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CoInMPro: Confidential Inference and Model Protection Using Secure Multi-Party Computation
In the twenty-first century, machine learning has revolutionized insight generation by using historical data across domains like health care, finance, and pharma. The effectiveness of machine learning solutions depends largely on the collaboration between data owners, model owners, and ML clients, without privacy concerns. The existing privacy-preserving solutions lack efficient and confidential ML inference. This paper addresses this inefficiency by presenting the Confidential Inference and Model Protection, also known as the CoInMPro, to solve the privacy issue faced by model owners and ML clients. The CoInMPro technique is suggested with an aim to boost the privacy of model parameters and client input during ML inference, without affecting the accuracy and by paying a marginal performance cost. Secure multi-party computation (SMPC) techniques were used to calculate inference results confidentially after sharing client input and model parameters privately from different model owners. The technique was implemented in Python language using the open-source SyMPC library to support the SMPC function. The Boston Housing Dataset was used, and the experiments were run on Azure data science VM using Ubuntu OS. The result suggests CoInMPros effectiveness in addressing privacy concerns of model owners and inference clients, with no sizable impact on accuracy and trade-off. A linear impact on performance was noted with an increase of secure nodes in the SMPC cluster. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
CONFIDENTIAL TRAINING AND INFERENCE USING SECURE MULTI-PARTY COMPUTATION ON VERTICALLY PARTITIONED DATASET
Digitalization across all spheres of life has given rise to issues like data ownership and privacy. Privacy-Preserving Machine Learning (PPML), an active area of research, aims to preserve privacy for machine learning (ML) stakeholders like data owners, ML model owners, and inference users. The Paper, CoTraIn-VPD, proposes private ML inference and training of models for vertically partitioned datasets with Secure Multi-Party Computation (SPMC) and Differential Privacy (DP) techniques. The proposed approach addresses complications linked with the privacy of various ML stakeholders dealing with vertically portioned datasets. This technique is implemented in Python using open-source libraries such as SyMPC (SMPC functions), PyDP (DP aggregations), and CrypTen (secure and private training). The paper uses information privacy measures, including mutual information and KL-Divergence, across different privacy budgets to empirically demonstrate privacy preservation with high ML accuracy and minimal performance cost. 2023 SCPE. -
A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine Learning
Machine learning (ML) techniques are the backbone of Prediction and Recommendation systems, widely used across banking, medicine, and finance domains. ML techniques effectiveness depends mainly on the amount, distribution, and variety of training data that requires varied participants to contribute data. However, its challenging to combine data from multiple sources due to privacy and security concerns, competitive advantages, and data sovereignty. Therefore, ML techniques must preserve privacy when they aggregate, train, and eventually serve inferences. This survey establishes the meaning of privacy in ML, classifies current privacy threats, and describes state-of-the-art mitigation techniques named Privacy-Preserving Machine Learning (PPML) techniques. The paper compares existing PPML techniques based on relevant parameters, thereby presenting gaps in the existing literature and proposing probable future research drifts. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Balancing work and life inacademia: unraveling theemployee engagement mystery
Purpose: This study aims to further the understanding of employees engagement by explaining their organizational commitment through their perception of the availability of work-life benefits in the organization. This study also investigates the mediating role of job satisfaction in this context. Design/methodology/approach: The model was tested on the primary data collected in two phases from 270 teaching professionals in higher education institutes in Northern India. Barren and Kennys algorithm and hierarchical regression analysis were used to test the hypotheses. Findings: The results reveal that employees perception of work-life benefits strongly influences their organizational commitment. Also, the results support that employees job satisfaction mediates the above-mentioned relationship. Research limitations/implications: Self-reported data could be considered as a key limitation of this study and for more accurate results supervisors (line managers) perspective could also be included in future studies. Also, in addition to perceived work-life benefits, supervisors support could also have an impact on employees commitment, thus its inclusion in the model could draw a clearer picture. Originality/value: This research has two key contributions: first, it adds to the limited literature examining the employees engagement issues in the academic sector. Second, this research is one of, if not the first, to investigate perceived work-life benefits among third-level teaching staff in India to explain employees commitment to their organizations. 2024, Emerald Publishing Limited. -
Moderation effect of flexibility in projects on senior management commitment in achieving success in financial services IT projects
Senior management commitment and flexibility improve project responsiveness to volatile and high-impact scenarios, especially in large projects and programs. The aim of this study is to determine how project flexibility interacts with and affects the relationship between senior management commitment and success in IT projects. A cross-sectional survey of 166 managers was used to derive empirical data from the financial services industry and used to test the conceptual framework based on recent project management literature. Ordinal regression analysis demonstrated a significant relationship between senior management commitment and success in projects which is influenced by significantly positive moderations established through flexibility in projects. The study findings can assist project managers and senior leaders to accomplish their short-term and long-term project goals and achieve success in projects by reducing the chances of failures. This paper adds value to existing research in the context of IT projects and the role of project flexibility on their performance. Copyright 2023 Inderscience Enterprises Ltd. -
Moderating Role of Project Innovativeness on Project Flexibility, Project Risk, Project Performance, and Business Success in Financial Services
Project risk management is crucial for project success and for achieving short-term and long-term project goals. This study examines the linkage between the management of project risks and project flexibility for information technology projects in Financial Services. A conceptual framework establishing the link between project risks, project flexibility, project performance, and business success, with project innovativeness as a moderating variable, has been introduced. To test the model, data were collated from over 400 managers working in Financial Services projects. The empirical outcomes through a Ordinal regression analysis demonstrate a substantial association between the management of project risks, project flexibility, and success of projects. Project innovativeness moderates the effects of project risks and project flexibility on project performance. Furthermore, managing project risks is vital to reduce the likelihood of failures in projects. This paper enriches existing research by applying a contingency perspective to project risk management and provides practical guidance for managing risks in projects professionally and also the relevancy of project flexibility. 2021, Global Institute of Flexible Systems Management. -
Nonlocal thermoelastic waves inside nanobeam resonator subject to various loadings
The present article focuses on the new meticulous model based on the postulate of memory-dependent derivatives to analyze the thermo-mechanical interactions inside the nano-beam-based machined resonators. Also, the size effect on dynamic responses of thermoelastic vibrations of homogeneous and isotropic nano-beam is considered. The fundamental expressions are formulated in the frame of non-local generalized thermoelasticity with paired relaxation times by operating the results of Euler-Bernoulli beam theory, non-local effect, and memory-dependent derivative. The proposed model is applied to study the nano-beam-based machined resonator subjected to the ramp-type heating and exponentially decaying time-dependent load. Closed-form solutions of the physical fields are examined by applying the Laplace transform mathematical mechanism. However, the coherence of the new thermal conductivity framework, a collation has been bestowed among the results obtained in the presence or absence of the memory-dependent derivative; also, the size effect is analyzed on the significant parameters of nano-beam such as deflection, temperature, displacement as well as bending moment. Moreover, the prominent influence of the distinct affecting parameters such as constituents of memory-dependent derivative (kernel function and time delay) and ramping time parameter with an applied load on the physical fields have been investigated with the help of quantitative results. 2022 Taylor & Francis Group, LLC. -
Memory response on generalized thermoelastic medium in context of dual phase lag thermoelasticity with non-local effect
Theory of non-local continuum is contemporary appraised and is found to be supplementary coherent to capture the impacts of each and every point of the material at its single point. The conviction of memory dependent derivative is also newly appraised and is observed to be more intuitionistic for predicting the realistic character of the real-world obstacles. Attractiveness of the belief of a memory dependent derivative lies in its unique properties such as its significant constituents a kernel function and time-delay are freely selected according to the requirement of a problem. The present study comprises a new meticulous thermoelastic heat conduction model for the homogeneous, isotropic, thermoelastic half space medium concerning memory effects and non-local effects. Governing equations are constructed on the basis of the newly appraised non-local generalized theory of thermoelasticity with two phase lags in the frame of a memory dependent derivative. Exact analytical solutions of the physical fields such as dimensionless temperature, displacement as well as thermal stress are evaluated by using a suitable technique of the Laplace transform. Quantitative results are determined in a time-domain for different values of time by taking the numerical inversion of the Laplace transform. Noteworthy role of the constituents of the memory dependent derivative such as kernel function as well as time-delay factor has been scrutinized on the crucial field variables of the medium through computational outcomes. Moreover, the impact of non-local parameter is examined on the variations of field quantities through the quantitative results. 2022 by IPPT PAN, Warszawa. -
Effects of variable thermal properties on thermoelastic waves induced by sinusoidal heat source in half space medium
Aim of the present study is to characterize the effects of changing thermal conductivity on the propagation of thermoelastic waves in the half space medium when it is exposed to a periodic heat source. Closed form solutions of all significant physical fields such as conductive temperature, stress and displacement are evaluated in their dimensionless form in the Laplace transform domain. Impact of changing thermal conductivity parameter is exhibited on all field variables with the help of quantitative outcomes in time-domain. Following this pattern, the effects of time parameter is also observed on the field quantities. 2022 -
Investigation of memory influences on bio-heat responses of skin tissue due to various thermal conditions
Advancement of new technologies such as laser, focused ultrasound, microwave and radio frequency for thermal therapy of skin tissue has increased numerous challenging situations in medical treatment. In this article, a new meticulous bio-heat transfer model based on memory-dependent derivative with dual-phase-lag has been developed under different thermal conditions such as thermal shock and harmonic-type heating. Laplace transform method is acquired to perceive the analytical consequences. Quantitative results are evaluated for displacement, strain and temperature along with stress distributions in time domain by adopting the technique of inverse Laplace transform. Impacts of the constituents of memory-dependent derivativeskernel functions along with time-delay parameter are analysed on the studied fields (temperature, displacement, strain and stress) for both thermal conditions separately using computational results. It has been found that the insertion of the memory effect proves itself a unified model, and therefore, this model can better predict temperature field data for thermal treatment processes. 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Decoding the Impact of Social Media on IPL Player Retention Using Sentiment Analysis and Ensemble Learning
This research query contrasted the use of player performance measures and fan sentiment rating scores in predicting player retention in the Indian Premier League (IPL). With the use of quantitative variables such as batting averages and bowling economies and qualitative variables including sentiment and visibility scores in over 1000 Reddit comments and posts, the research utilizes machine learning algorithms such as the Balanced Random Forest Classifier and Easy Ensemble Learning algorithms for enhancing decision-making on retention. In contrast to earlier approaches, which have largely disregarded the role of retention decision-making and qualitative data use in this context, the present study closes this gap by combining fan sentiment measures with visibility. The findings show that although conventional metrics hold, public opinion and sentiment have increasingly become factors in retention policy, particularly in the recent past. The highest performing model, which is a blend of both qualitative and quantitative traits, has an overall accuracy of 88%. The study finds the shift toward a more holistic, integrated approach, with the focus being on the data-driven nature of IPL team management for marketability, off-field pertinence, and on-field performance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Exploratory Analysis and Pattern Recognition in Energy Production and Demand: A Data-Driven Approach Using Multi-Source Energy Metrics
Hydropower is a leading renewable energy source due to its high efficiency and low operational costs. However, it still faces significant environmental, operational, and forecasting challenges. This paper explores the use of machine learning (ML) models such as SARIMA, Random Forest (RF), and Neural Basis Expansion Analysis for Time Series (NBEATS) to optimize hydropower operations. By analyzing diverse data sets, including hydrometeorological data, plant operations, sensor inputs, and other energy production and demand metrics such as solar, wind, coal, nuclear, and storage, ML enhances decision-making in areas such as inflow forecasting, predictive maintenance, and environmental sustainability. The paper presents an exploratory analysis of 48 -hour energy production and demand patterns across multiple sources (Hydro, Coal, Solar, Wind, Nuclear, and Storage), offering insights into interdependencies and system behavior. It also reviews current ML applications in hydropower, highlights challenges such as data quality and model interpretability, and discusses emerging technologies such as reinforcement learning, explainable AI (XAI), and digital twins as promising future directions. 2025 IEEE. -
Harnessing the Transformative Potential of Blockchain Technology to Accelerate the Global Transition Toward a Scalable, Transparent, and Resilient Circular Economy Framework
Blockchain technology applied to circular economy has a world-changing potential to increase transparency, traceability, and the sustainability of the resources management. With the shift towards regenerative industries, it is of the essence to have the capacity to track the lifecycle of used products, used materials, and waste securely. A new method (Circular Blockchain-Resource Lifecycle Management (CB-RLM) algorithm) is proposed in this work that is supposed to maximize the monitoring and incentivization resource flow in the context of decentralized systems. Using a blockchain as a platform allows conducting real-time monitoring of materials backtracking, and the proposed approach will provide a way of materials following all stages of production, consumption, reuse, and recycling. The algorithm combines the information presented by the means of the IoT-enabled devices to guarantee permanent verification of product statuses and ownership, whereas smart contracts automatically sanction the policy compliance of reuse and returns. The system also creates digital tokens as rewards to sustainable behavior during processes, which gives the stakeholders incentive to engage in the circular economy. The mechanism will not only facilitate resource efficiency and accountability but it will promote trust among the members of the supply chain. The suggested CB-RLM algorithm also has the potential to solve such key design problems as data integrity, stakeholder cooperation and lifecycle openness, thus paving the way to the creation of scalable and smart circular systems. This model brings a more environmentally responsible and resilient industrial ecosystem by means of decentralized enforcement and automated incentives. The suggested CB-RLM technique achieves an overall accuracy of 99.4%. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Multi-dimensional changes in drought patterns across India
Indias hydroclimatic systems are undergoing unprecedented transitions in a warming climate, marked by shifts in temperature extremes, altered precipitation patterns, and increasing drought risk. This study presents a comprehensive assessment of drought trends and hydroclimatic variability across six major geographical zonesWestern, Central, Himalayan, Indo-Gangetic Plain (IGP), Peninsular, and Northeast Indiaduring the period 1971 to 2020. Using a set of advanced climate change metricsStandardized Local Anomalies (SLA), Novel Climate Scores (NCS), and changes in probability of local climate extremes alongside the Standardized Precipitation Evapotranspiration Index (SPEI), we quantify changes in drought conditions and the emergence of non-analogue climates. Changes in climatic extreme are computed using high-resolution daily gridded temperature and rainfall datasets, comparing recent decades against a 19511980 baseline. SLA quantifies deviations from historical variability, highlighting intensified warming over the Indo-Gangetic Plain, western India, and the southern peninsula. NCS reveales the emergence of novel climatescombinations of temperature and precipitation conditions not previously observed, particularly in Southeast India and the Himalayan region. The probability of local climate extremes shows a substantial increase in extreme events across India indicating enhanced climate volatility. These metrics are then integrated with drought analysis using SPEI to incorporate both precipitation and temperature-driven evaporative demand. SPEI trends indicate increasing dryness in Northeast India, the Himalayas, and the Indo-Gangetic Plain, linked to declining monsoonal rainfall and rising temperatures. Meanwhile, Western and Peninsular regions show wetting trends, driven by increased rainfall and convective precipitation events. The rainfall is the dominant drought driver during the monsoon, while high maximum temperatures intensify drought conditions in pre- and post-monsoon seasons by enhancing evaporative demand. Minimum temperature exhibits regional effects, showing a drying influence in the IGP and Himalayas, but a slight moistening signal in Peninsular India. By combining drought indices with climatic extremes metrics, this study offers a comprehensive framework to monitor hydroclimatic shifts and their regional impacts. The findings underscore the need for region-specific adaptation strategies that incorporate early warning systems, sustainable water management, and climate-resilient agriculture to address Indias evolving drought risks. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
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. -
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.