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Mathematical modelling and mechanics of acoustic waves in piezoelectric layers between n-type semiconductor plates: an irreducible Cardano method coupled with a functional iteration scheme
This study presents a comprehensive analyticalnumerical investigation of acoustic wave dispersion, attenuation, and energy dissipation in piezoelectricsemiconductor heterostructures composed of SiPZTSi and GePZTGe layers. The governing electromechanicaldiffusive equations for the coupled media are formulated with full continuity conditions, leading to a cubic characteristic equation solved using a hybrid irreducible Cardano method and functional iteration scheme. A detailed convergence analysis demonstrates stable, monotonic residual decay for both symmetric and asymmetric modes, confirming the robustness of the adopted solver. Numerical results reveal strong sensitivity of phase velocity, attenuation, and specific loss to wave number, semiconductor mobility, convergence and carrier concentration. GePZTGe consistently exhibits higher phase velocity, reduced attenuation, and lower dissipative losses than SiPZTSi, primarily due to the higher carrier mobility and weaker acoustoelectric drag in Ge. Additional parametric plots highlight the influence of semiconductor quality and PZT layer thickness on acoustic energy confinement. The findings provide actionable design guidelines for optimizing SAW-based filters, delay lines, sensors, and signal-processing devices, where low loss, high velocity, and efficient energy trapping are critical. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2026. -
GraCoD: a disruptive graph-aware drift detection algorithm with a GCN-based time-varying module for concept drift detection in short text streams
Detection of concept drift in time-varying short text streams has numerous challenges since the data are volatile. According to research, 30% to 40% of the traditional drift detection methods are not able to detect change of the concept in the text stream and, therefore, produce high false positives and slow response time. To address the above issues, the proposed Graph based Concept Drift Detection (GraCoD) method suggests a novel concept drift detection (CoD) framework. GraCoD uses ConvBERT with Hopfield layers and temporal convolution to capture linguistic context and temporal dependencies. The model constructs a graph representation of text data using a text GCN with Time Varying Spatio Temporal-Graph Attention Module (TVST-GAT) and uses the Graph Aware Drift Detection Algorithm (GADD) to classify the change in the graph metrics such as node centrality and edge density. The approach is more helpful and effective than the traditional approaches of detecting the occurrence of drift. To react to the detected drifts proactively, Deep Reinforcement Learning (DRL) is merged with Deep Q-Learning to automatically adapt parameters and behaviors based on the outcomes of detected drifts. The severity and classification modules detect the severity and classify the detected drifts for further investigation. The proposed model demonstrates exceptional performance in CoD across five diverse datasets: Twitter datasets 1 and 2, Enron, News 20, and Amazon Reviews. It achieves high accuracy (98.7%-99.5%) and F1-scores (96%-98%), with low false positive (0.020.04) and false negative (0.010.03) rates. The model effectively identifies 2329 drifts, with drift indicators ranging from 81.3% to 86.6%, showcasing its robustness in handling dynamic data streams across various domains. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Electrochemical behaviour of optically transparent, nanoporous LiFePO4cathodes grown via RF magnetron sputtering
The rapid growth of smart technology has accelerated the need for compact and durable microbatteries. Fabrication of thin-film microbatteries is effective to address the requirements of the evolving technology. In the present work, pristine, optically transparent, nanoporous LiFePO4 (LFP)is synthesized via RF magnetron sputtering. The effect of nanoporosity on the electrochemical properties and charge storage mechanisms of LFP is explored. The galvanostatic studies revealed an initial discharge capacity of 32 Ah cm2?m1 and stabilised to 17.5 Ah cm2?m1 after 100 cycles. The capacity fading can be attributed to the increased formation of SEI caused by the enhanced interaction between the cathode and electrolyte due to the nanoporosity. The films demonstrate good rate capability and reversibility. Optical studies reveal a bandgap of 3.74eV, highlighting the potential for usage in optically transparent microbatteries. This work provides key insights into the intrinsic electrochemical behaviour of pristine nanoporous LFP thin films, creating a pathway for its implementation in microbatteries. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026. -
Silvergraphene composite: a coating on polyethersulfone membrane for superior water purification with antibacterial, catalytic and antifouling properties
Membrane fouling, originating from a diverse range of sources such as organic matter, inorganic particulates, biological agents, and industrial contaminants, continues to pose a significant challenge in water purification processes. This fouling results from complex nonspecific interactions between the membrane surface and foulants, leading to a substantial decline in filtration performance, including reduced permeability, selectivity, and operational lifespan. To address these limitations, there is an urgent need to engineer advanced membranes with integrated antibacterial, catalytic, and antifouling functionalities to enable efficient and sustainable water treatment. In this context, we developed an innovative approach to mitigate membrane fouling of polyethersulfone (PES) membrane by coating with silver-decorated reduced graphene oxide (rGO). This coating imparts exceptional antibacterial efficacy, catalytic dye degradation properties, antifouling performance and remarkable filtration capacity to the PES membrane. The antibacterial assessments conducted against Staphylococcus aureus (S. aures) and Escherichia coli (E. coli) bacteria revealed that increasing concentrations of silver in rGO composites resulted in a pronounced inhibitory effect on bacterial growth, with the most significant activity observed for membranes with higher silver loadings (rGO A500). Moreover, catalytic studies performed on the rGO A500 membrane emphasize the degradation of Congo Red, Methyl-Orange, and as well as the conversion of Nitrophenol to Aminophenol, occurring within 46min, 25min, and 23min, respectively. Furthermore, the rGO A500 membrane exhibits notable antifouling properties, evidenced by a flux recovery ratio of 98% and a minimal irreversible fouling ratio of 1.7% during Bovine Serum Albumin (BSA) protein filtration. Additionally, the composite membrane demonstrates an impressive water flux of 50 L m?2h?1 along with dye rejection efficiency of 92% for Congo Red, 86% for Rhodamine-B, and 81% for Methylene Blue. Overall, the findings underscore the multifunctional performance of the rGO A500 composite membrane, showcasing its antibacterial, catalytic and antifouling capabilities, and positioning it as a robust and practical solution for next-generation wastewater treatment technologies. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
An intelligent secure and efficient workflow scheduling (SEWS) model for heterogeneous cloud computing environment
This study recognizes the critical role of the cloud computing platform in scientific workflow applications yet identifies vulnerabilities in existing cloud workflow systems, such as information leaks, unauthorized access, and compromised data integrity during task scheduling. Mainly, attackers exploit the lack of security for intermediate-level task information. To address these security threats, this work introduces the secure and efficient workflow scheduling (SEWS) model for heterogeneous cloud computing environments. The SEWS model identifies malicious attacks on all workflow tasks and focuses explicitly on safeguarding intermediary data. The SEWS model employs intelligent techniques to enhance security and introduces a comprehensive metric to measure the security of workflow tasks, considering factors like integrity, confidentiality, and availability. Beyond security improvements, the SEWS model aims to elevate the overall quality of service (QoS) in workflow scheduling applications. This includes reducing simulation time, enhancing overall power efficiency, and minimizing average energy consumption. Results: Results from the SEWS model demonstrate substantial improvements over the energy-minimized scheduling (EMS) model, with a reduction of 79.41% in average simulation time, 87.92% in average power sum, 41.35% in average power average, and 89.62% in average energy consumption. These findings underscore the SEWS models effectiveness in providing enhanced security and improved QoS in cloud workflow scheduling. The overarching goal of this work is to contribute to developing a more secure and efficient cloud workflow scheduling system, aligning with the increasing demands for robust security measures and optimized performance in heterogeneous cloud environments. Findings: Compared to the energy-minimized scheduling (EMS) model, the findings of this study demonstrate that the secure and efficient workflow scheduling (SEWS) model yields superior outcomes across key performance metrics. Specifically, the SEWS model excels in average simulation time, power sum, power average, and energy consumption. These results underscore the effectiveness of the SEWS model in enhancing the efficiency and resource utilization of cloud workflow scheduling. Importantly, the study identifies a notable gap in the existing work related to workflow task scheduling. Many prior studies still need to address the critical aspects of security and QoS in this context. While some jobs have attempted to enhance security, a significant limitation is the failure to extend these security measures to intermediary data. This gap in the literature highlights the unique contribution of the SEWS model, which addresses security concerns comprehensively and prioritizes QoS in the workflow task scheduling process. The observed superiority of the SEWS model in comparison with the EMS model serves as a testament to the models efficacy in concurrently addressing security and QoS challenges. By focusing on intermediary data, the SEWS model presents a holistic solution that aligns with the increasing demand for comprehensive security measures in cloud workflow environments. The findings emphasize the significance of integrating security and QoS considerations to establish a more robust and efficient workflow scheduling framework in heterogeneous cloud computing environments. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025. -
Effective on-site waste minimisation approaches in Indian construction projects
Indias rapidly expanding construction sector generates substantial material waste, creating environmental and economic challenges that are often intensified by weak on-site waste management (WM) practices. This study investigates effective approaches for minimising material waste in building construction projects using a mixed-methods research design. The research integrates a systematic literature review, expert interviews, and a large-scale questionnaire survey to identify and prioritise waste minimisation strategies. From an initial set of 38 practices, 15 key strategies were shortlisted through descriptive and inferential statistical analysis. Their practical effectiveness was then evaluated through longitudinal monitoring of material waste across seven construction stages at four active residential sites. The results indicate a consistent reduction in material wastage following the implementation of targeted interventions. By triangulating expert insights, industry perceptions, and empirical site-based evidence, this study moves beyond perception-based assessments and provides empirical validation of waste minimisation strategies under real construction conditions in a developing-country context. The findings demonstrate that sustained waste reduction depends on an integrated approach combining human behaviour, managerial control, and proactive planning from early project stages. The study offers practical guidance for improving material efficiency, reducing costs, and advancing sustainable construction practices in India. The Author(s), under exclusive licence to Springer Nature Japan KK, part of Springer Nature 2026. -
Can mobile wallet usage contribute towards environmental sustainability? Evidence from a moderated mediation approach
This research empirically examines the antecedent factors influencing the relationship between mobile wallet use and environmental sustainability. The study adopted the UTAUT2 model with constructs of effort expectancy, performance expectancy, price value, mobile wallet usage, perceived security, and environmental sustainability. The research model was tested using a questionnaire-based response from 535 Indian Northeast tribal customers through a cross-sectional approach and examined the moderated mediation effect among the latent constructs. Partial least squares structural equation modelling was used to analyse the hypotheses and explain the variance, effect size, predictive relevance, and IPMA matrix. The results of this paper indicate that antecedent factors statistically and positively impact mobile wallet usage, contributing towards environmental sustainability. The outcome of this study encourages customers to adopt digital financial technology to promote environmental protection and reduce all forms of pollution. The findings help policymakers and mobile wallet service providers prioritise marketing attributes to enhance mobile wallet adoption. Moreover, managers should design tactics to augment confidence among older clients. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. -
Enhancing Early Seedling Stage Salinity Tolerance in Rice Through Brassinosteroid Priming
Rice (Oryza sativa), astaple food for the major global population, faces substantial productivity challenges due to salinity stress, an increasingly prevalent issue exacerbated by climate change. Salinity impacts rice at critical growth stages especially at seedling stage, root development, shoot elongation and ultimately seedling establishment. This study evaluated the effect of brassinosteroid (BR) priming as astrategy to improve seedling stage salinity tolerance in rice seedlings across 15genotypes subjected to moderate (140?mM NaCl), and severe (200?mM NaCl) salinity stress in comparison with control. BR-primed seeds demonstrated enhanced germination rates, seedling vigor index, shoot length and root length under salinity conditions compared to non-treated seeds. BR priming led to a35% improvement in SVI under control conditions and up to 30% under severe salinity, suggesting that BRs may facilitate osmotic regulation and ion homeostasis, key for maintaining growth under stress. Furthermore, BR priming significantly increased root development, essential for water uptake and nutrient acquisition in saline environments. Our results showed the prospect of BR priming as an effective approach to enhance rice resilience to salinity stress, providing afoundation for further field-based research on BR-mediated stress tolerance mechanisms. This study underscores the relevance of BR priming in improving rice productivity in saline-prone areas, contributing to food security in the face of increasing soil salinization. The author(s), exclusively licensed to Springer-Verlag GmbH Germany, a part of Springer Nature 2025. -
Deep learning-driven correction of motion-induced artifacts in microfluidic on-chip fluorescence microscopy for robust cell classification
Fluorescence microscopy combined with microfluidic platforms allows for the analysis of single cells and the whole biomedical process to be done at high speed, however, it is often a very delicate method that can be heavily affected by motion-induced distortions during the high-speed flow. These artifacts, such as motion blur, misalignment, and shape deformation significantly lower automatical accuracy of the cell classification. The suggested research suggests that on-chip fluorescence microscopy employs an AI-based framework of distortion correction using Vision Transformers (ViT) and Generative Adversarial Networks (GAN) to remove motion artifacts in real-time. The combination of the GAN-ViT architecture does not only manage to reconstruct image quality but also to preserve fine cellular features when flowing system rates increase to 200 4L/min, which provide PSNR = 38.6 dB and SSIM = 0.98. When the system was used in both synthetic and experimental microfluidic data, it was able to reach a classification accuracy of 99.9, thereby indicating consistency in the system despite varying flow rates. The speed of the framework is 950 frames per second (fps), almost equal to the 1000-fps smartphone camera acquisition rate, thereby, demonstrating its suitability to the real-time, high-throughput imaging. As opposed to the past CNN or transformer techniques, a hybrid GAN-ViT architecture offered by the authors of this study directly implements in the imaging pipeline, thus enabling the simultaneous motion correction and diagnostic classification to occur immediately. The study results highlight the fact that AI-based distortion correction not only increases the accuracy of the diagnosis, but also personnel and laboratory response in microfluidic fluorescence microscopy. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026. -
Investigation of surface and interface effects of piezoelectric quasicrystal different models with propagation of shear horizontal and anti-plane shear horizontal wave; [??????????????????????????????????]
Based on the theoretical representation of piezoelectric quasicrystal, a generalized dynamic model is built to represent the transmission of wave aspects in surface acoustic pulse nano-devices. Surface elasticity, surface piezoelectricity, and surface permittivity help to include the surface effect, which equals additional thin sheets. It is shown that, under certain assumptions, this generalized dynamic model may be simplified to a few classical examples that are appropriate for both macro and nano-scale applications. In the current work, surface piezoelectricity is used to develop a theoretical model for shear horizontal (SH) waves where it contains the surface piezoelectricity theory and a linear spring model to quantitatively and qualitatively explore SH waves in an orthotropic piezoelectric quasicrystal layer overlying an elastic framework (Model I), a piezoelectric quasi-crystal nano substrate, and an orthotropic piezoelectric quasicrystal half-space (Model II). The theoretical model stimulates the numerical results, which establish the critical thickness. As the piezoelectric layers thickness gets closer to nanometres, surface energy must be included when analyzing dispersion properties. Furthermore, the effects of surface elasticity and density on wave velocity are investigated individually. The authors establish a parameter, precisely the ratio of the physical modulus along the width direction to along the direction of wave travel. The surface effects impact on the general characteristics of piezoelectric structures is seen as a spring force acting on bulk boundaries. Analytical presentation of frequency equations for both symmetric and anti-symmetric waves pertains to the case of an electrical short circuit in Model II. The project aims to analyze SH waves in orthogonal anisotropic, transversely isotropic piezoelectric layered nanostructures, providing a practical mathematical tool for surface effects analysis and adaptability to other wave types, including Rayleigh waves and acoustic surface waves. The Chinese Society of Theoretical and Applied Mechanics and Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
The Role of Humility in School Counselling Relationships: A cross-cultural Comparison
In this multi-continent, cross-cultural study, we investigated the role of humility in counselling relationships between school counsellors and students across Southeast Asia, South Asia, and Central Europe. A culturally diverse research group interviewed 45 school counsellors and analysed the data thematically. The findings suggest that humility is a relational and context-dependent trait, highlighting how cultural understandings and enactments of humility shape counsellor-student relationships. The study has implications for developing culturally relevant counselling practices in schools, where cultural characteristics, organisational factors, and counsellors professional practices interact. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Analyzing enablers of artificial intelligence for decarbonization: implications for circular supply chains
This study comprehensively explores the pivotal position that Artificial Intelligence (AI) enables on the advancement of decarbonization efforts, mainly in the context of Circular Supply Chains (CSCs). Employing a two-stage methodology, this study delves into identifying and analyzing the enablers essential for leveraging AI in the pursuit of decarbonization objectives. In the first stage, a literature review and an exploratory factor analysis are performed to discern the key enablers of AI for decarbonization initiatives. This process resulted in the identification of 15 significant enablers and categorization of enablers into environmental, organizational, institutional, and technological categories. Building upon the findings from the first stage, this study progresses to its second stage, wherein the Grey-Ordinal Priority Approach (G-OPA) is applied to analyze the identified enablers. The results indicate that adopting recyclable materials to enhance the efficiency of supply chains, emphasizing local production for recovery practices through advanced technology, and managing product life-cycle through intelligent and additive manufacturing technologies are the top three enablers. The application of the G-OPA enriches the robustness and comprehensiveness of the analysis, enabling an understanding of the complex interplay among the enablers. By clarifying the key enablers,business planners and designers can migrate from traditional linear supply chains to more sustainable CSCs through the careful implementation of enablers for decarbonization. The Author(s) 2025. -
Deciphering the plant growth-promoting traits of bacteria capable of sodium dodecyl sulfate removal from graywater: a sustainable approach for water reuse for irrigation
Sodium dodecyl sulfate (SDS), an anionic detergent found in cleaning products and cosmetics, is one of the chemical pollutants in waterways. SDS-utilizing bacteria were isolated from soil and water samples using 0.05% SDS basal medium. Three bacterial isolates were selected for 16S rRNA sequencing based on their ability to solubilize phosphate, potassium, and zinc, and they were identified as Pseudomonas putida MSK86 OR192890, Klebsiella pneumoniae NET12 OR345422, and Enterobacter sp. MSK86 OR398804. Enterobacter sp. MSK86 and K. pneumoniae NET12 lowered the SDS concentration in the sample 84.78% and 75.65%, respectively, while P. putida MSK86 reduced it 33.43% on the sixth day of incubation. A phosphate-potassium-zinc co-inoculum was prepared using Enterobacter and Pseudomonas species. Laundry wash water was added with the bacteria, individually and co-inoculum, and the fortified water was used to irrigate the Capsicum annuum L. seedlings. On the 45th day, the plants were harvested, and total glucose, protein, chlorophyll, and proline were checked by comparing control plants. Enterobacter sp. MSK86 increased carbohydrate and proline levels by 37.22mg/g ( 0.54 SE) and 2.44mg/g ( 0.1 SE), while K. pneumoniae NET12-treated plants showed an increase in chlorophyll by 1.95mg/g ( 0.02 SE) and total protein by 1.94mg/g ( 0.03 SE). The bacteria in this study showed they could lower SDS levels in graywater and improve farming by adding nutrients to the soil and plants, offering a sustainable way to tackle detergent pollution, fertilizer use, and water scarcity. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
The Role of Regular Meditation Practice, Trait Mindfulness, and Psychological Characteristics in Affective Startle Modulation: A Psychophysiological Study
Meditation practices, including mindfulness, are linked with adaptive emotional processing and regulation. Although startle response modulation among meditators has been studied using habituation and prepulse-induced startle inhibition paradigms, affective startle modulation, which refers to potentiation by negative stimuli and attenuation by positive stimuli (both relative to neutral stimuli), remains unexplored. This study examined how regular meditation practice, dispositional mindfulness, and affective difficulties influence affective modulation of the acoustic startle reflex. Seventeen meditators and thirty non-meditators were exposed to pleasant, neutral, and unpleasant images while their eye-blink startle responses were recorded. Participants also completed self-report measures of dispositional mindfulness, alexithymia, emotion regulation difficulties, depression, anxiety, and stress. Meditators, compared to non-meditators, reported higher dispositional mindfulness, particularly in the Observing and Non-reactivity domains, lower stress, and fewer difficulties in goal-oriented behaviour during negative emotions; they also had longer startle onset latencies, potentially indicating lower state anxiety, across the entire experiment regardless of the valence of visual images. Higher dispositional mindfulness correlated with lower scores on alexithymia, emotion regulation difficulties, depression, anxiety, and stress across the pooled sample. These findings suggest that mindfulness, whether cultivated through meditation or as a trait, reduces negative emotionality, highlighting its potential for emotional regulation and stress reduction. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
The potential of hydrolyzed chicken feather meal as a partial replacement for fish meal and its effects on the growth and health status of African catfish (Clarias gariepinus) fingerlings
The application of agricultural by-products as alternative feed has received tremendous interest from the aquaculture industry. The current study explored the potential of hydrolyzed chicken feather meal (CFM) at different percentages as fish meal (FM) replacement and the impacts on growth, feed stability, apparent protein digestibility, digestive enzyme, body amino acid profiling, body proximate analysis, hematology, and morphology of African catfish (Clarias gariepinus) fingerlings. Five isonitrogenous (32% crude protein) CFM diets were prepared [0% CFM (T1), 5% CFM (T2), 15% CFM (T3), and 30% CFM (T4)] and applied in a 70-day feeding trial. At the end of the experiment, fingerlings fed with the T2 diet exhibited the best final length, final weight, net weight gain, weight gain, specific growth rate, intraperitoneal fat, and condition factor than other treatment groups. Furthermore, the highest digestive enzyme activity and apparent protein digestibility (APD) were highest in the T2 diet. There were significant differences between the groups in the liver, muscle, and intestine amino acid profiles and proximate analysis. Moreover, the T2 group recorded the best villus length, width, and crypt depth in the anterior and posterior regions. The highest white blood cells, lymphocytosis, monocytes, red blood cells, hemoglobin, and hematocrit were also found in the T2 diet group. Meanwhile, albumin, globulin, and creatine levels were the lowest in the T4 diet group. Notably, fingerlings supplemented with the highest CFM percentage demonstrated the highest morphological deterioration in the liver and intestine. In conclusion, 5% CFM is a promising FM replacement to improve the growth, apparent protein digestibility, digestive enzyme, liver and intestine histology, and blood indices of African catfish fingerlings. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Gravitational wave distance estimation using intrinsic signal properties: dark sirens as distance indicators
Gravitational Waves (GWs) provide a powerful means for cosmological distance estimation, circumventing the systematic uncertainties associated with traditional electromagnetic (EM) indicators. This work presents a model for estimating distances to binary black hole (BBH) mergers using only GW data, independent of EM counterparts or galaxy catalogs. By utilizing the intrinsic properties of the GW signal, specifically the strain amplitude and merger frequency, our model offers a computationally efficient preliminary distance estimation approach that could complements existing Bayesian parameter estimation pipelines. In this work, we examine a simplified analytical expression for the GW luminosity distance derived from General Relativity (GR), based on the leading-order quadrupole approximation. Without incorporating post-Newtonian (PN) or numerical relativity (NR) corrections, or modeling spin, eccentricity, or inclination, we test how closely this expression can reproduce distances reported by full Bayesian inference pipelines. We apply our model to 87 events from the LIGO-Virgo-Kagra (LVK) Gravitational Wave Transient Catalogues (GWTC), computing distances for these sources. Our results demonstrate consistent agreement with GWTC-reported distances, further supported by graphical comparisons that highlight the models performance across multiple events. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
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. -
Blockchain-based node authentication algorithm for securing electronic health record data transmission
The advent of Internet of Things (IoT) technologies in healthcare has heightened risks to Electronic Health Records (EHRs), including authentication vulnerabilities and data privacy concerns. This study proposes a novel blockchain-based node authentication algorithm for IoT healthcare, integrating Hyperledger Fabric, Homomorphic Encryption, and Recurrent Neural Networks (RNN). Employing a dual-layer security approach, the methodology utilizes a challenge-response mechanism and dynamic key exchange to ensure tamper-proof data transmission. Encrypted processing preserves confidentiality, while machine learning enhances anomaly detection accuracy to 99.01%, achieving a security rate of 99%. Comprehensive evaluations demonstrate significant improvements in efficiency, scalability, and robustness, addressing latency and computational overhead challenges. By fusing blockchains immutability with intelligent encryption and authentication, this solution revolutionizes EHR protection in IoT environments and scalable healthcare data management. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Resource allocation in cloud auction-based market by hybrid optimization algorithm
Effective resource allocation is essential in the rapidly changing cloud computing landscape to maximize provider revenue and user satisfaction. Through competitive bidding procedures, the auction-based market model has become a potent tool for allocating cloud resources among users. In this paper, a new method for cloud computing environments is presented: Double Auction-based Resource Allocation (DARA). The auction model and optimal resource allocation are the two main parts of the DARA methodology. The Double Auction mechanism is used as the auction model in the suggested DARA framework. In this model, resource prices and allocations are decided through a competitive auction process that involves both buyers and sellers.The highest price that buyers are willing to pay for resources is expressed in bids, and the lowest price that sellers are willing to accept is expressed in asks. There are many intricate tasks involved in this two-way auction process, including matching bids and asks, determining market prices, and handling transactions. Finding the equilibrium price requires the method to solve complex optimization problems in order to balance supply and demand. In order to overcome these obstacles, the study suggests the Hippopotamus Updated Pufferfish Optimization (HUPO) algorithm for the best possible resource distribution. The HUPO algorithm is made to handle limitations like truthfulness, resource density, execution time, and operating expenses. In order to ensure that users pay fair prices and service providers make the most money, it is crucial to implement effective resource allocation strategies that balance the cost of resources with their availability. According to the mean statistical metric, the resource density for the HUPO model is 17.862, which is greater than the values of all other traditional approaches, including BES at 14.960, AOA at 12.546, ACO at 14.274, COA at 13.693, SMO at 13.452, HOA at 13.686, and POA at 13.907. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
From Dharma to Dialogue: A Scoping Review of Couple Interventions Based on Buddhist Wisdom
There has been a surge of interest in interventions based on Buddhist traditions in the domain of relational therapy research. Our scoping review aimed to present a comprehensive overview of the current research landscape on this topic. Through systematic selection criteria, we identified 16 studies. We discovered that these interventions predominantly focused on mindfulness or compassiontwo pillars taken from the Buddhist tradition. Although the findings are varied, the collated evidence indicates that Buddhism-based interventions are promising in improving physical, mental, and relational health for individuals and dyads. However, the sustainability of these benefits needs to be examined. A point of concern is the possible dilution of the practices effectiveness when stripped of their comprehensive, traditional Buddhist context. We conclude from this review that while interventions such as mindfulness- and compassion-based programs can positively affect well-being, their efficacy might be constrained when these practices are detached from their broader, original Buddhist context. Therefore, future research should expand the field to develop intervention programs that maintain the integrity of holistic Buddhist wisdom to enhance relationship health and well-being. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.