Volume 13 | Issue 4

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Zein ,Edible coatings, Triphala ,Tomato

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IoT, Poultry Farming, DHT22, ESP32, Automated Temperature Control, Ammonia Sensor, Cloud Monitoring, Smart Agriculture.

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In this article, we shall prove a few of the most useful results on Hilbert-Schmidt Operators in our own technique. We shall also prove the following inclusions between different classes of operators on ?, each of which is a two-sided, self-adjoint ideal of operators in .

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Hilbert-Schmidt Operators, Trace Class operators, Compact operators, Bounded operators.

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This study looks at how nature is portrayed in Western and Indian literature, and how it reflects and shapes society values, cultural norms, and environmental concern. The study's goal is to expose the differing and converging ways in which nature is depicted by evaluating chosen works from both literary traditions, functioning as a mirror to the civilizations from which these narratives originate. The research also investigates the changing connection between humans and nature as represented in literature, as well as the consequences for current environmental debate. The fundamental goal of this research is to better understand how nature shapes cultural identities and societal attitudes, as well as to evaluate how literature might promote environmental consciousness. The research aims include:: examining (1) how nature is portrayed in Western and Indian novels, (2) how these representations represent cultural settings and social ideals, and (3) how literature might affect environmental advocacy and consciousness. In order to evaluate important works like Mary Shelley's Frankenstein, Thomas Hardy's Tess of the d'Urbervilles, R.K. Narayan's The Guide, and Amitav Ghosh's The Hungry Tide, the research technique uses a qualitative approach and eco-critical and comparative literary frameworks. Thematic patterns, cultural quirks, and the symbolic usage of nature are all identified through an analysis of these works. According to the findings, Indian novels illustrate the contradictions between industrialization and Romantic ideals, whereas Western novels frequently portray nature as a space of struggle or transcendence. However, both faiths call for environmental stewardship and analyze social ills through the perspective of nature. This study emphasizes how literature has the ability to influence and mirror cultural perceptions of nature.

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: Nature in Western and Indian novels, industrialization, ecological traditions

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This study investigates the cultivation, productivity, and social and economic impact of millets grown in the Taranagar region of Churu district, Rajasthan. Millets, known for their drought resistance and nutritional value, play a significant role in the agrarian economy of arid and semi-arid regions like Taranagar. The research focuses on major millet varieties cultivated in the area, such as pearl millet (bajra), and evaluates factors influencing yield, including soil quality, irrigation practices, and traditional versus modern farming techniques. Data was collected through field surveys, farmer interviews, and local agricultural records. The study highlights the adaptability of millets to the region's adverse climatic conditions and their potential to enhance food security, farmer income, and climate resilience. Challenges such as market access, storage infrastructure, and awareness about millet-based diets were also explored. The findings underscore the need for targeted policy support and awareness campaigns to promote millet cultivation and consumption, thereby ensuring sustainable agricultural development in the region. This study focuses on the cultivation and agronomic performance of two important millet crops--pearl millet (Pennisetum glaucum) and sorghum (Sorghum vulgare) in arid and semi-arid regions, with an emphasis on their adaptability, nutritional value, and role in sustainable agriculture. Both crops are staple cereals in regions with limited rainfall and poor soil fertility, making them crucial for food security and rural livelihoods. The research examines varietal characteristics, growth conditions, and productivity under traditional and improved agricultural practices. Data was gathered through field trials, farmer surveys, and soil and climatic analysis. Results indicate that both pearl millet and sorghum exhibit strong drought resistance, short growing periods, and significant resilience to pests and diseases. Additionally, their high fiber, protein, and micronutrient content position them as key crops in combating malnutrition. The study concludes that promoting the cultivation of these millets, supported by better market access, extension services, and processing infrastructure, can contribute to climate-resilient and nutrition-sensitive farming systems in dryland areas.

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Pearl millets, Sorghum, Taranagar, Churu, Rajasthan.

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Through the utilization of statistical and calculus-based methodologies, this study examines the influence of gender wage policies on urban income inequality, as measured by the Gini coefficient. Gender wage inequality, driven by systemic undervaluation of women's work and limited access to high-paying roles, remains a significant barrier to equitable economic participation in urban areas. Through an analysis of data from 2005-2016, this paper evaluates the impact of the UK's Equality Act 2010, a significant legal framework, on reducing income inequality. The study employs statistical methods, including regression analysis and the Mann-Whitney U test to assess the impact of the Act. Results indicate a statistically significant decline in the Gini coefficient post-implementation, highlighting the role of gender wage policies in narrowing wage gaps and stabilizing income distribution. Furthermore, this research explores the limitations of such policies, emphasizing the need for comprehensive systemic reforms, including equitable access to education, childcare support, and workplace inclusivity.

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: The project presents the Implementation and Analysis of CDRA antenna operating at 6GHz frequency on Rogers RT substrate using ANSYS HFSS simulation software. The cylindrical geometry is selected due to its fabrication simplicity, and its capacity to deliver a uniform radiation pattern, which is essential for reliable communication. Dielectric resonator antennas are widely recognized for their exceptional radiation efficiency and broad operational bandwidth, making them highly suitable for advanced applications such as Internet of Things (IoT), 5G communication networks, and Satellite Communication systems. By employing HFSS, critical antenna performance metrics, including Return loss (S11), VSWR, Radiation patterns can be accurately derived and optimized.

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CDRA antenna, HFSS, Return loss, VSWR, Radiation pattern

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Skin cancer is one of the most common types of cancer worldwide, and early detection is crucial for effective treatment. Traditional diagnostic methods rely on clinical expertise, which can be subjective and time-consuming. In recent years, machine learning (ML) techniques have gained significant attention for their potential in automating skin lesion detection and classification, improving diagnostic accuracy, and reducing human dependency. This survey provides a comprehensive review of various ML-based approaches for skin lesion analysis, including feature extraction techniques, classification algorithms, and evaluation metrics. It explores different datasets used for training ML models and highlights challenges such as data imbalance, variability in lesion appearance, and interpretability of models. Additionally, recent advancements in deep learning, particularly convolutional neural networks (CNNs), are discussed, along with their impact on improving classification performance. The survey also examines the role of ensemble learning and hybrid models in enhancing diagnostic accuracy. Finally, potential future research directions are outlined to address existing limitations and further advance ML applications in dermatology.

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Skin cancer, Early detection, Machine learning (ML), Skin lesion detection, Skin lesion classification, Diagnostic accuracy, Feature extraction.

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This study explores public perception and awareness of green taxes as a motivator for developing a sustainable circular economy in Bangalore. Green taxes are considered to act as a discourager for pollution and motivator for environmental-friendly behavior, thus playing a very important role in transforming consumer and production patterns. The study was based on the responses of 101 participants by using a structured questionnaire. It collected demographics, knowledge of green taxes, attitudes, and perceptions in regard to their efficiency towards sustainability. It showed levels of awareness and support ranging from respondent to respondent concerning green taxes. They were more skeptical of their long-run economic impact but recognized they reduced pollution. It used chi-square tests and ANOVA to test how the effect of green tax awareness may be associated with consumers' behavior, decision to buy, and perception of the long-term environment. There is a gap in public knowledge and effectiveness in reaching existing channels, which suggest ways to which policymakers may benefit in driving the sustainability consumption through circular economies, with regard to using green taxes as drivers of consumption in the pursuit of environmental concerns.

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Circular Economy, Environmental Benefits, Green Taxes, Public Perception, Sustainable Practices

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The implementation of a Massive MIMO-based 6G communication system with Full Duplex (FD) and Half Duplex (HD) aims to enhance spectral efficiency and network capacity while addressing self-interference challenges. The project is executed in two phases: first, implementing FD with and without Self-Interference Cancellation (SIC) and Reconfigurable Intelligent Surfaces (RIS); second, comparing FD with HD in terms of spectral efficiency, interference management, and overall system performance. SIC techniques, including digital and analog cancellation, are employed to mitigate self-interference in FD systems, enabling simultaneous transmission and reception on the same frequency band. Key performance metrics such as Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), Mean Square Error (MSE), and data rates are analyzed using MATLAB to compare FD with SIC and RIS against traditional HD systems. The results demonstrate that FD, when integrated with SIC and RIS, significantly improves spectral efficiency and data throughput while reducing latency, making it a promising technology for next-generation 6G networks.

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6G Communication, Full Duplex (FD), Half Duplex, Self-Interference Cancellation (SIC), Reconfigurable Intelligent Surfaces (RIS), Massive MIMO, Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), Mean Square Error (MSE), Throughput Optimization, Terahertz (THz) Communication, mm Wave Technology, Spectral Efficiency, Zero Forcing (ZF) Method, MATLAB Simulation.

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The IoT-based intelligent parking system is designed to enhance parking management by automating real-time space monitoring, vehicle authentication, and billing processes. The system integrates IoT components such as ESP8266, RFID technology, and line sensors to ensure efficient and accurate tracking of parking slot occupancy. RFID authentication enables secure vehicle entry and exit, preventing unauthorized access while ensuring a seamless user experience. Real-time data transmission to a cloud server allows users to check parking availability remotely, reducing congestion and improving space utilization. The automated billing system accurately calculates parking fees based on entry and exit times, eliminating manual errors and enhancing transparency. Wireless communication ensures seamless integration with digital payment systems, further improving convenience. The system is designed to be energy-efficient and scalable, making it suitable for deployment in urban environments and smart city infrastructure. By leveraging IoT and cloud computing, the proposed system offers a cost-effective and reliable solution to modern parking challenges, optimizing space management while improving security and operational efficiency. The implementation of this system promises to alleviate parking-related issues in urban settings, providing an innovative approach to managing parking resources effectively

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IOT, Intelligent Parking System, RFID Authentication, Automated Billing, ESP8266 Micro Controller, Real-Time Monitoring.

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Waterborne diseases remain a significant global health concern, particularly in developing regions where access to clean drinking water is limited. This study explores machine learning techniques for predicting waterborne diseases using physicochemical water quality parameters. The research employs various models, including Decision Trees, Random Forest, and Neural Networks, to enhance prediction accuracy. Data pre-processing, feature engineering, and model evaluation are performed to ensure robust results. The findings suggest that machine learning can significantly improve early detection and prevention strategies for waterborne diseases.

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Waterborne Diseases, Machine Learning, Water Quality Prediction, Environmental Health, Public Health Surveillance, Random Forest, SVM, Predictive Analytics, Real-time Monitoring, Disease Risk Assessment

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Chronic diseases such as diabetes, cardiovascular conditions, and respiratory illnesses represent some of the most persistent and resource-intensive challenges in modern healthcare. With the increasing global burden of these conditions, the healthcare industry is turning to digital technologies for sustainable solutions. This paper explores the integration of wearable technology and artificial intelligence (AI) as a transformative approach to managing chronic diseases. Wearables, equipped with sensors and connectivity features, continuously collect real-time physiological data such as heart rate, glucose levels, and physical activity. When combined with AI-driven analytics, this data becomes a powerful tool for early detection, personalized treatment, and continuous monitoring. The paper examines the core technologies underpinning wearable-AI synergy, reviews current applications across chronic disease domains, and evaluates ethical, regulatory, and operational considerations. Additionally, it outlines challenges such as data fragmentation, user adherence, and algorithmic transparency. Future innovations like federated learning, smart fabrics, and explainable AI hold immense promise in improving the scalability and reliability of these systems. Through a comprehensive analysis, this study underscores how wearable and AI integration can revolutionize chronic disease care, shifting healthcare from reactive treatment to proactive, data-driven intervention.

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AI, Chronic Diseases, Healthcare

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An autoimmune response against two hemidesmosomal proteins in the dermal-epidermal junction, BP180 and BP230, is the characteristic of bullous pemphigoid (BP), a blistering dermatosis. BP180 is an extracellular domain-bearing transmembrane glycoprotein, while BP230 is intracellularly bound and associated with the hemidesmosomal plaque. Most BP patients possess autoantibodies that bind to the noncollagenous 16A domain (NC16A) of BP180, an immunodominant region of BP180 that is extracellularly located adjacent to the protein's transmembrane domain. Autoreactive T and B cell responses to BP180 in BP patients have been reported. In mice, a bullous skin disease with a very close analog of human blood pressure is induced by passive antibody transfer to the mouse BP180 ectodomain. In this model of animals, the formation of lesions relies on mast cell degranulation, complement activation, and neutrophil and eosinophil recruitment. Dermal-epidermal separation occurs in cryosections of human skin when autoantibodies against BP180 are co-incubated with leukocytes. Granulocytes secrete proteinases that cause loss of cell-matrix adhesion. The development of new treatment strategies for BP may be facilitated by our increasing knowledge of its pathophysiology.

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Autoimmunity . Bullous pemphigoid . BP180 . BP230 . Hemidesmosome

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Artificial intelligence (AI) is revolutionizing drug discovery, accelerating the identification of novel therapeutics and redefining pharmaceutical research. As we approach 2025, AI-driven innovations particularly generative AI and advanced data analytics are not only expediting drug development but also enabling precision medicine and reducing costs. This transformation is fueled by deep learning models capable of designing entirely new molecular structures, predictive analytics that optimize clinical trials, and federated learning that enhances collaborative drug discovery while maintaining data privacy.

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Drug discovery, pharmaceuticals, Machine learning, innovations, Quantum computing, analytics, biomarker, Regulatory, Ethical

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Hybrid neural-fuzzy systems are used for gender estimation of dental panoramic radiographs, coupling methods such as convolutional neural net- works (CNNs), fuzzy logic, and contrast-limited adaptive histogram equaliza- tion. Stratified cross-validation is performed for analysis after pre-processing on a dataset comprising 947 images. The most successful approach of SVM augmented using expanded fuzzy rules comes with accuracy rating of 94.93% and finds a better success than traditional models. Results highlight the mer- its of hybrid strategies in maintaining accuracy and interpretability in dental diagnostics and forensic purposes.

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eural networks, fuzzy logic, dental radiography, gender classification.

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Echoes of the Past: Trauma, Memory, and the Spectral in Gabriel García Márquez and Isabel Allende’s Magical Realist Works

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Collective memory , magical realism, predictive trauma

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The research paper presents the development of a 17DOF Humanoid Robot, titled "Humanoid 17DOF - Precision Robot with Advanced Servo Control," designed to achieve human-like movements with high precision. Utilizing an ESP32 microcontroller, 17 MG995 servo motors, and a PCA9685 servo controller, the robot is controlled wirelessly via a Bluetooth-enabled joystick. The project focuses on implementing Inverse Kinematics (IK) for coordinated joint movements, enhancing manoeuvrability and natural motion. The proposed system adopts an Embedded C programming approach with real-time motion control and robust power management using a 7.4V lithium polymer battery. Extensive testing demonstrates the robot's responsiveness, stability, and efficiency in performing complex tasks. The study highlights potential applications in research, education, and human-robot interaction, contributing to advancements in affordable, versatile humanoid robotics.

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17 Degrees of Freedom (DOF), Precision Robotics, Advanced Servo Control, ESP32 Microcontroller, PCA9685 Servo Controller, Human Robot Interaction, Wireless Control, and PWM Signal Synchronization.

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Customer satisfaction is a critical factor in the e-commerce industry that affects consumer behaviour, brand loyalty, and market dynamics. This study looked at the main factors influencing customer satisfaction, including customer service, return and refund policies, delivery speed, and product quality. This study provides a thorough analysis of consumer expectations and experiences with online shopping by comparing survey data from 148 participants with previous research. According to the findings, customer satisfaction was largely dependent on delivery speed (39.5%) and product quality (46.7%). Even though e-commerce platform satisfaction is generally moderate (3.5/5), problems with payment security (18.9%) and return/refund procedures (26.8%) still exist. Additionally, 34.2% of respondents said they were unhappy with the customer service they received. The knowledge gained from this study will help businesses improve their plans to satisfy evolving consumer needs.

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Customer Satisfaction E-commerce Industry Online Shopping Consumer Behavior Digital Retail Product Quality Delivery Service Online Customer Experience Pricing Strategy Return and Refund Policy User Interface Customer Support Indian E-commerce Market Online Buying Trends Consumer Expectations

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