGAuthNet, a deep learning based setup that takes raw EEG signals from 64 channels and authenticates without the need to extract features manually.The model combines one-dimensional Convolutional Neural Networks (CNN) to pull out spatial features and then it adds Bidirectional Long Short-Term Memory (Bi-LSTM) layers that go both ways to handle the longer patterns in time across the EEG bits. We tested it on PhysioNet dataset for motor movements and imagery which contains 109 subjects.The accuracy for identifying came out to be 81.47%. Equal Error Rate (ERR) was 2.0%. To check what each part does, we did an ablation study which confirmed that each component contributes meaningfully with combination of CNN and Bi-LSTM which beats only CNN by 3.8Overall this shows EEG could be a good secure way for biometrics if you pair it with this hybrid setup.

There is a voluminous data generated by people every day on geoportals which is being used tremendously. These geoportals are the life line to many people to get quick information about the location including address, geographical location, category and reviews of the place as well. The geo tagged data available on these portals is useful for better planning to improve in various sectors such as business, public services, event planning and disaster management. However, there is no way to verify the information available on these portals as these are not always contributed by domain experts. This data is collected through crowd sourcing therefore it is an important to make sure the accuracy of this crowd sourced data available on the geoportals. All existing verification methods are based on the crowd sourced information which again can’t claim as the reliability of this data.  To address this challenge, it becomes vital to explore the insight of geotagged data available on geoportal to understand the features affecting the accuracy most. There must be some ground truth data which can be taken as the reference data while exploring the crowd sourced data. In this paper, the exploratory statistical analysis is provided for crowd sourced geotagged data including the data set preparation using ground truth data as a reference data. In-depth analysis on prepared dataset will lay the foundation for developing automated accuracy measuring models which makes this vast data more useful for planning various citizen centric services specifically in developing nations.

Mental illness is a huge issue in global public health, and it affects people no matter what their gender or age. While more people are learning about mental health, many still struggle to get help quickly. This is often due to social stigma (prejudice), discrimination, and a lack of professional knowledge. Additionally, the high cost of treatment prevents many people from getting the care they need.

AI technology is improving fast, making conversational bots feel more natural and understand context better than before. These new tools make it easier to provide quick, simple support to people dealing with emotional issues.

That is why we created MindGuide, an AI chatbot designed to help users understand their emotions and provide initial mental health support.

MindGuide runs on Ollama (a large language model) and is built using the LangChainframework. MindGuide is an AI-powered chatbot designed to act as a private, 24/7 mental health companion. It helps people by providing support, guidance, and a safe space to talk, supplementing traditional therapy. MindGuide is not a replacement for a professional doctor or therapist. It is meant to provide extra support for immediate guidance.

The researchers are looking into how to make the chatbot better and safer.

Vehicle Identification is being done by Vehicle Number Plate Recognition (VNPR) systems in applications such as traffic monitoring, toll collection, and security surveillance. However, many existing VNPR systems are intended for large-scale environments such as toll gates and city-wide monitoring infrastructures. This paper presents a VNPR system which is simple and cost-effective majorly designed for smaller regions such as public parks, institutional campuses, and restricted residential buildings.

The system automatically captures vehicle images either from stored input images or through a live camera interface. Image processing techniques is used to analyse vehicle images to detect the region of number plate and extract the characters through a technology called Optical Character Recognition (OCR). In addition, a Convolutional Neural Network (CNN) based deep learning approach is used to enhance the performance of character recognition.

The recognized vehicle number can further be used for access control, vehicle monitoring, and maintaining entry records within the premises. The proposed system therefore provides an efficient automated solution for vehicle identification in small and controlled environments while reducing manual effort and improving security .

In order to improve hands-on learning in IoT systems for educational purposes, this article outlines the design and development of a modular IoT trainer kit that integrates three popular microcontroller and microprocessor platforms: the Arduino UNO, ESP32, and Raspberry Pi. A motor driver module for actuation and several sensors, including motion, temperature, humidity, and gas detectors, are included in the kit to provide practical experience of sensing, communication, and control in real-time settings. These three platforms, namely Raspberry Pi, ESP32, and Arduino UNO, enable wireless data transmission via WiFi/BLE, an interface for sensor data collection, and motor control. Managing data gathering and visualization via a local web interface and optional cloud integration for distant monitoring, the Raspberry Pi serves as a central gateway. This kit gives experience in IoT application development, communication protocols (UART, I2C, and MQTT), and heterogeneous system design using this multi-board architecture. Initial tests show how well the kit replicates actual Internet of Things situations, promoting a better comprehension of edge computing, distributed sensing, and system integration. The designed trainer kit provides educators with an affordable, adaptable, and scalable platform to connect theoretical ideas with real-world IoT education abilities.

Digital filters have become an integral part of modern signal processing systems, enabling the selective modification or suppression of specific frequency components. This paper presents the design and simulation of a second-order Infinite Impulse Response (IIR) Anti-Notch filter aimed at attenuating unwanted narrowband frequency components from digital signals. The Anti-Notch filter, also known as a notch-reject or notch-suppression filter, effectively eliminates disturbances such as power-line hum or interference from communication channels. The filter was designed using MATLAB, focusing on key parameters such as pole radius, notch frequency, and sampling rate. Simulation results show a distinct frequency attenuation at the targeted frequency while maintaining minimal distortion in the passband. Compared to FIR designs, the proposed IIR Anti-Notch filter achieves a compact structure, low computational complexity, and reduced delay, making it highly suitable for real-time audio and communication applications. The design was further implemented on an FPGA platform using Xilinx ISE to validate its real-time performance. The hardware and MATLAB results show complete suppression of the 300 Hz component from a composite input signal containing 100 Hz, 300 Hz, and 700 Hz frequencies, verifying the filter’s accuracy and timing efficiency. The optimized second-order structure minimizes hardware utilization while ensuring numerical stability and high signal fidelity, demonstrating its practical effectiveness in embedded and high-speed digital signal processing systems. The designed IIR Anti-Notch filter achieves approximately 50.2 dB attenuation at the rejection frequency of 300 Hz, with less than 0.5 dB passband ripple and a −3 dB bandwidth of 12 Hz. The FPGA implementation operates at a maximum frequency of 34.25 MHz, confirming real-time feasibility.

For voltage step-up applications, boost converters are a common DC–DC power electronic circuit. A review of the literature on boost converter topologies created to date is presented in this paper. The development of sophisticated high-gain topologies from the traditional boost converter is examined. Based on voltage gain, efficiency, component count, and application suitability, several configurations, including quadratic, cascaded, interleaved, coupled-inductor, and switched-capacitor boost converters, are examined and contrasted. A clear understanding of contemporary boost converter topologies is provided by this review.

In this research paper, we are suggested to come up with an efficient method for solving the parking problem of a big crowded city depending on the demand for number of available parking slots because we know that the transport systems in urban areas are heavily impacted by inadequate parking just as it results in fuel wastage, traffic jams and raising pollution. To overcome these challenges, this paper proposes an internet of things (IoT) based smart parking model using First-Come-First-Served (FCFS) priority scheduling mechanism to keep efficient allocation of the parking space. The status of Parking Slot is retrieved in real-time with sensor based detection units and cross-cores to a central control system

Real-time Parking Slot status is obtained using sensor-based detection units and transmitted to a central control system. Parking spaces are assigned strictly according to vehicle arrival order, eliminating conflicts and ensuring fair utilization of available resources. . The proposed approach avoids the risk of the complex reservation strategies and it can reduce computational overhead while maintaining real-time responsiveness. The system improves parking efficiency; it minimizes unnecessary vehicle movement, and enhances user convenience. The proposed model demonstrates the way of its suitability for smart city applications by providing a scalable, transparent, and cost-effective way of parking management solution which saves time and risks.

"FCFS scheduling and IoT for conflict-free parking allocations"

Brushless Direct Current (BLDC) motors are universally used in industrial and domestic applications owing to their high efficiency, reliability, compact design, and low maintenance. Conventional Hall-sensor-based BLDC motors provide precise rotor position detection feedback and closed-loop regulation. These techniques commonly lead to problems such as the nonlinearity of motor properties, complexity, and higher system costs. Moreover, the non-appearance of mechanical commutators in BLDC motors requires innovative microelectronic control systems for commutation and drive regulation. This paper presents an effective method via Pulse Width Modulation (PWM) driven BLDC motor speed controller. The developed system uses an ESP32-WROOM microcontroller for wireless control and includes components such as an Infrared (IR) sensor, Electronic Speed Controller (ESC), and a 16X2 LCD display for better monitoring, control, and speed measurement. The proposed technique is anticipated to provide a cost-effective, flexible, and reliable solution for advanced motor control systems.

This paper characterises the locally available waste material i.e. cow-dung for affordable and sustainable development in construction sector. By introducing "cow-dung," as a new construction material, focus is on bringing this material into a new and sustainable domain. It is concluded that cow dung can be used as a main/bulk material or supplement material in brick masonry and in plastering the walls. The paper summaries that the use of cow-dung and cow dung ash in brickwork significantly enhance the structural integrity and strength of structure. The improved strength, binding property, thermal insulation, water resistance property and carbon neutrality of cow dung create the opportunity to use it as a transformative element in sustainable construction. However, due to low mass density the use of cow dung is limited to patrician walls and not recommended for load bearing walls.

Sound absorbing materials play an important role in various fields like automobiles, conference halls, industries and many more. Synthetic fibers acoustic materials came in existence around 1970 due to public health concerns of the asbestos-based material. Now, these synthetic materials are required to be replaced by natural fiber-based materials due to their sustainability issues. In this research paper the acoustic impedance of various natural fiber pads has a been analyzed with the help of an impedance tube. It is a compact measuring apparatus to determine the acoustic impedance for a wide frequency range. The results show that the acoustic impedance of various natural fiber pads is found to be comparable with synthetic materials. Also the acoustic property of these materials is almost same in some range of frequencies with slight variations.

The nature of complex interactions between machine, material and process parameters frequently leads to quality inconsistencies in injection molding processes. The present paper introduces an autonomous quality control framework based on AI and having real-time data on cavity pressure and temperature sensors of industrial injection molding cycles. Process data in large quantities are used to train learning models which learn to detect quality-sensitive cavity conditions and predict deviations which cause defective products. The system dynamically modifies the input parameters to the machine based on a real-time comparison with the learned reference patterns to stabilize in-cavity conditions. The feasibility of the proposed approach shows how AI-based control can help to ensure the stability of the quality of the products in the case of industrial injection molding.

The rapidly increasing dominance of electric vehicles in automibile industry is leading towards the requirement of an appropriate battery thermal management systems to ensure effiecnt integration and extended life. This review outlines the battery thermal behaviour and further, how it affects the performance and life of a battery. It has been observed from the study that battery perform optimally within a specified temperature range. There are several factros which triggers the heat generation inside a battery such as fast charging and discharging, high load conditions, unfavourable environmental conditions, etc. Therefore, a sophisticated thermal management systems is advised which can be helpful during varied driving conditions and safeguard by runaway accidents. A compilation is done for existing thermal management technologies/methods incluiding passive and active types. The integration issues have also discussed in this review to get acquainted with the compatibility, safety and reliability consideraitons along with regulatory compliances. This review highlights the challenges, emerging trends and futuristic solutions in battery thermal management systems in electric vehicles to ensure safe ride.

The present works shows a comaparative analysis of Indian Fast Moving Consumer Goods (FMCG) industries. A detail study is done to determine inventories tied up by the industries on the basis of inventory turnover ratio. The top performing FMCG industries are compared and useful insights are drwan from it. It has been found that Britania Industries has maintained highest inventory turnover ratio since last many years. The paper also investigates the sustainable practices implement by the FMCG industries.

The efficient design of finned cross-section is crucial in various engineering applications, such as electronic cooling, automotive radiators, and industrial heat exchangers. Fins are commonly used to increase the surface area for heat dissipation, and their cross- sectional geometry plays a significant role in enhancing heat transfer. This study presents a comparative analysis of different fin cross-sections analytically and ANSYS is used for simulations.

Due to rapid changes in demands from customers, particularly in Fast Moving Consumer Goods (FMCG) segment, it becomes essential that forecasts that are used for predicting demands should have acceptable level of accuracy. Accurate forecasting in FMCG, becomes even more difficult, when the businesses use promotional activities in form of discounting, price reductions, providing s with purchase of specified number of units on selective week days on selective items. This paper studies the impact of promotions on FMCG. It compares a promotional enabled forecasting with the existing forecasting techniques on a real life FMCG data.

Transfemoral amputation, commonly known as above-knee amputation, presents significant biomechanical and functional challenges due to the loss of the knee joint and associated musculature. This paper explores the causes, clinical considerations, and post-operative rehabilitation strategies for transfemoral amputees. A detailed classification of prostheses—particularly the differences between exoskeletal and endoskeletal designs—is presented, with emphasis on the critical role of the knee joint in restoring gait stability and mobility. The paper compares single-axis and polycentric (four-bar) prosthetic knee joints, analyzing their biomechanics and suitability for different user profiles.

The study also investigates material selection for prosthetic components across developed and developing countries. Overall, this review underscores the importance of context-specific prosthetic design and material selection, aiming to improve affordability, functionality, and quality of life for transfemoral amputees globally.