Volume 21 Issue 3 2026

Kashmir witnessed the longest and strongest rebellion after the killing of Burhan Wani, militant leader of Hizbul Mujahideen, on 8th July 2016. The number of people at his funeral procession sent a direct message to the authorities that not everything was right in Kashmir. Longest curfew, suspension of internet services and banning of local press hinted strongly at media gagging by the government. During the period, curfew passes issued to the local journalists were not honoured by the security forces hence, curtailing their movement. While Kashmir based local media was extensively reporting about use of pellet guns by the security forces to control the protesting people, the New Delhi based national media was focusing more on attacks on security forces by the protestors. New Delhi based Indian national media emphasised on the government’s security centric line which asserted that Kashmir is an integral part of India and all efforts were made to prevent Pakistan sponsored terrorism in Kashmir. Indian National media seldom reported human rights violations in Kashmir while local Kashmir based media and international media were vocal about such issues. The focus of this study was to find out whether New Delhi based newspaper reported what happened on the ground in Kashmir and also to analyse how the reporting of New Delhi based media was different from reporting of Kashmir based local media.

Writing is an extremely complex cognitive activity in which the writer is required to demonstrate control of variables simultaneously. Strong writing skills may enhance students' chances for success. Writing is an essential factor of language. Good writing skills are needed for all the students in order to accomplish their educational and employable requirements. Process Approach stresses writing activities which move learners from the generation of ideas and the collection of data through to the publication of a finished text. Process approach is learnercentered in which learners’ needs, expectations; goals, learning styles, skills and knowledge are taken into consideration.

Cancer immunotherapy involves the use of therapeutic modalities that determine a manipulation of the immune system by using immune agents such as cytokines, vaccines, cell therapies and humoral, transfection agents. Immunotherapy of cancer has to stimulate the host’s anti-tumour response by increasing the effector cell number and the production of soluble mediators and decrease the host’s suppressor mechanisms by inducing tumour killing environment and by modulating immune checkpoints. Immunotherapy seems to work better in more immunogenic tumours. Compared with previous standards of care (including chemotherapy, radiotherapy, and surgery), cancer immunotherapy has brought significant improvements for patients in terms of survival and quality of life. Immunotherapy has now firmly established itself as a novel pillar of cancer care. In this review we will see the evolution of immunotherapy

In traditional cloud storage systems, all data is stored in the cloud server, which could lead to major issues including key misuse and privacy data leaking. However, because the traditional cloud storage system depends on centralized storage, a single point of failure could cause the system to fail. With the advancement of blockchain technology, decentralized storage has become more well-known. Decentralized storage can overcome the problem of a single point of failure in typical cloud storage systems while also providing a variety of benefits over centralized storage, including low cost and fast throughput. In this work, focus on data storage and sharing in decentralized storage systems and propose a framework that includes IPFS, an encryption technique, and blockchain technologies. The data holder can distribute secret keys to data users and encrypt shared data location by Blockchain network. In this research contribute to data integrity verification in the blockchain network.

In recent years, climate change has posed a serious threat to us. Women experience climate variability differently and have varying adaptive capacities to the risks it poses. Therefore, this study examines different gender-sensitive adaptation strategies to tackle the severe effects of climate change in two agrarian regions of North India: Western Uttar Pradesh and Western Bihar. Drawing on primary data collected from 1,600 households across 64 villages, the findings reveal that women’s participation in adaptation initiatives remains limited despite policy interventions such as the Mahila Kisan Sashaktikaran Yojana and Self-Help Group-linked credit schemes. Despite some progress in adaptation strategies in terms of access to credits, cooperatives, and Community-Based Resource Management (CBRM), the majority of respondents viewed these strategies as ineffective, citing insufficient training, weak institutional coordination, and limited community engagement. The study concludes that current strategies suffer from a disconnect between policy intentions and lived realities, necessitating a shift toward more inclusive, context-specific execution to ensure equitable climate resilience.

Objectives: This study aims to produce a literature-based ethnographic synthesis of death-related beliefs and ritual practices among nomadic Yörük communities in Anatolia. It seeks to (i) map recurring patterns across the literature, (ii) classify the practices under analytically coherent thematic clusters, and (iii) identify under-researched dimensions to guide future interdisciplinary work in folklore, anthropology, and intangible cultural heritage studies. Methods: The research employed qualitative document analysis and qualitative content analysis within a secondary ethnography/meta-synthesis design. Postgraduate theses, peerreviewed articles, books, and book chapters were retrieved through multi-source searches (e.g., YÖK National Thesis Center, Google Scholar, and academic indexes). Publications without direct ethnographic or descriptive data on death-related practices were excluded. Included texts were coded through an iterative process (open coding → thematic clustering → category refinement) and organized under five theme codes: (4.1) ritual time and mourning calendar, (4.2) food and charity practices, (4.3) mobility and burial space, (4.4) gendered roles, and (4.5) sacred-ecological beliefs. Results: The synthesis demonstrates that the literature is most concentrated on ritual time (3rd–7th–40th–52nd-day cycles) and food/charity practices (funeral meals, halva/lokma distribution), followed by gendered roles largely documented through lament performance and domestic labor in mourning. Nature- and ecology-related beliefs (trees/plants, animal omens, sacred ecology) are present but are frequently treated descriptively, with limited cosmological or cultural-ecological interpretation. By contrast, mobility-based burial strategies and the relationship between migration routes, cemeteries, and spatial belonging remain comparatively underrepresented, indicating a notable gap in the field. Conclusion: Yörük death rituals constitute a multi-layered cultural system that reorganizes social life through time, food-based solidarity, gendered labor, ecological symbolism, and spatial mobility. Beyond “folkloric detail,” these practices function as mechanisms of collective memory, social cohesion, and cultural continuity, aligning with community-based intangible cultural heritage perspectives. The study contributes by offering a thematic map and comparative analytical model for the literature and by outlining priority areas for future research, particularly on mobility-based burial logics, intergenerational mourning experiences, and cross-cultural comparisons across nomadic Turkic communities.

Vyom Try-On is an AI-based virtual fitting room implemented as a client-side single-page application developed in React 19 and TypeScript. Based on generative try-on technology, the system delivers interactive garment try-on by providing a seamless integration of inputs from a webcam or photo upload, processing of the image with HTML5 Canvas, and connection to the Gemini-2.5-flash-image model through a secure proxy layer. The architectural design of the application is centered around a user-friendly e-commerce platform consisting of a product catalog, shopping cart, user signup/login, and an administrator dashboard. The application utilizes Context API for state management and a localStorage-based Object-Relationship mapping library for data persistence without a dedicated backend database. In this paper we describe the end-to-end development and deployment of the application including system design, implementation details, empirical assessment with quantitative metrics, user experience evaluation, and detailed reproducibility documentation. Reporting of methodology, simulated experimental results, user-centric evaluation, and open-source resources are provided in the paper with an aim to disseminate practical knowledge about virtual fitting room systems and reproducible applied machine learning for online retail.

Water contamination by pathogenic microorganisms and the increasing problem of antimicrobial resistance (AMR) pose serious threats to public health and environmental sustainability, highlighting the need for efficient visible-light-driven antibacterial materials. In this study, a Zinc Oxide/Biochar (ZnO/BC) nanocomposite was synthesized, where biochar derived from onion peel waste through pyrolysis served as a support for ZnO nanoparticles prepared by the sol–gel method. The materials were characterized using X-ray diffraction (XRD), UV–Visible diffuse reflectance spectroscopy (UV– Vis DRS), and Fourier transform infrared spectroscopy (FTIR), confirming the formation of crystalline ZnO with a hexagonal wurtzite structure and its dispersion over the porous biochar surface. Band gap analysis showed a reduction in band gap energy for the ZnO/BC nanocomposite compared to pure ZnO, indicating improved visible-light absorption. Photocatalytic studies under sunlight using crystal violet dye revealed that the ZnO/BC nanocomposite exhibited higher photocatalytic efficiency than pure ZnO due to improved charge separation and enhanced generation of reactive oxygen species (ROS). However, no antibacterial activity against E. coli was observed under normal conditions. These findings suggest that the ZnO/Biochar nanocomposite is a promising eco-friendly material for visible-lightdriven photocatalytic applications in water purification and environmental remediation.

The demand for energy has been rising quickly in recent years. Innovative approaches to energy conservation are being put forth. To reduce electricity costs and increase the return on investment for solar module purchases, an environmentally friendly system should be developed. When compared to other conventional sources, the photovoltaic industry can be more competitive since its systems are more cost-effective and efficient. Irradiation and panel temperature present greater difficulties for PV modules due to their instability. As a result, PV panel electricity generation is inherently unstable. To harvest the most power from PV modules, the Maximum Power Point Tracking (MPPT) technology is employed. To maximize power extraction from PV modules or strings of PV modules, a DC-to-DC converter has been utilized to match the impedance between PV modules or arrays of modules. Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Perturb and Observe (P&O) techniques have all been used in this work. These algorithms' MPPT performance has been confirmed in the MATLAB/Simulink environment.

As a highly controversial yet ethically philosophical classic work by Nabokov, Lolita has seen its cross-era ideological value become increasingly prominent through multiple film and television adaptations. Based on the dual support of literary ethical criticism theory and community theory, and closely focusing on the educational context of forging a strong sense of the Chinese national community in universities in Northern Anhui, this paper re-excavates the contemporary ethical significance and diverse community connotations of the work. From the four core dimensions of family, identity, emotion and destiny communities, it analyzes the complex process of the construction and deconstruction of communities by Lolita in different ethical choices: the legal family community formed by her and Humbert based on natural will, the special identity community constructed by her and peers based on growth cognition, the diverse emotional community established by virtue of the Sphinx factor and free will, and the opposing destiny community formed by Humbert and Quilty due to emotional confrontation. All of these provide a vivid literary mirror for understanding the individual generation mechanism of national community consciousness. On this basis, the paper further explores the integration path of the community narrative in Lolita and ideological and political education in Northern Anhui universities, and provides theoretical references and practical plans for creating a characteristic educational model of "Literature+Ideological and Political Education" and forging students' sense of the Chinese national community.

The demand for power has increased the necessity for renewable energy sources and the challenge of generating clean, green electricity. Relying solely on solar energy during daytime has prompted individuals to seek improved and more consistent prospects in the wind sector. This research investigated the potential expansion of the Indian wind sector and formulated a research hypothesis that offers a comprehensive evaluation of the challenges and issues linked to grid integration for wind turbines. This research explored the evolution of converters in wind turbines and the issues that have arisen in them over the past years. It also explored the need for reactive power in WEC and the role of the FACT device. The final section reviews and analyzes the techniques for monitoring the Maximum Power from WEC. The function of algorithms is examined as they proved useful for monitoring Maximum Power while enhancing power quality and stability

The present study was conducted to evaluate and compare the pharmaceutical quality of generic and branded Vildagliptin 50 mg tablets available in the market by performing various in-vitro quality control tests in accordance with Indian Pharmacopoeial standards. The investigation included the assessment of key physical parameters such as tablet thickness, hardness, friability, weight variation, disintegration time, and dissolution profile. In addition, the drug content was determined using UV–visible spectrophotometric analysis. Vildagliptin is a selective dipeptidyl peptidase-4 (DPP-4) inhibitor widely used in the treatment of Type 2 diabetes mellitus. The purpose of this evaluation was to determine the pharmaceutical equivalence between the generic and branded tablet formulations. The experimental findings revealed that both formulations complied with the pharmacopeial quality requirements for the tested parameters. Minor variations were observed in the dissolution and disintegration characteristics, with the branded product showing slightly faster performance compared to the generic formulation. Overall, the results suggest that generic Vildagliptin tablets can be considered effective and economical alternatives to branded products without significant compromise in quality or therapeutic performance. However, further in-vivo bioequivalence studies are recommended to establish a direct correlation between in-vitro findings and clinical efficacy.

This paper shows the concept & application of data structure from how the computer solves the practical problems through program design, based on the analysis of the connection & difference between data structure & algorithm, the general principles followed by data structure are given, & further illustrates the practical significance of data structure application through examples. Data structure is the logical structure of data, the physical storage structure & the encapsulation of algorithm. This paper discusses how to apply data structure to solve the practical problems of non – numerical calculation from these three aspects & illustrates the application of data structure with a concrete example.

This paper presents an AI-Powered College Inquiry Agent designed to handle real-time inbound calls from prospective students and stakeholders in an automated and efficient manner. Incoming calls from customers are first received by the AI agent, which orchestrates the end-to-end interaction workflow. Audio data is sourced and streamed using the integration of Twilio and LiveKit, enabling reliable and low-latency voice communication. The incoming speech is processed through a speech-to-text module to generate realtime transcriptions, which are then passed to a response management module.The response management module leverages a centralized knowledge base combined with AI logic to generate accurate, consistent, and context-aware responses to college-related inquiries such as admissions, courses, and general information. In cases where the agent is unable to confidently answer a query, the system automatically forwards the call details and transcribed conversation in text format to the respective department head for further review. If the head identifies the inquiry as relevant or high-priority, they may directly contact the customer to continue the interaction. The generated responses are converted into natural-sounding speech using a text-tospeech engine and delivered to the customer in real time. This approach ensures continuity of communication while reducing dependency on live human intervention. The proposed system enhances inquiry handling efficiency and improves decision-making in academic communication workflows.

Deep learning models have demonstrated strong performance in automated chest radiograph interpretation; however, their deployment in clinical practice is hindered by severe class imbalance, label uncertainty, and poorly calibrated predictive confidence. In this work, we present a comprehensive uncertainty-aware analysis of thoracic pathology classification on the CheXpert dataset using Bayesian deep learning methods, including Monte-Carlo Dropout, Mean-Field Variational Inference, Stochastic Weight Averaging Gaussian (SWAG), and Bayesian Deep Ensembles built on modern convolutional backbones. We systematically evaluate these approaches across 13 clinically relevant pathologies using discriminative performance (AUROC), probabilistic calibration (Expected Calibration Error and Brier score), and a clinically motivated risk metric that jointly accounts for annotation uncertainty and predictive error. Our results show that Bayesian deep ensembles consistently achieve the highest AUROC, while Mean-Field Variational Inference and MC Dropout often yield better-calibrated predictions. We further demonstrate that pathologies characterized by high annotation ambiguity, such as pneumonia and atelectasis, exhibit elevated clinical risk across methods, underscoring the necessity of uncertainty-aware evaluation. Together, these findings highlight the trade-offs between accuracy and calibration in Bayesian inference strategies and emphasize the importance of reliable uncertainty estimation for safe and clinically meaningful deployment of deep learning systems in medical imaging.

The deployment of Deep Learning models on resource-constrained microcontrollers requires significant reduction in model size and computational complexity. This paper presents an updated comparative analysis of Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) applied to Keyword Spotting (KWS) tasks, incorporating critical advances from 2025-2026 research. Using the Google Speech Commands dataset, we evaluate the performance of Depthwise Separable CNNs (DS-CNN) and BC-ResNet architectures under both quantization paradigms. Recent findings demonstrate that while PTQ remains the dominant fast-deployment route—especially when paired with hardware zero-skipping techniques—fixed-point QAT (FXP-QAT) now enables sub-8-bit models (down to 4- bit) with significant latency and accuracy gains. Notably, 8-bit FXP-QAT models achieve 4–6% improvement in relative false discovery rate at fixed false reject rate compared to full-precision models, together with a 68% reduction in execution time. We further analyze the emerging trend of hardwarealgorithm co-design, where architectures like BCResNet are optimized for ultra-low-power accelerators with hybrid time-feature-frequency-domain zeroskipping, achieving energy consumption as low as 36 nJ per decision. This work synthesizes 98 papers from 2025-2026 to provide a comprehensive view of the current state-of-the-art in quantized KWS for edge deployment.