Disaster Management, a strategy or an activity that involves risk reduction planning and effective preparations for all stages of a crisis cycle, faces challenges in synchronization of the flow of information resulting in constant communication problems. This coordination imbalance leads to vulnerabilities in communities and makes them unprepared to respond and recover effectively. To fill these gaps, a new integrated inclusive, and accessible AI-driven platform, Surakshit Bharat has been introduced to mitigate the existing gap as mentioned above. It would integrate government agencies, NGOs, volunteers, and citizens, ensuring those who are most at risk can receive assistance and aid relief as fast as possible. Equipped with multiple technologies, the platform would be capable of sending out alerts in real time, remote assistance, a voice interface, and gamified educational training to improve self-efficacy and efficiency during preparation and rescue efforts. Elders and people with low literacy levels can easily access the platform through a simple navigated interface and multilingual guides. Utilizing cutting-edge technologies with a focus on inclusivity, the platform portrays itself as a comprehensive, integrated, and innovative solution in the crisis management system.

Nowadays, many people are shifting towards eco friendly materials because synthetic ones cause harm to the environment. Natural fibre based composites like jute, hemp, banana, and sisal are becoming popular since they are cheaper, biodegradable, and easy to produce. These materials are now being used in place of synthetic composites in civil, automotive, and packaging industries. Researchers have tested different combinations of natural fibres and biodegradable plastics to make strong and sustainable materials. This review talks about various types of natural fibres and their properties like strength, moisture resistance, and durability. It also explains how fibre treatment and the use of nanofillers can improve performance of composites. Some studies also show how these composites are being used in civil engineering for structural repair work and with the help of AI methods which have more influence on predicting, optimizing, and improving the mechanical and thermal qualities of composites made from natural fibres. AI can also be used in composite for major improvement in industries of automobiles and different constructions,with the help of AI we can do predictions for production process and maintaining high product quality with more efficiency.

In this paper, we explore snake classification as a binary classification problem (venomous vs. non-venomous) based on a large dataset of images. This study aims to tackle the massive difficulty of separating species that are morphologically very similar, i.e., species that are commonly confused when using classical identification methods and the need for reliable snake species identification to manage the risk of snakebite envenoming. These datasets were evaluated through rigorous preprocessing and involved the innovative application of data augmentation techniques to refine features, thereby establishing a novel and diverse dataset of snake images under differing environmental conditions to model against.

Agriculture played an important role in maintaining human life, and technological advances contributed significantly to improving agricultural productivity. The project was a sensor-based smart agricultural monitoring system using Web and IoT, which integrated web-based surveillance and nanotechnology (IoT), optimized farming efficiency, optimized resource use, and improved harvest revenue. These sensors sent real-time data to cloud-based servers via IoT-enabled devices such as microcontrollers ESP32. The collected data was displayed on a web-based dashboard, allowing farmers to monitor and control a variety of agricultural parameters from afar. The most important features of the system included automatic irrigation control. In this irrigation control, the level of soil moisture determined the actual warnings of water needs and important conditions such as drought, excessive moisture, and temperature fluctuations. Additionally, the system provided data analysis and visualization that enabled farmers to make well-informed decisions to improve plant health and productivity. The proposed solution was aimed at creating a sustainable, inexpensive, and user-friendly approach for modern precision breeding.

This paper suggests a drone system for automated trash discovery and collection that integrates robotic manipulation and computer vision to combat environmental declination in hard- to- reach places. The YOLOv8 object identification algorithm, which has an average perfection of 89.7 in a variety of outside circumstances, is used by the proposed system for real-time waste identification. Drone is incorporated with a specially designed 3- DOF robotic arm powered by MG996R servo motors to recoup waste. This arm is capable of precise and stable grasping and retrieval operations during flight or stationary hovering. The drone navigates autonomously using GPS and onboard sensors, while the YOLOv8 algorithm continuously processes camera input to detect and track waste. Experimental evaluations demonstrate the system's effectiveness in accurately detecting and retrieving litter across different terrains and conditions.

The pharmaceutical industry faces unique challenges, including strict regulatory demands, complex production processes, and the need for precise quality control. This research introduces Smart Pharma TwinNet, a system that uses AI-powered digital twins and Industrial Internet of Things (IIoT) technology to make pharmaceutical manufacturing more efficient, adaptive, and intelligent. A digital twin is a virtual model of a physical asset that updates in real time with data from sensors and other sources. In Smart Pharma TwinNet, digital twins monitor equipment health and predict potential breakdowns, helping manufacturers perform maintenance before issues occur. This predictive maintenance approach reduces unexpected downtime, lowers costs, and extends the life of expensive machinery. Smart Pharma TwinNet also improves quality control by allowing real- time monitoring of production conditions, which helps catch any issues early and ensures consistent product quality. Additionally, the system promotes resource optimization by reducing waste and energy use, supporting a more sustainable manufacturing process. This paper discusses the design and technology behind Smart Pharma TwinNet, including the IIoT and AI tools used to build a network of digital twins. Our experiments show that this approach enhances production efficiency and accuracy while creating a scalable and sustainable solution for the pharmaceutical industry.

Traffic congestion in urban areas leads to capacity issues, intersection delays, increased congestion, fuel consumption, and air pollution. Advanced traffic management systems, including adaptive signals and intelligent transportation systems, offer solutions to mitigate congestion and improve road network efficiency. Utilizing live camera imagery and AI for real-time traffic density assessment, alongside adaptive signal control algorithms, reduces congestion and optimizes traffic flow, aligning with the trend of technology-driven transportation systems for environmental benefits. YOLO (You Only Look Once) is a renowned AI object detection algorithm, enabling a vehicle-activated traffic signal to adjust timing based on traffic density, reducing congestion, wait times, and pollution. A simulation and prototype demonstrate the proposed system's effectiveness compared to fixed-time signals. It dynamically adjusts green signal durations based on traffic density, prioritizing high-traffic directions to minimize delays, congestion, and fuel consumption. Results show significant improvements in vehicles crossing intersections, with potential enhancements through real-world data calibration.

A Passenger Car Unit (PCU) is a metric used in traffic engineering to quantify the influence of various vehicle types on traffic flow, expressed as a ratio relative to a standard passenger car. PCU factors are crucial for intersection design, as they allow for accurate estimation of traffic capacity and performance by standardizing the effects of different vehicles. Traditionally, static PCU values have been used for design purposes. However, advancements in vehicle technology over the past few decades necessitate a re-evaluation of these values through field studies. This research provides an empirical analysis aimed at determining dynamic PCU values for various vehicle types, enabling a comparison with the factors recommended by the Indian Highway Capacity Manual (Indo-HCM). Data collection took place at two signalized intersections, utilizing the headway ratio method and the queue clearance rate method to estimate PCUs for different vehicle categories. Field data on delay and saturation flow were collected following Indo-HCM guidelines, employing both static and dynamic PCU values. A comparative analysis of these values was conducted to highlight discrepancies and offer insights into the relevance and accuracy of dynamic PCU values in assessing traffic flow.

This study explores the potential of use steel slag as a biocover for dumpsite gas mitigation. Landfill sites are significant sources for greenhouse gases (GHGs), particularly methane (CH₄) and carbon dioxide (CO2), which contribute to global warming and create environmental and health problems. This study explores the potential use of steel slag, an industrial byproduct, as an alternative bio cover material for LFG emission control and the study found that steel slag achieved a higher carbon dioxide reduction efficiency compared to soil covers. This research highlights the dual benefits of utilizing steel slag for gas control and promoting sustainable waste management by reuse of industrial byproducts. Based on the series of batch test slag showed sequester of 64.10mg/g carbon dioxide at moisture condition tested in 24hours. The primary objective of this study is to know the feasibility of using steel slag as a biocover material for mitigating gas emissions from landfills. This study aims to provide an alternative, cost-effective, and sustainable solution for landfill gas management while addressing the issue of industrial waste utilization, this work concludes that steel slag is a promising alternative biocover material for landfill gas mitigation.