: In general, two different types of loads act on structure artificial and natural loads. Loads due to some material comes in artificial and wind, earthquakes are neutral load, but in this modern world blast load also consider. Blast load applied sudden and extreme on structure, cause of structure can be vary depending on situation either it is gas cylinder blast, chemical blast or terrorist activity and many more. During the explosion blast waves propagate rapidly and exert high-pressure of building surface that could damage the structural elements and lead to structural failure. The primary objective is to establish correlations between floor shape and blast resistance, offering insights into design improvements for enhanced structural performance. For this study four different shapes (L, T, C and Hexagon) of plans of building modelled in E-tabs. Midair blast was considered, and load pressure was calculated. A triangular function for blast load applied in E-tabs and analysis was performed. Deformed shape of structure, storey drift, shear force, overturning moment were analyzed and columns sections which are in front of blast, are also analyzed based on axial force. After all the analysis found that hexagon shape of floor plan performed batter than the other floor shape. Displacement was 65–70% lower than the C-shape. Overturning moment was 55–60% less than in T and C shapes. Storey drift was consistently minimal across all floors. Critical column forces were the lowest in axial force. Its symmetrical and closed layout ensured uniform force distribution, minimal stress concentration, and superior blast resistance.

As the threat posed by climate risks has grown, several worldwide cities have modified their approaches to urban planning and architecture. These changes aim to counter typical infrastructure practices by utilizing natural solutions that combine green (such as parks, gardens, playgrounds, and forests) and blue elements (such as lakes, rivers, seas, wetlands, and water utilities). This project research paper examines the new idea of "BGI" and evaluates plans and initiatives that are now underway in India and around the world. Additionally, it points out ways that India's cities might take advantage of the blue-green area to promote resilience and equity, respond to climate dangers, and spark economic shifts that would lead to sustainable urban futures.

The demand for precise, real-time fraud detection systems is growing in direct correlation with the proliferation of online purchases, since credit card fraud is still a major problem in the banking industry and poses significant dangers to both customers and businesses.This review paper explores various approaches and methodologies developed to detect fraudulent credit card activities, focusing on their applicability, effectiveness, and adaptability in dynamic transaction environments. The paper categorizes existing research based on learning paradigms and detection strategies, highlighting the importance of pattern recognition, anomaly detection, and behavioral analysis in combating fraud.

Examined are important obstacles include data asymmetry, changing fraudulent behavior, and scarce labeled datasets. The paper also discusses common evaluation metrics and performance indicators used to assess detection models. Additionally, it reflects on the role of data preprocessing, feature selection, and system scalability in enhancing detection accuracy.

The review concludes by identifying gaps in current research and proposing future directions, including the development of more adaptive, interpretable, and privacy-preserving fraud detection systems capable of operating efficiently in real-world, large-scale environments.

In a world where technology evolves faster than ever, finding ways to interact with it naturally is more important than ever. If we talk about “drawing” it's very significant because it is the most basic and powerful form of communication. It's a personal and universal tool which connects thinking and feeling. The transition from drawing on walls of ancient caves to creating digital art on smartphones is an example of the remarkable journey of humans in innovation and technology. This technology gave us various options for drawing. “Air Canvas” helps to portray the idea of drawing in air. This study introduces a gesture-based system that lets people draw digitally in real time—using just their hands and a regular webcam. The primary objective of this study is to enable users to draw on a digital screen without any physical contact, simply by moving a colored object (such as a fingertip covered with a blue cap) in front of a webcam providing an easy, accessible and touchless way of engaging with digital spaces. The uniqueness of this study lies in using technologies like OpenCV and deep learning and building features like recognizing hand or object gestures accurately, enabling them to make refined shapes and designs. A lot of practical applications can be supported through this - in domains like education - digital boards, accessibility-focused tools like screen readers, screen magnifiers, art and games. It can increase collaboration in online education and meeting systems where people can convey their ideas and points on a shared canvas which everyone can see. There’s a lot which this application provides like allowing users to change tools, change ink colours, changing background colour, undo-redo and to save their work and all these can be accessed through simple hand movement, making it more effortless and engaging. Overall, it will help to span the distance between user and virtual technology. This study emphasizes the power of gesture recognition technology in transforming the way we engage with computers.

With the increasing availability of structured and semi-structured data on websites, there is a need for efficient tools to extract such information. Web scraping, the process of automatically extracting data from web pages, has been very important in applications like financial analytics and job market studies. This study presents a powerful, multifunctional web scraper created specially to retrieve employment and stock market data. Implemented using Python and its companion tools, including Beautiful Soup and Selenium, the scraper fixes every problem caused by dynamic site content, JavaScript rendering, and CAPTCHA methods. It has been verified and tested on websites for employment portals and livestock.

Facial expression recognition (FER) has become a prominent field of research in computer vision, human-computer interaction, and artificial intelligence due to its potential applications in various domains such as healthcare, education, marketing, and entertainment. This survey presents a comprehensive review of the methods, techniques, and advancements in FER, focusing on the identification and analysis of human emotions through facial expressions. The paper explores traditional techniques, including geometric feature-based methods, and compares them with more recent advancements that utilize "deep learning, particularly convolutional neural networks (CNNs) and other neural network architectures". These deep learning approaches have revolutionized FER by improving accuracy, robustness, and real-time performance, especially in complex and unconstrained environments. The paper also discusses the challenges in FER, such as variations in lighting, pose, and occlusions, and emphasizes the importance of large, diverse datasets for training effective models. Furthermore, the survey reviews the most widely used FER datasets and evaluation metrics, highlighting their impact on the development of reliable systems. Finally, it provides insights into future research directions, including cross-cultural considerations, multimodal emotion recognition, and the integration of FER with other artificial intelligence technologies for more sophisticated, human-like interactions.

In this paper, we investigate and compare the ambiguity functions (AFs) of LFM and Stepped LFM signals when bandwidth, pulse duration, and operating condition are taken to be identical. By matching these parameters, the comparisons in this study would enable direct separation between waveform effects and system mismatches/limitations. This observation emphasizes the different range resolution, Doppler tolerance and sidelobe properties of these two signals. The results indicate that, although both waveforms can achieve high-resolution radar imaging capabilities, each provides different design tradeoffs between implementation effort and performance. In contrast to analogous works that treat both approaches independently, our evaluation of the two chosen methods side by side allows us a practical view on waveform selection in high-resolution SAR.

This study examines the Viability Assessment of On-Grid (HPS) Hybrid power (HPS) system s integrating photovoltaic (PV) and wind turbine technologies for rural applications in the Jaisalmer region of Rajasthan, situated at 26.91° N latitude and 70.91° E longitude near the Indo-Pak border. The research assesses the potential of wind and solar energy using data from various sources. The analysis is conducted using the Hybrid Optimization Model for Electric Renewable (HOMER) software to evaluate the cost-effectiveness of the proposed system. Two system configurations—On-grid and Standalone—are modeled and optimized for comparison. Furthermore, a sensitivity analysis is executed to examine the impact of grid electricity price variations on overall system costs. The simulation results indicate that the On-grid hybrid system is the most economically viable and suitable solution for the selected area.

Road traffic accidents caused by driver fatigue pose a significant threat to public safety, often resulting in severe injuries and fatalities. The Guardian Eye Driver Assistance System (DAS) is designed to mitigate this risk by continuously monitoring driver alertness and implementing real-time corrective actions. The system employs an Arduino UNO, based on ATmega328P microcontroller, an infrared (IR) sensor, and a relay mechanism to detect excessive blinkinga key indicator of drowsiness. Upon detection, a sound alarm is triggered to alert the driver, while a signal is transmitted to the Arduino controller to gradually decelerate the vehicle, ultimately bringing it to a safe stop if necessary. Additionally, an automated emergency notification is sent to a designated contact in case of driver incapacitation. The Guardian Eye system is designed to be cost-effective, scalable, and easily integrable into existing vehicle architectures. By providing a proactive response to driver fatigue, this technology has the potential to significantly reduce road traffic fatalities and enhance overall transportation safety.

This study presents an approach for the optimal allocation and sizing of a Battery Energy Storage System (BESS) to mitigate power losses and voltage deviation indices in the Renewable Energy Source (RES) incorporated distribution system. The optimal positioning and capacity determination of BESS within RES-connected test systems are derived using a metaheuristic optimization algorithm. Utilizing the ETAP simulation platform, a comprehensive investigation is performed to estimate the impact of BESS deployment. The ETAP software facilitates detailed modeling and simulation of BESS integration by incorporating critical parameters such as RES generation variability, load fluctuations, and network constraints. The projected optimization approach is implemented on the IEEE-33 and IEEE-34 bus test systems, demonstrating a substantial drop in voltage deviations and power losses following the amalgamation of RES and BESS units. The findings provide significant insights for power system operators, aiding in the strategic locationing of BESS to enhance system efficiency, stability, and reliability

The sources and power system equipment's performance is impacted by the load neutral current. Consequently, compensating the current will help to enhance the power system equipment performance. The design for test system and execution of DSTATCOM for improve the power quality with a T-Connected transformer for load output neutral current for compensation in the existence of a 3-ϕ unbalanced non-linear load connected. The proposed DSTATCOM was managed utilizing a control methodology derived from the unit vector template approach. These analogies used for increase the power quality and mitigate the harmonic distortion and unity power factor. The SIMULINK/MATLAB environment has been used design test system and simulate for mitigate harmonics and uncertain disturbance in system. The outcomes of the simulations demonstrate how successful the suggested algorithm is.

The mechanism of working of the motor vehicle is the same from past many years which has let the security of the vehicle down and thieves to improve themselves to break the locks and start the vehicle easily, in order to eradicate this problem to a great extent, this project have been built, in which the owner would have the complete control and can track over owner’s vehicle even being miles apart from it. The proposed project comprises of GPRS & GSM, Arduino, Relay, etc. installed inside the two-wheel vehicle. With this, user can control the power supply, ignition and location tracking operations of vehicle through an application installed in the owner’s phone which will be connected to the cloud. When the vehicle has been stolen, it can control the ignition and every motion will be captured through the application, from wherever in the globe (as this device is not distance obliged) This proposed idea is a one step ahead in the revolution and innovation of the two-wheel vehicle. The purpose of working on this project is to build a safety device for motor vehicles in an era where vehicle theft is just a normal thing that happens almost every day. The device does not allow the vehicle to get start with out receiving the signals sent by the application. It also lets us know the location of a vehicle just in case the thief picks it up and carries it to a distance.

Environmental concerns and fluctuating fossil fuel prices necessitate a shift toward cleaner energy sources. Diesel engines, prevalent in industry and transportation, face stringent emission regulations. This study evaluates the performance and emissions of a CI engine using ternary fuel blends of diesel, diethyl ether, and ethanol in varying ratios: D-DEE5-E5, D-DEE5-E10, and D-DEE5-E15. The D-DEE5-E10 blend demonstrated optimal results, achieving the lowest brake-specific fuel consumption (0.245 kg/kWh) and highest brake thermal efficiency (39.83%). The improved cetane number of DEE and the oxygen availability in ethanol enhanced combustion, significantly reducing smoke (6.8%), hydrocarbons (18 ppm), and carbon monoxide (0.12% vol.). However, the elevated combustion temperatures led to increased nitrogen oxide emissions. Overall, the D-DEE5-E10 blend emerges as a promising cleaner alternative to conventional diesel, effectively balancing improved performance with reduced emissions.

This study explores the effectiveness of non-linear regression and artificial neural network (ANN) in predicting percentage shrinkage during plastic injection molding processes for HDPE material. By analyzing critical process parameters such as mold temperature, melt temperature and injection pressure, the research aims to identify optimal modeling techniques to improve % shrinkage accuracy so that better parts can be produced. A comparative analysis has been done between non-linear regression (NLR) and learning-ANN which shows understandings into the accuracy and limitations of each type of investigation method. Outcomes shows that while learning-ANN models offer greater adaptability, non-linear regression gives a more interpretable approach, having reliable shrinkage predictions with minimized error.

This study explores the various application of Coded Genetic Algorithm (CGA) along with the solving of two complex mathematical optimization problems. Coded Genetic Algorithm can be used in design optimization, injection moulding, facility layout and scheduling, information security etc.  In this study, the first function a trigonometric-exponential function was maximized using CGA, while the second, a convex Sphere-function, was minimized using CGA. Coding of GA was implemented in Python without relying on external libraries, ensuring wide accessibility. To visually and interpret the optimization performance the high-resolution 3D surface plots, contour plots, and convergence curves were generated. The results confirm the effectiveness of CGA in accurately locating global optima in nonlinear models and convex search spaces. This paper contributes to the growing use of the metaheuristic optimization studies by offering mathematical insights.

This manuscript deals with the Earth’s most impulsive and perilous natural disaster well known as Tsunami mathematical model. Tsunami propogate with an utmost speed and have incredible energy. The governing equations are of partial differential which are further handled by the Maching learning based Back Propagation method, known as Levenberg-Marquardt approach.  Here we generate 1000 data points. The 70% of data are used for training, 15% for testing and 15% for validating. The plot of mean squre error (MSE) between the exact solution and ANN are discussed through graphs.