International Journal of Engineering and Science Research
FloraFusion: Valorization of Agro-Waste-Based Natural Dyes for Sustainable Textile Applications
The global textile industry is a major contributor to environmental degradation, primarily due to the use of synthetic dyes, which are derived from petrochemicals and pose severe risks to ecosystems and human health. Synthetic dye effluents contaminate water bodies, disrupt aquatic ecosystems, and often require expensive wastewater treatment processes. As a result, there is a growing interest in identifying biodegradable and renewable alternatives. This study explores the dyeing potential of agro-waste-based natural dyes derived from betel nut, marigold, and pomegranate peel on three fabric substrates: cotton knit, cotton woven, and silk. The primary objectives are to assess the color strength (K/S values), fastness properties (washing, rubbing, light, saliva), changes in fabric GSM after dyeing, and the antibacterial properties of the dyed fabrics. Aqueous dye extracts were obtained from dried agro-waste powders and applied to pre-mordanted fabrics using an alum mordant. Standard ISO testing protocols were employed to evaluate color fastness, while the zone of inhibition method was used to assess antibacterial efficacy. Spectrophotometric analysis revealed that silk consistently exhibited the highest dye uptake, followed by cotton woven and cotton knit. Among the dyes, pomegranate peel not only produced a strong color yield but also demonstrated significant antibacterial activity. The results demonstrate that agro-waste-derived dyes can offer performance comparable to conventional synthetic dyes. These findings support the valorization of agricultural by-products as sustainable, cost-effective, and functional dye sources. This research contributes to the advancement of environmentally responsible textile processing and aligns with global sustainability objectives in the fashion and apparel industry. DOI: https://doi-ds.org/doilink/01.2026-93288788
Captcha: Visual Passwords For Robust Authentication Through Difficult AI Tasks
Many security primitives are based on hard mathematical problems. Using hard AI problems for security is emerging as an exciting new paradigm, but has been under-explored. In this project, we present a new security primitive based on hard AI problems, namely, a novel family of graphical password systems built on top of Captcha technology, which we call Captcha as graphical passwords (CaRP). CaRP is both a Captcha and a graphical password scheme. CaRP addresses a number of security problems altogether, such as online guessing attacks, relay attacks, and, if combined with dual-view technologies, shoulder-surfing attacks. Notably, a CaRP password can be found only probabilistically by automatic online guessing attacks even if the password is in the search set. CaRP also offers a novel approach to address the well-known image hotspot problem in popular graphical password systems, such as Pass Points, that often leads to weak password choices. CaRP is not a panacea, but it offers reasonable security and usability and appears to fit well with some practical applications for improving online security.
Application of Fuzzy Analytic Hierarchy Process to Assess Key Financial Indicators Influencing Indian Stock Market Investments
This research examines the principal financial factors affecting investment decision-making in the Indian stock market through the application of the Fuzzy Analytic Hierarchy Process (FAHP). Financial data were obtained from the top ten companies listed on the Bombay Stock Exchange (BSE), while expert evaluations were gathered from two senior specialists—an experienced investor and a finance professor—each possessing more than fifty years of professional expertise. The analysis focused on four key financial criteria: Return on Equity (ROE) (C1), Earnings Per Share (EPS) (C2), Price-to-Earnings (P/E) ratio (C3), and Debt-to-Equity (D/E) ratio (C4). The FAHP results indicate that Return on Equity (ROE) (C1) is the most influential criterion in investment decision-making. The findings further reveal that indicators related to profitability carry greater weight in investor preferences, while liquidity-related measures have comparatively limited impact. Overall, the study offers valuable insights for investors by underscoring the importance of prioritizing profitability and growth-oriented metrics over short-term solvency considerations when constructing investment portfolios.
Fuzzy Analytic Hierarchy Process (FAHP); Investment Decision-Making; Indian Stock Market; Financial Performance Indicators; Return on Equity; Multi-Criteria Decision Making (MCDM).
Anomaly Detection For Industrial Application: Spotlight On Patchcore And Autoencoder Insights With Resnet Backbone
Ensuring product quality is one of the key requirements in industrial manufacturing, where early detection of defects helps in reducing losses, improving safety, and maintaining customer satisfaction. Manual inspection is often time-consuming and prone to human error, which has motivated the use of Artificial Intelligence (AI) for automated anomaly detection. This project focuses on developing a basic yet effective anomaly detection system using image data. Two simple deep learning approaches are employed: an Autoencoder and a Patch-based analysis method. This project focuses on building a simple anomaly detection system for industrial applications. The goal is to identify whether a product image is normal or defective. To achieve this, we use a basic Convolutional Neural Network (CNN) for feature extraction and apply two easy approaches. A small dataset of normal and defective product images is used for testing. The project demonstrates that even with basic deep learning techniques, it is possible to detect defects in industrial images with good accuracy. This highlights the usefulness of AI in improving product quality and reducing manual inspection effort.
Graphical User Authentication: Enhancing Multi-Factor Authentication With Images
The aim project is to develop a robust and user-friendly Graphical Password Authentication that enhances the security of digital access by incorporating image-based authentication alongside traditional alphanumeric credentials.In the current digital landscape, where cyber threats are becoming increasingly sophisticated, traditional alphanumeric passwords often fall short of providing sufficient protection. This project addresses these vulnerabilities by introducing an innovative authentication method that leverages graphical passwords. Users are required to select specific images from a predefined set, creating a personalized and unique password. This graphical layer works in tandem with conventional passwords, significantly improving security and making unauthorized access more challenging.Additionally, the project focuses on striking a balance between security and usability. While many advanced authentication systems can compromise the user experience due to their complexity, the graphical password system offers a simple yet effective solution. By providing a visual, intuitive method for authentication, users can easily recall and utilize their passwords without sacrificing security. Graphical Password Authentication highlights the potential of combining innovative image-based techniques with modern technologies, offering a more secure, personalized, and user-friendly alternative to traditional authentication methods. The system aims to reduce the risks associated with unauthorized access to sensitive information and ensure a more secure online environment for users.
Risk-Aware and Trustworthy Artificial Intelligence Systems: A Review of Learning, Generative, and Governance-Driven Approaches for High-Impact Applications
The growing number of Artificial Intelligence (AI) systems used in high-impact sectors of finance, healthcare, smart transportation, cybersecurity, and critical infrastructure has increased the desire to implement risk-conscious, trustworthy, reliable, and effective models of decision-making. Although the conventional machine learning models focus on predictive reliability, they tend to be unable to deal with uncertainty, rare, adversarial, and moral responsibility. Consequently, recent studies in AI have been moving towards those architectures which explicitly support risk modeling, robustness, interpretability, and governance practices. The current review gives a syntactic overview of the recent developments in risk-conscious and trustworthy AI systems. The paper discusses the concept of anomaly detection by using deep learning, uncertainty-sensitive predictive models, generative AI-driven stress simulation, optimization-focused learning pipelines, and explainable AI models. Several applications in finance, healthcare, cyber-physical systems, and intelligent networks are critically examined in order to point out the frequent obstacles and design principles. The review also addresses the issue of ethical concerns, privacy protection, and compliance with regulations as the inseparable elements of the reliable AI systems. Lastly, the research directions are put forward to help in developing resilient transparent and human-friendly AI architectures that can be applied in the real world. DOI: https://doi-ds.org/doilink/01.2026-65995577
Risk-Aware Artificial Intelligence; Trustworthy AI; Anomaly Detection; Generative AI; Explainable AI; Secure Machine Learning; Ethical AI Governance.
A Comprehensive Review of Artificial Intelligence, Machine Learning, and Generative Models for Intelligent Decision-Making Across Modern Computational Domains
Machine Learning (ML) and Artificial Intelligence (AI) have found their way into the perennial categories of valuable tools used to make intelligent decisions in data-intensive and complex spaces. The ongoing accelerated research and development in the supervised learning field, deep learning, generative algorithms, and signal processing applications have facilitated effective solutions to financial, medical, intelligent transportation system, Internet of Things (IoT), and cyber-physical infrastructures. Rule-based and statistical methods prove often to be not scalable or generalizable in regard to heterogeneous, in large dimensional, and dynamically changing data. This in turn is leading to the adoption of data-driven and learning-based solutions that can be used to aid in prediction, automation and adaptive intelligence. The current review paper provides a synthesis of the most recent research advances in the field of AI-based decision-support systems and dwells upon the algorithmic core, specific areas of application, and trends. The article summarizes the findings of papers dealing with financial anomaly detection, learning analytics, radiomics-aided medical imaging, IoT-powered intelligent systems and generative artificial intelligence as well as cutting-edge signal processing systems. The most critical challenges associated with the interpretability, data quality, scalability, security, and ethical governance are under consideration. The review ends with an overview of the future research directions to explainable, multimodal, and responsible AI systems that will be able to work properly in the real-world settings. DOI: https://doi-ds.org/doilink/01.2026-18381397
Artificial Intelligence; Machine Learning; Deep Learning; Generative AI; Intelligent Systems; Financial Analytics; Medical Imaging; IoT; Responsible AI
An Inventory Model Incorporating Deterioration And Partial Backordering
This study develops an inventory model for deteriorating items that incorporates partial backordering under realistic operational and financial conditions. The model addresses the practical challenges of managing inventory where products experience time-dependent deterioration and shortages occur due to demand uncertainty or supply disruptions. Unlike traditional inventory models, this framework integrates exponential time-dependent demand, variable deterioration rates, and partial backordering to reflect real-world customer behavior where only a fraction of unmet demand is willing to wait. The mathematical model employs differential equations to determine optimal replenishment times, cycle lengths, and order quantities that minimize total average costs including holding costs, deterioration costs, shortage costs, and lost sales. The analysis considers multiple scenarios involving trade credit policies, interest rates, and payment delays between suppliers and retailers in a two-echelon supply chain. Numerical illustrations demonstrate that optimal solutions differ between individual stakeholders and the overall supply chain, with system-wide cost minimization typically achieved at moderate order frequencies. Results indicate that progressive credit periods marginally increase order quantities but significantly reduce annual total costs. The model provides decision-makers with a practical tool for optimizing inventory policies under deterioration, shortages, and financial constraints in contemporary market conditions. DOI: https://doi-ds.org/doilink/01.2026-68834452
Deteriorating inventory, Partial backordering, Time-dependent demand, Trade credit policy, Supply chain optimization
Influence of Spiritually Enriched Family Environment on Emotional Regulation and Psychological Adjustment of Adolescents
Adolescence is a very important stage of development when the family setting plays a major role in determining psychological well-being. This paper has explored the connection between spiritually enriched family setting, emotional control and psychological adaptation of Indian adolescents. The survey was cross-sectional and involved 420 teenagers between the age of 14-18 years living in Northern Indian urban and semi-urban regions. The standardized measures were standardized such as the Family Environment Scale, Difficulties in Emotion Regulation Scale-Short Form, Bell Adjustment Inventory, and Index of Core Spiritual Experiences. The hypothesis was that adolescents raised in spiritual enhanced family conditions would show excellent emotional regulation and psychological adaptation than their colleagues. Findings indicated strong positive correlations existed between family spiritual practices (r = 0.68, p < 0.001), emotional regulation abilities (r = 0.72, p < 0.001) and psychological adjustment scores (r = 0.64, p < 0.001). The results indicate that family spirituality is a protective factor to improve adolescent mental health, and it has implications on family-based interventions and counseling programs in Indian cultural settings.
Spirituality, Family Environment, Emotional Regulation, Psychological Adjustment, Adolescents
Influence of Optical Properties of Glazing and Reflector Configurations on The Thermal Efficiency Of Integrated Solar Water Heating Systems
Integrated solar water heating systems (ISWHS) rely on flat-plate or evacuated tube collectors to harness solar energy for domestic and industrial hot water needs. Glazing materials, with their transmittance, absorptance, and reflectance properties, govern the amount of solar radiation reaching the absorber plate, while reflector configurations like compound parabolic concentrators (CPCs) or V-troughs enhance incident radiation. This article explores how variations in glass thickness, anti-reflective coatings, and reflector geometries such as mirror spacing, angles, and types affect overall thermal performance. Drawing from experimental and simulation studies, it examines heat transfer mechanisms, optical efficiency gains, and practical optimizations. Key findings indicate that 4 mm low-iron glass yields peak efficiencies around 35%, while reflectors at 5 cm spacing and 30-degree angles can elevate temperatures to 318 K and efficiencies to 85%. Challenges like overheating and angle-dependent losses are addressed, alongside future directions involving nanofluids and advanced simulations. The analysis underscores the potential for 20-50% efficiency improvements, promoting ISWHS adoption in diverse climates.
A Comparative Study of Care Leadership Models and Their Influence on Older Adults' Health and Well-Being
The aging global population necessitates effective leadership models in elder care settings to optimize health outcomes and well-being. This comparative study examines three predominant care leadership models transformational, servant, and person-centered leadership and their differential impacts on older adults' health, quality of life, and satisfaction outcomes. A cross-sectional comparative design was employed involving 302 nursing home staff and 620 care facility assessments across multiple settings. The study hypothesizes that relationship-oriented leadership models demonstrate superior outcomes compared to passive-avoidant approaches. Results indicate that transformational leadership explained 18-23% variance in staff satisfaction and care quality, while servant leadership contributed an additional 15-26% variance in organizational commitment and job satisfaction. Person-centered care models showed 90% satisfaction rates among residents regarding family contact and autonomy. Statistical analyses revealed that transformational and servant leadership significantly improved patient safety culture, reduced adverse events, and enhanced residents' quality of life scores by 31.82±7.18 compared to institutional care. The discussion emphasizes that relationship-oriented leadership fosters collaborative environments, staff empowerment, and resident-centered approaches. This study concludes that integrating transformational and servant leadership principles with person-centered care frameworks optimizes elder care quality, staff well-being, and resident health outcomes.
ransformational leadership, Servant leadership, Person-centered care, Older adults, Health outcomes
Distribution of Prime Numbers and Recent Advances Toward the Riemann Hypothesis
The distribution of prime numbers lies at the heart of analytic number theory. The Prime Number Theorem (PNT) gives the first-order asymptotic behaviour of the prime-counting function (\pi(x)), while the finer structure of the primes appears to be governed by the zeros of the Riemann zeta function (\zeta(s)). The Riemann Hypothesis (RH), asserting that all nontrivial zeros of (\zeta(s)) lie on the critical line (\Re(s)=\tfrac12), would yield near–optimal error terms in the PNT and far-reaching consequences for the distribution of primes in short intervals, arithmetic progressions, and many other problems. This paper reviews the classical framework connecting primes and zeta zeros via explicit formulas and discusses the role of zero-free regions and zero-density estimates. It then surveys selected recent advances connected—directly or indirectly—to RH: improved explicit zero-free regions, refinements in the proportion of zeros known to lie on the critical line, results on the pair correlation of zeros and its implications, developments around the de Bruijn–Newman constant, and progress on bounded gaps between primes. While a proof of RH remains elusive, these developments significantly sharpen our understanding of both prime distribution and the landscape of zeta zeros, suggesting promising directions for future research.
prime number theorem, Riemann zeta function, Riemann Hypothesis, zeros of zeta, zero-free region, pair correlation, bounded gaps between primes
Non-Governmental Organizations have emerged as vital partners in addressing India's community health challenges through innovative service delivery models and grassroots interventions. This study examines the multifaceted role of NGOs in strengthening community health systems across India through primary healthcare delivery, maternal and child health services, disease prevention programs, and health education initiatives. The research employed a descriptive cross-sectional design analyzing secondary data from government health reports, WHO databases, and published literature spanning 2020-2025. Analysis of 33 lakh registered NGOs in India, representing one NGO for every 600 people, reveals that NGOs contribute significantly to bridging healthcare gaps, particularly in underserved rural and tribal areas. Results demonstrate that NGO interventions have led to improved immunization coverage, reduced maternal mortality, enhanced community participation in health programs, and strengthened primary healthcare infrastructure. The study found that states with robust NGO partnerships achieved 15-20% higher health outcome indicators compared to states with limited NGO engagement. Discussion highlights NGO strengths including community trust, flexible programming, and innovative service models, while acknowledging challenges of funding constraints and coordination gaps. The research concludes that strategic NGO-government partnerships are essential for achieving Universal Health Coverage and sustainable community health improvements in India's diverse healthcare landscape. DOI: https://doi-ds.org/doilink/01.2026-62318779
Non-Governmental Organizations, Community Health, Primary Healthcare, Maternal Child Health, Health Service Delivery.
Oxygen-Bridged Transition Metal Chelates with Alkali & Alkaline Earth Metal Salts
Oxygen-bridged transition metal chelates are an important category of coordination compounds that are used in a variety of applications in catalysis, materials science and biological systems. This paper examines the synthesis, characterization and properties of oxygen bridged transition metal complexes in combination with alkali and alkaline earth metal salts. The study will focus on the structural changes, stability values, and possible uses of these heterometallic systems. To characterize compounds synthesized, a systematic approach by using spectroscopic techniques such as UV-Visible, FTIR, NMR, and thermal analysis were used. The hypothesis was that on incorporation of the alkali and alkaline earth metals the stability would increase and the electronic property of the transition metal chelates would be altered due to oxygen bridging. The findings indicated that there were substantial differences in stability constants, calcium and magnesium salts exhibited a higher level of coordination than sodium and potassium counterparts. The resulting complexes that were produced had changed spectroscopic characteristics and had higher thermal stability. It was discussed that the character of alkali and alkaline earth metals has a considerable impact on the bridging geometry and the stability as a whole of complexes. The paper finds that oxygen-bridged heterometallic chelates have a potential of providing a future development of high-dual-function materials.
Oxygen-bridged complexes, Transition metal chelates, Alkali metals, Alkaline earth metals, Heterometallic coordination
Growth, Yield, and Economic Analysis of Commercial Floriculture Crops under Controlled Environmental Conditions
Controlled environmental agriculture has revolutionized commercial floriculture by enabling year-round production and superior quality flowers. This study investigates the growth, yield, and economic viability of major commercial floriculture crops (rose, gerbera, carnation, and chrysanthemum) cultivated under polyhouse conditions in India. The primary objective was to evaluate agronomic performance and conduct comprehensive economic analysis comparing controlled environment cultivation with open-field systems. A factorial randomized block design was employed across four commercial floriculture farms in Maharashtra during 2023-2025, monitoring growth parameters, yield metrics, and input-output economics. Results demonstrated that controlled environmental conditions significantly enhanced flower yield (45-68% increase across crops), stem length (40-41% improvement), and vase life (52-53% extension) compared to open cultivation. Economic analysis revealed benefit-cost ratios ranging from 2.89 to 3.23 across different crops, with gerbera exhibiting maximum profitability. Gerbera cultivation generated highest net returns of ₹1,023,000 per hectare annually with benefit-cost ratio of 3.23, while rose yielded net returns of ₹847,000 per hectare with benefit-cost ratio of 2.89. The study concludes that controlled environmental floriculture presents substantial economic opportunities for Indian farmers despite higher initial investment requirements, with gerbera emerging as the most economically attractive crop followed by carnation and chrysanthemum. DOI: https://doi-ds.org/doilink/01.2026-77971983
Controlled environment agriculture, floriculture economics, polyhouse cultivation, yield optimization, commercial flower production
Nanotechnology Applications In Water Purification: Metal Oxide Nanoparticles And Membrane Nanofiltration
Water scarcity and contamination pose significant global challenges, necessitating innovative purification technologies. This study examines nanotechnology applications in water purification, specifically focusing on metal oxide nanoparticles and membrane nanofiltration systems. The primary objective is to evaluate the effectiveness of various nanomaterials in removing contaminants from water and to assess their practical implementation potential. A systematic methodology was employed, reviewing empirical data from published studies on nanoparticle efficiency, membrane performance, and removal rates of different pollutants. The hypothesis posited that nanotechnology-based systems demonstrate superior removal efficiency compared to conventional methods. Results indicate that metal oxide nanoparticles, particularly TiO₂, ZnO, and Fe₃O₄, achieve removal efficiencies exceeding 95% for heavy metals and organic contaminants. Membrane nanofiltration systems demonstrate 85-99% rejection rates for various pollutants. Discussion reveals that while nanotechnology offers exceptional purification capabilities, challenges including cost, scalability, and potential environmental impacts require attention. The study concludes that nanotechnology represents a promising solution for water purification, with metal oxide nanoparticles and nanofiltration membranes showing significant potential for addressing global water quality issues, though further research on long-term sustainability and economic viability is essential.
Nanotechnology, Water Purification, Metal Oxide Nanoparticles, Nanofiltration, Heavy Metal Removal
Navigating the Digital Bazaar: A Comprehensive Analysis of Social Media Marketing's Impact on Subscription-Based Services in the Indian Market
This study investigates the unique dynamics of social media marketing's impact on subscription-based services (SBS) in India, the world's fastest-growing digital economy. Through a mixed-methods approach analysing 150 Indian SBS companies (2021-2024), platform-specific user behaviour data, and cultural sentiment analysis, we identify distinct patterns shaped by India's demographic diversity, platform preferences, and value consciousness. Key findings reveal: (1) Regional language content on social media drives 3.2x higher conversion rates for SBS in tier 2/3 cities compared to English content; (2) WhatsApp-based marketing channels achieve 47% lower Customer Acquisition Cost (CAC) but 18% higher churn rates than platform-native advertising; (3) "Sachet pricing" previews on social media increase conversion by 68% for price-sensitive segments; (4) Influencer marketing ROI varies dramatically by platform, with YouTube influencers delivering 5.1x higher lifetime value than Instagram influencers for educational subscriptions. The study introduces the "India Social Media Adaptation Index" (ISMAI) to measure cultural and linguistic alignment, finding that services scoring above 7.0/10 achieve 42% better revenue retention. We conclude that success in India's SBS market requires hyper-localized, platform-diverse, and value-transparent social media strategies that account for the country's unique digital ecosystem.
Digital India, Subscription Economy, Social Media Marketing, Regional Content, WhatsApp Commerce, Influencer Marketing, Price Sensitivity, Tier 2/3 Markets
Affordable Sanitation Systems and Their Role in Inclusive Public Health
Affordable sanitation systems are foundational to inclusive public health because they reduce exposure to fecal pathogens, protect dignity and safety, and narrow health inequities that disproportionately affect low-income households, women and girls, persons with disabilities, and informal-settlement residents. Despite progress, sanitation gaps remain vast: in 2022, 57% of the world (≈4.6 billion people) used safely managed sanitation, while about 3.5 billion still lacked it; 419 million people continued to practice open defecation. These deficits translate into preventable illness and lost productivity; diarrhoeal disease alone kills ~443,832 children under five annually, and a significant proportion is preventable through safe water, sanitation, and hygiene (WASH). This paper synthesizes evidence on “affordable sanitation” (low-capex and low-opex options appropriate to local context) and explains how well-designed, inclusive sanitation services—paired with behavior change, fecal sludge management, and governance—advance public health, equity, and progress toward SDG 6. We propose a practical framework for selecting affordable systems (technical feasibility, life-cycle cost, inclusion/safety, climate resilience, and service-chain performance) and outline policy and program actions to accelerate equitable sanitation outcomes.
affordable sanitation, inclusive public health, fecal sludge management, SDG 6, equity, WASH, low-cost toilets, safely managed sanitation
Exploring Lexical Features of Indian English Newspapers for English Language Pedagogy
Indian English newspapers represent a distinctive variety of English that reflects the sociolinguistic landscape of India. This research explores the lexical features present in Indian English newspapers and examines their pedagogical implications for English language teaching in Indian contexts. The study aims to identify characteristic lexical patterns, borrowings, code-mixing practices, and semantic variations that distinguish Indian English print media from other English varieties. Using corpus linguistic methodology, a systematic analysis of five major Indian English newspapers was conducted over a six-month period, collecting approximately 500,000 words. The research employed quantitative and qualitative analytical techniques to identify frequency patterns, collocation structures, and context-specific lexical choices. Results reveal significant presence of Indianisms, hybrid constructions, cultural borrowings, and localized semantic extensions. The findings demonstrate that Indian English newspapers employ a unique lexical repertoire that bridges formal Standard English with indigenous linguistic elements. These lexical features provide authentic, culturally relevant materials for English language pedagogy in India. The study concludes that incorporating newspaper-based lexical features into curriculum design can enhance learner engagement, cultural awareness, and communicative competence in context-appropriate English usage.
Indian English, lexical features, newspapers, English language pedagogy, corpus linguistics
Electrical and Luminescent Behavior of Calcium Silicate Doped with Erbium and Terbium Ions
Calcium silicate phosphors doped with rare-earth ions exhibit remarkable luminescent and electrical properties for optoelectronic applications. This study investigates the electrical conductivity and photoluminescence characteristics of calcium silicate (Ca₂SiO₄) doped with erbium (Er³⁺) and terbium (Tb³⁺) ions at various concentrations. The synthesis was performed using solid-state reaction method at 1200°C. We hypothesized that dual doping would enhance both electrical conductivity and luminescence intensity compared to single-doped samples. Structural characterization was conducted using X-ray diffraction, while luminescent properties were analyzed through photoluminescence spectroscopy under UV excitation. Electrical measurements revealed that 2 mol% Er³⁺ and 1 mol% Tb³⁺ co-doped samples exhibited optimal conductivity of 3.8 × 10⁻⁴ S/cm at room temperature. Photoluminescence spectra showed characteristic green emission from Tb³⁺ at 544 nm and red emission from Er³⁺ at 660 nm. The energy transfer efficiency between Tb³⁺ and Er³⁺ was calculated as 42%. Results demonstrate that co-doping significantly improves material performance, suggesting potential applications in solid-state lighting, display devices, and optical sensors.
Calcium silicate, Erbium doping, Terbium doping, Photoluminescence, Electrical conductivity
Advanced AI Techniques For Cyber Hacking Breach Forecasting
Examining cyber event data sets is a crucial approach for enhancing our comprehension of the evolving threat landscape. This is a somewhat novel research area, and numerous investigations are yet to be conducted. This study presents a statistical analysis of a dataset pertaining to breach incidents during a 12-year period (2005–2017) including cyber hacking operations, including malware attacks. We demonstrate that, contrary to existing literature, both the inter-arrival periods of hacking breach incidents and the sizes of breaches should be described using stochastic processes rather than distributions, as they display autocorrelations. We offer specific stochastic process models to fit the inter-arrival periods and breach sizes, respectively. We additionally demonstrate that these models can forecast the inter-arrival intervals and the breach magnitudes. To gain deeper insights into the evolution of hacker breach incidents, we do both qualitative and quantitative trend studies on the dataset. We derive a collection of cyber security insights, indicating that the frequency of cyber intrusions is indeed growing, yet the severity of their damage remains unchanged. DOI: https://doi-ds.org/doilink/12.2025-65663568
An Improved Framework for Detecting Thyroid Disease Using Filter-Based Feature Selection and Stacking Ensemble
Machine learning (ML) had become quite significant in determining what type of thyroid disease an individual has and in informing individuals that they may possess one in the last several years. Single ML models have been examined but their capability to produce accurate forecasts is usually impaired by information imbalance and model-specific constraints. These problems were addressed using a number of ML techniques applied to a thyroid dataset primarily to increase the accuracy of the predictions. The most useful features that could make a correct evaluation were found by means of feature selection methods. Some of the models used to determine thyroid disease were support vector machines (SVM), decision trees (DT), k-nearest neighbors (KNN), logistic regression (LR), and artificial neural networks (ANN). There was also the ensemble approach of stacking and voting a classifier to combine multiple models to improve performance. There were significant improvements in the stacking ensemble including LightGBM, SVM, DT, KNN, LR, ANN and vote classifier including Boosted Decision Tree and ExtraTree. The best of these was the voting predictor whose accuracy was 98% reliable. This demonstrates that ensemble techniques may be useful in addressing imbalance of data and achieve improved outcomes in the prediction of what thyroid disease a patient has.
Artificial intelligence, healthcare, machine learning, filter-based stacking ensemble learning, thyroid disease”.
Interplay Of Aquatic Flora And Wetland Health: A Water Quality Study In Assam's Dhemaji And Jorhat Districts
Wetlands serve as critical transitional ecosystems between terrestrial and aquatic environments, providing indispensable ecological services including water purification, biodiversity conservation, and carbon sequestration. This research investigates the intricate relationship between aquatic macrophytes and water quality parameters in selected wetlands of Dhemaji and Jorhat districts of Assam, India. The primary objectives encompass documenting macrophyte diversity, assessing physicochemical water quality parameters, examining correlations between aquatic vegetation and water quality indicators, and evaluating overall wetland health status. A mixed-method approach integrating field sampling, laboratory analysis, and statistical correlation was employed across six wetland sites during 2019-2021. The hypothesis posited that macrophyte abundance significantly influences water quality parameters, thereby determining wetland health. Results revealed significant variations in dissolved oxygen (4.5-9.4 mg/L), pH (6.8-7.6), and nutrient concentrations across sites, with macrophyte-dominated areas exhibiting distinct water quality profiles. Statistical analysis confirmed significant negative correlations between free-floating macrophyte density and dissolved oxygen levels (r=-0.72, p<0.05). The study concludes that aquatic flora composition serves as a reliable bioindicator of wetland health, necessitating integrated conservation strategies for sustainable wetland management in northeastern India.
Aquatic Macrophytes, Water Quality Index, Wetland Health, Brahmaputra Floodplain, Bioindicators
Assessing Freshwater Integrity Using Planktonic Biological Monitoring Methods
The dire state of freshwater ecosystems in an era of worsening anthropogenic threats necessitates a timely development of effective water quality monitoring approaches based on biology. The objective of this study was to test the role of plankton as bioindicators to quantify integrity of freshwater systems in Indian water bodies. General aims included analysis of diversity patterns of phytoplankton and zooplankton, relating planktonic abundance to physicochemical parameters and assessment of the degree of usefulness of some Bio-indices to assess pollution. Description: A mixed-method approach combining field sampling and laboratory analysis was employed over a number of freshwater lakes and rivers. First, that the planktonic diversity indices are significantly related to water quality variables so that they can reflect the health of the ecosystem. Results showed Shannon-Weiner diversity index values between 1.2 and 3.4 within sites indicative of trophic conditions. Palmer's Pollution Index also indicated that organic pollution in water bodies under anthropogenic influence was moderate to high The dominance of Cyanophyceae and Chlorophyceae groups implied the eutrophic levels of the phytoplankton, whereas the composition of zooplankton was associated with the pollution stress. The dissolved oxygen, pH and planktonic density all correlated statistically significantly with these design parameters. It was discussed that biological monitoring, particularly in freshwater ecosystems, is a known and cost-effective reliable assessment of integrity, as opposed to or chemical methods alone (Suter, 1993). Planktonic biological monitoring constitutes a tool potentially suited to provide a more holistic assessment of freshwater quality that should be included in frameworks for environmental management.
Bioindicators, Freshwater integrity, Phytoplankton, Zooplankton, Water quality index
A Study on the Design and Analytical Evaluation of Liquid PVT Systems for Building-Integrated Renewable Energy
The integration of photovoltaic thermal (PVT) systems in building structures represents a significant advancement in sustainable energy generation, enabling simultaneous production of electricity and thermal energy from a single collector area. This study aims to evaluate the design parameters and analytical performance of liquid-based PVT systems for building-integrated renewable energy applications. The research objectives include assessing thermal and electrical efficiency variations across different absorber configurations, examining the influence of operational parameters such as mass flow rate and solar radiation intensity, and evaluating the overall energy performance of building-integrated photovoltaic thermal (BIPVT) systems. The methodology adopted a mixed-method research design incorporating both quantitative analysis of experimental data from existing literature and comparative evaluation of various liquid PVT collector configurations. The hypothesis proposed that liquid-based PVT collectors with optimized absorber designs demonstrate superior combined thermal-electrical efficiency compared to conventional standalone photovoltaic systems. The results indicate that spiral flow absorber configurations achieve the highest PVT efficiency of 68.4%, comprising 13.8% electrical and 54.6% thermal efficiency at optimal operating conditions. The discussion reveals that mass flow rate optimization and absorber design significantly influence system performance. In conclusion, liquid PVT systems present viable solutions for building integration, contributing substantially toward renewable energy targets and sustainable building development.
Photovoltaic Thermal Collector, Building-Integrated Renewable Energy, Liquid PVT, Thermal Efficiency, Sustainable Buildings
Design And Optimization Of A Robotic Arm Using Advanced Materials
This Project highlights the importance of digital prototyping over traditional prototyping in manufacturing engineering. In today’s fast paced industrial era, every company strives for speed and efficiency in manufacturing to meet client demands, with the agricultural sector being a notable exception. Robots now perform tasks more quickly, cost effectively, and accurately than humans ever could. The moving parts of a robotic arm experience significant elastic deformations and stresses, often operating at ve locities comparable to the tool itself. This project focuses on the shearing operation, with the primary challenge being the design and optimization of a lightweight robotic arm component that is both durable and reliable. Through extensive study, we have developed and analysed a jointed robotic arm where the base remains fixed, and the remaining joints can move vertically and horizontally. The design and analysis were conducted using three different materials: Epoxy Carbon Woven, Steel Alloys , and AL NI . A force of 4000N was applied to the robotic arm to evaluate its structural and thermal performance. Ultimately, the study identifies the most suitable material among these three options for use in the robotic arm.
Thermal Analysis Of A Nuclear Power Plant Heat Pipe Exchanger Using Ansys
Heat pipes are a unique type of heat transfer device that can transport large amounts of heat through a small cross sectional area, with minimal temperature differences. A heat pipe is made up of a pipe or tube and a base fluid. In practice, the heat pipe is poured into a mould that is compatible with the cooling media. These devices have found uses in a variety of fields, including space apparatus, solar energy systems, electronic equipment, and air conditioning systems, due to their simplicity of design and ease of manufacture and maintenance. Using heat pipe-cooled micro-reactors in power plants is one of the newest technologies in small power plants. In these power plants, heat is taken from the reactor by several heat pipes and transferred to the working fluid in the main heat exchanger of the power plant. The present work focused on the design and thermal analysis of heat pipe used in nuclear power plant. Three models namely Model-1, Model-2 and Model-3 were modeled in CATIA software. Thermal analysis was carried out on these models with steel and copper material properties in ANSYS software. Finally optimum model with suitable material was suggested for the heat pipe.
Modeling And Analysis Of A Cnc Milling Machine Bed With Composite Materials
The materials utilized as a part of a machine device have an unequivocal part in deciding the efficiency and exactness of the part made in it. The regular basic materials utilized as a part of exactness machine apparatuses, for example, cast iron and steel at high working velocities create positional mistakes because of the vibrations moved into the structure. Quicker cutting rates can be procured just by structure which has high firmness and great damping attributes. We know that by experiences life of a machine is inversely proportional to the levels of vibration that the machine is subjected. The further procedure is completed to experience the distortion, normal recurrence and removal utilizing Static examination, Modal investigation and Harmonic individually. Since the bed in machine apparatus assumes a basic part in guaranteeing the exactness and precision in segments. Is a standout amongst the most critical apparatus structures which have a tendency to retain the vibrations coming about because of the cutting operation. To break down the bed for conceivable material changes that could build solidness, diminish weight, improve damping characteristics. In this paper constant load is applied on a bed with and without Nanocoating on the material and with composite materials. Graphene is used for coating basing on strength and thermal properties. Modeling of the bed was carried out in CATIA V5 R24 and ANSYS is used for structural analysis. By comparing the stress distribution and deformation in the bed by changing its material with the previous. Finally this thesis summarizes the suitable materials can be used as machine bed material.
Machine Tool, Machine Bed, Stiffness, Damping, CATIA V5 R24, ANSYS
Design And Structural Analysis On A Frameless Chassis Construction Of Bus For Different Loads
Automotive chassis is a skeletal frame on which various mechanical parts like engine, tires, axle assemblies, brakes, steering etc. are bolted. The chassis is considered to be the most significant component of an automobile. It is the most crucial element that gives strength and stability to the vehicle under different conditions. Bus chassis is the design and quality of bus chassis depends on the capacity of bus. It can be tailor made according to the needs and can be availed with features like transverse mounted engine, air suspension as well as anti-roll bars. A well manufactured bus chassis offers various benefits like high torque from low revs, superior brake performance and more. Bus chassis designed for urban routes differs from the one manufactured for suburban routes. For bus frameless chassis construction is used. In this frame less chassis type all the components is attached to the body. All the functions of the frame carried out by the body itself. Due to elimination of long frame it is cheaper and due to less weight most economical also. Only Disadvantage is repairing is difficult. This type of frames will affect more in collision of vehicle. The aim of the project is to analyze the frameless chassis with existing material at different load conditions. Presently steel is used for chassis construction. Modeling of the frameless chassis will be done by using modeling software CATIA and structural analysis was performed in ANSYS software to determine deformations and stresses.
Design And Analysis To Analyse The Heat Transfer Of Space Heater For Boat
As fossil fuels are depleting gradually with the course of time there is a need in developing innovative products that can extract maximum potential that the system can deliver. One of the key areas that still make use of diesel fuels for experiencing thermal comfort is the boat heating system. Due to its user-friendly characteristics with low maintenance requirements has still been the benchmark for all other types of heaters. Boat cabin heater is an essential setup for all kinds of boats and ship not just for the warmth but also to eliminate the dampness that can spread throughout the boat in cold weather. The project presents a detailed analysis of thermal behavior of a space heater using steady state thermal analysis technique to study the effect of fin numbers and fin location inside the space heater. Initially base model of space heater with two numbers of fins was prepared and analyzed with steel alloy and copper alloy material properties. In the optimized model an increase in the number of fins inside the space heater was made and analyzed with same material properties. Steady state thermal analysis with same boundary conditions was made in both cases. Modeling of space heater was done in CATIA software and the analysis was carried out in ANSYS software.