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Urban Heat Dynamics in Pune: The Influence of Land Cover and Local Climate
Urban areas with high population density and extensive infrastructure development have been experiencing an increasing strain on the local heat budget, leading to a surge in heat-related illnesses and discomfort. This study examined the impact of climate and land use as heat islands in Pune, India, from 2012 to 2023 at six different locations representing varying degree of urbanization. Satellite land cover observations revealed that 55.17% of the total area was urbanized in the city itself, which was limited to 44.8% in 2012. This urbanization has significantly impacted the increasing tendency of maximum temperature (Tmax; 0.13? year?1 to 1.63? year?1) at almost each study site and minimum temperature (Tmin; 0.06? year?1 to 0.23? year?1) at a specific location during night. The mutual effect of land cover changes and meteorological conditions have evidenced the heat islands with varying intensities (2? to 8?) at four of the six sites, with significantly intensifying rates from 0.05? year?1 to 0.39? year?1. The estimation of dominating land cover type for the formation of heat islands demonstrated a significant simple determination (r2 = 0.001 to 0.013) and probability (P < 7.910?13 to 2.330?5) with heat island temperature identifying urban land cover as the primary factor at two sites, while the other two were affected by mixed land covers influenced by local meteorological characteristics. The outcomes of this study offer valuable insights into the development of heat islands in Pune and could guide strategies for alleviating urban heat, ultimately improving climate resilience and thermal comfort citywide. 2025, Binghamton University Libraries. All rights reserved. -
Transition in Kpen Climate Zones and Its Impacts on Hydroclimatic Extremes Across India
Shifting climatic zones across India are reshaping the country's hydroclimatic balance, with significant consequences for drought behaviour and water security. This study examines how spatial changes in KpenGeiger climate zones between two climatological periods (19611990 and 19912020) are influencing long-term drought characteristics. Using high-resolution gridded rainfall and temperature data from the India Meteorological Department, the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI) are used to assess drought intensity and extent across five major climate categories: tropical, arid, temperate, continental and polar. Results reveal a noticeable expansion of the arid zone by 3.86% and a contraction of the temperate zone by 6.94%, indicating a transition toward warmer and drier climates. These spatial shifts have altered regional drought behaviour, with formerly moderate zones experiencing more frequent and intense droughts. The arid and tropical zones, where expansion is observed, show increasing drought severity, largely driven by rising evapotranspiration due to temperature increases of 0.12C0.25C/decade (Tmax) and 0.10C0.20C/decade (Tmin). In contrast, regions where the temperate climate is receding are showing a loss of climatic buffering capacity against drought. SPEI captures more widespread and severe drought events than SPI, underscoring the increasing role of thermal stress in water balance anomalies. This study highlights that changes in the spatial extent of climate zones are a key driver of evolving drought patterns in India. Recognising these shifts is essential for improving temperature-sensitive drought monitoring and formulating zone-specific adaptation strategies in the face of accelerating climate change. 2026 Royal Meteorological Society. -
Regional Drought Modulation by ENSO and IOD as Indicated by the Standardized Precipitation Index
Understanding the modulation of drought by large-scale oceanatmosphere teleconnections is crucial for strengthening drought prediction and resilience in India. This study investigates the influence of the El NiSouthern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on meteorological drought characteristics across India from 1950 to 2024 using the Standardized Precipitation Index (SPI) at a 12-month timescale. Drought events were quantified in terms of frequency, duration, severity, and intensity and linked to ENSOIOD variability through composite, correlation, and mediation analyses. Results reveal that El Ni events consistently correspond to widespread and severe droughts, particularly over central and southern India, with drought frequency exceeding 30% and SPI < ?1.5. Conversely, La Ni phases enhance monsoon rainfall and alleviate drought conditions across much of the subcontinent. Spatial correlations demonstrate that ENSO exerts a stronger, more coherent influence on both rainfall and SPI than the IOD, while positive IOD phases can partly offset El Ni-driven drought in limited regions. Mediation and wavelet coherence analyses confirm ENSOs dominant control at interannual (48 year) timescales and reveal secondary, episodic modulation by the IOD. These findings highlight the complex, evolving dynamics between Pacific and Indian Ocean drivers in shaping Indias hydroclimate variability. The study underscores the need for integrated ENSOIOD monitoring and inclusion of multi-ocean indicators in Indias drought early warning and seasonal forecast frameworks. 2026 Binghamton University Libraries. All rights reserved. -
Exploration of aldazine Schiff bases as promising bioactive agents: A synergistic approach using DFT, ADME, antibacterial and cytotoxicity analysis
A straightforward method for synthesizing four new asymmetric Aldazine Schiff base derivatives using aromatic aldehydes and hydrazine precursors was successfully demonstrated under moderate conditions. These compound are designated as follows: 1-((E)-(((E)-2-ethoxy benzylidene) hydrazineylidene) methyl)naphthalene-2-ol (2-EHMN) (L1), 1-((4-ethoxy benzylidene) hydrazineylidene) methyl) naphthalene-2-ol (4-EHMN) (L2), 1-((2?hydroxy-4-methoxybenzylidene) hydrazineylidene) methyl) naphthalene-2-ol (HMHMN) (L3), and 1-((2?chloro-6-hydroxyybenzylidene) hydrazineylidene) methyl) naphthalene-2-ol (CHHMN) (L4). The compounds obtained were analyzed via FT-IR, 1H-/13CNMR spectroscopy, HRMS spectrometry techniques, and elemental analysis. Infrared (IR) spectroscopy, UVVis spectroscopy, and accurate melting point determination all contribute to the improved study of synthesised compounds. A comprehensive solubility analysis was conducted for all synthesized compounds, demonstrating their solubility in dichloromethane (DCM), tetrahydrofuran (THF), and dimethylformamide (DMF). Thermoanalytical studies of all the ligands were also examined and compared. Furthermore, a single-crystal X-ray diffraction (SCXRD) analysis of L1 was conducted using a single-crystal diffractometer, with unit cell calculations and data collection performed using MoK? radiation (? = 0.7107 . Density functional theory (DFT) computations were used to optimise the structures of molecules and assess reactivity, durability, and electronic characteristics of the developed ligands. Molecular docking of L1, L2 and L3 has been done in different proteins, which gives precise results to show the activity for cytotoxicity and antibacterial studies. In silico, the ADME process calculations showed that the synthesised compounds have favourable drug-like features. In vitro antibacterial (L2 and L3) and cytotoxicity (L1) tests were also performed to assess their efficacy as therapeutic agents. 2025 Elsevier B.V. -
Development of Privacy Preserving Machine Learning Techniques Using Secure Multi-Party Computation
Machine learning (ML) has brought about a paradigm shift in insight generation across various domains, including healthcare, finance, and pharma, by leveraging historical data. However, the effectiveness of ML solutions hinges on the seamless collaboration between data owners, model owners, and ML clients while ensuring that privacy concerns are meticulously addressed. Unfortunately, existing privacy-preserving solutions have not been able to offer efficient and confidential ML training and inference. This has led to an increased focus on Privacy-Preserving Machine Learning (PPML), which has become a flourishing area of research aimed at safeguarding the privacy of machine learning stakeholders. In this regard, the present research introduces novel techniques for private ML inference and training of models using Secure Multi-Party Computation (SMPC) and Differential Privacy (DP) methods on horizontally and vertically partitioned datasets. The proposed techniques are implemented using Python with open-source libraries such as SyMPC and PyDP to ensure confidential inference and model protection. The findings across various parameters illustrate the effectiveness of the suggested techniques in addressing the privacy concerns of model owners and inference clients, with no significant impact on accuracy and a linear influence on performance as the privacy parameters, such as secure nodes count within the SMPC cluster. are increased. Furthermore, the privacy gain is substantiated by information privacy measures such as Mutual Information and KL-Divergence across different privacy budgets, which demonstrate empirically that privacy can be preserved with high ML accuracy and minimal performance cost. -
Role of project management methodologies in achieving project success and business value creation in financial services IT provides
Increasingly, project-based organizations, have been implementing
project management methodology for specific projects based on the dynamic environment. Financial services face challenges in the form of unstable market conditions, economies and technology, strict controls, and higher stress to meet customer demands and maximize profits. They are compelled to provide services that are tailored to changing customer interests as a result of technological innovations and developments. Financial services especially information technology-based businesses find it difficult to sustain considering the dynamic behavior of the market in terms of efficiency and controlling the cost based on market changes. Moreover, financial services are also constrained to manage stakeholders while maintaining flexibility and responsive processes and focusing on technological advancement to improve success in projects. Many IT initiatives fail to provide the desired outcomes as per the project budgets, project types, and project contexts. Some are also unable to deliver business value to the customers due to the adoption of an unsuitable project management methodology. Regardless of the wide variety of choices, project managers often fail to decide on available alternatives. Project managers tend to tailor projects based on criteria that may not be associated with the overall project objectives rationally. The goal of this research work is to assess the role of hybrid and agile project management approaches in achieving success and creating business value considering project contexts for financial services IT projects. -
Role of project management methodologies in achieving project success and business value creation in financial services IT projects
Increasingly, project-based organizations, have been implementing project management methodology for specific projects based on the dynamic environment. Financial services face challenges in the form of unstable market conditions, economies and technology, strict controls, and higher stress to meet customer demands and maximize profits. Many IT initiatives fail to provide the desired outcomes as per the project budgets, project types, and project contexts. Some are also unable to deliver business value to the customers due to the adoption of an unsuitable project management methodology. Regardless of the wide variety of choices, project managers often fail to decide on available alternatives. Project managers tend to tailor projects based on criteria that may not be associated with the overall project objectives rationally. The goal of this newlineresearch work is to assess the role of hybrid and agile project management approaches in newlineachieving success and creating business value considering project contexts for financial newlineservices IT projects. A conceptual research framework and various propositions were built on the literature analysis. To analyse the theoretical support, data was gathered through an online questionnaire. The survey was administered to project managers who were newlineinvolved at different levels in IT project development in financial service organizations. The study findings demonstrate that the various key success factors vary significantly across hybrid and agile project management methodologies considering different success measures in IT projects. And, such factors and processes play a vital role, especially in large-size high-technology projects. This research work enriches the project management literature and offers practical guidance by applying the contingency approach for comparing the effect of key success factors applicable to financial service IT projects from the viewpoint of hybrid and agile project management approaches. -
Enhancing Regional Language Proficiency in Large Language Models Through Translated Datasets
Although Large Language Models (LLMs) have made significant progress in Natural Language Processing the lack of high-quality training data frequently limits their ability to perform well in regional languages. To improve LLM competency this study methodically translates an English dataset into the low-resource language of Bhojpuri. On this new dataset we apply a structured translation methodology and then refine an LLM that has already been trained. The models capacity to produce contextually relevant and culturally appropriate responses in Bhojpuri has significantly improved according to a comparison of its performance before and after fine-tuning. Our findings show that this translation-centric approach provides a practical and affordable way to enhance the usefulness and inclusivity of LLMs increasing the effectiveness and accessibility of these potent AI tools for underrepresented linguistic groups globally. For linguistic groups that are marginalized globally. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
AI-enhanced approaches for personalized cardiac treatment: insights from ECG data
The analysis of drug-induced alterations in the electrocardiogram (ECG) is essential in measuring cardiac safety, but manual analysis is not always accurate enough to identify subtle but important effects. This paper examines how machine learning (ML) models can be used to categorize various pharmacological treatments according to their distinct ECG patterns to establish a platform of individualized therapeutic evaluation. Using the public ECG Effects of Dofetilide, Moxifloxacin, Dofetilide+Mexiletine, Dofetilide+Lidocaine and Moxifloxacin+Diltiazem (ECGDMMLD) database, key electrophysiological features were extractedincluding heart rate variability (HRV) and standard cardiac intervals (RR, PR, QT, QRS) to train and compare three different classifiers: XGBoost, Random Forest, and a Support Vector Machine (SVM). The analysis showed that tree-based ensemble techniques were very useful in this task. The XGBoost model had a better classification accuracy of 98.1%, which was closely followed by the random forest at 97.3%. Conversely, the SVM had much lower accuracy, implying that it was not as well adapted to the complexity of the high-dimensional ECG data. These results establish that ML models, particularly XGBoost, can accurately decode complex drug-induced cardiac signatures from ECG data. This work is a powerful demonstration of the proof-of-concept of automated and data-driven analytics integration into clinical processes to enhance drug safety and promote personalized medicine. The Author(s) 2026. -
Green credibility: Unlocking employee engagement through environmental responsibility
Today, the adoption of environmental policies has gained significance in the corporate scenario not only for sustainability but to also improve organizational dynamics. These environmental policies are discussed in terms of their effects on the overall performance, satisfaction, and general well-being of the employees, thus forming the company culture and behavior. Another objective of this book chapter is to come up with a developmental model, other than what exists so far, which will study the impact of environmental policies using Organizational image as a moderator. Also, including how employee motivation, organizational communication, and leadership can create an enabling environment for sustainable development. This book chapter aims at offering useful insights and recommendations to companies keen to exploit environmental efforts not only as a means of complying with the requirement but also as a stimulant for a motivated and engaged workforce in the modern corporate climate. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Growth and characterization of glycine potassium nitrate NLO crystals
Single crystals of glycine potassium nitrate were grown using slow evaporation technique. The solutions were prepared mixing glycine with potassium nitrate in different ratios stirring continuously for an hour to get a saturated solution. It was then kept at room temperature for controlled evaporation. Optically clear and well shaped crystals were obtained and these were characterized by (FTIR) studies, EDAX and X-ray powder diffraction. 2011 American Institute of Physics. -
Self-care practices, professional quality of life and challenges : An exploration among counsellors in Kerala
The profession of counselling continues to prove its importance in today's fast- paced world, where pausing down and listening to someone are becoming an odd and luxurious concern of people. The counsellor may feel as though they are in a marathon, which demands them to continuously offer unconditional positive regard and empathy for their clients. This may leave them incapacitated to look into themselves and to recognize what is happening to them in this process of caring for others. Though profound discourses take place in the international scenario about the criticality of rendering to the need and well- being of counsellors, evidence-based studies, and effective interventions in this regard are still lacking in the Indian context. This study seeks to fill this gap by exploring, how the positive and negative feelings of continuous caring can affect the professional quality of life of counsellors, what are the self-care mechanisms they adopt, and which are the professional issues they find most concerning. To meet these objectives, the present research employs a mixed- method design in a sequential explanatory fashion. The study encompasses three different phases wherein phase I and II; the sample consisted of counsellors working in various government projects in Kerala selected through dense sampling method and purposive sampling method respectively. In phase III, counsellor experts with more than 20 years of experience in the field are considered through purposive sampling. Findings describe that counsellors tend to follow an unbalanced self-care routine with significantly less focus on professional aspects. The presence of a high risk of burnout and secondary traumatic stress with a moderate feeling of compassion satisfaction call for immediate interventions for counsellors. Lack of benefits, safety issues, concerns about professional identity, poor working conditions, absence of career prospects, and lack of professional credentials are the major challenges identified by the counsellors. Considering these challenges, a set of recommendations are proposed by counsellor experts, which suggest reformations at both the systemic level and academic formation. Specific recommendations are also listed on the development of personal self and professional self for safe, effective and ethical practice of counsellors across a variety of practice settings. Implications exist for policymakers and counsellor educators to create an avenue for supporting a healthier and sustainable counsellor workforce. -
Determinants of employee eco-initiatives in Indian hotel industry
Results of a questionnaire survey completed by 402 respondents who were all employees of hotels that have adopted eco-friendly practices showed that eco-initiatives are significantly and positively correlated to conservatism, commitment to the cause of the environment, and monetary rewards and recognition; significantly and negatively correlated to self-transcendence and environmental training; and bear no significant relationship with environmental communication and self-enhancement. Future research should consider the role of guests in promoting employee eco-initiatives. Copyright 2019 Inderscience Enterprises Ltd. -
Analytics in e-learning
Predictive analytics play an important role in the evolving dynamics of higher education. There has been a steady up rise in use of technology in the field of education. e-learning is seen as a futuristic approach of learning. Hence, the study of factors influencing success in e-learning courses is relevant to the current scenario. Use of predictive analytics in virtual learning environment would provide insight on learning patterns of students. The learning data available in the traditional teaching environment is different from the one, which is available from virtual learning. This paper tries to identify various attributes associated with e learning which can help in making the learning process effectual. International Research Publication House. -
Tag indicator: a new predictive tool for stock trading
In this paper, TAGan indicator for stock market prediction in which volume-based means for measuring potential trading and investing decision-making is introduced. This task has been in correlation of the changes in the volume with the changes in the actual trade volume. Using this, a concise trading strategy is formulated. Hoping to outperform the market and analyze the results by back testing across intraday, price data for the last 1 year, 2019, is performed. It was discovered that about 48.9% of the time, the volume-based trading strategy outperformed and the returns from market are also healthy enough to support the claim. Statistical methods like linear regression, mean square error in prediction and stochastic gradient descent are applied. Furthermore, while the scope of the study was limited to a few stocks in Nifty in order to mitigate selection bias, nonetheless, we hypothesize that numerous other assets that similarly possess a predictable correlation to volumes based on daily high and low are likely to exist. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Implementation of integer factorization algorithm with pisano period
The problem of factorization of large integers into the prime factors has always been of mathematical interest for centuries. In this paper, starting with a historical overview of integer factorization algorithms, the study is extended to some recent developments in the prime factorization with Pisano period. To reduce the computational complexity of Fibonacci number modulo operation, the fast Fibonacci modulo algorithm has been used. To find the Pisano periods of large integers, a stochastic algorithm is adopted. The Pisano period factorization method has been proved slightly better than the recently developed algorithms such as quadratic sieve method and the elliptic curve method. This paper ideates new insights in the area of integer factorization problems. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Evaluating the impact of BRICS+ trade policies: A comparative analysis before and after the Russia-Ukraine conflict
This chapter explores shifts in trade dynamics among BRICS+ nations (Brazil, Russia, India, China, South Africa, and extended partners) in response to the Russia-Ukraine conflict. The study assesses changes in trade volumes and patterns between Russia and other BRICS+ nations, comparing pre-conflict (2018-2021) and post-conflict (2022 onwards) periods. Utilizing secondary data from credible sources like Bloomberg, WTO, and national trade statistics, the research ensures data authenticity and reliability. The findings reveal significant growth in trade activities between Russia and certain BRICS+ nations post-conflict. Moreover, a comparative analysis with other trade blocs like the European Union highlights contrasting geopolitical strategies, showcasing the resilience and adaptability of BRICS+ in navigating global challenges. The study offers strategic insights for policymakers within the alliance to effectively adjust to evolving trade dynamics. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Self-care practices, professional quality of life and challenges: An exploration among counsellors in Kerala
The profession of counselling continues to prove its importance in today's fastpaced world, where pausing down and listening to someone are becoming an odd and luxurious concern of people. The counsellor may feel as though they are in a marathon, which demands them to continuously offer unconditional positive regard and empathy for their clients. This may leave them incapacitated to look into themselves and to recognize what is happening tothem in this process of caring for others. Though profound discourses take place in the international scenario about the criticality of rendering to the need and wellbeing of counsellors, evidence-based studies, and effective interventions in this regard are still lacking in the Indian context. This study seeks to fill this gap by exploring, how the positive and negative feelings of continuous caring can affect the professional quality of life of counsellors, what are the self-care mechanisms they adopt, and which are the professional issues they find most concerning. To meet these objectives, the present research employs a mixedmethod design in a sequential explanatory fashion. The study encompasses three different phases wherein phase I and II; the sample consisted of counsellors working in various government projects in Kerala selected through dense sampling method and purposive sampling method respectively. -
Bioconvective flow of nanofluid past a cylinder subject to ThompsonTroian slip
The bioconvective flow of a nanofluid across a cylinder under the impact of ThompsonTroian slip conditions is studied in this work. The nonzero velocity at the boundary, which affects the distribution of shear stress and, in turn, the overall flow pattern, is explained by this slip condition. Additionally, the paper covers the dynamics of nanofluid flow and its mass and heat transfer characteristics. Partial differential equations (PDEs) that characterize the momentum, energy, concentration and species movement in the fluid, are used to simulate the flow. Through similarity transformations, these PDEs are transformed into a system of ordinary differential equations (ODEs), simplifying the intricate flow phenomena. After applying the similarity transformations, the resulting system of ODEs is solved via the RungeKuttaFehlberg (RKF45) technique. The study emphasizes how important precise modeling and numerical solutions are for managing and predicting bioconvective flows in real-world applications, including cooling systems, chemical reactors and microfluidic devices. The results provide a basis for further research into more complex flow scenarios as well as for the creation of cutting-edge materials and technologies that take advantage of nanofluid dynamics. The changes in the slip parameter resulted in 12.23% changes in the Nusselt number, whereas the changes in the magnetic field parameter accounted for 1.22.4%. However, the velocity of the nanofluid was found to decrease for a stronger magnetic field. 2025 World Scientific Publishing Company. -
Visualization of Data Structures and Algorithms with Dynamic Memory Allocation
Data Structures and Algorithms (DSA) is fundamental to computer science education, yet novice learners face significant challenges in grasping abstract concepts and their system-level implications, such as dynamic memory allocation. This paper presents a novel web-based platform designed to enhance learning outcomes for beginner to intermediate students through interactive step-by-step visualizations of DSA, including arrays, linked lists, stacks, queues, and searching and sorting algorithms. A distinctive feature is the integration of dynamic memory allocation visualization, illustrating stack and heap to elucidate system-level operations. Developed using Next.js, Tailwind CSS, D3.js, and Framer Motion, the platform offers a space-themed responsive interface with synchronized code, data structure, and memory views. By addressing pedagogical gaps in tools like VisuAlgo, this work aligns with Sustainable Development Goal 4- Quality Education, promoting accessible and equitable learning. 2025 IEEE.