Journal of Advances in Science and Technology
Journal of Advances in Science and Technology (JAST) is a scholarly online, open access, peer-reviewed, interdisciplinary, half yearly, and fully refereed journal focusing on theories, methods and applications in Science,Technology and relevant fields. It is an international scientific journal that aims to contribute to the constant scientific research and training, so as to promote research in the field of Science and Technology.The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence.
Preparation and Application of ZnO-Nb-V Nanocomposites for Photocatalytic Dye Degradation under UV Irradiation
Zinc oxide (ZnO) and its ternary composite ZnO-Nb-V were synthesized successfully via a sonochemical route using an ultrasonic frequency of 20 Hz, power of 500 W, and a reaction time of 120 minutes. The resulting precipitates were subjected to drying at 80°C followed by calcination at 600°C to achieve phase purity and crystallinity. Comprehensive characterization was carried out using UV-Visible spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), and selected area electron diffraction (SAED) techniques, confirming the formation and nanostructured nature of the synthesized materials. The composite exhibited a bandgap of 2.6 eV, attributing enhanced photocatalytic properties. Application studies for the degradation of Chloro-Sulpha (Cl-Sulpha) dye under UV light showed marked photocatalytic efficiency across varying pH levels (4, 7, and 9). The highest degradation efficiency was observed at pH 4, indicating the material's optimal performance under acidic conditions. These results demonstrate the potential of ZnO-Nb-V nanocomposites as efficient photocatalysts for wastewater treatment applications.
Copyright (c) 2025 Journal of Advances in Science and Technology
Impact of Solar and Interplanetary Dynamics on Geomagnetic Activity
Assessing Earth's geomagnetic variability requires knowledge of the observed variability in solar and interplanetary interactions. Solar wind, solar flares, and coronal mass ejections (CMEs) are all products of the sun's magnetic field and charged particle emissions, which are the principal causes of space weather. The interplanetary magnetic fields (IMF) and "the Earth's magnetosphere" are both created by these same processes. The impact of variations in solar and interplanetary factors on the geomagnetic parameters Dst, Kp, and AE were investigated in this study. The parameters in question were solar wind velocity, CME speed, plasma density, and Bz's south geomagnetic pole (IMF). Timeseries and statical studies were performed to explore the relationships between geomagnetic and solar activity during distinct solar cycles, including research from NOAA, NASA, and the World Data Center for Geomagnetism. According to the data, geomagnetic storms that occur during the soth IMF orientation phases coincide with periods of very strong solar winds. Another indicator of spatial analysis is geomagnetic patterns and variations of certain intersolar cycle periods. With the use of solar and interplanetary connection dynamics monitoring, we can establish a stronger geomagnetic effect on geomagnetic variability. We can enhance our forecasting systems and better protect satellites, navigation systems, and power systems on the ground from extreme space weather thanks to this work, which teaches us more about the Sun-Earth relationship.
Enhancing Stability and Efficiency in Tandem Perovskite- Silicon Based Solar Cells
Tandem Solar Cells that combine perovskite and silicon absorbers are gaining attention for their ability to overcome the efficiency ceilings faced by single- junction technologies. This work systematically investigates methods for simultaneously improving the durability and energy conversion efficiency of perovskite silicon tandem cells through material engineering, interface optimization, and advanced encapsulation techniques. A detailed investigation of perovskite composition modifications, interface passivation layers, and barrier coatings is conducted to suppress degradation mechanisms caused by moisture, UV exposure, and thermal stress. The study also examines the impact of scalable fabrication processes, such as vapor deposition and low-temperature solution methods, on device reliability and uniformity. Experimental results demonstrate improved stability under accelerated aging conditions and a significant gain in overall efficiency due to optimized charge transport and reduced recombination losses. The findings contribute to advancing the commercial viability of tandem perovskite–silicon solar technologies for next-generation renewable energy systems.
LLMs and Compound AI Systems - Exploring Potential Applications in Real-World Scenarios
Compound AI Systems are the emerging phenomena integrating “large language models (LLMs)” with additional components like agents, retrievers, orchestrators, and tools to deal with issues of individual models in tasks which need reasoning, memory, multimodal knowledge, and real-time grounding. These systems compose various specialized modules into cohesive flows for more context-aware and capable behaviors. Irrespective of rising adoption in both industry and academia, the landscape of compound AI systems has been fragmented, with a lack of unified model for taxonomy, analysis, and evaluation. In LLMs, recent advancements and AI systems have made a significant change in optimization and design of complex workflows. Compound AI systems have been adept with various components when it comes to perform smart tasks. With the rise in complex systems, there are new challenges when it comes to optimizing both interactions and components. While traditional optimization models are foundational, such as “reinforcement learning (RL)” and “supervised fine-tuning (SFT)”, there are promising new avenues with the rise of “natural language processing (NLP)”, especially when it comes to optimizing different systems. This study offers a systematic review of recent developments for compound AI systems. It validates the notion of optimizing compound AI systems, highlights open research challenges, and classifies current approaches apart from major dimensions.
Correlation Between Ergonomic Practices and Workplace Injury Rates: A Sector-Wise Analysis
This study examines the correlation between ergonomic practices and workplace injury rates across three major sectors: manufacturing, logistics, and administrative services. With growing attention to occupational health and safety, organizations are increasingly implementing ergonomic interventions including workstation design improvements, equipment upgrades, training programs, and policy enforcement to mitigate employee injuries and enhance productivity. Using a combination of survey responses, observational assessments, and organizational injury records, the study applies descriptive and inferential statistical analyses, including correlation analysis, ANOVA, and regression models, to evaluate the relationship between the extent of ergonomic implementation and the frequency and severity of workplace injuries. The findings reveal a strong negative correlation, indicating that workplaces with comprehensive ergonomic practices experience significantly lower injury rates, with sector-wise variations reflecting differences in physical workload, task complexity, and adherence to safety protocols. Manufacturing and logistics sectors showed the most pronounced benefits from ergonomic interventions, while administrative settings demonstrated moderate but meaningful improvements. The study underscores the critical importance of ergonomics in promoting employee health and safety, minimizing operational disruptions, and fostering a proactive safety culture, offering actionable insights for managers, policymakers, and occupational health professionals seeking to implement effective ergonomic strategies across diverse workplace environments.
Integral Equation Methods in Solving Problems of Elasticity and Potential Theory: A Unified Approach
The employment of integral equation approaches has resulted in a revolution in the analytical and numerical treatment of boundary value concerns in the domains of elasticity and potential theory. This revolution has contributed to the advancement of these sciences. This research's objective is to offer a comprehensive account of the creation and use of integral formulations for the aim of resolving conventional concerns in a variety of fields of study. The article begins with basic similarities between linear elasticity and potential theory, and then on to cover major formulations within the field, such as Somigliana's identity and boundary integral approaches. An emphasis is placed on the benefits of reducing domain issues to boundary-only formulations, which provide computational efficiency, manage complicated geometries, and naturally integrate boundary conditions. These advantages are highlighted in the following sentence. There is a review of the significant contributions made by Betti, Somigliana, and Muskhelishvili, which is then followed by contemporary numerical implementations. In the last section of the paper, a discussion of difficulties and potential future paths is presented, with particular emphasis on nonlinear and three-dimensional situations.
A Study of the THZ Generation via Magnetized hot Plasma-Laser Interaction
Terahertz (THz) radiation by beating of two laser beams in a groove density and magnetized hot plasma is discussed theoretically. In this mechanism, the ponderomotive force leads to a nonlinear oscillatory current that resonantly excites THz radiation at the frequency upper hybrid. And the study in which discussed about Terahertz radiation Generation, Magnetized plasma, Detection of terahertz radiations
Study of Zooplankton Diversity and its Ecological Role in the Son River Ecosystem
This research focuses on the diversity and abundance of zooplankton in the Son River over a two-year period. The study identified 34 zooplankton species, including 19 rotifers, 9 cladocerans, 4 copepods, and 2 ostracods, indicating a high level of biodiversity. The abundance of these species fluctuated throughout the year, with maximum species richness observed during the winter months of February and March, and lowest during the monsoon season in June and July. The study also examined the feeding habits and population growth of zooplankton species under different feeding conditions, suggesting that organic feeds such as chicken feed and baker's yeast significantly enhance zooplankton growth. The findings provide insight into the ecological dynamics of freshwater ecosystems and the factors affecting zooplankton populations in the Son River.
A Study on Treatment of Wastewater using Electrochemical Oxidation Techniques
Electrochemical oxidation process has gained increasing interest due to its exceptional technical features to eliminate a wide range of pollutants exist in various types of wastewaters, e.g., refractory organic matter, nitrogen species, microorganisms, etc. Serve as a clean, adaptable and powerful tool in removing pollutants, this review paper focuses on the fundamental mechanisms of electrochemical oxidation process and provides discussions on the possible applications in wastewater treatment. Electrochemical technique is suitable for the removal of various pollutants from industrial wastewater. The effect of electrolytic treatment on the properties of different effluents was evaluated. Three different electrodes namely iron, aluminium and steel were used. Three factors pH, voltage and time were varied in each experiment and the results were evaluated on the basis of COD and metal removal efficiency.
Effect of Solar and Interplanetary Disturbances on Geomagnetic Field: A Comprehensive Review
The Earth’s geomagnetic field serves as a crucial protective barrier, shielding the planet from the potentially hazardous effects of solar and cosmic radiation. Despite its stabilizing presence, this magnetic shield is highly dynamic and susceptible to disturbances originating from solar and interplanetary phenomena. These disturbances often manifest as geomagnetic storms, which can have serious implications for technological infrastructure, including satellite integrity, communication and navigation systems, and ground-based power grids. This review provides a comprehensive synthesis of historical and contemporary research that explores the intricate pathways through which solar activity particularly Coronal Mass Ejections (CMEs), solar flares, and high-speed solar wind streams interacts with the interplanetary magnetic field (IMF) and subsequently with the Earth’s magnetosphere. Central to this interaction is the process of magnetic reconnection, especially when the IMF exhibits a southward Bz component, which enhances the coupling between solar and terrestrial magnetic fields and triggers energy transfer into the magnetosphere. These interactions are quantitatively assessed using geomagnetic indices such as the Dst (Disturbance Storm Time) index, the Kp index, and the AE (Auroral Electrojet) index, which offer measurable proxies for storm intensity and auroral activity. Furthermore, statistical analyses conducted across solar cycles 24 and 25 have provided critical insights into long term solar-terrestrial dynamics, revealing patterns and thresholds associated with storm onset and severity. The review also highlights the evolution of space weather forecasting methodologies, noting a growing shift towards machine learning and data-driven models that can integrate multi-parametric solar, interplanetary, and geomagnetic data to enhance prediction accuracy. As space weather events pose increasing risks in a technologically dependent world, understanding and forecasting geomagnetic disturbances remain pivotal scientific and operational priorities.
Perceptions of Graduates on Artificial Intelligence and Automation: Implications for Employability
This research investigates graduates’ opinions of Artificial Intelligence (AI) and automation, emphasizing their awareness, readiness via reskilling and upskilling, and the consequences for employment in India. Employing a descriptive study approach, data were gathered from 150 graduates and postgraduates in Gwalior, Madhya Pradesh, via structured questionnaires. The study used descriptive statistics, Pearson’s correlation, and regression methods to examine the link among awareness, readiness, and employability outcomes. The results indicate that the majority of respondents had a reasonable understanding of AI, with 55% categorizing themselves as “somewhat knowledgeable.” Sixty-five percent of respondents saw AI as both a danger and an opportunity, while fifty percent had already registered for AI-related courses, demonstrating proactive reskilling initiatives. Technical competencies, including coding and data science, were seen as the most essential for employment (45%), in conjunction with soft skills such as communication and flexibility (30%). Correlation and regression studies indicated strong positive associations between AI awareness, preparation, and employability, but unfavourable views of AI in recruiting procedures correlated with less optimism for future career possibilities. These results underscore the need of incorporating AI-centric curriculum, government-sponsored reskilling programs, and cooperation between industry and academics to address skill deficiencies and equip graduates for an AI-oriented labour market.
Insects as Bioindicators: Using Entomofauna to Monitor Ecosystem Health
Insects play an important role in many ecosystem processes, including pollination, decomposition, nutrient cycling, and predation within food webs. They make up more than 80% of animal species. Their variety, short life spans, and sensitivity to environmental disturbances make them useful bioindicators for assessing ecosystem health. This research assessed insect diversity in three habitats of Bhopal Van Vihar National Park, agricultural areas, and Upper Lake, to determine ecosystem conditions. A total of 87 species were detected and classified into sensitive and tolerant categories. The findings indicated that Van Vihar National Park demonstrated the most variety and richness, agricultural lands presented intermediate diversity, while Upper Lake revealed the least, suggesting considerable ecological stress. Statistical investigations validated robust connections between insect diversity and environmental variables, including plant cover and pH levels. These results highlight the significance of entomofauna as bioindicators for ecological assessment and conservation strategy development.
A study of dairy effluent by using phycoremediation treatment
As a result of its efficacy and extensive application, there are a range of biological therapeutic approaches available today. The vast majority of dairy waste is biodegradable, has an unpleasant odour, and contains a substantial quantity of oil. The contamination of water by untreated dairy waste led to the spread of several diseases among humans and animals. Because of this, it is probable that the majority of currently functioning dairies utilise activated sludge-based treatment facilities. The nutritional content of dairy effluent cannot be eliminated using this method of treatment. Dairy effluent has sufficient nutrients for biological growth, and biological treatment approaches are believed to be more successful and economical. The processing of milk and other dairy products generates foul-smelling effluent due to the presence of a substantial amount of organic materials. This wastewater also contains a notable quantity of COD and BOD. The growth in human population has resulted in a rise in the amount of water contamination. Concerns over the diverse types and amounts of garbage that are created and deposited into natural water sources have led to the implementation of new regional water quality regulations.
Can Artificial Intelligence Experience Spirituality?
Artificial Intelligence (AI) has transformed almost all aspects of human life with rapid advancements, from business to healthcare to creative expression and interpersonal relationships. Currently, influence of AI has gone beyond applications, being in the realm of existential crisis and spiritual existence. Influence of AI has expanded over the years beyond just apps, being in the realm of spirituality. AI is constantly working on spirituality, providing new paths to seek guidance, discover faith, and engage in excellent experiences. AI tools like meditation apps, chatbots, and religious text analysis have become the marker to raise significant questions around the role of technology and nature of spiritual experiences. Some people report that they feel connected and comfortable by interacting with AI, while others ponder the question whether AI can mediate spirituality or if it just mirrors human desires and beliefs. This study investigates the potential of AI as channel for spirituality, its applications, theological implications, and future of AI as a spiritual guide. This study seeks to explore whether spiritual experiences can be improved by AI or if there is a risk automating or commodifying the journeys which are deeply personal. Evolution of AI and its integration in spirituality signals both hype and caution for people engaged in spiritual journeys.
A Comparative Review of Structural, Electrical, and Magnetic Properties of Bulk Versus thin Film Functional Oxide Materials
Functional oxide materials have garnered significant research attention owing to their adjustable structural, electrical, and magnetic characteristics, rendering them suitable for diverse applications such as microelectronics, memory storage, spintronics, sensors, and energy devices. These characteristics are significantly influenced by the morphology and dimensionality of the material, particularly regarding whether the oxide is in bulk or thin film form. This paper thoroughly examines and contrasts the structure–property correlations in both bulk and thin film functional oxides, focussing specifically on perovskite, spinel, and columbite-type materials. Significant distinctions in crystallographic orientation, grain boundaries, phase purity, defect density, and anisotropic phenomena are examined concerning their impact on dielectric properties, electrical conductivity, ferroelectric behaviour, and magnetic characteristics. The examination includes synthesis procedures, characterisation tools, and application relevance. This study seeks to elucidate design options for enhancing performance via morphology management, hence informing the future advancement of oxide-based electrical and magnetic devices.
A study of Pulse slippage of Cosh- Gaussian lasers in corrugated magnetized plasma generates terahertz radiation
The combined effects of pulse slippage and the transverse magnetic field are studied on terahertz radiation excitation by nonlinear beating of two cosh-gaussian (chg) laser pulses propagating in corrugated plasma. The beating lasers exert nonlinear ponderomotive force on plasma electrons. The oscillating electrons couple with corrugations presents in the plasma and resonantly excites a nonlinear current (at different frequencies) which drive the terahertz wave at proper phase matching conditions and the study in mainly discussed about terahertz radiation generation ,excitation of the wave generation by obliquely incident pump lasers on a hot magnetized plasma,terahertz radiation generation by pulse slippage of cosh-gaussian lasers in a corrugated magnetized plasma
Copyright (c) 2023 Journal of Advances in Science and Technology
Evaluation of In Vitro Diagnostic Technologies for Sars-Cov-2 Detection
The World Health Organization's classification of SARS-CoV-2 as a pandemic on March 11, 2020, has had a lasting influence on global health systems, continuing until March 2025. This study evaluates in vitro diagnostic (IVD) technologies for SARS-CoV-2 detection, focusing on datasets from the Indian Council of Medical Research (ICMR) and the Web of Science (WoS) repository. Analysis covers 2,882 approved commercial devices and test kits manufactured by 1,257 companies, alongside 12,886 related research publications. Among assay categories, antigen-based kits dominated (44%), followed by antibody assays (31%) and nucleic acid-PCR tests (23%). Validation studies revealed Nucleic Acid-PCR as the gold standard, with clinical sensitivity of 98.6%±2.0% and specificity of 99.3%±1.2%, while antigen-based kits offered the fastest detection, returning results within 15–30 minutes. False result analysis showed antigen kits with lower false positives (2.3–4.1%) and antibody kits with slightly fewer false negatives (4.6%). Limit of detection (LOD) evaluation indicated that 90% of antigen kits and 80% of PCR kits successfully identified positives at 1,000 viral copies or higher. This study highlights India’s significant role in diagnostic innovation during the pandemic and underscores the need for harmonized validation metrics, rapid testing platforms, and regulatory preparedness to strengthen global readiness for future outbreaks such as Disease X
Role of Zooplankton as a Bioindicator in Assessing Physico-Chemical Changes in the Son River
The Son River, an important water body in India, plays a crucial role in sustaining its surrounding ecosystems and communities. This review examines the physicochemical factors present in the river and their implications for aquatic biodiversity, with a particular focus on zooplankton communities. Zooplankton, as important primary consumers and bioindicators, provide crucial information about water quality and ecosystem health. The study explores various factors affecting their density, diversity, and role in energy transfer within aquatic food webs. Key parameters such as temperature, dissolved oxygen, and organic matter are discussed as important determinants of zooplankton dynamics. The review further highlights the interconnectedness between zooplankton populations and larger ecological and environmental changes. This underscores the importance of maintaining a balanced aquatic ecosystem to support biodiversity and ensure the sustainability of dependent communities.
Phycoremediation technique using for the treatment of Dairy Effluent
The degradation of the environment poses an ever-greater threat to human health, with the danger being compounded in urban areas with high concentrations of industrial activity. The addition of industrial pollutants has resulted in a significant decline in the quality of the water supply, which is a significant challenge for large industrial communities. Both the quality of the water and the aquatic life in it are negatively impacted when effluents are discharged into larger bodies of water without proper regulation. By altering the worldwide cycles of elements or by introducing chemicals, industrial effluents, pesticides, and so on into the environment, human beings are able to affect the environment and even the entire eco-sphere. A biosphere that has undergone such transformation poses a danger to the continued existence of humans on this planet. As a result of the presence of biodegradable components, the effluent from the dairy is primarily organic. Because of the high concentration of nutrients in these effluents, microalgae may be able to grow in them. In addition to the production of potentially valuable biomass that can be used for a variety of purposes, such as the production of biogas and biofuels, composting, animal feed, aquaculture, and the production of chemicals, microalgae culture in wastewater treatment offers a contemporary solution for tertiary wastewater treatment.
Estimating Heterogeneous Birth Probabilities using the EM Algorithm: A Probabilistic Approach to Population Dynamics Modeling
Improving our ability to calculate birth probabilities among diverse subgroups in a population is crucial for understanding population growth dynamics. The various reproductive behaviours inherent in real-world communities are typically ignored by traditional demographic models, which generally presuppose uniformity. Using the Expectation-Maximization (EM) technique, this research presents a probabilistic framework for predicting heterogeneous birth probability. The population is modelled as a finite mixture of Bernoulli distributions, with a different birth probability for each subgroup. In order to uncover hidden demographic patterns in binary birth outcome data, the EM technique is used to estimate the parameters of the latent mixture model repeatedly. In spite of imbalances and noise, the method converges quickly and accurately in synthetic dataset trials. A real-world case study also shows how the approach may be applied to find hidden subgroups with different reproductive trends. To find the best amount of subpopulations, model selection techniques like the Bayesian Information Criterion (BIC) might be utilised. The suggested strategy increases both the accuracy of estimates and their interpretability in demographic research, according to the results. Because it provides a detailed, data-driven picture of fertility trends in different populations, this method shows potential for long-term population forecasting, resource allocation, and policy modelling.
PM10 Associated Heavy Metals Interference with Olfactory Enzymes: Evidence from Air Sampling and Molecular Docking Studies
Airborne particulate matter (PM10) serves as a primary carrier for heavy metals in urban environments, posing significant threats to human health through respiratory and neurological pathways. The olfactory system represents a direct route for heavy metal entry into the central nervous system, bypassing the blood-brain barrier. This study investigates the interference of PM10-associated heavy metals with key olfactory enzymes through environmental sampling and molecular docking analysis. Air samples were collected from three urban locations in Kanpur, India between November 2024 and January 2025. Heavy metal concentrations were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Molecular docking studies examined interactions between heavy metals (Cd²⁺, Ni²⁺, Pb²⁺, Hg²⁺) and three critical enzymes: cytochrome P450 (PDB ID: 1OG5), aldehyde dehydrogenase (PDB ID: 1O01), and monoamine oxidase (PDB ID: 1GOS). Lead (Pb) concentrations ranged from 10.2-19.2 μg/m3across sampling sites, with P. Road showing the highest levels. Molecular docking revealed Pb²⁺ exhibited the strongest binding affinity across all enzymes (docking scores: 7.665-8.806), followed by Cd²⁺ (5.097-6.057), Ni²⁺ (4.375-5.626), and Hg²⁺ (3.907-5.914). Statistical analysis demonstrated significant correlations between metal concentrations and binding affinities. PM10-associated heavy metals demonstrate substantial potential for olfactory enzyme interference, with lead emerging as the most concerning neurotoxic threat. These findings underscore the urgent need for air quality management strategies targeting heavy metal emissions.
The Improvement of Industrial Safety Measures Using AI and IOT
The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as a transformative approach to enhancing industrial safety by enabling intelligent decision-making, predictive analytics, and real-time monitoring. Industrial environments are inherently exposed to risks such as equipment failure, toxic exposure, and human error, all of which can lead to accidents, injuries, and financial losses. AI-driven predictive analytics, when combined with IoT-enabled sensor networks, offers a proactive framework to mitigate these hazards before they escalate into critical incidents. Smart sensors continuously capture data related to equipment performance, environmental conditions, and worker behavior, which is then analyzed by AI algorithms to detect anomalies, predict failures, and trigger timely alerts. This integration enhances workplace safety, minimizes unplanned downtime, optimizes resource utilization, and ensures compliance with regulatory standards. Moreover, AI-powered automation and robotics can take over high-risk tasks, reducing direct human exposure to hazardous conditions. Wearable IoT devices further strengthen worker protection by monitoring vital health indicators and adherence to safety protocols. Overall, the integration of AI and IoT transforms reactive safety systems into proactive and predictive strategies, fostering an industrial ecosystem that is safer, more intelligent, and environmentally sustainable.
Use of EM Algorithm for Classification Issues and Parameter Estimation in Gaussian Mixture Models
One effective iterative method for estimating parameters in latent variable probabilistic models is the Expectation-Maximization (EM) algorithm, which finds extensive use in classification issues. A versatile probabilistic framework for modeling data from multiple Gaussian distributions, Gaussian Mixture Models (GMMs) employ EM for parameter estimation, which is explored in this article. Problems with class membership estimation (including means, covariances, and mixing coefficients) are the primary focus of this study. Starting with initial parameter estimations, estimating posterior probabilities in the E-step, and repeatedly updating model parameters in the M-step until convergence, the technique entails applying the EM algorithm on both synthetic and real-world datasets. In order to measure performance, we compare it to K-means and other baseline classifiers using measures like classification accuracy, Adjusted Rand Index (ARI), and log-likelihood progression. When data is distributed according to a multimodal Gaussian distribution, the findings show that EM-GMM provides better parameter estimation and better classification accuracy. Initialization procedures have a substantial effect on performance, as convergence study further shows. In addition to reiterating the EM algorithm's usefulness for probabilistic classification, this paper also describes its advantages and disadvantages and offers suggestions for improving its performance in settings with high-dimensional data and noise.
Electrochemical Oxidation For Wastewater Treatment
Electrochemical oxidation (EO) as electrochemical method is unique by three aspects. The first is that is the most versatility process in water treatment area and covers: various industrial effluent treatment including, amongst others, distillery, agrochemical, pulp and paper, textile dyes, oilfield and metal plating wastes; hazardous effluent treatment including hospital wastes; removal of pathogens and persistent, pharmaceutical residues and biological from municipal wastewater treatment plant; removal of organic micro - pollutants such as pesticides and heavy metals such as arsenic and chromium from water. Another aspect is that EO is complementary with most other methods: chemical or electrochemical, and is often combined with one or more of them. And finally, this procedure is the most interdisciplinary of all. It includes: material science, (micro)biology, (electro)chemistry, environmental protection, water supply systems, etc Many human activities result in the production of wastewater. Usually, physical, chemical and biological processes are successfully combined for the treatment of municipal wastewater, attaining good removal efficiencies. However, some industrial processes introduce anthropogenic recalcitrant pollutants in wastewater that are quite difficult to remove or degrade using conventional means and that should be removed due to their hazardousness
Implementing Lean and Green Strategies for Sustainable Efficiency in the Indian Steel Sector
This research presents a comprehensive case study on the integration of Lean and Green manufacturing strategies at a small scale ABC Alloys Ltd non-ferrous metal processing enterprise. The company faced multiple operational and environmental challenges, such as high levels of production waste, excessive energy consumption, extended lead times, and inadequate compliance with environmental regulations. To address these issues, a structured transformation initiative was undertaken that combined Lean tools namely 5S, Kaizen, Single-Minute Exchange of Dies (SMED), and Value Stream Mapping (VSM) with Green practices like wastewater recycling, LED lighting retrofitting, and the implementation of noise reduction systems. This integrated Lean-Green approach led to notable improvements, including reduced scrap generation, optimized energy and water usage, shorter production lead times, and increased employee engagement through participatory methods. The case demonstrates that even resource constrained Small and Medium Enterprises (SMEs) can successfully adopt Lean-Green strategies to achieve operational efficiency, environmental sustainability, and long term cost savings. The findings underline the viability of a systematic and phased implementation model, offering a scalable roadmap for similar manufacturing units across India.
The extended jacobi polynomials in two variables: A generalization
This work presents an extension of the Jacobi polynomials to two variables and generates a number of generating functions. In addition, certain applications of Jacobi polynomials are examined, along with Bateman's and Brafman's generating functions, Rodrigues formula, and the relationship between Legendre and Jacobi polynomials. The main objective of this study is to construct and describe certain properties of an analogue of extended Jacobi polynomials that operates on two variables. The authors provide recurrence relations that use extended ϱJacobi polynomials in two variables, along with a variety of differential equations for these polynomials.
Behavioural Safety and Human Factors in High-Risk Industries
In high-risk industries such as nuclear power, oil and gas, mining, aviation, and construction, behavioural safety and human factors are central to risk management and accident prevention. While technological safeguards are critical, human behaviour, decision-making, and organisational dynamics play an equally vital role in ensuring workplace safety. Behavioural safety emphasizes cultivating a strong safety culture, reducing human error, and systematically observing, analysing, and correcting unsafe practices. In parallel, the study of human factors examines the interactions between people, technology, and organisational systems, addressing issues such as fatigue, stress, ergonomics, training, and communication. Taken together, these approaches provide a comprehensive framework for improving operational safety by recognising that accidents often arise from a combination of human error, organisational weaknesses, and equipment failure. Applying human factor principles ensures systems are designed to support optimal human performance under demanding conditions, while behavioural safety programs, leadership initiatives, and employee engagement foster accountability and vigilance. Integrating these two perspectives is crucial for achieving sustainable safety performance, reducing accidents, and protecting both workers and communities in today’s increasingly complex, technology-driven industries.
An Examination of Particular Curves and Metal Structures Regarding Manifold Properties
This paper delves into the intricate relationship between particular curves and metal structures in the context of manifold properties. By investigating the geometric and topological characteristics of these curves, we aim to uncover their influence on the stability and performance of metal structures. The study utilizes differential geometry and manifold theory to analyze the curvature and torsion of specific curves, examining their role in stress distribution and structural integrity. Additionally, we explore how these curves interact with the manifold properties of metal surfaces, such as smoothness, continuity, and boundary behavior. Through both theoretical analysis and practical experimentation, we demonstrate that the integration of precise curve design can significantly enhance the resilience and adaptability of metal structures. This research not only contributes to the fundamental understanding of manifold properties in applied mathematics but also provides valuable insights for engineering applications, particularly in the fields of aerospace, civil engineering, and materials science. The findings suggest potential pathways for optimizing metal structures by leveraging advanced geometric techniques, thus paving the way for innovations in structural design and manufacturing processes.
Applications of I-function in solving fractional differential equations and boundary value problems
An extension of the famous H-function by Fox, the I-function, which in turn extends to the incomplete I-functions. Solving the one-dimensional heat flow problem using incomplete I-functions is the focus of this work. Additionally, we derive a large number of special situations from our primary conclusions. The Struve's function with its enlarged integral with the I-function in two variables is a generic class of polynomials. This study also addressed a boundary value problem concerning the steady-state temperature distribution of a rectangular plate by using the I-function, Struve's function, and the Extended General Class of Polynomials.
Study of generalised hypergeometric functions and fractional calculus with applications
A strong foundation for tackling complicated issues in engineering, physics, and applied sciences is offered by the study of generalised hypergeometric functions and fractional calculus, which is a major junction of classical analysis and current mathematical theory. The generalised hypergeometric functions are naturally generated in the solutions of diverse differential equations and have rich analytical features. They expand the classic hypergeometric functions over a bigger parameter space. For memory-dependent and hereditary phenomena that cannot be effectively represented by standard integer-order calculus, fractional calculus offers more sophisticated tools by dealing with integrals and derivatives of arbitrary (non-integer) order. An examination of the foundations and relationships between fractional calculus and generalised hypergeometric functions, as well as their operational methods, integral transforms, and special function representations, is the goal of this research. An analysis of fractional differential equations analytically solved by generalised hypergeometric functions is highlighted, illustrating their practical use in problems of viscoelasticity, anomalous diffusion, and signal processing, among others. By providing concrete examples, we go deeper into the applications and show how these mathematical tools have contributed to theoretical advancements as well as practical applications. The importance of fractional-order operators and special functions in modern scientific research and mathematical modelling is highlighted by this study, which ultimately adds to our knowledge of their synergy.