Authors: Dr Amit Kumar Awasthi

Abstract: Nawabganj Bird Sanctuary, a Ramsar-designated wetland in the Unnao district of Uttar Pradesh, India, serves as a critical wintering habitat and stopover site for a multitude of migratory bird species traversing the Central Asian Flyway (CAF). This comprehensive review paper synthesizes four decades of ornithological data, ecological studies, and management reports to analyze the patterns, drivers, and conservation status of avian migration at this vital sanctuary. The analysis confirms Nawabganj’s role as a key refuge for over 250 bird species, with a significant influx of Palaearctic migrants between November and March. Dominant families include Anatidae (ducks, geese), Ardeidae (herons, egrets), Rallidae (coots, moorhens), and a diverse array of waders (Charadriiformes). Migration timing and species composition are primarily driven by photoperiodic cues in breeding grounds and the availability of wetland habitat, forage resources, and thermal cover in the sanctuary. However, the review identifies a multifaceted crisis threatening this ecological function. Severe anthropogenic pressuresincluding water scarcity due to upstream diversion and erratic rainfall, invasive plant species (Eichhornia crassipes, Prosopis juliflora) encroachment, agricultural runoff leading to eutrophication, unsustainable tourism, and increasing human-wildlife conflict in the surrounding landscapeare degrading habitat quality. Emerging evidence suggests shifts in arrival/departure timings and a potential decline in populations of certain diving ducks and sensitive waders, possibly linked to climate change and local habitat degradation. This paper concludes that while Nawabganj remains a biodiversity haven, its long-term viability as a migratory bird sanctuary is precarious. The review advocates for an urgent, science-based, and integrated management approach. Key recommendations include securing ecological water flows, implementing systematic habitat restoration (invasive species removal, creation of deeper zones), strengthening community-based conservation, establishing long-term ecological monitoring programs, and promoting regulated, eco-sensitive tourism. The findings underscore that the sanctuary's future is contingent on translating its protected status into effective, on-ground ecological security.

DOI: http://doi.org/10.5281/zenodo.18629460

Published by: vikaspatanker

Authors: Prof. Sonali Ingole, Mr. Rohit Rajpurohit, Mr. Kartikesh Pachkawade , Prof. Deepa Shivshimpi

Abstract: The rapid growth of technology and lifestyle modernization has significantly influenced the human mind, behavior, and emotional balance. This study investigates the impact of the modern lifestyle on the subconscious mind — the part of the human psyche that governs thoughts, emotions, and decisions beyond conscious awareness. A structured questionnaire was administered to 305 respondents, including students and professionals, to examine how daily habits such as screen time, sleep patterns, stress, and mindfulness practices affect subconscious stability. Findings show that excessive device use, irregular sleep, and frequent stress strongly affect subconscious calmness and self-awareness. Participants who maintained mindfulness routines reported greater emotional balance. The study concludes that while modernization improves efficiency, it disrupts subconscious harmony, emphasizing the need for balanced routines and conscious mental care.

DOI: http://doi.org/10.5281/zenodo.19182113

Published by: vikaspatanker

Authors: Anitha Rajathi, Pellakuru Mahathi, Bhavya G, B Harshitha Reddy

Abstract: Agriculture continues to face significant challenges due to crop diseases that result in reduced yield, economic losses, and delayed intervention, particularly in developing regions where access to expert diagnosis is limited. Traditional disease identification methods rely on manual inspection, which is time-consuming, subjective, and not scalable. This paper presents a Smart Crop Disease Detection System using Convolutional Neural Networks (CNNs) for automated and accurate identification of plant diseases from leaf images. The proposed system leverages deep learning techniques trained on real-world agricultural image data obtained from the PlantDoc dataset, which contains healthy and diseased crop leaves captured under diverse field conditions. A lightweight and efficient CNN architecture, MobileNetV2, is adopted to enable real-time disease detection with reduced computational overhead, making the system suitable for mobile and low-power devices. The model performs image classification to identify disease categories and assess plant health conditions. Experimental evaluation demonstrates that the proposed model achieves an accuracy of 85%, outperforming other baseline architectures. To enhance deployability, the trained model is converted into TensorFlow Lite, enabling seamless integration into mobile and web-based applications. The proposed framework facilitates early disease detection, supports timely preventive measures, and contributes to improved agricultural productivity through intelligent decision support.

Published by: vikaspatanker

Authors: Deepa Kumar M, Sutha K

Abstract: The systemic mismanagement of surplus food and clothing creates significant economic and social waste, necessitating a transition from manual, fragmented charity methods to automated, data-driven platforms. This paper analyzes a web-based Digital Redistribution System developed using PHP and MySQL to facilitate real-time resource allocation between donors (restaurants, individuals) and orphanages. By shifting from a "reactive" model—where surplus often spoils before discovery—to a "proactive" digital ecosystem, the system ensures timely collection and transparent tracking. The study highlights the effectiveness of Centralized Data Management and Validation Testing in reducing manual overhead and ensuring data integrity, ultimately proposing a scalable framework for minimizing waste in urban environments. By shifting from manual, often inefficient donation methods to an automated online system, this project aims to reduce hunger and minimize environmental waste.

Published by: vikaspatanker

Authors: Sourabh Upadhyay, Professor Jitendra Chouhan

Abstract: One of the main purposes of Highway bituminous pavement failure and its maintenance is to provide a better road surface for the road users and carry traffic smoothly and safely with minimum cost. Paved roads in tropical and sub-tropical climates often deteriorate in different ways to those in temperate regions, because of the harsh climatic conditions, lack of proper design and quality control, high loads and inadequate assessment for identifying causes of distresses before carrying out maintenance and rehabilitation. A pavement distress that occurs at the surface can have a number of different causes which must be properly identified before corrective action is taken. Proper maintenance is very essential for longer life of the road surface.

Published by: vikaspatanker

Authors: Jay-An T. Biscocho, Keanne R. Noval, Lebron F. Calunsag, Nathalie G. Tangonan

Abstract: The objective of this study is to develop an automated surface vehicle prototype called AQuaRIUSV (Aquatic Quality Real-Time Information Using Surface Vehicle) designed to monitor water quality in marine ecosystems. The sensor being utilized by the prototype consists of pH, turbidity, and TDS sensors measuring key water quality parameters in real time. The system consists of a pH, turbidity, and TDS sensor; an Arduino Uno R4 Wi-Fi microcontroller for data processing and control; a Neo-6M GPS module for location tracking; an L298N motor driver operating dual DC motors for movement; SG90 micro servo motors for steering; and an ultrasonic sensor for obstacle detection. Monitoring was enabled by Blynk through an IoT dashboard. Performance was evaluated for accuracy, consistency, and reliability. Results show effective real-time monitoring via the IoT platform. The study concludes that AQuaRIUSV is a reliable, efficient, and sustainable system for continuous marine water quality monitoring.

DOI: http://doi.org/10.5281/zenodo.18627861

Published by: vikaspatanker

Authors: Tony Kangwa Mumba, Mr Lewis Chisengele

Abstract: This study investigated the efficacy of Chinese cabbage (Brassica Rapa) liquid manure as an organic fertilizer and evaluated its role in promoting sustainable vegetable production compared to conventional synthetic practices. A field experiment was conducted in Lusaka over an eight-week period to assess its impact on the growth and yield of eggplant (Solanum melongena). The main objective of the research was to assess the effects of Chinese cabbage (brassica Rapa) organic fertilizer and synthetic fertilizers on the growth and productivity of eggplants and the specific objectives was to determine the effects on vegetative parameters such as plant height, leaf length, and stem diameter rand yield attributes, including fruit number and weight. The experiment followed a completely randomized design with three treatments: T1 (Chinese cabbage liquid manure), T2 (synthetic fertilizer), and T3 (control, no fertilizer). Data collected from replicated plots were analysed using ANOVA, which revealed significant differences in leaf length, stem diameter, and fruit yield among treatments. The results demonstrated that the Chinese cabbage-based manure significantly enhanced fruit production and soil health, supporting its potential as a viable organic alternative for small-scale farmers seeking to reduce reliance on costly synthetic inputs while maintaining productivity.

Published by: vikaspatanker

Authors: Taruna pachurekar, Professor Shashikant B. Dhobale

Abstract: A thesis of the five main theories describing the interaction mechanisms in the bitumen/aggregate system was conducted: theory of weak boundary layers, mechanical theory, electrostatic theory, chemical bonding theory, and thermodynamic theory (adsorption theory). The adhesion assessment methods in the bitumen/aggregate system are described, which can be divided into three main groups: determination of adhesion forces for bitumen with different materials, determination of bitumen resistance to the exfoliating action of water with different materials, and determination of adhesion as a fundamental value (contact angle measurements, interfacial fracture energy, adsorption capacity and others). It is proposed to evaluate the quality of adhesive interaction in the bitumen/aggregate system in two stages. The authors recommend using the adhesion determination methods for these two stages from the second group of methods the determination of bitumen resistance to the exfoliating action of water with different materials. In the first stage, the adhesion in the bitumen/aggregate system is determined by an accelerated technique in which the used bitumen binder and mineral material are considered as test materials. After the first stage, there are positive results in the second tests on compacted mixtures (indirect tensile strength test, Modified Lottman indirect tension test, immersion-compression test, and Hamburg wheel tracking test).

Published by: vikaspatanker

Authors: Komal Bhatkar, Gauri Gadhave, pragati Ingale, Ankita Gunjite, prof. Prachi Walunj

Abstract: The “Creating Wi-Fi Using Arduino Robot Car System” project focuses on the design and implementation of a smart robotic car that can be controlled wirelessly through a Wi-Fi network. The main objective of this project is to develop a low-cost, flexible, and user-friendly robotic system capable of remote operation using a smartphone or computer. The system utilizes an Arduino microcontroller integrated with an ESP8266 Wi-Fi module to establish wireless communication between the car and the user’s device. Through this setup, the user can send commands via a web-based interface or mobile application, which are then processed by the Arduino to control the car’s motion, such as forward, backward, left, and right movements.

Published by: vikaspatanker

Authors: Balaganesh.S, Mrs.S. Indhumathi,M.E, Dr.A.Shiny Pradeepa, M.E

Abstract: Electric vehicles rely heavily on battery performance, safety, and lifespan, making efficient battery management essential. Existing battery systems face drawbacks such as inaccurate state estimation, poor thermal management, cell imbalance, and limited real-time fault detection, leading to reduced efficiency and safety risks. A Battery Management and Monitoring System addresses these issues by continuously supervising battery parameters to ensure safe, reliable, and optimal EV operation. Therefore, this project proposes a smart, connected, and predictive solution for effective battery management in electric vehicles. The system utilizes both an ESP32 and a Raspberry Pi Pico as central controllers to enhance data processing and control capabilities. Sensors such as voltage, current, and temperature (DHT11) are used to continuously monitor the battery’s key parameters. The ESP32 handles IoT connectivity, transmitting real-time data to a cloud platform (like Blynk), and allowing users to remotely monitor battery status and control motor operations via the internet. Meanwhile, the Raspberry Pi Pico is employed to manage local data acquisition, signal processing, and protective control logic. This division ensures faster and more reliable responses to critical conditions. A relay driver and electronic relay are used to regulate the DC gear motor, ensuring optimal power management based on the sensed data. In case of abnormalities such as overvoltage, overcurrent, or overheating, the system can automatically trigger protective actions to prevent battery damage. This intelligent and connected solution not only improves operational efficiency and reliability but also promotes the advancement of sustainable electric vehicle technology through smart, dual-controller energy management. The combined use of ESP32 and Raspberry Pi Pico provides both robust cloud integration and precise local control, making the system highly responsive and reliable.

DOI:

Published by: vikaspatanker