Volume 6 Issue 12 2025

An automatic medicine dispenser with a dynamic remote monitoring system has been developed using IOT. This is to help people in rural areas who cannot find a good/best doctor and do not have 24/7 hospital services. During night emergencies, poor people cannot travel a long distance for simple health problems such as high fever, cold, nausea, and other acute illnesses. The automatic medicine dispenser ATM is a good example of a computerized process. This project provides a solution technique to save time and avoid inconvenience, reduce the workload of most pharmacists, and provide customers with the medications they need. The device consists of a heartbeat, weight, ultrasonic, and temperature sensors to instantly measure the patient's health status and send it to the doctor. The connection to the doctors is established via GSM technology. The patient can talk to the doctors and tell them about his problems. A webcam is installed to have conversations in real-time. The main advantage of this device is that the medicines prescribed by the doctor can be received instantly from the same device with the help of a single relay and a motor. The patient can send a request to the server to know the times to take the pills.

This study presents an approximate analytical solution for a fractional-order bacterial growth model by applying the Mahgoub–Adomian Decomposition Method (MADM). This technique combines the Mahgoub Transform with the classical Adomian decomposition approach to treat nonlinear fractional partial differential equations. The model captures the growth behavior of Bacillus subtilis colonies expanding on nutrient agar. Fractional derivatives are considered in the Caputo sense to better represent the memory-driven diffusion and reaction processes. Numerical results show that this method is easy to implement and accurate when applied to fractional partial differential.

The Warana River Basin, an important tributary of the Krishna River in western India, exhibits varied physiographic, climatic and ecological characteristics that shape its geomorphic and environmental framework. Serving as a crucial region for agriculture, water resources and socioeconomic development, the basin has experienced increasing stress due to land-use changes, anthropogenic activities and irregular rainfall patterns. This study analyzes key geo environmental parameters such as elevation, slope, slope aspect, drainage density, soil texture, contour, hillshade and rainfall erosivity to understand their interrelations and impact on basin evolution. Remote Sensing (RS) and Geographic Information System (GIS) techniques were employed to integrate multi-source datasets, including satellite imagery, topographic maps and Digital Elevation Models (DEMs). The basin’s geomorphology consists of structural hills, plateaus, pediments and river terraces that influence runoff dynamics, groundwater recharge and agricultural potential. The predominance of dendritic to sub-dendritic drainage patterns indicates lithological control imposed by basaltic formations. Moreover, high monsoonal rainfall intensifies soil erosion and sedimentation, posing challenges to sustainable land and water resource management. The integrated geo environmental evaluation thus provides a scientific basis for watershed prioritization and the formulation of soil and water conservation strategies for sustainable development within the Warana River Basin.

Flooding is a recurrent and damaging hazard in riverine cities of India due to intense monsoonal rainfall, rapid urbanisation, and modification of natural drainage systems. Kolhapur City, located along the Panchganga River in Maharashtra, has experienced frequent flooding with significant impacts on infrastructure and socioeconomic activities. This study assesses flood inundation and flood risk in Kolhapur City using an integrated hydrological modelling and GIS-based multi-criteria framework. Rainfall–runoff processes were simulated using the Soil Conservation Service–Curve Number (SCS-CN) method, while flood hydrographs were generated using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS). The model was calibrated and validated using observed rainfall and discharge data, and performance was evaluated using NSE, R², RMSE, PBIAS, and RSR. Simulated hydrographs were used to generate flood inundation extent and depth maps. Results indicate that low-lying floodplain areas along the Panchganga River are highly susceptible to inundation during high-flow conditions. Flood hazard, vulnerability, and risk were assessed using the Analytical Hierarchy Process (AHP) integrated with GIS by combining physical, hydrological, socioeconomic, and infrastructure-related factors. The analysis reveals that several parts of Kolhapur City fall within high to very high flood risk zones due to the combined effects of elevated flood hazard and high socioeconomic vulnerability. The study demonstrates that hydrological modelling coupled with GIS-based multi-criteria analysis provides a robust and transferable framework for urban flood risk assessment and supports effective flood mitigation and disaster management planning.

Post-Quantum Cryptograohy is indispensable to ensure that future digital systems remain secure against quantum attacks. CRYSTALS-Kyber is a lattice-based KEM that was standardized by NIST and provides strong security; however, software implementations of Kyber have latency and power limitations for real-time and embedded systems. This paper presents a modular FPGA-optimized hardware architecture of Kyber that is implemented in Verilog and validated using Cadence simulation tools and Xilinx Vivado. The architecture includes an optimized Keygen, Encrypt, and Decrypt module, with simplified polynomial operations based on NTT and an FPGA-friendly memory and control arrangement. A complete top-level pipeline guarantees deterministic operation, while functionality is verified through testbenches. Synthesis has been performed on an Artix-7 FPGA, presenting improved clarity, modularity, and deploy ability for low-cost PQC hardware. The contribution at hand provides a practical basis for the implementation of Kyber based secure communication on resource-constrained FPGAs.

Textile industry effluents are discharged on land and into adjacent water bodies. These effluents cause soil and ground water pollution. Textile effluents carry heavy metals which are harmful to vegetables crops. The objective of the present study is to determine the heavy metals in soil treated with textile industry effluent. Textile industry effluent was obtained from district Sant Ravidas Nagar, Bhadohi and used in this study. A pot experiment including tomato was conducted adopting Completely Randomized Design with five treatments and three replications in the natural open weather conditions for 60 days. Five concentrations of effluents viz; 0%, 25%, 50%, 75% and 100% were used for present experiment. Zero per cent concentration was treated as control. Observations related to concentration of heavy metals (Zn, Cu, Cr, Pb and Ni) in soil were recorded at 45 and 60 days after transplanting. Results reveal that minimum amount was recorded in 0% concentration of effluent (control). As the concentration of effluent increased there is continuous increase in zinc, copper, chromium, lead and nickel content in soil. Maximum amount was recorded with 100% effluent concentration.

This project presents an enhanced bidirectional wireless power transfer (BWPT) system for electric vehicle (EV) applications, utilizing Particle Swarm Optimization (PSO)-based Sliding Mode Control (SMC) with Phase Shift Modulation (PSM) for superior performance. The BWPT system enables seamless Grid-to-Vehicle (G2V) and Vehicleto-Grid (V2G) operations, addressing key challenges such as power factor management, dynamic efficiency, and power transfer rates. While conventional dual-phase shift Pulse Width Modulation (PWM) techniques improve power factor correction (PFC) in unidirectional wireless power transfer (WPT) systems, bidirectional systems demand advanced control strategies to manage dual-side power converters effectively. The proposed PSO-SMC control framework optimizes the phase shift parameters, ensuring precise power flow regulation, enhanced stability, and reduced switching losses under varying load and grid conditions. Simulation results in MATLAB/Simulink demonstrate that the PSOSMC with PSM significantly improves power factor, transfer efficiency, and overall system reliability, making it a robust solution for future EV charging infrastructure and smart grid integration.

Purpose: This study aims to develop and assess a crop recommendation system driven by soil and climatic data utilising machine learning techniques. The objective is to optimise resource utilisation and enhance crop yields by aiding farmers in picking the most appropriate crops for their respective geographical regions. Method:The study employed a dataset that included rainfall, temperature, humidity, pH, phosphorus (P), potassium (K), and nitrogen (N). A number of machine learning models were trained and evaluated, including Random Forest, K-Nearest Neighbours, Naive Bayes, Logistic Regression, Decision Trees (Gini and Entropy), Support Vector Machines (with RBF and Linear Kernels), and Neural Networks (with ReLU and Sigmoid activations). It assessed each model's performance using criteria such as recall, precision, accuracy, and validation accuracy. Results: The Random Forest classifier had the best accuracy (98.90%) and strong precision/recall values (0.98/0.97) of all the models. It was closely followed by SVM with a linear kernel and Decision Tree (Entropy). The results showed that ensemble and kernelbased methods are quite good at predicting what kind of crop a plant is. The study also showed what the best environmental conditions were for 22 different crops based on average feature values. Conclusion:Machine learning techniques, particularly Random Forest and SVM, have the potential to produce highly accurate crop recommendations when trained on well-structured agro-climatic datasets. The study demonstrates how data-driven decision-making can significantly enhance precision agriculture. The technique might be incorporated into smartphone apps to assist farmers in making real-time crop selections, promoting agriculture's long-term growth.

Electric vehicles (EVs) have gained significant traction in recent years due to their environmental benefits and improved performance. Permanent Magnet Synchronous Motors (PMSMs) are widely used in EVs owing to their high efficiency, power density, and torque characteristics compared to others from 1990s. However, precise control of PMSMs is crucial for optimal performance and energy efficiency. Traditional control methods often face challenges in adapting to dynamic operating conditions and disturbances. This research explores the implementation of a Fuzzy Logic Controller (FLC) of PMSMs in EVs. The PID controller, while widely used, faces limitations in handling complex nonlinear systems. It depends on precise mathematical tuning can be time consuming. PID controllers may struggle to adapt to unexpected disturbances in operating conditions. Fuzzy logic controllers can effectively handle nonlinear systems, adapt to changing conditions, making them a suitable alternative to PID controllers in achieving performances like speed response and torque ripple reduction. The design and simulation of electric vehicle fed with PMSM by fuzzy logic controller (FLC) can be carried out in MATLAB/Simulink

Automated age and gender classification is increasingly relevant for various applications. This paper proposes an approach using multiple convolutional neural networks (CNNs). The method involves face detection, background removal, face alignment, multiple CNNs, and a voting system. The CNN model uses three CNNs with different structures and depths, trained on the AGFW dataset. A voting system combines their predictions for the final result.. Keywords: Age and Gender Classification, Convolutional Neural Networks, Facial Images, Face Detection, Face Alignment, Voting Systems