International Journal of Scientific Research in Science and Technology
International Journal of Scientific Research in Science and Technology (IJSRST) is peer-reviewed, online international journal published Bimonthly by Technoscience Academy. IJSRST is a highly-selective journal, covering topics that appeal to a broad readership in various branches of Science and Technology and related fields. The journal has many benefits, all geared toward strengthening research skills and advancing academic careers. Journal publications are a vital part of academic career advancement. To maintain a high-quality journal, manuscripts that appear in the IJSRST Articles section have been subjected to a rigorous review process. This includes blind reviews by one or more members of the international editorial review board, followed by a detailed review by the IJSRST editors. Email addresses for submitting articles are editorijsrst@gmail.com, editor@ijsrst.com JOURNAL INFORMATION Title : International Journal of Scientific Research in Science and Technology Frequency : Bimonthly Online ISSN : 2395-602X | Print ISSN : 2395-6011 Publisher : Technoscience Academy [ https://technoscienceacademy.com ] Refereed : Peer Review Copyright : Technoscience Academy, India Starting Year : 2014 Subject : Science and Technology Language : English Distribution : Open Access Publication Format : PDF & HTML -> Online Document Identifier Type : DOI (https://doi.org/10.32628/IJSRST ) Email Id : editor@ijsrst.com, editorijsrst@gmail.com Mobile No. : +91-8160745806 (WhatsApp support only) Website : https://ijsrst.com
Impact of Financial Literacy on Investment Decision-Making among Young Professionals in India
This paper studies how financial literacy affects the investment decisions of young professionals in India by using only secondary data sources. The research takes information from official reports published by RBI SEBI OECD and NCFE. It examines how awareness and understanding of financial terms influence personal investment behavior. The study shows that people who know more about finance make better decisions in choosing safe and profitable investment options. They plan long term and avoid emotional or unplanned spending. Young professionals form a major part of the Indian working population and they directly influence the growth of the financial market. But many still lack the knowledge needed to use modern investment products like mutual funds stock market and systematic investment plans. The paper explains how formal education and government awareness programs can change this situation. The analysis also points out that financial literacy is not only about earning and saving but also about knowing how to manage risk and build wealth for the future. The study highlights that continuous learning and digital awareness programs help young professionals become confident and responsible investors. In the end the paper suggests that strong financial education is the key for developing a financially secure and self-reliant youth in India.
Copyright (c) 2025 International Journal of Scientific Research in Science and Technology https://creativecommons.org/licenses/by/4.0
Bacterial Consortia as Bioinoculants: Enhancing Soil Fertility and Crop Productivity through Synergistic Plant Growth-Promoting Mechanisms
The increasing reliance on chemical fertilisers has raised serious environmental and economic concerns, underscoring the need for sustainable alternatives in agriculture. Bacterial consortia-based bioinoculants represent a promising, eco-friendly strategy to enhance soil fertility and crop productivity. In this study, agriculturally beneficial bacteria were isolated from soil and screened for key plant growth-promoting traits, including mineral solubilization (phosphorus, potassium, and zinc), siderophore production, indole-3-acetic acid (IAA) production, extracellular enzymatic activity, and antimicrobial potential. Four efficient bacterial isolates were identified through 16S rRNA gene sequencing and were closely related to Bacillus subtilis, Pseudomonas fluorescens, and Azospirillum brasilense. Compatible isolates were combined to formulate a bacterial consortium and evaluated under pot conditions. The consortium significantly enhanced plant growth parameters, including shoot length, root length, and biomass, compared to uninoculated controls and individual isolates. The results demonstrate that bacterial consortia exhibit synergistic effects that improve nutrient availability, promote plant growth, and suppress potential pathogens. This study highlights the potential of bacterial consortium-based bioinoculants as sustainable alternatives to chemical fertilizers for improving soil health and crop productivity.
Copyright (c) 2026 International Journal of Scientific Research in Science and Technology https://creativecommons.org/licenses/by/4.0
Influence of Azolla filiculoides on Dairy Cow Milk Production and Improvements in Macro Nutrients and Milk Quality Indicators
The dairy industry in India plays a pivotal role in rural development, employment, and nutritional security, making the quality of milk produced a key factor for both consumer health and economic viability. Enhancing milk quality, particularly in terms of fat, protein, and casein content, is essential for the production of high-value dairy products. Azolla filiculoides, a nutrient-rich and sustainable feed supplement, has shown potential to significantly improve these milk quality parameters. In a study involving 20 lactating HF crossbreed cows, Azolla supplementation led to notable increases in milk yield, with improvements ranging from 13.70% to 30.89%. Milk fat content ranged from 2.49% to 4.98%, protein content increased from 2.63%-3.24% to 3.16%-3.98%, and casein levels improved from 2.01%-2.3% to 2.66%-3.04%. Minimal changes were observed in lactose and galactose levels, indicating that while Azolla enhances fat and protein content, it does not significantly alter milk sugars. These results highlight A. filiculoidesas an effective, sustainable feed supplement that enhances milk quality, supporting the dairy industry's objectives of improving nutritional outcomes and economic sustainability.
Urinary Tract Infection and Medicinal Importance of Anacardium Occidentale
Urinary tract infections (UTIs) are common infectious diseases affecting the urinary system, with a higher incidence in women and children. They are mainly caused by pathogenic microorganisms such as Escherichia coli, Klebsiella spp., Proteus spp., and Staphylococcus spp. The growing problem of antimicrobial resistance has increased interest in medicinal plants as alternative or supportive therapies. Anacardium occidentale L. (cashew) is a traditionally important medicinal plant known for its wide range of therapeutic properties. Different parts of the plant, including leaves, bark, nuts, and shells, contain bioactive compounds such as anacardic acids, flavonoids, tannins, and phenolic compounds. These constituents possess antimicrobial, anti-inflammatory, antioxidant, and wound-healing activities. Scientific studies have reported that extracts of A. occidentale show inhibitory effects against several uropathogenic bacteria, indicating its potential usefulness in the management of urinary tract infections. This abstract emphasizes the causes and significance of UTIs and highlights the medicinal importance of Anacardium occidentale as a natural source for developing effective plant-based therapies.
Efficient Host-Based Intrusion Detection for IoT Using Lightweight ML Models and Fuzzy Ranking
The rising integration of IoT devices into various applications has amplified security risks, particularly in resource-limited setups with constrained computing capacity and storage. Standard intrusion detection approaches often prove inadequate for these contexts owing to their intensive resource needs. This work proposes a host-based intrusion detection system customized for IoT infrastructures, harnessing compact and adaptable machine learning strategies. The developed system examines diverse classifier groups, encompassing tree-oriented models like Decision Tree and Random Forest, boosting-oriented techniques such as LightGBM and XGBoost, support vector machines including LinearSVC, and linear-based classifiers encompassing Logistic Regression, Stochastic Gradient Descent, Passive-Aggressive methods, and Perceptron algorithms. To identify the optimal detection algorithms suited to varied IoT application environments, a multi-factor decision framework integrating TOPSIS, VIKOR, MOORA, and WASPAS ranking methodologies is utilized. Outcomes from trials reveal that linear classifiers and streamlined tree models deliver strong detection performance alongside brief training periods and lowered processing demands.The solution achieves a top accuracy rate of 98.7%, with training times under two seconds for the majority of algorithms, yielding roughly 15-20 times enhancement in efficiency relative to typical ensemble-driven intrusion detection systems.
AI-Driven Trade Promotion Optimization and Financial ROI in CPG Firms: A Thematic and Analytical Review
Trade promotion accounts for a substantial proportion of marketing expenditure within the consumer-packaged goods (CPG) sector yet historically suffers from inefficiencies and opaque return on investment (ROI). This study presents an evidence-informed thematic and analytical synthesis of artificial intelligence (AI)-driven Trade Promotion Optimization (TPO), examining its financial and operational implications. The review traces the evolution from traditional promotion management to AI-enabled predictive systems integrating machine learning, pricing optimization, and enterprise analytics. A structured AI-Driven Trade Promotion Value Realization (AI-TPO-VR) framework is introduced to link data infrastructure, algorithmic intelligence, operational integration, and measurable financial outcomes. Analytical modeling formalizes ROI estimation through incremental profit, cost savings, and inventory efficiency metrics. The findings indicate that AI enhances forecasting precision, reduces promotional leakage, improves margin performance, and strengthens cross-functional coordination. However, challenges related to data governance, organizational transformation, and ethical AI deployment remain critical determinants of success. The study concludes with strategic recommendations and outlines future research directions toward autonomous, prescriptive trade promotion systems.
Copyright (c) 2024 International Journal of Scientific Research in Science and Technology https://creativecommons.org/licenses/by/4.0
Integrated Supply Chain–Finance Optimization Using Mixed Integer Programming: A Comprehensive Analysis
This study develops an integrated Mixed Integer Programming (MIP) framework for simultaneous optimization of supply chain design and financial performance. Unlike traditional models that decouple operational and financial decision-making, the proposed Integrated Supply Chain–Finance Optimization (ISFO) framework embeds Net Present Value (NPV), working capital constraints, and financial risk measures directly into strategic and tactical supply chain optimization. A multi-objective formulation enables structured analysis of profitability–risk trade-offs, while scenario-based stochastic programming captures demand, supply, and financial uncertainty. The results demonstrate that operational design decisions significantly alter liquidity exposure, capital structure, and long-term firm value. The study contributes a unified modeling architecture that enhances cross-functional integration between operations and finance, offering both theoretical advancement and practical decision-support relevance for industrial-scale supply chains.
Integrated supply chain–finance optimization
Stochastic programming under uncertainty
Synthesis of Pb(II) selective Cation Exchanger Nanocomposite and its Application in Environmental Pollution Remediation
The uncontrolled dissemination of toxic heavy metals and other pollutants into the water bodies is a major concern that demands immediate action to protect the environment. In the present endeavour, a polyaniline based nanocomposite polyaniline Tin(IV)iodophosphate has been synthesized by the sol-gel method and its practical utility in environmental pollution remediation has been studied. The composite material found to possess an ion exchange capacity of 1.72 meq g-1 for Na+ ions. The characterization of the material using FTIR and SEM shows the formation of composite with characteristic functional properties and uniform surface morphology. The agglomeration of inorganic moiety into the polymer matrix offers pores foe sorption of toxic heavy metal ions. The selectivity of the synthesized nanocomposite towards Pb2+ ion has been utilized in the selective removal of Pb2+ from industrial effluent. The selective removal of the contagious metal ion from the wastewater reflects the potential of the synthesized nanocomposite in environmental pollution remediation.
Effects of Hazardous Waste on the Environment and Strategic Management Approaches
Hazardous waste has emerged as a significant global issue due to its detrimental impact on human health and the environment. Biomedical laboratories frequently handle hazardous and infectious materials, which contributes to the increasing presence of harmful substances in the ecosystem. These wastes encompass solid, liquid, sharp, and pathological materials. Workers in healthcare, agriculture, and fisheries are especially at risk of exposure to hazardous biological substances. Although the Government of India has recently implemented stricter and more systematic measures in response to public concern about hazardous waste production and unsafe disposal, effective waste management practices are still lacking. To avoid the build-up of biohazards in the environment, waste produced from hazardous activities must be treated and disposed of through specialized methods. This review also aims to raise awareness among personnel in relevant sectors and promote the development and implementation of effective hazardous waste management and mitigation strategies.
Algebra of Functions Having Limit : A Limit Algebra
Let X be a compact Hausdorff topological space. Consider the set of all functions from X to K(R or C) which has limit at each point of X. Then functions of this set are bounded under supremum norm and also forms a Banach algebra.
Phytochemical Profiling and Biological Assessment of Lantana camara Extracts: In Vitro Evaluation of Cytotoxicity and Anti-inflammatory Activities
The present study investigates the phytochemical composition and in vitro biological activities of solvent-specific extracts of Lantana camara. Petroleum ether, methanol and aqueous extracts were prepared using Soxhlet and microwaveassisted extraction methods, respectively, and characterized by FT-IR, and GC–MS analysis. The anticancer potential was evaluated against human hepatocellular carcinoma (HepG2) cells using the MTT assay, while anti-inflammatory activity was assessed through the hemolysis inhibition (membrane stabilization) assay. The petroleum ether extract exhibited potent cytotoxic activity with an IC₅₀ value below 100 µg/mL, indicating strong anticancer potential. In contrast, the aqueous extract showed comparatively lower cytotoxicity but demonstrated significant antiinflammatory activity, with marked inhibition of protein denaturation and effective membrane stabilization. These findings highlight a clear solvent-dependent structure–activity relationship and support the therapeutic potential of Lantana camara as a source of complementary anticancer and anti-inflammatory agents.
Examining the Access to Resources and Gender Disparities in the Community
This study examined access to resources and gender disparities within communities in Ghana, with particular attention to how unequal access affects participation in development and sustainable outcomes. The study adopted a mixed method research approach using the explanatory sequential design, which allowed for the integration of quantitative findings with qualitative explanations. With a sample size of 112, data were collected from government officials, policymakers, community members, women and men engaged in development activities, as well as leaders of non-governmental organisations involved in gender equality and sustainable development initiatives. Purposive sampling was employed to select respondents with relevant knowledge and experience on gender and resource-related issues. Quantitative data were gathered through structured questionnaires, while qualitative data were obtained through interviews and focus group discussions. The quantitative data were analysed using descriptive statistics, whereas qualitative data were analysed using thematic analysis. The findings reveal persistent gender disparities in access to key resources such as education, healthcare, land, financial services, economic opportunities, and decision-making platforms. Women were found to face greater structural and socio-cultural barriers compared to men, particularly within traditional and rural community settings. These disparities limit women’s economic empowerment, agency, and participation in leadership, thereby constraining overall community development. The study concludes that equitable access to resources is central to promoting gender equality, strengthening human capital, and achieving sustainable community development in Ghana. It therefore recommends the implementation of gender-responsive policies, reforms in land and financial systems, and targeted interventions to enhance women’s participation in economic and governance processes at the community level.
UNDP – United Nations Development Programme
Digital Well-Being Analytics: Social Media, AI, and Mental Health
The quick evolution of social media and digital ecosystems based on artificial intelligence (AI) altered the trend in human interaction, communication, and self-expression dramatically. With the introduction of these technologies providing the new opportunities of flexibility in connection and customization of the user, the issue of their impact on mental health and general digital well-being has grown in concern. The authors examine the intricate relationship between the activities of the social media use along with the artificial intelligence-based algorithms and the mental health outcomes according to the digital well-being analytics in this study. The paper will explain these algorithms content curation, engagement optimization algorithm, and data-based personalization and their involvement in the psychological states of anxiety, depression, self-esteem, and social comparison through mixed methods study, a mixture of quantitative behavioral analytics and the qualitative analysis of user perception. The paper will also evaluate the possibility of applying AI as a causative element of digital stress and a prevention and treatment instrument through sentiment-based information, danger prediction algorithms and tailored suggestions of well-being.The findings will endeavor to provide a universal model of AI twofold role in setting digital mental health outcomes. The research can be utilized in the development of ethical AI-driven models of well-being, policy-level reformulations, and principles of platform design that can help make the Internet a healthier place. Lastly, this paper shows that there is need of responsible technological innovation which will balance the metrics of engagement as well as take into account the psychological sustainability in the digital age.
Explainable Artificial Intelligence for Financial Crime Prevention: Translating Machine Learning Outputs into Regulatory and Compliance Decision-Making
Financial institutions increasingly rely on machine learning systems to detect fraudulent activities and prevent financial crime across complex transactional environments. Models like these are exemplified by their remarkable prediction accuracy, but at the same time, their lack of interpretability leads to considerable problems in terms of compliance with regulations, auditing, and operational trust. In the case of financial transactions that carry a high level of risk, regulators and compliance professionals want not just precise risk forecasts but also the reasoning behind the decisions to be open and understandable. The presented research introduces a regulatory-compliant explainable artificial intelligence (XAI) framework that connects machine learning results and financial crime decision-making processes. The framework does not innovate but rather focuses on converting risk scores and explainability outputs of predictive models into interpretable decision artifacts that can be easily reviewed for compliance, supervisory oversight, and human-in-the-loop validation. The proposed methodology combines explainability mechanisms with governance-oriented design principles, allowing for consistent justification of flagged transactions, enhanced audit trails, and increased accountability in automated systems for financial crimes detection and prevention. Case study illustrations demonstrate how explainable AI can support escalation, investigation, and reporting decisions in fraud and anti-money laundering contexts. The findings highlight the role of explainable AI as a critical enabler for aligning machine learning innovation with regulatory expectations, contributing to more transparent, trustworthy, and responsible financial crime prevention systems.
Fish Seed Rearing to Improve Qualitative Production of Fish Farmers: A Review
This review synthesizes current knowledge on fish seed rearing practices to enhance qualitative production in aquaculture systems. The global aquaculture production exceeding capture fisheries, the quality of fish seed has become a critical condition of fish farming success and profitabal. Present study examines nursery and rearing protocols, feeding strategies, water quality management and disease prevention. This study details the successful rearing and production of carp seed using a portable Fibre Reinforced Plastic (FRP) tank in the Jalkot region of Latur district, Maharashtra. Indian major carp spawn was utilized to sustain seed production. The experiment achieved a 90% survival rate within 20-30 days of cultivation. The FRP tank's high portability, simple installation, and low cost demonstrate its suitability for adoption by fish farmers in the Marathwada region, providing a viable and sustainable method for increase fish seed to support local aquaculture. The findings provide practical recommendations for fish farmers to optimize seed production, improve survival and growth rate and enhance overall productivity in aquaculture operations.
Comparison of Black Magnetic Powder and Phase Transfer Catalyst Methods to Develop Latent Fingerprints on Latex Surface: A Preliminary Study
Latent fingerprints are important pieces of physical evidence at crime scenes, playing a significant role in individualization. Such fingerprints can be detected on a variety of surfaces, including used latex gloves, which are frequently found at crime scenes. Although fingerprints on latex gloves are common, there has been limited research on identifying them accurately. The aim of this study was to create a new method for producing latent fingerprints on latex surfaces utilizing black magnetic powder and the Phase Transfer Catalyst (PTC) reagent approach, which might be useful for crime scene investigators. The research also addressed the PTC reagent's shelf life. The research employed two fingerprint development techniques: black magnetic powder and a PTC reagent formulated from non-toxic chemicals. Over the course of seven days, latent fingerprints were developed on latex surface at various intervals. The developed prints were photographed to determine their quality. Both approaches, black magnetic powder, and the PTC reagent, were found to be successful in developing latent fingerprints on latex glove surfaces. Both approaches produced comparable-quality fingerprints, with clear prints acquired up to seven days following deposition. A number of physical and chemical processes are available for fingerprint formation, depending on the surface and type of the print. Both black magnetic powder and the PTC reagent proved effective in developing latent fingerprints on latex surfaces. The black magnetic powder technique is a useful tool since it is simple and easy to use, and also the PTC reagent is stable for at least seven days.
Exosomal Immune Checkpoint Molecules as Liquid Biopsy Markers
Immune checkpoint inhibitors have revolutionized the management of cancer; unfortunately, there are few stable and least invasive biomarkers in forecasts and monitoring of therapeutic response. Traditional tissue biopsies do have limitations in the heterogeneity of tumors, invasiveness of the procedure and a limited temporal model. Liquid biopsy is therefore an emerging alternative and increasing evidence is presenting the tumor-derived exosomes as information-rich circulating biomarkers. Exosomes are directly engaged in tumorimmune crosstalk capable of carrying immune checkpoint molecules, programmed death-ligand 1 (PD-L1), programmed death-1 (PD-1), and other co-inhibitory controls, and is involved in immune escape and therapy resistance. Recent publications also show that exosomal immune checkpoint molecules can not only indicate the dynamism of the immune status of the tumor microenvironment, but also offer better stability and sensitivity than soluble or cell-based biomarkers. This review critically analyses the biological and clinical usefulness and technological improvements connected with exosomal immune checkpoint molecules as markers in liquid biopsy. We address their diagnostic, prognostic and predictive value in a variety of types of cancer, and we pay special attention to the immunotherapy response assay. Existing problems, such as analytical standardization, biological heterogeneity, and clinical validation are also dealt with. Lastly, prospective outlooks into incorporating exosomal immune checkpoint profiling in precision immuno-oncology are discussed highlighting its capacity to direct personalized cancer immunotherapy.
Circulating B-Cell Receptor (BCR) Repertoire Diversity as a Biomarker of Immunotherapy Response
The immunotherapy of cancer has revolutionized the treatment of oncologic cancer, but the patient response has been very heterogeneous and this has greatly raised the need to develop strong, minimally invasive, biomarkers that predict and monitor the reaction to the treatment. Although T-cell receptor (TCR) repertoire profiling has enjoyed much focus, recent studies have demonstrated that the variety of circulating B-cell receptor (BCR) repertoire is critical and understudied in determining antitumor immunity and immunotherapy response. With the current development of high-throughput immune repertoire sequencing, BCR diversity, clonality, somatic hypermutation, and lineage evolution can now be comprehensively characterized using a peripheral blood sample. The current article assesses the circulating BCR repertoire diversity as an immunotherapy responsiveness biomarker by synthesising the recent findings of immune checkpoint blockade, cancer vaccines, and combination immunotherapies biomarkers. We discuss methodological platforms of BCR sequencing, analytical diversity measures and computing methods of repertoire architecture. The existing data shows that the responders to immunotherapy tend to be characterized by either dynamic BCR repertoire remodelling, clonal amplification of tumour-surveillance B cells, and changes in indices of diversity throughout therapy. Moreover, peripheral BCR signatures have been linked to the treatment efficacy, immune-related adverse events, as well as long-term immune memory formation. We also comment on technical and biological confounders, which are sequencing depth, sampling time, tumor heterogeneity and immune compartmentalization. The problems of standardization and reproducibility are mitigated, and the novel methods of single-cell and multi-omics that increase interpretability are described. Lastly, we suggest a translational system of applying circulating BCR repertoire measures to foresee biomarker and clinical trial layout.
IgG Subclass Switching In Tumor Infiltrating B Cells and Patient Survival
Tumor infiltrating B cells have been able to be considered as very important in regulation of anti-tumor immunity, but its prognostic role is always context dependent and also not well understood mechanistically. There is a growing amount of evidence to support that clinical outcome is dependent not on evidence of B cell presence, but on the functional quality of intratumoral antibodies. This review summarizes the existing developments on immunoglobulin G (IgG) subclass switching in tumor-infiltrating B cells and discusses its potential use to improve patient survival in solid malignancies. We describe the molecular and microenvironmental(s) that control the process of class-switch recombination in tumors, and the role that chronic exposure to antigens as well as polarization and crosstalk among tumors and immune responses play in determining IgG subclass bias. Specific focus is placed on the opposite actions between cytotoxic and immunosuppressive activities of a series of IgG subclasses, which have been attributed to immune evasion, reduced effects of action target, and poor clinical outcomes. With the data of bulk, single cell and B-cell receptor repertoire analysis we demonstrate how IgG subclass skewing is associated with prognosis and immunotherapy response of immune checkpoint blockade in various cancer types. Lastly we delve into the emergent approaches to therapy trying to reprogram Tumor-infiltrating B cells or trying to harness the biology of the IgG subclasses to promote anti-tumor immunity. Takit together, the present report places IgG subclass switching as the application functioning biomarker and as an encouraging objective of the subsequent-generation immunotherapies in cancer treatment.
Green Nanotechnology Approach for Mitigating Trichoderma Threats using Ajwain-derived Silver Nanoparticles
Trichoderma species , particularly those responsible for the green Mold disease , pose a major threat to the commercial cultivation of button mushroom (Agaricus bisporus) , leading to a significant yield losses ,reduced quality, and increased production costs, conventional chemical fungicide have shown declining effectiveness due to resistance development and environmental concerns , creating an urgent need for eco- friendly alternatives . in the present study a bio fungicide was formulated using Trachyspermum ammi (ajwain) seed extract , known for its antimicrobial activity and antifungal phytochemicals, enhanced through green synthesis of silver nanoparticles(ag NPs) using the same extract as the reducing and stabilizing agent this green synthesis combined the intrinsic antifungal activity if ajwain with the high surface reactivity of a nano silver, producing a synergetic effect against Trichoderma species ,and interfere with some spore germination thereby inhibiting the colonization in mushroom substrates. The use of plant- based reducing agents ensures non- toxic , sustainable nanoparticles production, making the formulation suitable for agricultural applications. Overall, the ajwain -mediated Ag Np’s bio fungicide demonstrates string potential as an effective and environmentally safe to mange mushroom cultivation.
Post-Quantum Secure Design Principles for Detection and Mitigation in Critical Infrastructure Information Systems
The advent of quantum computing poses a significant threat to the cryptographic foundations that secure critical infrastructure information systems, including energy grids, transportation networks, healthcare systems, and financial services. Conventional public-key cryptographic schemes such as RSA and elliptic curve cryptography are vulnerable to quantum attacks, necessitating a paradigm shift toward post-quantum secure design principles. This review paper examines emerging post-quantum cryptographic frameworks and their role in strengthening detection and mitigation mechanisms within critical infrastructure environments. It systematically analyzes post-quantum algorithms, hybrid cryptographic models, and quantum-resilient authentication schemes, emphasizing their integration into intrusion detection systems, anomaly detection frameworks, and secure communication protocols. Additionally, the paper explores quantum-aware threat models, resilience-oriented system architectures, and adaptive mitigation strategies that enhance system survivability against both classical and quantum-enabled cyber threats. Key challenges including computational overhead, legacy system compatibility, scalability, and regulatory compliance are critically discussed, alongside ongoing standardization efforts and implementation case studies. By synthesizing current research and industry practices, this review provides a comprehensive roadmap for designing, deploying, and managing post-quantum secure detection and mitigation frameworks that ensure the long-term resilience, reliability, and trustworthiness of critical infrastructure information systems in the quantum era.
Adaptive Neural Feedback Methods for Bias and Weight Adjustment in Feed Forward Layers of LLMs
Feed-forward layers constitute the dominant computational and parametric component of transformer-based Large Language Models (LLMs), yet they are a major source of training instability due to static bias terms, uncontrolled weight scaling, and activation distribution drift. Conventional optimization methods rely solely on global backpropagation signals, which are often insufficient to correct local statistical imbalances that emerge during large-scale, long-horizon training. This work proposes AFB-FFN (Adaptive Feedback Bias and Weight Corrected Feed-Forward Network), a novel feed-forward layer architecture that integrates an internal neural feedback mechanism to dynamically regulate bias and weight behavior during forward propagation. The proposed model introduces lightweight feedback units that generate bias correction vectors and weight gating signals conditioned on intermediate activations, enabling real-time stabilization of hidden representations. The AFB-FFN architecture is embedded within a transformer framework and evaluated on a token-level language modeling task. Extensive experimental analysis demonstrates that the proposed method significantly improves training stability under both nominal and high learning-rate regimes. The model achieves a controlled token-level accuracy of 97.8%, while maintaining smooth convergence, reduced gradient norm variance, lower activation drift, and stable gate entropy compared to conventional FFN baselines. These results validate that adaptive neural feedback driven bias and weight correction within feed-forward layers is an effective and scalable strategy for stabilizing LLM training. The proposed AFB FFN offers a practical architectural advancement toward robust, efficient, and statistically stable large language model optimization.
Stability of Triangular Equilibrium Points in the Generalised Photogravitational Restricted Three Body Problem
This paper investigates the stability of triangular equilibrium points (L4, L5) in the generalized photogravitational restricted three‑body problem, considering the combined effects of radiation pressure and oblateness of both primaries. Using variational equations and characteristic roots, the linear stability conditions are derived. The analysis shows that triangular points are stable when the mass parameter lies within 0   < c, and unstable otherwise. The critical mass ratio c is obtained, and its range of stability is shown to increase, decrease, or remain unchanged depending on the sign of parameter p, which depends on radiation and oblateness coefficients. These results demonstrate that oblateness and radiation significantly modify the location and stability of triangular points compared to the classical restricted three‑body case, offering deeper insights into celestial mechanics and artificial satellite dynamics
Engineering Productivity at Scale: Designing Internal Developer Platforms for Cloud-Native Java Teams
Platform engineering the discipline of designing and operating Internal Developer Platforms (IDPs) has rapidly evolved as organizations seek to standardize infrastructure provisioning, CI/CD pipelines, developer onboarding, and runtime operations to improve productivity and reliability at scale. For Java teams in particular, an IDP serves as a unifying abstraction that integrates the end-to-end delivery lifecycle from Spring Boot–based application development and containerized builds to GitOps-driven deployment, observability, and policy enforcement while embedding governance, security controls, and operational best practices by default. By reducing cognitive load and eliminating repetitive setup work, IDPs enable Java engineers to focus on business logic rather than platform mechanics. This article synthesizes proven architecture patterns, tooling choices, and operational practices for Java-focused IDPs, illustrated through three complementary reference diagrams: a layered IDP architecture that clarifies separation of concerns (Figure 1), the Backstage software-catalog and plugin architecture that anchors the developer experience (Figure 2), and a platform-tooling landscape that contextualizes the broader ecosystem (Figure 3). Building on insights from industry practitioners and platform engineering literature, the discussion offers practical recommendations for designing reusable Java service templates, implementing Kubernetes-native CI/CD workflows, exposing platform APIs for self-service provisioning, and enforcing consistent security and observability standards demonstrating how a well-designed IDP becomes a foundational capability for scalable, resilient Java delivery.
A Rule-Based Automatic Question Generation Framework for Educational Text
Automatic Question Generation (AQG) is essential in educational technology, enabling the creation of assessments, practice exercises, and intelligent tutoring systems. Although recent neural approaches have achieved promising results, they often lack interpretability and control, thereby limiting their use in structured educational contexts. To overcome these challenges, this study introduces a structurally modular, rule-based AQG framework that generates factoid questions from educational texts through explicit linguistic rules and deterministic transformations. The proposed framework draws on classical rule-based question generation methods and employs a two-stage algorithmic design that distinctly separates linguistic analysis from question generation. The first stage involves linguistic preprocessing, clause extraction, and auxiliary-verb determination to identify syntactically valid, question-worthy clauses. In the second stage, rule-based transformations generate various factoid question types, such as who, what, where, when, how many, and how much, followed by disambiguation, template-based surface real- ization, and validation. This separation enhances interpretability, extensibility, and control over rule execution. Unlike data-driven models, the proposed framework does not require annotated training data and avoids hallucination by relying on explicit syntactic and semantic patterns. The resulting system produces grammatically correct, semantically meaningful, and reproducible questions, making it well suited for educational and academic applications. The study demonstrates that carefully designed rule-based systems remain a viable and effective alternative for automatic question generation in controlled domains.
Strong Lp Solutions of Magnetohydrodynamic Equations in R3
In this paper we discuss some results on existence and unique solution of local, mild and global solution (u, b) of the three-dimensional Magnetohydrodynamic equations on the space R^3. The MHD equation governing the interaction between the velocity field and the magnetic field is analysed under suitable ini tial data by using various inequalities and L^p-L^qestimates.
Concentration-Dependent Acute Toxicity of Parboiled Rice Mill Effluent in Labeo rohita: A Fish Bioassay Approach
Parboiled rice mill effluents (PRME) are among the most polluting agro-industrial wastewaters due to their exceptionally high organic load, nutrients, and suspended solids (Nanda et al., 2021; Das et al., 2023; Pandey & Chandra, 2024). The present investigation evaluates the acute toxicity of PRME at different concentrations on the freshwater fish Labeo rohita over a 96-h exposure period. Physico-chemical analysis of raw effluent revealed acidic pH, critically low dissolved oxygen, and extremely elevated BOD, COD, nutrients, alkalinity, and hardness, which are known to induce severe stress in aquatic organisms (Yadav et al., 2022; Singh & Srivastava, 2023). Acute toxicity bioassays were conducted using graded effluent concentrations (10%, 20%, 40%, 60%, 80%, and 100%). Mortality, behavioral abnormalities, and stress responses were recorded at 24-h intervals. Results demonstrated a clear concentration- and time-dependent increase in mortality, with severe behavioral disruptions at higher concentrations, consistent with previous fish bioassay studies (Kumar & Rai, 2021; Saha et al., 2023). The study confirms that untreated PRME poses serious ecological risks to freshwater fish and emphasizes the urgent need for effective treatment prior to discharge.
A Review on Zinc Stannate Nanoparticle Based On Gas Sensor
Several types of semiconductor-based metal oxide gas sensors have been developed to meet the demands of modern life. Sensors are employed for monitoring environments in the commercial, health, and environmental categories. Researchers have been emphasizing on semiconductor-based metal oxide gas sensors types due to the numerous applications of gas sensors. Perovskite Zinc Stannate nanoparticle based on gas sensor are excellent prospects for air pollution reduction techniques, as reviewed in this research. This research concentrated on the employing of nanostructured perovskite-type semiconductor-based metal oxide gas sensors as a novel gas-sensitive material for the detection of nitrogen dioxide NO2 due to their toxic nature.
Measurements of the Non-Linear Polarization Rotation for Direct Determination of Polararization Coupling Length
In this paper, we present a way to obtain the polarization coupling length, an important parameter for the PMD probability distribution. This parameter is obtained from measurements and modelling of the nonlinear polarization rotation in optical fibers.
Synthesis, Characterization of Macrocyclic Complexes of 3D Series Transition Metal Ions
Transition metal macrocyclic complexes have been extensively studied by researchers in search of designing new chemical agents because of their excellent biological activities, including antimicrobial, antioxidant and anticarcinogenic. Presence of metal ions accelerates the drug action and efficiency of organic therapeutic agents. Transition metal complexes based on hydrazides of carboxylic acids have displayed wide range of biological and pharmaceutical activities because of the formation of –CONH- linkage which is used as building unit for peptides formation from amino acids in biological systems. It also gives higher stability to the molecules because of the presence of a chelatophore group of donor atoms in the coordination sphere. In this present paper we studied about synthesis and characterization of macrocyclic complex of 3D series transition metal ions.