Volume 13 | Issue 4 |

 Abstract

The Citizenship (Amendment) Act, 2019 (CAA) has indeed ignited within it an ethical and also legal debate within India's constitutional framework. Citizenship is very fast-tracked specifically for Hindu, Sikh, Buddhist, Jain, Parsi, and Christian migrants from Pakistan, Bangladesh, and Afghanistan who entered India before December 31, 2014, because of religious persecution since this legislation amends the Citizenship Act of 1955. However, in the instance when they exclude certain Muslims and certain persecuted groups like Rohingya Muslims and Sri Lankan Tamils, they seriously concern the people in regard to religious discrimination, constitutional validity, and the secular character that the Indian state has. Criticism about the Act exists because of potentially violating Articles 14, 15, and 21 of the Indian Constitution since those do protect life and personal liberty, those do prohibit discrimination, and those ensure equality before any law. While the government defends entirely the Act because it addresses religious persecution in certain Islamic states in such a humanitarian manner, critics argue its quite selective inclusion of certain communities as well as countries does not classify reasonably under Article 14. The CAA has been greatly commended by people, from an ethical point of view, for protecting persecuted minorities, and it has also been strongly condemned for weakening India's pluralistic values. Public discourse and also judicial scrutiny do continue to be fueled by each of the constitutional, of the legal, and of the ethical dimensions of the CAA.

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2864

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Creative Commons Attribution 4.0 and The Open Definition

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This project details the development of a fully offline, privacy-centric voice assistant, utilizing a fine-tuned Large Language Model (LLM). By employing parameter-efficient fine-tuning (QLoRA), we adapt a pretrained model, such as Mistral-7B or Phi-2, on custom instruction-tuning datasets tailored for conversational tasks. The resultant model is optimized and quantized for local inference using llama.cpp and is hosted through OpenWebUI, a user-friendly interface for LLM interaction. Speech-to-text conversion is facilitated by Whisper, while Coqui TTS provides natural voice responses. The system operates entirely on local hardware, thereby ensuring user data remains private without dependence on cloud services. This architecture offers a scalable and customizable platform for intelligent, voice-first human-computer interaction in offline or low-connectivity environments

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 Keywords

Offline VoiceAssistant, Large Language Models(LLM), QLoRA,Speech Recognition, Text-to-Speech (TTS), Privacy-Preserving AI

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In this review, the information has been provided on the research studies and investigations which were reported earlier by the researchers. The main focus is based on deoxyribonucleic acid (DNA) studies of several tree species. It has been observed that isolation of pure DNA makes the base of those molecular studies which completely rely over the quality of DNA. Hence, several kind of DNA isolation protocols are available in the research world. Many are based on readily available kit methodology, modified cetyltrimethylammonium bromide (cTAB) protocols of genomic DNA isolation, etc. The major objective of these methods is to achieve quality DNA with adequate amount for best outcomes in the molecular studies.

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DNA Isolation, DNA markers, Molecular studies, Tree species

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This study investigates the effect of partial replacement of natural coarse aggregate with recycled coarse aggregate (RCA) on the compressive strength and workability of concrete. With the increasing demand for sustainable construction materials, the utilization of construction and demolition waste has become a critical strategy for reducing the environmental footprint of the construction industry. In this experimental research, RCA was used to replace natural aggregate at three levels: 10%, 20%, and 30%, by weight. Additionally, silica fume was incorporated at a fixed dosage of 10% by weight of cement to enhance the pozzolanic activity and improve overall concrete performance. Concrete mixes were prepared using a fixed mix ratio of 1:2:4 (cement: sand: aggregate) with a constant water-cement ratio. For each replacement level, mixes were prepared with and without silica fume, resulting in eight different mix combinations. Standard 150 mm concrete cubes were cast, cured for 28 days, and tested as per IS 516:1959 for compressive strength. Slump tests were also conducted to assess the workability of the mixes. Results showed that compressive strength decreased with increasing RCA content in the absence of silica fume. However, mixes containing silica fume showed a significant improvement in strength, recovering up to 90% of the control mix's compressive performance. Workability slightly declined with RCA content but improved with silica fume addition. The study concludes that up to 30% RCA replacement, especially when combined with silica fume, can be effectively used in structural concrete, promoting sustainable practices without significantly compromising strength or workability

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 Keywords

Recycled aggregate, concrete, OPC, Silica Fumes, Compressive strength

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Creative Commons Attribution 4.0 and The Open Definition

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ROOTSENSE offers a proactive IT support model using AI and predictive analytics to minimize system disruptions. By integrating machine learning, real-time monitoring, and automated diagnostics, it preempts failures and boosts operational efficiency. Key features include anomaly prediction, automated ticketing, and a dashboard-driven REI module for performance enhancement. Empirical results show improved reliability and responsiveness, marking a shift toward self-healing IT infrastructures.

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Predictive Analytics, Real-Time Monitoring, Proactive IT Support, Root Cause Analysis, Automated Ticketing, Machine Learning.

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This research PAPER contains an in-depth strategic analysis of Elon Musk, arguably the most prominent entrepreneur of the 21st century, alongside the various companies he actively manages. It takes into account the Musk's strategic vision and leadership, considering the myriad of industries he has influenced from electric vehicles and aerospace, to social media, neural technologies, and beyond. The study captures insights from his key companies, including but not limited to, Tesla, SpaceX, Neuralink, and The Boring Company to establish the business and operational strategies that are driving these ventures forward. Using some strategic management frameworks such as SWOT, leadership theories, and business model canvas, the paper analyzes what key strengths, weaknesses, opportunities, and threats confront the enterprises in constantly changing international markets. The study also looks at Musk's leadership practices which include but are not limited to tolerance of risks, innovation, engagement with the public, and assesses the implications on the performance of the respective businesses and the society at large. In addition, it examines the overall financial performance and market context of his companies, with full consideration of the controversies and debates on sustainability issues.

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Elon Musk , Strategic Leadership, Innovation Strategy, Entrepreneurial Vision , Business Model Analysis ,Tesla ,SpaceX ,Neuralink, The Boring Company, X (formerly Twitter) ,Risk-Taking Entrepreneurship , SWOT Analysis, PESTLE Analysis , Transformational Leadership, Technology Disruption ,Sustainable Energy ,Space Exploration , Brain-Machine Interfaces ,Crisis Management , Financial Performance , Market Positioning ,Strategic Management, Future of Innovation, Corporate Sustainabil

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In this study, cobalt ferrite (CoFe2O4) nanocrystals were synthesized via a green, plant-mediated combustion route using Ocimum sanctum (Tulsi) leaf extract as a natural fuel and chelating agent. The methodology presents a sustainable alternative to conventional chemical synthesis by eliminating the need for toxic reagents, reducing energy consumption, and minimizing waste generation. The synthesized nanocrystals were systematically characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), UV-Vis diffuse reflectance spectroscopy (DRS), photoluminescence (PL) analysis, and Brunauer-Emmett-Teller (BET) surface area measurements. XRD confirmed the formation of a single-phase spinel structure with average crystallite size around 25 nm. Morphological analysis revealed spherical and porous particles with a uniform distribution. The nanocrystals exhibited a band gap of 2.12 eV, indicating good absorption in the visible region. Photocatalytic activity was evaluated using methylene blue (MB) dye under visible light irradiation, where more than 91% degradation was achieved within 90 minutes. The photocatalyst showed excellent reusability and structural stability over four successive cycles. The enhanced photocatalytic efficiency was attributed to a synergistic effect of narrow band gap, high surface area, and efficient charge carrier separation. This study demonstrates a green and efficient approach for developing spinel ferrite nanomaterials for environmental remediation.

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Spinel ferrite nanocrystals; Green synthesis; Cobalt ferrite; Photocatalytic degradation; Visible light; Tulsi extract; Wastewater treatment.

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The concept of sugarcane migrant workers is relevant to understanding the major socio-economic problems faced by these workers after they migrate to the sugarcane cutting fields. The socio-economic issues include wages, income, and savings. And what are the before-migration and after-migration wage rates? Is the wage rate increasing or not? All such questions have arisen. This study identifies the socio-economic problems faced by sugarcane workers at the workplace. To estimate the income earned during the sugarcane cutting period., This paper is based on the primary data collected by questionnaires and field studies. The researcher used Microsoft Excel for data analysis, and the Paired samples test was used for hypothesis testing comparing the results before and after sugarcane migration.

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Socio-economic problems, Sugarcane migrant workers, Seasonal migration, Before and after migration.

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Creative Commons Attribution 4.0 and The Open Definition

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Actinomycetes are a diverse group of Gram-positive bacteria known for their ability to produce a wide variety of bioactive secondary metabolites with significant medicinal properties. These microorganisms play a critical role in the development of antibiotics, anticancer agents, immunosuppressants, antioxidants, and anti-inflammatory compounds. Notably, Actinomycetes such as Streptomyces species have been the source of life-saving antibiotics like streptomycin, erythromycin, and vancomycin, which have revolutionized the treatment of infectious diseases. The ecological and evolutionary significance of Actinomycetes in producing these bioactive compounds for survival and competition further underscores their importance. Despite these advances made, several challenges such as difficulties in culturing, the rise of antibiotic resistance, and limited exploration of Actinomycetes in understudied environments limits its widespread use. This paper delves into the various therapeutic compounds produced by Actinomycetes, ranging from anticancer drugs like doxorubicin and bleomycin to immunosuppressants like rapamycin, demonstrate the far-reaching implications of Actinomycetes in modern medicine. A future research direction includes exploration of the underexplored species, optimizing metabolite production, and developing combination therapies. Overall, Actinomycetes remain an invaluable resource for combating emerging health challenges, and continued investment in research and innovation is essential to harness their full potential for medical and industrial applications.

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Actinomycetes; Medical; Industrial; cancer; diabetes

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Creative Commons Attribution 4.0 and The Open Definition

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This study focuses on applying and comparing various ML algorithms Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF) to classify different types of network attacks such as DoS, Probe, R2L, and U2R. The CICIDS2018 dataset, known for its modern traffic features and attack diversity, was used as the benchmark. The dataset underwent preprocessing and feature selection to optimize model performance. Among the evaluated models, Random Forest outperformed others, achieving 98.76% accuracy . The results validate the effectiveness of ensemble learning methods in intrusion detection, offering a reliable solution for real-time cyber security monitoring.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Intrusion Detection System, CICIDS2018, Random Forest, Cyber security, Machine Learning, Network Traffic Analysis

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Creative Commons Attribution 4.0 and The Open Definition