Volume 14 | Issue 3 |

 Abstract

Plastic pollution is still an important issue for the environment as synthetic polymers stay in natural ecosystems for an extended period of time. Because of its strength and chemical resistance, polypropylene is one of the most commonly used materials; however, these same properties also make it highly difficult to breakdown. The current study examines the function of the alkaline hydroxylase enzyme derived from Pseudomonas putida in the initiation of polypropylene degradation. Biochemical tests indicated that the enzyme was made and that it worked. In a controlled laboratory environment, the enzyme was used to treat polypropylene films. We examined the degradation process using weight loss measurements and chromatographic techniques like GC-MS and HPLC. The findings demonstrated that the polypropylene's weight had decreased, indicating a breakdown in the polymer structure. The development of intermediate compounds like alcohols and aldehydes was more proof that enzymatic oxidation had taken place. Two analytical methods that demonstrated the presence of these degradation products were HPLC and GC-MS. Long hydrocarbon chains had been disintegrating into smaller molecules, they confirmed. This means that alkaline hydroxylase starts the breakdown of polymers by adding oxygen to the structure. According to the study, utilizing enzymes to break down plastic waste is a viable and eco-friendly solution. It also demonstrates the possibility of creating long-term strategies to reduce environmental pollution.

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 Keywords

Polypropylene, Alkaline Hydroxylase, Biodegradation, Pseudomonas putida, HPLC, GC-MS

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 Abstract

ABSTRACT: KNN--predicts the asthma exacerbation by using the information entered by the user, previous medical records and the environment. The site also includes a virtual asthma diary, breathing exercises modules, and educational information, which along with prediction allows patients to participate actively in self-management. An integrated chatbot assistant provides real-time support whereas automated emergency Asthma is among the most common chronic respiratory diseases, with millions of patients worldwide with it, and a high health burden because of frequent exacerbations, emergency visits, and lower quality of life. Traditional management measures do not offer any real-time tracking and unprofiled interventions, and patients can fall victim to sudden attacks. These gaps are what Astreva-AsthmaWellness framework is conceptualized to be based on and fill; bringing together predictive analytics, personalized care and patient empowerment tools into one digital healthcare solution. The system also uses machine learning algorithms, which are developed at high levels- such as alerts are raised in case of critical conditions, so that there is proper communication to the healthcare providers and caregivers in a timely manner. Astreva also focuses on the holistic wellness aspect by incorporating lifestyle management aspects like medication reminders, physical activity tracking and stress reduction tips. This is a two-fold aim of predictive care and general well-being to minimize hospital hospitalization, increase compliance in treatment and also improve the quality of life among individuals with asthma. Astreva-AsthmaWellness offers a scalable, usable, and smart model of healthcare that bridges the gap between clinical expertise and digital innovation and is appropriate in managing chronic diseases in the era of modernity.

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 Keywords

Exercise, AI Chat-bot, Smart Reminder, AOI insight.

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Brain tumor detection is a critical task in medical diagnosis that requires high accuracy and reliability. Magnetic Resonance Imaging (MRI) is widely used for identifying brain tumors, but manual analysis is time-consuming and prone to human error. This paper proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNN), GoogLeNet, and Long Short-Term Memory (LSTM) networks for automated brain tumor detection. The proposed system utilizes GoogLeNet for efficient spatial feature extraction from MRI images, while LSTM captures sequential dependencies across multiple MRI slices. This combination enables the model to analyze both spatial and temporal information, improving classification accuracy. The system includes preprocessing techniques such as image normalization, resizing, and noise reduction, followed by feature extraction, sequence modeling, and classification using a softmax layer. The model is implemented using deep learning frameworks and evaluated using standard performance metrics such as accuracy, sensitivity, and specificity. The results indicate that the hybrid CNN-GoogLeNet-LSTM model provides improved performance compared to traditional CNN-based approaches. This system can assist radiologists in faster and more accurate diagnosis.

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 Keywords

Brain tumor detection, Convolutional Neural Network (CNN), GoogLeNet, LSTM, MRI classification, Hybrid deep learning, Medical image analysis.

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Durability is one of the most important properties of cement-based materials, as it determines the long-term performance and service life of structures. Conventional cement mortar is often affected by environmental factors such as acid attack, carbonation, and chloride penetration, which can lead to deterioration over time. In this study, metakaolin was used as a supplementary cementitious material to improve the durability performance of cement mortar. Metakaolin was incorporated as a partial replacement for cement at different percentages of 0%, 5%, 10%, and 15%. The durability performance of the mortar was evaluated using acid attack tests, carbonation tests, and chloride penetration tests. The results indicated that the addition of metakaolin significantly improved the durability characteristics of cement mortar. Among all the mixes, the 10% replacement level showed the best overall performance. The improvement in durability is mainly due to the pozzolanic reaction of metakaolin, which reduces calcium hydroxide content and refines the pore structure. This leads to reduced permeability and enhanced resistance to aggressive environmental conditions. The study concludes that metakaolin is an effective material for improving durability and sustainability in cement-based construction.

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 Keywords

Metakaolin, Durability performance, Modified cement mortar, Compressive strength, Water absorption, Permeability, Sustainability, Supplementary cementitious material (SCM), Long-term performance.

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In the contemporary organizational landscape, effective recruitment and selection have become critical determinants of workforce quality and organizational performance. This study examines the effectiveness of the multi-stage hiring process in enhancing recruitment outcomes, with a specific focus on organizations operating in Vadodara, India. The research explores how structured hiring stages--such as resume screening, assessment tests, interviews, and background verification--contribute to improved decision-making, reduced hiring risks, and better alignment between candidate competencies and job requirements. The study adopts a descriptive research design and is based on primary data collected through structured questionnaires from HR professionals across various industries, including manufacturing, IT/ITES, and service sectors. The findings indicate that the multi-stage hiring process significantly improves the quality of hiring decisions by enhancing objectivity, transparency, and evaluation accuracy. It also contributes to improved employee performance, reduced turnover, increased fairness in selection, and higher satisfaction among hiring managers. Additionally, the integration of digital tools in recruitment processes was found to enhance efficiency and streamline hiring activities. However, certain challenges were identified, including increased time consumption, candidate fatigue, and the need for skilled HR professionals to manage complex hiring systems. The study concludes that organizations implementing structured multi-stage hiring frameworks are better positioned to achieve effective talent acquisition, minimize recruitment errors, and ensure long-term organizational success. The research offers valuable insights for HR practitioners and organizational leaders aiming to strengthen hiring effectiveness through systematic and data-driven approaches.

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 Keywords

Multi-Stage Hiring Process, Recruitment and Selection, Hiring Effectiveness, Structured Hiring, Human Resource Management, Employee Performance, Organizational Efficiency, Decision-Making, India

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Soil degradation has emerged as a critical environmental issue due to continuous exposure to pollutants, particularly heavy metals. These contaminants persist in soil systems for long durations and adversely affect plant growth, microbial diversity, and overall ecosystem stability. Conventional remediation techniques, including chemical treatments and soil washing, are often expensive and may further disturb soil structure and native microbial communities. Hence, there is a growing need for sustainable and environmentally friendly remediation strategies. In recent years, enzyme-mediated and redox-based approaches have gained significant attention for their efficiency and eco-compatibility. The Murburn concept introduces a novel framework in which diffusible reactive oxygen species (ROS) play a central role in pollutant transformation. Unlike traditional enzyme-substrate interactions, this concept emphasizes the role of freely diffusing reactive species in facilitating biochemical reactions. Enzymes such as horseradish peroxidase (HRP), in the presence of hydrogen peroxide, are capable of generating ROS that can oxidize and transform complex contaminants into less harmful forms. This review focuses on the mechanisms of soil degradation, the impact of heavy metals, and the role of ROS in remediation processes. It also highlights the significance of enzyme-mediated systems and analytical techniques such as UV-Visible spectroscopy and Atomic Absorption Spectroscopy (AAS) in evaluating treatment efficiency. Overall, the study suggests that Murburn-based enzyme systems provide a promising, eco-friendly, and efficient approach for restoring degraded soil health and ensuring long-term environmental sustainability.

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Soil degradation, Heavy metal contamination, Murburn concept, Reactive oxygen species (ROS), Horseradish peroxidase (HRP), Enzyme-mediated remediation, Soil remediation, Redox reactions, Environmental sustainability, Bioremediation

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Rapid urban growth has exacerbated surface thermal stress in LB Nagar, Hyderabad, increasing susceptibility to heat-related disasters. Land Use/Land Cover (LULC), Land Surface Temperature (LST), Urban Heat Island Index (UHI), and geographical patterns of urban expansion are integrated to analyze thermal dynamics from 2013 to 2024. Multi-temporal Landsat data were utilized to get land surface temperature (LST) and categorize land use and land cover (LULC) changes. To evaluate thermal anomalies Urban heat island (UHI) index was computed by which heat intensity zones and transition maps were prepared. These maps show those regions which were once open land, vegetation, and water bodies were present are now converted to impermeable built-up areas. The developed areas, especially primary transportation routes and adjacent regions exhibited very high land surface temperatures and urban heat island classifications in the results. Higher Land Surface temperatures and proliferation of impermeable surface show that they are directly proportional in high temperature clusters. Those Regions which undergo transformation from dense vegetation and mixed open land to developed areas consistently align with the rise of thermal hotspots. The research aims to devise spatial decision-support system for disaster risk mitigation by integrating land modification activities with temperature indices. There are strategic solutions to incorporate heat sensitive zoning into urban planning, expansion of green infrastructure, establishment of blue-green corridors, and implementation of regulated urban densification techniques to inculcate climate responsive urban design and aim for long term urban resilience in LB Nagar.

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 Keywords

Land surface temperature, Urban heat island, Thermal hotspots, Climate responsive Urban planning, Disaster Risk Reduction.

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Creative Commons Attribution 4.0 and The Open Definition

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Despite extensive advancements in consumer behaviour research, its application to managerial decision-making remains fragmented. To bridge this gap, the study introduces the Consumer Decision Architecture Model (CDAM), a unified decision-oriented system that synthesizes key behavioral theories such as the Theory of Planned Behaviour, Elaboration Likelihood Model, Stimulus-Organism-Response framework, SERVQUAL/INDSERV, UTAUT2, and Commitment-Trust theory. In contrast to conventional review studies, this paper introduces a structured, equation-based framework that allows managers to quantify and optimize the effects of various decision drivers, such as psychological factors, social influence, service quality, pricing fairness, and digital engagement. The model identifies satisfaction and trust as key mediating factors that link these drivers to behavioral outcomes like purchase intention, choice, loyalty, and word-of-mouth. The proposed framework additionally incorporates a managerial interpretation layer, enabling organizations to convert theoretical coefficients into actionable strategies for pricing, customer experience, digital design, and service operations. By shifting the focus from descriptive determinants to decision architecture, the study makes both theoretical and practical contributions to consumer behavior literature. The paper concludes by outlining implications for strategic resource allocation and future research directions in multi-level behavioral modeling.

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 Keywords

Consumer decision architecture, consumer behaviour, decision-making models, satisfaction, trust, behavioural frameworks, pricing strategy, service quality, digital engagement, managerial decision-making.

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Solar variability fundamentally influences Earth's climate system over multiple timescales, with a strong emphasis on decadal scales. This research investigates how fluctuations in sunspot cycles, Total Solar Irradiance (TSI), spectral solar irradiance (SSI), and solar magnetic activity modulate decadal climate prediction models. By integrating observational datasets, reconstructed solar forcing indices, and simulations from coupled climate models, the study quantifies solar contributions to predictability. While solar effects are modest relative to anthropogenic forcing, their accurate inclusion markedly enhances forecast skill, particularly for surface temperatures and atmospheric patterns. The paper advocates embedding refined solar parameters into operational prediction frameworks to improve long-term climate outlooks.

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Solar Variability, Sunspot cycles, Total Solar Irradiance (TSI), spectral solar irradiance (SSI), Solar parameters

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Abstract It is very difficult to say what exactly Indian Society means. Naturally the discussion about social change in modern India is a complex issue. After the achievement of Independence the transformation of change began in the lives of the people of India. There are some revolutionary changes in the lives of people in India. The things that have changed are: Acquisition of the development programme follows the socialist idea, communication and improvements in media, extensive application of modern science and technology, urbanization and industrialization etc. In this point of view there have been various aspects of the social life of Indians since some decades.

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 Keywords

Key-words: Social Change, Urbanization, Industrialization, Modern science and Programme etc.

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