Volume 14 | Issue 4 |

 Abstract

Abstract-- The structural performance of bridge piers plays a critical role in ensuring the safety, durability, and serviceability of bridge infrastructure under varying loading conditions. This study presents a comprehensive analysis of bridge piers with different geometric shapes subjected to static, impact, and cyclic loading. Using computational simulation through ANSYS Workbench along with theoretical evaluation, the behavior of piers is examined in terms of stress distribution, deformation patterns, and fatigue performance. The analysis incorporates material properties of M35 grade concrete and steel reinforcement, along with varying load cases ranging from 850 kN to 10000 kN. Results indicate that geometric configuration significantly influences load-bearing capacity, stiffness, and resistance to failure. It is observed that piers maintain structural integrity within the elastic range up to a certain load threshold, beyond which plastic deformation and eventual failure occur. The study also highlights that steel reinforcement plays a crucial role in sustaining loads beyond concrete failure strain, thereby preventing sudden collapse. Furthermore, fatigue analysis reveals that higher loads drastically reduce the life cycle of the structure. The findings emphasize the importance of optimizing pier geometry for enhanced structural performance, improved load distribution, and long-term durability under real-world loading conditions.

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Keywords: Bridge Piers, Geometric Shapes, Structural Performance, Cyclic Loading, Stress-Strain Behavior etc.

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Epilepsypilepsy is a brain-related disorder in which sudden disturbances in electrical activity lead to repeated seizures. Early and accurate detection of these seizures plays an important role in proper diagnosis and patient care. Electroencephalogram (EEG) signals are commonly used to monitor brain activity and identify abnormal patterns associated with seizures. However, manual analysis of EEG data is time-consuming and requires expert knowledge. This study introduces an automated approach for detecting epileptic seizures using deep learning techniques. The proposed system involves preprocessing of EEG signals, learning meaningful patterns, and classifying them using deep neural network models. The results show that deep learning methods are capable of identifying complex patterns in EEG data and improving detection performance. Moreover, the use of deep learning reduces the dependency on manual feature extraction and enhances the system's ability to detect subtle changes in brain signals. This makes the proposed method useful for developing reliable and efficient seizure detection systems in clinical environments.

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Epileptic seizure detection, EEG analysis, deep learning, CNN, biomedical signal processing.

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Rejection sensitivity has been identified as an important psychological factor influencing mental health outcomes among sexual minority individuals. The present study examined its relationship with depressive symptoms among Indian gay men, a population that experiences distinct psychosocial challenges within a stigmatizing sociocultural context. A sample of 100 participants was assessed using the Gay-Related Rejection Sensitivity Questionnaire (GRSQ) and the Beck Depression Inventory-II (BDI-II). Both instruments demonstrated high internal consistency (GRSQ ? = .965; BDI-II ? = .939). Pearson correlation analysis revealed a statistically significant positive relationship between rejection sensitivity and depression, r (98)= .356', p < .001, indicating that individuals who are more likely to anticipate social rejection also tend to report higher levels of depressive symptoms. These findings highlight the role of rejection-related cognitive and emotional processes in shaping mental health outcomes. Although the cross-sectional design limits causal interpretation, the study provides important insight into factors associated with depression among Indian gay men.

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Rejection sensitivity, Depression, Sexual minority, Minority stress, Indian gay men

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Water contamination by synthetic dyes poses a serious environmental and health challenge due to their persistence, toxicity, and resistance to biodegradation. In this study, a novel kerosene-based magnetic peanut husk adsorbent was developed using oleic acid-coated Fe?O? nanoparticles and evaluated for the removal of methylene blue from aqueous solutions. The magnetic nanoparticles were synthesized via a coprecipitation method and confirmed by XRD analysis to possess a cubic spinel structure with an average crystallite size of ~12 nm. DLS results indicated a well-dispersed system with hydrodynamic particle sizes in the range of 50-120 nm, while VSM analysis confirmed superparamagnetic behavior with a saturation magnetization of approximately 48-50 emu/g. FTIR analysis verified successful oleic acid coating and surface functionalization. Adsorption experiments were conducted over a concentration range of 10-200 mg/L using 30 mg of adsorbent with a contact time of 3 hours. UV-Vis analysis at 665 nm showed a significant reduction in absorbance intensity after treatment, indicating effective dye removal. Maximum removal efficiency of approximately 97% was achieved at lower concentrations (10 mg/L), while efficiency decreased to around 68% at higher concentrations (200 mg/L) due to saturation of adsorption sites. Importantly, no shift in the absorption maximum was observed, confirming that the removal mechanism is governed by adsorption rather than chemical degradation. The adsorption process is driven by electrostatic interactions, hydrogen bonding, hydrophobic interactions, and ?-? stacking between the dye molecules and the adsorbent surface. The magnetic property of the adsorbent enables rapid and efficient separation using an external magnetic field. Overall, the developed magnetic peanut husk demonstrates high efficiency, low cost, and environmental sustainability, making it a promising material for wastewater treatment applications without generating secondary pollutants.

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Magnetic peanut husk, methylene blue, adsorption, UV-Vis spectroscopy, superparamagnetic, wastewater treatment.

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Hypertension is a major global health burden and a leading contributor to cardiovascular morbidity and mortality. Despite the availability of effective antihypertensive agents, blood pressure control rates remain suboptimal, necessitating a critical evaluation of pharmacological strategies. This review aims to compare the efficacy, safety, and clinical outcomes of monotherapy classes versus combination regimens in the management of hypertension, with emphasis on guideline recommendations, patient-centered outcomes, and evolving therapeutic paradigms. A narrative synthesis of clinical trials, meta-analyses, and guideline updates was conducted to evaluate the role of major antihypertensive drug classes--angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, calcium channel blockers, thiazide diuretics, and beta-blockers--when used as monotherapy or in combination. Monotherapy remains effective in patients with mild hypertension and specific comorbidities, but often fails to achieve target blood pressure in moderate to severe cases. Combination regimens, particularly those involving renin-angiotensin system blockers with diuretics or calcium channel blockers, demonstrate superior blood pressure reduction, improved cardiovascular outcomes, and enhanced adherence. Fixed-dose combinations further optimize compliance by reducing pill burden. However, patient-specific factors such as age, ethnicity, comorbidities, and socioeconomic considerations influence therapeutic choice. Pharmacological management of hypertension has shifted toward early adoption of combination therapy, reflecting its superior efficacy and patient-centered benefits. While monotherapy retains a role in selected populations, combination regimens--especially fixed-dose formulations--are increasingly favored in contemporary practice. Future directions include precision medicine approaches, biomarker-driven therapy, and integration of digital health tools to personalize hypertension management and improve global control rates.

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Hypertension, Antihypertensive therapy, Monotherapy, Combination therapy, Fixed-dose combinations.

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Diabetes is a growing global health issue that can cause serious complications if not detected early. This paper proposes a hybrid prediction system combining Machine Learning and Deep Learning to improve accuracy. It uses clinical data (such as glucose, BP, BMI, and age) with Logistic Regression, and retinal images with a CNN (EfficientNetB0) for detection. Implemented as a Flask-based web application, it allows users to input data or upload images and receive predictions with basic health suggestions. Results show effective performance, supporting early diagnosis and preventive healthcare.

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Diabetes Prediction, Machine Learning, Deep Learning, Logistic Regression, Convolutional Neural Network (CNN), EfficientNetB0, Retinal Fundus Images, Clinical Data Analysis, Web Application, Flask, Early Disease Detection, Healthcare Analytics

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Literature is considered to be a mirror of life, as it replicates the way we approach it. It conceives various human emotions and feelings in the form of words. Each words have an emotion, which is analyzed by Senses. This research paper tries to explore the world of literature through a vehicle called Senses. There are five senses: sight, sound, smell, taste and touch. Literally, they are called as Vision, Audition, Olfaction, Gustation and Haptics. In general, sensory studies are an interdisciplinary field that examines how literary texts represent human perception and the five senses as a cultural, historical and aesthetic phenomenon. Though religious book and other literary works like Thirukural speaks about how to control the emotions to be enlightened, Indian literature stands as pillar of senses to hire all the mixed emotions in the form of literary piece. It dives deeply to strengthen the shades of memory, history, taste and culture. This paper throws light on how literature treats senses as a literary expression which is used as Vision for light and dark, Audition for silence and sound, Smell for foul and fragrance, Taste for numbness and delicious, Haptics for love and alienation. These senses not only act as fundamental language of expression but also demonstrates emotional connections forged through the sensory experiences.

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Aesthetics, Culture, Vision, Audition, Gustation, Olfaction, Haptics

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In today's digital era, AI-driven language learning is critical for effective global communication, as traditional methods lack real-time interactivity and personalized feedback. This paper presents "Talk Genius AI", an intelligent web application that improves pronunciation, vocabulary, and speaking fluency. Integrating speech recognition and natural language processing, the system delivers instant, adaptive feedback on user input. Key features include language selection, interactive practice sessions, structured lessons, and performance analytics. Built with HTML, CSS, JavaScript, and AI APIs, the application is fully responsive across devices. Additionally, the system tracks user progress over time, offers contextual sentence correction, and simulates realworld conversations to enhance practical communication skills. The modular architecture allows easy integration of multiple languages and third-party AI services for future scalability. Talk Genius AI exemplifies how AI transforms conventional education into a smart, interactive, and efficient learning experience, making language acquisition more accessible and engaging for learners worldwide

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Natural Language Processing (NLP), Artificial Intelligence (AI), E-Learning / Educational Technology, Human-Computer Interaction (HCI), Automated Feedback, Speech Recognition, Language Acquisition

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The deployment of robotics in hazardous environments has become essential for reducing risks in industrial, disaster, and defense applications. Current robotic platforms often face an operational trade-off, where systems designed for unstructured terrain mobility lack effective manipulation capabilities, while those with high dexterity are typically limited to controlled environments. This paper introduces D.E.F.U.S.E., the Dual-Arm Electronic Field Unit for Surveillance and Extraction, a teleoperated robotic system developed to overcome this limitation. By combining a rocker-bogie suspension system with dual Selective Compliance Assembly Robot Arm manipulators, the platform enables advanced bi-manual operations across dynamic terrains. The electro-mechanical design incorporates an ESP32 microcontroller, a PCA9685 servo driver, and a differential drive mechanism to achieve reliable, low-latency wireless teleoperation. Experimental evaluations demonstrate the system's ability to maintain stability, traverse obstacles efficiently, and perform precise remote manipulation during simulated extraction tasks. The proposed system provides a scalable and cost-effective solution suitable for applications such as explosive ordnance disposal, search and rescue, and remote industrial inspection

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Field Robotics, Mobile Manipulation, Rocker-Bogie Suspension, SCARA Manipulator, Dual-Arm Robotics, Wireless Teleoperation, Embedded Systems, Search and Rescue, SAR, Explosive Ordnance Disposal, EOD, Unmanned Ground Vehicle, UGV, Robot, Dual Arm.

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Abstract: The 21st century has brought unprecedented social, technological, and economic transformations that demand a fundamental reconceptualization of the teacher's role in education. This paper examines the evolving responsibilities of teachers in the context of India's National Education Policy (NEP) 2020, situating the discussion within broader global trends of digitalization, globalization, and changing learner needs. Employing a qualitative content analysis methodology, the study draws on both primary sources -- including the NEP 2020 policy document and government reports -- and secondary scholarly literature to explore how the policy reshapes teacher identity, preparation, and professional practice. The paper identifies a decisive paradigm shift from teachers as knowledge transmitters to teachers as facilitators, mentors, digital integrators, and lifelong learners. NEP 2020 introduces several structural reforms to drive this transformation, including the introduction of a four-year integrated B.Ed. programme, mandatory Continuous Professional Development of at least 50 hours annually, the establishment of National Professional Standards for Teachers (NPST), merit-based recruitment, and enhanced teacher autonomy. The policy also emphasizes technology integration, inclusive education, mother tongue pedagogy, competency-based learning, and formative assessment as essential dimensions of the reconceptualized teaching role. However, the study reveals significant challenges that threaten the policy's implementation, inadequate digital infrastructure, insufficient teacher training, resistance to pedagogical change, increased administrative workloads, a persistent policy-practice gap, and barriers to inclusive education. The paper proposes key transformation strategies -- including experiential learning, multilingual pedagogy, community engagement, and reflective practice -- to bridge these gaps. The findings underscore that NEP 2020's vision can only be realized through sustained institutional support, systemic investment in teacher development, and a deeper societal reimagining of the teacher's role as central, dignified, and irreplaceable in shaping human potential.

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Teacher Reconceptualization, National Education Policy (NEP) 2020, 21st Century Education, Continuous Professional Development (CPD), Pedagogical Transformation

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