Metallurgical and Materials Engineering (E-Journal)
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CPW Fed Fractal Based Four-Port Mimo Antenna With A Noval Method For Isolation Improvement
Full text linkA compact multiple-input multiple-output whose ports are exited with CPW feeding configuration is proposed here.This configuration having very good isolation properties between the antennas incorporated (>-20 dB).The four antennas are placed symmetrical around the axis gives a band with in UWB range specified by FCC (3.1GHZ-10.6GHZ).Each UWB antenna element in this configuration having a hybrid fractal which composed of Sierpinski Carpet and Koch geometry.Hybrid fractal geometry along with CPW feed gives wider band width. In order to Improve the mutual coupling between the antennas two stubs are attached in the ground plane for each antenna element. The Hybrid MIMO antenna works on frequency band 2.4GHz-11GHz with a mutual coupling lower than -20dB and Envelop Correlation Coefficient lower than 0.01
A Deep Learning-Based Framework for At-Risk Student Detection Using Educational Data Mining with a Focus on Learning Health
Full text linkDetection of at-risk students through Educational Data Mining (EDM) with emphasis on learning health seeks to determine students who are likely to underachieve or drop out by monitoring trends in their learning behavior, performance indicators, and attendance levels. Current models in this Area are plagued by several issues, such as poor capability to capture deep relational features, low generalization through shallow structures, inadequate treatment of imbalanced and high-dimensional educational data, and the absence of adaptive optimization for varied learning patterns. To overcome these constraints, this study introduces a new deep hybrid model named Harris Hawk Optimization based Capsule Deep Residual and Dense Network model (HHO-CapDeReD-Net), which integrates four strong architectures Capsule Network, Deep Neural Network, ResNet-50, and DenseNet-121 to extract multi-level features from encoded student data. The model also incorporates Harris Hawk Optimization (HHO) to fine-tune hyperparameters for optimal learning performance dynamically and minimize overfitting. By concentrating on learning health, the model continuously monitors and predicts educational risk so that early intervention and tailored help can be ensured. The intended HHO-CapDeReD-Net model shows higher prediction accuracy and stability in pinpointing students who are at risk, thus furthering more successful educational interventions and student retention
A Comprehensive Review Of Phishing Detection Techniques Based On Machine Learning
Full text linkPhishing attacks continue to pose significant security threats to individuals and organizations worldwide, resulting in financial losses and compromised sensitive information. This comprehensive review examines various machine learning (ML) techniques employed for detecting phishing attempts across multiple vectors, including websites, URLs, and emails. By analysing recent literature, we explore feature selection methodologies, prominent algorithms, dataset characteristics, and performance metrics. Our findings indicate that supervised machine learning approaches, particularly Random Forest and Convolutional Neural Networks, demonstrate superior detection accuracy, often exceeding 97%. Traditional ML algorithms combined with effective feature selection techniques provide practical solutions with reasonable computational requirements, while deep learning approaches offer higher accuracy at the cost of increased complexity. Notable research gaps include limited attention to zero-day attacks, insufficient multimodal phishing detection techniques, and ethical considerations surrounding privacy and consent. This review provides valuable insights for security researchers and practitioners seeking to advance the state-of-the-art in phishing detection through machine learning
An Analysis Of Intensity Of Livelihood Diversification Among Rural Households In Cuddalore District Of Tamil Nadu
Full text linkPurpose: The study was undertaken to analyse the intensity of livelihood diversification among rural households in Cuddalore district of Tamil Nadu.
Methodology: The study is based on primary data collected from the respondents through a well-structured interview schedule. 374 respondents have been selected and a simple random sampling technique has been used to select the respondents.
Materials and methods: The Simpson index of diversity has been used to measure the intensity or pattern of livelihood diversification among the rural households in Cuddalore district of Tamil Nadu.
Results: The results of the study revealed that apart from crop production farm and livestock trading, Handicrafts and artisanship are the most important activities in the study area and the SID in the study area is 0.32 which is less than the midpoint.
Suggestions: The study suggested that government should continue its efforts to generate income earning opportunities in the rural areas and support the farmers to enhance agricultural productivity through supportive policies including input utilization and creating market for their product
Rapid And Sensitive Colorimetric Detection Of Caffeine For Forensic Applications
Full text linkCaffeine, a methylxanthine alkaloid, is a naturally occurring stimulant found in plants such as Coffea arabica (coffee), Camellia sinensis (tea), and Theobroma cacao (cocoa). It is widely present in beverages, pharmaceuticals, and dietary supplements. While caffeine is generally considered safe in moderate doses, excessive consumption or misuse can lead to toxicity, making its detection crucial in forensic investigations. Cases involving drug adulteration, overdose, poisoning, and forensic toxicology often require rapid and reliable methods for caffeine identification. However, conventional analytical techniques like chromatography and spectrophotometry, though highly accurate, require sophisticated instruments and time-consuming sample preparation, limiting their use in field applications.
This study introduces a novel colorimetric method for caffeine detection using iodine and methanol, which has not been previously reported in forensic science. When iodine is dissolved in methanol, and caffeine is introduced, a distinct color change occurs due to the formation of a charge-transfer complex. Caffeine donates electrons to iodine resulting in a visually detectable shift in color. This test is rapid, cost-effective, and highly sensitive, capable of detecting trace amounts of caffeine, making it ideal for preliminary forensic screening.
The method was systematically evaluated across a range of sample dilutions to establish its effective detection limits. A distinct and consistent colorimetric response was observed within the concentration range of 0.1 mg/mL to 2 mg/mL, ensuring reliable visual detection even at lower caffeine levels. Below 0.1 mg/mL, the color change became too faint for clear identification, while concentrations exceeding 2 mg/mL did not produce any significant increase in visual intensity. Due to its simplicity, this technique is highly suitable for on-site forensic applications such as drug seizure inspections, toxicological evaluations, and detection of beverage adulteration. It offers a rapid, qualitative indication of caffeine presence enabling quick preliminary assessments. However, because other electron-donating compounds may interfere with the test, confirmatory analysis through more advanced techniques like chromatography or spectrophotometry is still essential for precise identification and quantification. This study thoroughly investigates the underlying reaction mechanism, sensitivity range, dilution effects, and potential interferences associated with the proposed colorimetric method. With optimization, it holds promise as a valuable initial screening tool in various forensic and toxicological scenarios
Computation-Driven Control For Hybrid Electric Vehicles Ensuring Optimal Energy Utilization And Reduced Latency
Full text linkHybrid Electric Vehicles (HEVs) are the next big leap towards cleaner means of transport since they act as a bridge between conventional vehicles and full-electric vehicles therefore require complex control strategies for optimal energy management, battery and vehicle durability as well as instantaneous power availability. This chapter aims for Optimal Energy Management and Latency Minimization by Intelligent Designing a Controller System which explores the computation techniques in the optimization of energy and reduction of latent impacts. The frameworks incorporate the best current methods; model predictive control and machine learning controls to periodically and in real time distribute the power between the internal combustion engine and the electrical motor. It also solves problems including the control of regenerative braking, State of Charge (SOC), temperature control, and fault detection. Particular attention is paid to minimizing computational lag to support streaming adaptation to fluctuating driving environment and other conditions to improve functionality and usability for drivers. This chapter also considers the likelihood of using renewable energy resources, accurate prediction to reduce maintenance cost for HEV components, and the development of new generation batteries to further boost the efficiency of HEVs. By presenting a number of examples and analyzing the mimicked situation, the efficiency of the proposed controller design is shown, and possible emissions and cost decrease is outlined. This work offers useful information for numerous scholars, professionals and authorities who have interest in finding new approaches to enhance the overall performance of HEVs
Efficiently Identifying Fake Audio And Images Using Transfer Learning
Full text linkWith the advent of AI-generated content in today's digital world, identifying forged images and audio has become more difficult. The system suggested here utilizes deep learning to enhance the precision and dependability of detecting fake media. A strong Convolutional Neural Network (CNN) known as VGG19 is utilized to label fake images. VGG19 uses deep features toanalyze images and detect discrepancies in textures, illumination, and pixel patterns that signal manipulation. The fine-tuned pre-trained VGG19 model with a large database of real and fake images improves prediction accuracy. For detecting fake audio, the system employs a Recurrent Neural Network (RNN), which is best suited for processing sequential data. By analyzing spectrogram features and waveform patterns, the RNN model detects anomalies in pitch, tone, and frequency typical of AI-generated or manipulated audio. The model is also trained on synthetic and real speech datasets to identify authentic and deepfake audio successfully. Through the combination of VGG19 for image detection and RNN for audio classification, the suggested system offers a powerful method for fake multimedia content detection. The proposed solution improves security, digital forensics, and misinformation avoidance, providing more trustworthy authentication of visual and audio data in real-world applications
A Secure Authentication Method Based On Digital Twins For Vehicle Cloud Networking
Full text linkAutonomous vehicles (AVs) provide various services but are usually plagued with sensing, processing, and communication delay issues. To mitigate these challenges, we introduce a secure cloud-based digital twin framework that allows real-time synchronization and data fusion with physical vehicles. This solution minimizes communication overhead, enhances system responsiveness in general, and promotes a better passenger experience. In addition, it provides data security and privacy during the interaction process. In comparison to other vehicular communication protocols, our approach is less computationally intensive and thus extremely appropriate for real-time applications. The system is intended to allow secure and efficient data transfer from the vehicle and its digital twin without impairing performance. Optimizing communication and computation equally, our solution offers a scalable platform for autonomous vehicle deployments in the future. In general, this work is a milestone toward the integration of digital twin technology with AV systems in a seamless and secure manner.  
A Financial Performance Analysis On Pharmaceutical Companies With Reference To Nse
Full text linkThe pharmaceutical industry plays a critical role in ensuring public health while also contributing significantly to economic growth and employment in India. As one of the core sectors listed on the National Stock Exchange (NSE), the financial health of pharmaceutical companies is vital not only for stakeholders but also for long-term national development. This study, titled "A Financial Performance Analysis on Pharmaceutical Companies with Reference to NSE," provides a comprehensive evaluation of selected leading pharma companies — Cipla, Sun Pharma, and Dr. Reddy’s — through detailed financial ratio analysis, correlation assessment, and factor analysis. The research spans five financial years (2019–2024) and assesses key performance indicators such as liquidity, profitability, earnings retention, and valuation metrics. The study applies analytical techniques including Kaiser-Meyer-Olkin (KMO) testing, Bartlett’s test, and factor loading to determine the most influential financial variables driving performance. The findings reveal that while Dr. Reddy’s consistently shows strong profitability and dividend distribution, Sun Pharma exhibits a comparatively weaker liquidity position. Cipla, on the other hand, maintains stable financial metrics with notable improvements in quick and current ratios. The results also show declining enterprise values and profit margins across all firms, suggesting the need for strategic financial restructuring. The research emphasizes the importance of liquidity optimization, inventory efficiency, and balanced reinvestment strategies to enhance financial resilience. The outcomes of this study will be valuable for corporate managers, investors, analysts, and policymakers seeking insights into the financial appraisal and long-term sustainability of India’s pharmaceutical sector
Low Power Based Sense Amplifier By Using Conditional Bridging Technique
Full text linkOne of the major challenges in modern VLSI design is power consumption, right up there with space and performance. Digital systems rely on the flip-flop. In sub-threshold operation, we examine and contrast four different flip-flop topologies: IP-DCO, MHLFF, CPSFF, and CPFF. Both pulse-triggered and conditional approaches are included in these topologies. Very low power consumption applications are now within reach, thanks to sub threshold technology. One advantage of this technique is that it decreases the number of power-hungry flip-flops. Compared to a strong inversion circuit, a subthreshold circuit consumes less power while running at the same frequency. Tanner uses 18nm technology in cmos for design. We test the flip-flops' power delay, power delay product, and average power at a 1V power supply voltage and look at them from every perspective