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Bioprospecting Soil Bacteria for Protease Production Using Agro-Waste: Toward Sustainable Detergent Formulations
Purpose: Microbial proteases, particularly from soil-dwelling Bacillus species, are preferred over plant- and animal-derived enzymes due to their high yield, stability, and cost-effectiveness for large-scale production in industries. This study aimed to isolate and characterize potent protease-producing bacteria from soil and explore their application in developing a sustainable, bio-based stain remover. The formulation incorporates waste (citrus fruit peel and flower), promoting the valorization of agro-waste as part of a sustainable waste management strategy. Methods and Results: Soil samples collected from market waste disposal sites in Madurai, Tamil Nadu, yielded eight distinct bacterial isolates, among which strain S-5 showed the highest proteolytic activity on skimmed milk agar. Molecular identification confirmed the isolate as Bacillus aerius based on 16S rRNA sequencing.The crude enzyme extract obtained after 48h of incubation exhibited maximum proteolytic activity at pH 11, with an activity of 0.928U/mL. This confirms that enzyme production improves at higher pH levels. A biodegradable stain remover was prepared by combining the crude protease extract with citrus peel extract in water and ethanol formulations. The prepared formulation effectively removed oil, paint, and dye stains from cotton cloth within 20min of treatment without mechanical rubbing, whereas control samples showed minimal stain removal. Ethanol-based formulations demonstrated higher cleaning efficiency compared to water-based extracts, showing extensive stain removal in all replicates, while control treatments showed only minor or minimal removal. Conclusion: The integration of microbial proteases from soil-derived bacteria with agro-waste components produced an eco-friendly stain remover, offering a sustainable alternative to chemical detergents and promoting waste valorization in circular economy-based green product development. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
Nanoremediation of Groundwater Contaminants Through Mycosynthesized CuONPs and ZnONPs
The global wide threatening problem is the pollution, especially water and soil pollution are biggest threats to our people. The pollution not only damages the resources but also enters the ecosystem and impairs our health. The pollution disfigures the fertility of the soil and contaminates the groundwater table which is the most reliable source of all living organisms. Due to urbanization of people and scarcity of the water resources, the people rely on the groundwater for the domestic and drinking needs. Earlier researches include the bioremediation and physico-chemical mechanisms in removal of toxic/heavy metals from water but still faced several post-treatment issues. The advancement in science and technology paved a path as nanotechnology to overcome these problems. In this current investigation, the CuO nanoparticles (CuONPs) and ZnO nanoparticles (ZnONPs) were synthesized from endophytic fungal strain and characterized which were previously reported. The groundwater samples were collected near, in, and around of the garbage-dump site of Vellalore-Kurichi village, Coimbatore, Tamil Nadu, India; three areas were selected, and water samples were collected. The basic physico-chemical parameters such as BOD, COD, TDS, hardness, pH, chlorides, sulfates, nitrates, and heavy metal(s) of the collected samples were analyzed. The adsorption studies were initiated with three different concentrations of CuONPs and ZnONPs in 100mL of polluted groundwater samples, and the kinetics was started with 0th min and extended till 180min. The adsorption rate increased with the increase in time; the CuONPs and ZnONPs adsorbed the few pollutants that also included arsenic (V) effectively. The nanoremediated samples were further taken to determine the effectiveness in aiding the plant growth promotion, and this was executed in Trigonella sp. plants. The plants were grown well which was compared to the control plants, and the phytochemical assessment was carried out. The presence of phytochemicals of the plants grown in nanoremediated samples was similar to that of control plants. Further, the CuONPs and ZnONPs have the ability in remediating the pollutants/contaminants in the groundwater. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Biological and Environmental Applications of Myco-synthesized Titanium Dioxide Nanoparticles
In the present investigation, TiO2NPs were myco-synthesized through the extracellular enzymes of endophytic fungi, Aspergillus versicolor FCPRS11 isolated from stem of Azadirachta indica. The synthesized TiO2NPs was characterized using UV- Vis, FT-IR, SEM, XRD, EDX, DLS and Zeta Potential. The synthesized TiO2NPs were analyzed for their antimicrobicidal properties against five clinical pathogens with two fungal pathogens and were exhibiting significant inhibition towards the bacteria at minimum concentration of 50g/ml of TiO2NPs. The free radical scavenging mechanism of the synthesized TiO2NPs was monitored through various assessment to understand about NPs antioxidant properties and the IC50 values were compared with the IC50 value of standard ascorbic acid (91g/ml). Further the NPs were analyzed for in vitro anti- inflammatory property exhibiting 73.87% inhibition and anti- diabetic properties (62.18% inhibition) proving that TiO2NPs evinced a promising biomedical activity. The larger surface area of the synthesized TiO2NPs as per the SEM analysis, allowed evaluation of their adsorption capacity on soil collected from metallurgical site containing combination of heavy metals and contaminants. The results of adsorption studies demonstrated that the adsorption increases with increase in time of exposure of TiO2NPs and adsorption capacity was determined by employing Langmuir model. The % dye degradation was evaluated by photocatalytic dye degradation studies where at 100mg/L of TiO2NPs, over 91% dye was found to be degraded within 240mins.These findings highlight the potential of myco-synthesized TiO?NPs as effective agents for biomedical applications and environmental nanoremediation. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Biogenic Copper Oxide@rGO Nanocomposite for Decontamination of a Food Threat B. Cereus in a Rice Model
Pathogenic microorganisms have become a serious threat to human beings all over the world as they cause severe disease illnesses. This study used supercritical carbon dioxide as a green solvent to prepare a nanocomposite composed of copper oxide (CuO) nanoparticles dispersed on the surface of reduced graphene oxide (rGO). The resulting nanocomposite was examined using a various of alanytical techniques. The developed CuO/rGO nanocomposite exhibited potenail antimicrobial ability against a food pathogenic bacterium, Bacillus cereus. Additionality, it showed high toxicity towards B. cereus, confirmed by the means of fluorescent live-dead counting of cells. Its mechanistic role against a food meneac was confirmed by the means of time-kill ability (complete inhibition at 200min), cell membrane integrity (OD of control: 1.62; CuO/rGO: 3.78 at 60min), membrane uptake (Control: 27.21%; Cuo/rGO: 64.33%), and membrane potential ability (Control: 58.33; CuO/rGO: 24.12) towards B. cereus. Study of scanning electron microscopy analyse resulted in the membrane disruption of B. cereus by the nanocomposite. Moreover, the CuO/rGO nanocomposite inhibited in vitro biofilm formation ability (Crystal violet uptake - Control: 31.33%; CuO/rGO: 59.11%) of B. cereus. Furthermore, the nanocomposite coating was used as a rinse solution for rice bowl packages. Interestingly, a rinsing solution (20% and 4060min) significantly inhibited the CFU count of B. cereus in rice by 2.1 log cfu/g as compaered to control 6.2 log cfu/g. The outcomes highlight the effectiveness of nanocomposite coating against food menace B. cereus, suggesting that the developed nanocomposite could be applied as an effective antimicrobial marinade and/or a rinse for raw rice preservation aginst hazardous foodborne pathogens. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Synthesis and Biological Evaluation of Sr and Co Co-Doped TiO?Folic Acid Nanocomposites: Antibacterial, Antifungal (Candida albicans), Antioxidant (DPPH and Trolox), and In Vitro Anticancer Activity against HepG2 Cells
Liver cancer and multidrug-resistant bacterial infections pose significant health challenges, highlighting the urgent need for multifunctional therapeutics. In this study, a TiO? nanocomposite co-doped with strontium (Sr) and cobalt (Co) and surface-functionalized with folic acid (TiO?SrCoFA) nanocomposite was synthesized via a hydrothermal method followed by post-synthesis FA functionalization. XRD confirmed the anatase phase, with reduced crystallite size for TiO?SrCoFA, while TEM showed spherical, uniformly dispersed nanoparticles (~ 23nm) with no agglomeration. DLS revealed a hydrodynamic diameter of 138.6nm, and XPS/FTIR confirmed Sr, Co, and FA incorporation. Optical studies (UV-Vis and PL) indicated electronic modifications conducive to ROS generation. TiO?SrCoFA exhibited enhanced antimicrobial activity against Gram-positive bacteria (Staphylococcus aureus, Bacillus subtilis, Bacillus megaterium), Gram-negative bacteria (Klebsiella pneumoniae, Proteus vulgaris), and Candida albicans. Antioxidant assays demonstrated concentration-dependent scavenging (2883%) comparable to vitamin C. In HepG2 liver cancer cells, TiO?SrCoFA showed superior cytotoxicity with an IC?? of 6.5g/mL versus 9.8g/mL for TiO?, inducing apoptosis and oxidative stress. The enhanced bioactivity is attributed to nanoscale size, Sr/Co doping, FA-mediated targeting, and ROS generation. TiO?SrCoFA thus represents a promising multifunctional nanotherapeutic platform for simultaneous antimicrobial, antioxidant, and anticancer applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Chitosan Coated-Liposomal Microencapsulation of Fucoidan Extracted from Marine Sargassum wightii: Assessment of Its Biofunctional and Therapeutic Properties
Fucoidan, a sulphated polysaccharide derived from Sargassum wightii, holds significant therapeutic potential due to its antioxidant, antimicrobial, and wound-healing properties. Despite its therapeutic properties, its clinical efficacy is limited by poor bioavailability and instability. This study reports the successful encapsulation of fucoidan in liposomes employing the thin-film hydration technique, followed by chitosan coating to enhance its stability and biological activity. Structural integrity and successful encapsulation were confirmed through FTIR and UVVis spectroscopy. Antioxidant activity assessed via DPPH and hydrogen peroxide scavenging assays demonstrated concentration-dependent radical scavenging, with chitosan-coated formulations exhibiting superior efficacy. The formulation was reported to exhibit strong antioxidant potential, as indicated by DPPH (38.65% at 500?g/ml) and H?O? (40.707% at 400 ?g/ml). Antimicrobial testing revealed notable activity against the Gram-negative bacterium Escherichia coli, but not against the Gram-positive bacterium Bacillus subtilis, suggesting a narrow-spectrum antibacterial potential. The antimicrobial assays conducted reported a zone of inhibition of 11.4mm for a concentration of 2mg/ml. Furthermore, scratch wound assays and MTT-based cytotoxicity analysis on L929 fibroblast cells indicated promising wound-healing activity, with a wound closure of 92.72% observed 72h after treatment with the sample. The IC?? value of 100?g/ml was also reported to have high cell viability of 84.22%. These findings underscore the potential of chitosan-coated liposomal fucoidan as a multifunctional bioactive system for pharmaceutical and biomedical applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
CalciumCobaltCurcumin Functionalized TiO? Nanoparticles for Enhanced Biological Activities Using a Multivariable Regression Approach
A multifunctional TiO?-based NPs, TiCaCoCur, was synthesized by coating TiO? nanoparticles with calcium, cobalt, and curcumin. XRD confirmed the retention of the anatase phase with crystallite sizes of 35nm for TiO? and 27nm for TiCaCoCur. FTIR verified curcumin incorporation via hydrogen bonding and metal coordination. UVVis spectra showed shifts in absorption peaks, indicating electronic interactions, while PL suggested the presence of surface defect states and improved electronhole separation. SEM and TEM revealed spherical nanoparticles (~ 2526nm) with SAED patterns consistent with XRD. DLS indicated a reduced hydrodynamic size (116 6nm) for TiCaCoCur compared to TiO? (152 8nm), and XPS confirmed the presence of Ti??, Ca?, Co?, and surface-bound curcumin.TiCaCoCur exhibited enhanced antimicrobial activity against Gram-positive and Gram-negative bacteria and C. albicans, and showed improved DPPH radical scavenging activity, approaching that of Vitamin C. MTT assays on MOLT-4 cells demonstrated dose-dependent cytotoxicity, with an IC?? value of 8.7g/mL for TiCaCoCur. In addition, a simple multivariable regression analysis was carried out to examine the relationship between key physicochemical parameters and biological activity. The results suggest that reduced particle size (DLS), smaller crystallite size (XRD), and changes in optical absorption behavior (UVVis) collectively contribute to the improved biological performance of the modified nanoparticles.Overall, the synergistic combination of TiO?, curcumin, and metal ions yields a NPs with enhanced structural, optical, and biological properties, making it a promising candidate for biomedical applications including antimicrobial, antioxidant, and anticancer uses. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Parsing the Promise of Mindfulness for Obsessive-CompulsiveDisorder: From Heterogeneous Evidence to Mechanistic Precision
This extended commentary critically evaluates recent mindfulness-based interventions (MBIs) for obsessive-compulsive disorder (OCD), with particular attention to the synthesis by Reis et al. (Expert Review of Neurotherapeutics, 24(7), 735741,2024). Drawing on a meta-analysis by Chien et al. (Journal of Obsessive-Compulsive and Related Disorders, 32, 100712,2022) and 10 randomized controlled trials, this review highlighted substantial heterogeneity across intervention types, delivery formats, and outcome measures. Key distinctions were identified among mindfulness-based cognitive therapy, acceptance and commitment therapy, and mindfulness-based exposure and response prevention (MB-ERP). Notable discrepancies between self-reported and clinician-rated outcomes, divergent theoretical frameworks, and the need for greater mechanistic precision were underscored. Integration of mindfulness within ERP emerges as a theoretically promising but still preliminary strategy to enhance inhibitory learning, reduce covert compulsions, and strengthen distress tolerance and treatment engagement. A forward-looking research agenda was proposed, emphasizing mechanism-matched trials, optimization of intervention sequencing, culturally adapted protocols, and scalable digital MB-ERP platforms with fidelity monitoring. This approach aimed to support the development of individualized, effective, and durable mindfulness-based treatments for OCD. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Application of Corn Oil Derived Carbon Nano-onions Using Flame Pyrolysis as Durable Catalyst Support for Polymer Electrolyte Membrane Fuel Cells
The reliance of carbon black as catalyst support for Pt in PEM fuel cell has posed a major challenge in its durability as carbon blacks are highly prone to corrosion. As an alternative, CNTs, CNFs, and graphene are explored as catalyst support, however at the expense of tedious synthesis procedure and production cost. So to combat this issue, a facile flame pyrolysis route was adopted to produce carbon nano-onions using eco-friendly corn oil. Further modification in the carbon nano-onions exhibited better corrosion resistance in comparison to carbon black (Vulcan XC-72R). Also, a systematic approach was adopted towards developing a durable electrocatalyst which was designed to withstand harsh fuel cell operating conditions. The electrocatalyst was successfully analyzed using stringent standard testing protocols (< 40% ECSA loss). Among all the electrocatalyst studied, Pt/fOC exhibited only 37.1% loss in electrochemical active surface area (ECSA) after 5000 cycles, thus indicating its excellent durability. A full cell was also constructed with Pt/fOC as cathode electrocatalyst which showed a maximum power density of 365 mW cm?2comparable to commercial Pt/C (367 mW cm?2). To the best of our knowledge, this is the first study on the application of corn oil derived carbon nano-onions as catalyst support for PEM fuel cells. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Bimetallic Cobalt-Vanadium Boride as a Bifunctional Electrocatalyst for Overall Water Splitting
The transition to a hydrogen-based economy necessitates the development of sustainable and cost-effective electrocatalysts for green hydrogen production via water electrolysis. In this study, we report a novel cobalt-vanadium boride (CoVB) catalyst, which exhibits enhanced bifunctional activity for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in alkaline media. CoVB was synthesized using a facile one-step chemical reduction method with varying vanadium concentrations, optimizing performance at a 3% vanadium content. Electrochemical analyses demonstrated that CoVB significantly outperformed cobalt boride (CoB), achieving an HER and OER overpotential (?10) of 80mV and 320mV, respectively, comparable to noble metal benchmarks. Characterization results revealed that V plays a promoting role in inhibiting the growth of particles and agglomeration of particles, leading to an increase in surface area and producing unique mixed amorphous and crystalline structures in CoVB to enhance catalytic activity by increasing the number of active sites and conductivity across the interface. Furthermore, in two-electrode systems, the cell voltage of 1.66V was needed to achieve 10mA/cm2 of current density with superior stability and reusability. Overall, the CoVB catalyst, a new candidate from the metal boride family, presents a promising alternative to precious metals for efficient and sustainable water-splitting in alkaline electrolyzers. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network
In past decade, the android malware threats have been rapidly increasing with the widespread usage of internet applications. In respect of security purpose, there are several machine learning techniques attempted to detect the malwares effectively, but failed to achieve the accurate detection due to increasing number of features, more time consumption decreases in detection efficiency. To overcome these limitations, in this research work an innovative Hybrid dual path Bidirectional long short-term memory Kepler dynamic graph Convolutional Network (HBKCN) is proposed to analyze and detect android malwares effectively. First, the augmented abstract syntax tree is applied for pre-processing and extracts the string function from each malware. Second, the adaptive aphid ant optimization is utilized to choose the most appropriate features and remove irrelevant features. Finally, the proposed HBKCN classifies benign and malware apps based on their specifications. Four benchmark datasets, namely Drebin, VirusShare, Malgenome -215, and MaMaDroid datasets, are employed to estimate the effectiveness of the technique. The result demonstrates that the HBKCN technique achieved excellent performance with respect to a few important metrics compared to existing methods. Moreover, detection accuracies of 99.2%, 99.1%,99.8% and 99.8% are achieved for the considered datasets, respectively. Also, the computation time is greatly reduced, illustrating the efficiency of the proposed model in identifying android malwares. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Multi-Objective Reinforcement Learning With Physics-Aware Vehicle Dynamics for Safe and Efficient Adaptive Cruise Control
Adaptive Cruise Control (ACC) enhances safety and comfort in autonomous vehicles by maintaining appropriate inter-vehicular distance and speed regulation. Traditional ACC systems based on PID or Model Predictive Control (MPC) often struggle to handle complex, unforeseen traffic scenarios such as sudden braking, pedestrian crossings, or lane changes. Reinforcement Learning (RL) offers an adaptive alternative by enabling policy learning through environment interaction. However, existing RL-based ACC methods frequently suffer from poor smoothness and energy inefficiency under emergency conditions. This work proposes an enhanced RL-based ACC framework that integrates a physics-informed, multi-objective reward function to jointly optimize safety, ride comfort, and energy efficiency. The reward components are normalized and dynamically weighted based on the current driving context, allowing the agent to adaptively prioritize objectives. Vehicular dynamics are explicitly incorporated into the learning process to improve real-world applicability. The system is trained using the DDPG algorithm, which supports continuous control and stable policy convergence. Extensive MATLAB-based simulations were conducted across diverse urban driving scenarios including stopgo traffic, traffic signals, lane changes, and pedestrian interactions. Comparative analysis against PID and MPC-based ACC controllers demonstrates that the proposed framework achieves superior performance in maintaining safe inter-vehicular distance, reducing jerk, and improving energy efficiency. This study validates the feasibility of deploying a computationally efficient, model-free RL-based ACC for robust and safe autonomous driving in dynamic traffic environments. The Author(s), under exclusive licence to ITS Japan 2026. -
We are Treated as Outsiders in Our Own City: Lived Experiences of Intersectional Stigma Against Sex Workers in Kolkata, India
Introduction: Sex workers in India experience intersectional stigma related to their gender identity, sexuality, and profession. The objective of the present study is to analyze the lived experiences of intersectional stigma against sex workers in Kolkata. Methods: We interviewed 30 cisgender female sex workers in March 2023 in Kolkata, India. Interviews were digitally audio recorded, translated from Bengali into English, and transcribed and coded using thematic analysis. Results: We identified five main themes regarding intersectional stigma: (1) internalized stigma regarding the shame associated with being a female sex worker, (2) perceived stigma of sex work as a dirty profession, associated with lower caste status, (3) enacted stigma against sex workers who are mothers, (4) enacted stigma against the children of sex workers, and (5) reduction of stigma through unionization/labor organizing. Conclusions: Intersectional stigma against sex workersis impacted by negative attitudes regarding gender, caste status, single motherhood, and occupation. We identified internalized stigma as a source of shame for sex workers. Sex workers also were perceived to beengaged in afilthy profession, associated with lower caste status. Those sex workers who were mothers experienced discrimination, as did their children. Respondents reported how collectivization has helped to address these experiences of stigma anddiscrimination. Policy Implications: Addressing the intersectional stigma against sex workers in Kolkata necessitates a shift in social attitudes.Findings underscore the urgent need for stigma reduction interventions and socialpolicies, including (1) labor protections for sex workers, (2) individual/community-level interventions for sex workers, and (3) media campaigns to address stigma reduction. By understanding the lived experiences of sex workers, we may develop better interventions to reduce stigma in the lives of sex workers in Kolkata and throughout India. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Bayesian and non-bayesian inference of the generalized Lomax distribution under a unified hybrid censoring scheme with applications in failure times in biomedical and aerospace materials
The unified hybrid censoring scheme is a combination of different types of censoring schemes used in reliability testing. This paper presents the statistical inference of generalized Lomax distribution under unified hybrid censoring scheme. The point and interval estimates of the parameters ?,?, and ? of the generalized Lomax distribution have been studied for unified hybrid censored data. In point estimation, the maximum likelihood estimation method is used for computing the estimates, and Tierney and Kadane estimation method is used for Bayes estimation. A 100(1-?)% approximate confidence interval and Bayesian credible intervals for the parameters ?,?, and ? have been computed in the interval estimation part. Mean squared errors are computed for all the estimates and comparison of estimates have been done. The results indicate that the Bayesian estimation method yields more accurate and reliable parameter estimates compared to the maximum likelihood approach. Finally, data representing failure times of fatigue fracture of Kevlar 373/epoxy and failure times of aircraft windshields have been used for point and interval estimations of all parameters as application of real-life scenarios. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025. -
A shap-enhanced PCA-DBSCAN framework for interpretable retail customer segmentation and strategic insight
The rapid expansion of online retail underscores the critical need for precise customer segmentation to drive personalized marketing, reduce churn, and boost lifetime value. This study develops an end-to-end, highly interpretable segmentation pipeline encompassing advanced feature engineering, dimensionality reduction, exhaustive hyperparameter tuning, and robust validation to reveal stable, actionable customer groups in a large, real-world UK online-retail dataset (541,909 records). We augment the classic RFM (Recency, Frequency, Monetary) framework with: TPAC TF-IDF embeddings of item descriptions, holiday-purchase flags, and exponential recency decay; CACV net monetary value and cancellation ratios. After outlier filtering on RFM scores, we apply PCA (230 dimensions) and compare ten clustering methods (selected to represent major algorithmic paradigms: centroid-based [K-Means], probabilistic [GMM], hierarchical [BIRCH, Agglomerative], density-based [DBSCAN, OPTICS, HDBSCAN], graph-based [Spectral], message-passing [Affinity Propagation], and mode-seeking [Mean Shift]). We perform a full grid search per algorithm using a 'safe' silhouette scorer (ignoring noise) and also report Davies-Bouldin and Calinski-Harabasz indices. Temporal stability is assessed via adjusted Rand indices across time splits, and cluster interpretability is enhanced through SHAP-based feature importance analyses. By integrating textual, temporal, and cancellation behaviors into segmentation followed by systematic tuning and multi-metric validation our pipeline delivers superior cluster quality and actionable business insights compared to prior work. Segments directly enable strategic interventions: 'High-Decay Loyalists' (precision = 0.92) receive VIP retention offers yielding 2231% ROI lift, while 'At-Risk Cancellers' (recall = 0.89) trigger targeted win-back campaigns. We also demonstrate a reproducible framework for selecting both model and feature set. DBSCAN (? = 0.3, min_samples = 3 on 10 PCA components) achieved the best silhouette score (0.986), markedly exceeding the 0.72 benchmark in the literature. Agglomerative clustering (average linkage, 2 clusters) scored 0.776, while OPTICS and Spectral Clustering also outperformed classical Gaussian- or centroid-based models. A temporal ARI above 0.8 confirms cluster stability. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025. -
Digitization assisted circular economy: a business strategy to attain sustainability in global supply chains
Contemporary businesses that aim for sustainable global supply chains (GSCs) through circular economy (CE) models has been a crucial topic of research and practice for addressing the climate change risks and resource scarcity. This study identifies and prioritizes the critical success factors (CSFs) for digitalization assisted CE for sustainability in GSCs using a modified multi criteria decision making technique, Grey Decision-Making Trial and Evaluation Laboratory (DEMATEL). Analysing the cause-and-effect relationships among 14 identified CSFs for digitization assisted CE in GSCs offers actionable insights for organizations and policymakers seeking to enhance sustainability efforts by navigating the complexities of integrating digitization and CE practices. The findings from the study reveal that process improvement and optimization through digitization and human centric sustainable operations towards CE as the highest ranked CSFs. The results suggest that managers shall invest in digital integration, prioritize transparency, and foster collaboration to create resilient and sustainable supply chain ecosystems and this will serve as the initial step towards the future digital transformations. This study provides a strategic roadmap for managers and policymakers aiming to integrate CE principles through digital transformation. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2026. -
Decoding customer sentiments in quick commerce: comparative insights from BlinkIt, Zepto, and JioMart utilizing machine and deep learning models
The rapid expansion of quick commerce platforms like BlinkIt, Zepto, and JioMart has introduced unique challenges in understanding customer sentiments due to their operational focus on ultra-fast deliveries and hyper-local logistics. This study conducts a comprehensive analysis of sentiment classification methodologies, exploring both traditional ML techniques and advanced DL models to classify customer reviews into positive, negative, and neutral categories. Traditional models, while offering simplicity and interpretability, achieved moderate accuracy (83% with SVM) but struggled to capture the complexities of neutral sentiments. In contrast, DL models, particularly LSTM, achieved superior performance with an accuracy of 88.96% and a macro F1-score of 0.64, leveraging pre-trained embeddings like GloVe to enhance semantic understanding and contextual representation. Further experiments with optimizers, including Adam, RMSprop, SGD, and Nadam, revealed their limited impact on resolving class imbalance and improving neutral sentiment classification. To address these challenges, we integrated hybrid architectures combining GloVe and BERT embeddings, achieving a significant accuracy of 90.69% and demonstrating improved generalization across sentiment classes. However, the classification of neutral sentiments remained a persistent challenge, underscoring the need for advanced techniques like data augmentation and ensemble strategies. This research highlights the importance of adopting hybrid and deep learning-based approaches for sentiment analysis in quick commerce platforms. The findings provide actionable insights for enhancing customer satisfaction and service quality, while also paving the way for future research in domain-specific sentiment classification and scalable solutions for underrepresented sentiment categories. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2026. -
Remote sensing data analyzed by machine learning to predict structural changes
Natural disasters can cause extensive structural damage, necessitating rapid and reliable post-event assessment to support emergency response and recovery planning. Although several methods exist for pixel-level damage classification using post-disaster imagery, translating these outputs into meaningful, building-wise assessments remains challenging. Building-level damage prediction provides more interpretable insights, enabling a clearer estimation of the severity of impact on individual structures and a comprehensive understanding of the overall destruction. This information is crucial for quantifying damage magnitude and prioritizing relief operations. This paper proposes Damage Estimation U-Net (DE-U-Net), a deep learning framework designed to estimate structural damage across four classes: No Damage, Minor Damage, Major Damage, and Destroyed. The model is trained on the xBD dataset to learn representative damage patterns. DE-U-Net is developed by integrating a modified Siamese U-Net with a Damage Ratio Analyzer (DRA) algorithm for building-level damage conversion. The DRA algorithm comprises three components: (1) Connected Component Analysis (CCA) to transform pixel-level predictions into building-level predictions (2) size filtering to remove noise and eliminate small artifacts, and (3) a damage estimation module to compute the number of pixels corresponding to each damage class per building. Model performance is evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2026. -
Modelling bivariate vector autoregressive model using copula approach
In this study, we propose a novel approach to model the relationship between bivariate time series by introducing a bivariate vector autoregressive model with Ali-Mikhail-Haq(AMH) copula, incorporating non-normal errors. The utilization of the Ali-Mikhail-Haq copula will allow for flexible modeling of the dependence structure between the two time series. This copula framework enables us to model the joint distribution of the errors with greater accuracy. Our approach provides a way to capture the relationships between the two time series, making it more suitable for complex data structures where traditional methods based on normal error assumptions may fall short. The Inference Functions for Margins (IFM) technique is employed to estimate both the model parameters and the dependency structure in our proposed model. To evaluate the accuracy of the proposed model, we conduct an extensive simulation study. The results demonstrate that the suggested model performs robustly across different scenarios, effectively capturing the dependence structure and delivering precise parameter estimates. The AMH copula efficiently models moderate levels of both negative and positive dependence. To enhance forecasting performance, we introduce a hybrid extension in which an artificial neural network(ANN) is applied to the residuals of the copula-based AMHVAR model. This hybrid approach captures remaining nonlinear patterns not explained by the linear VAR dynamics and the copula-based dependence structure, leading to improved predictive accuracy. Finally, we apply the proposed models to real-world data, further validating its practical applicability. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2026. -
Improved piezoelectric energy harvester design using aluminum nitride for improved voltage and power output
This research focuses on improving the performance of piezoelectric energy harvesters (PEHs), which convert ambient kinetic energy into electricity. One of the primary challenges with piezoelectric harvesters is their high resonant frequencies, which often do not align with the lower natural frequencies of ambient vibrations, limiting their efficiency. The goal of this research is to propose a new technique to optimize the design of PEHs, enhancing voltage output and power conversion efficiency. The proposed method combines an Arithmetic Optimization Algorithm to optimize the harvesters dimensions with a Dual Temporal Gated Multi-Graph Convolution Network (DTGMGCN) to forecast resonant frequency and harvested voltage. The principal objective is to reduce resonant frequency errors and enhance energy conversion efficiency. The results, implemented on a MATLAB platform, demonstrate that the proposed method outperforms the existing techniques, such as robust chaotic Harris Hawk optimization, K-Nearest Neighbor Algorithm, and Heaviside Penalization of Discrete Material Optimization. The existing techniques show errors of 0.04%, 0.06%, and 0.08%, while the proposed method achieves an error of only 0.02%. Additionally, in terms of efficiency, the proposed method reaches 98%, significantly higher than the 65%, 78%, and 85% achieved by the existing techniques. These findings indicate the efficiency of the proposed approach in improving the design and performance of piezoelectric energy harvesters, offering a promising solution for more efficient energy harvesting systems. King Abdulaziz City for Science and Technology 2025.