Service-based business models (either product-, use-, or result-oriented) have gained attention as a way for manufacturing firms to transition toward a circular economy and foster the implementation of closing-the-loop strategies such as reuse, remanufacturing, and recycling. However, the relationship between the adoption of service-based business models and the achievement of greater circular economy performance remains complex and not fully understood. Adopting the Supply Chain Practice View and paradox theory as theoretical lenses, this paper addresses a literature gap by examining how supply chain practices shape the effectiveness of service-based business models in enhancing circular economy performance. Drawing on survey data from 380 Indian manufacturing firms and employing covariance-based Structural Equation Modeling, the study finds a direct effect of both the adoption of service-based business models and the implementation of supply chain practices on circular economy performance. It further shows that supply chain practices positively moderate the relationship between service-based business models and circular economy performance, strengthening their effectiveness in generating positive circularity outcomes. The study extends the Supply Chain Practice View by clarifying how transferable and imitable supply chain practices such as supply chain tracking, green partner selection, and co-design enable an effective implementation of service-based business models for circularity. Drawing on paradox theory, it also suggests that such practices help firms manage tensions triggered by service-based business models, sustaining a dynamic equilibrium that reinforces circularity performance. From a managerial and policy perspective, these results highlight a valuable opportunity for firms in developing economies to simultaneously support business growth and reduce environmental burdens through the joint implementation of service-based business models and supply chain practices.
Anxiety is a prevalent concern among college students, impairing academic performance and increasing attrition risk for universities. Effective interventions are needed to reduce overall and test anxiety during high-stakes assessments. This study explored the effect of massage therapy (MT) in reducing overall and test anxiety in traditional-aged undergraduate business students compared to a comparison group engaged in a relaxation activity (adult coloring). A private liberal arts university in the southeastern United States. Undergraduate business students (aged 18-23 years) enrolled in accounting, business administration, finance, economics, or marketing. Quantitative, quasi-experimental, repeated-measures design with both within-subjects (pre-post) and between-subjects (MT vs. adult coloring) evaluations. The experimental group received a 30-min MT session administered by a licensed massage therapist 2 h prior to a final exam. The comparison group engaged in a 30-min adult coloring activity. Anxiety levels were assessed pre- and post-intervention with the Adult Manifest Anxiety Scale-College Version (AMAS-C). MT significantly reduced overall anxiety (mean (M) = 58.79 to 51.68, t(37) = 4.103, p < 0.001, Cohen's d = 0.666) and test anxiety (M = 59.29 to 55.24, t(37) = 2.623, p = 0.013, Cohen's d = 0.426). Analyses of variance confirmed significant main effects of MT on overall anxiety (η 2 = 0.314, p < 0.001) and test anxiety (η 2 = 0.155, p = 0.015), unaffected by sex at birth or academic classification. Between-group differences with the comparison activity were not statistically significant (p = 0.114), though MT showed a small-to-moderate effect size. A 30-min MT intervention effectively reduced overall and test anxiety among traditional-aged undergraduate business students when administered within a 2-h window prior to a final exam. MT demonstrates promise as a non-pharmacological strategy to support students during high-stakes academic assessments.
Veterinary medicine is shaped by the business-care paradox, a persistent tension between commercial sustainability and ethical care commitments. Emotional labor is central to how practitioners navigate this paradox, yet the subjective processes through which they construct and sustain professional identity amid these competing demands have received little empirical attention from a subjectivity standpoint. Existing studies illuminate how emotions mediate this paradox yet stop short of examining the distinct identity pathways that practitioners adopt. Employing Q-methodology, 30 small-animal veterinarians in employee roles across private practices in southeastern China sorted 42 statements. The statements were developed around four core mechanisms of emotion-mediated paradox navigation: emotional salience, flexible emotional labor, emotional traces, and ongoing learning. Principal component analysis with varimax rotation identified shared subjectivity patterns, while semi-structured post-sorting interviews elicited rationales for extreme placements, strengthening interpretive validity. A three-factor solution explained 58% of variance, revealing three distinct identity trajectories. The pragmatic service provider reframes business decisions as necessary for sustainability, prioritizing clinic viability, and employs emotional detachment as a protective strategy. The conflicted caregiver experiences moral distress and identity dissonance when financial constraints limit care, feeling torn between ideal and feasible treatment. The resilient integrator treats constraints as parameters for creative problem-solving, drawing on adaptive learning while sustaining core ethical commitments. These viewpoints represent qualitatively distinct modes of emotional labor engagement. The findings reveal three distinct modes of emotional labor engagement, each reflecting a different pathway of professional identity construction. For veterinary educators, these findings highlight the need for differentiated support strategies, each tailored to a distinct identity trajectory. Spanning moral distress mitigation to resilience cultivation, such strategies are essential for sustaining both practitioner wellbeing and care quality.
Manure is an abundant waste source in dairy production with disposal creating significant challenges for producers. Recent work has shown that black soldier fly, Hermetia illucens (Linnaeus) (Diptera: Stratiomyidae), larvae can digest dairy manure; however, its high cellulose content and low nutrient value impact larval performance. Adding probiotics to black soldier fly feed can increase feed digestibility and weight gain. The goal of this study was to determine the influence of probiotic additions on black soldier fly larval performance when fed dairy manure. This study evaluated life-history traits of black soldier fly larvae fed dairy manure with single or combined probiotic treatments: Rhodococcus sp., Paenibacillus sp., and 1:1 combination, compared to a no-microbe control. Larvae were mass-reared on fresh dairy manure, and key metrics-bioconversion rate, survivorship, total biomass, and larval weight-were compared across treatments. Results showed a significant trial effect for all variables measured. Mean peak weights and the largest larval weights were 3.5-20.0% greater in at least one probiotic treatment compared to the control, while other life-history traits did not differ significantly. However, probiotic treatments reduced variation up to 17.0% in black soldier fly larval weight across trials, a trait not historically discussed in black soldier fly research. Developing processes that optimize larval weight in combination with reducing variation are critical for developing precise business models for predicting future production.
Clinicians' adoption of interoperability tools influences care quality, but evidence of actual use is limited. We analyze clinicians' use of outside records delivered via Epic Care Everywhere (CE), focusing on use frequency and predictors such as gender, experience, specialty, and role. Differences between pre-pandemic (2018-2019) and pandemic (2020-2021) periods are also examined to see how COVID-19 affected use of outside records. De-identified EHR metadata from UCSF clinicians (n = 1442) during pre-pandemic and pandemic periods, totaling 686 797 clinician-day observations, were analyzed. We measured usage intensity (mean CE lookups per appointment) and breadth (percentage of appointments with ≥1 lookup). Generalized linear models (GLMs) with a negative binomial distribution for overdispersion were used to estimate predictors of intensity. CE usage intensity rose by 43.0% post-COVID-19 onset compared with the pre-pandemic. Clinician specialty most strongly predicted use, with Nephrology and Cardiology showing the highest breadth (56.0% and 53.0% of visits, respectively), while Dermatology (13.2%) and Pediatrics (23.4%) were lowest. Residents used CE at 23.0% greater intensity than attendings, and each additional year of experience was linked to a 0.57% decrease in intensity. Clinician use of interoperability tools was higher in specialties such as Nephrology and Cardiology that require more care coordination, and among less experienced clinicians including resident physicians. Use increased after the pandemic began, likely due to ongoing adoption trends and increased clinical demands during system strain and uncertainty. These findings underscore the critical importance of considering clinician behavior and contextual factors, such as specialty care needs, in addition to technical capabilities, when promoting the adoption and use of interoperability tools.
As a novel plant-based milk alternative, the stability and flavor of quinoa milk are key to its marketability and meeting consumers' diverse requirements. Moreover, they might be significantly affected by protein characteristics, which are closely associated with different pretreatment methods. This study aimed to compare physicochemical stability and flavor characteristics of quinoa milk prepared by three different pretreatment methods, including milling, blanching, and microwave. The possible differential mechanisms were explored by analyzing protein properties and microstructure of quinoa milk samples. The results showed that compared with the control with the largest sediment (5.82 mm) and particle size (1.72 μm), the stability of quinoa milk was improved after pretreatment. The quinoa milk sample of blanching exhibited the greatest stability with the lowest sediment layer (1.54 mm), lowest instability index (0.15), and smallest particle size (0.52 μm), followed by the microwave and the milling groups. In addition, the decreased surface tension (43.71 mN/m) and increased free sulfhydryl content (41.21 μmol/g), zeta-potential (-26.15 mV), surface hydrophobicity, as well as the presence of high-molecular-weight polypeptide bands, were considered to be the reasons for the stability of the blanching-pretreated quinoa milk sample. For flavor profile, blanching pretreatment significantly increased the levels of desirable volatile compounds associated with grassy and fruity notes (such as 1-Hexanol, 2-ethyl-, and 2-Hexenal, (E)-), contributing to the improvement of sensory attributes. These findings provided new insights that the positive effect of appropriate heat pretreatment (blanching) into the quality enhancement of novel additive-free quinoa milk and other plant-based milk alternatives.
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Examining how the relationship between alcohol outlet density and alcohol-related harms may vary by neighborhood-level factors is important for informing the evaluation and implementation of environmental alcohol interventions and identifying other community conditions that can be modified to reduce alcohol-related harms and inequities. This study aims to extend prior research by testing whether housing eviction-a cause of housing insecurity and neighborhood destabilization-modifies the impact of alcohol outlet density on hospitalizations for assault and alcohol use disorder (AUD) in Pennsylvania ZIP codes. We used Bayesian hierarchical space-time misalignment models to examine the associations between inpatient assault and AUD hospitalizations and housing eviction filing rates, alcohol outlet density, and the proportion of outlets selling alcohol for off-premise consumption at the ZIP code level in Pennsylvania 2018-2022 (n = 7257 space-time units), reporting relative rates (RR) and 95% credible intervals (CIs). We tested whether eviction filing rates modified the associations between outlet density/the proportion of off-premise outlets and hospitalizations for AUD and assault. In main effects models, a one percent increase in the eviction filing rate was associated with a 1.2% increase in AUD hospitalizations (95% CI: 1.01, 1.015) and a 1.9% increase in assault hospitalizations (95% CI: 1.002, 1.038). The addition of one outlet per square mile was associated with a 0.3% increase in AUD hospitalizations (95% CI: 1.002, 1.004) and a 0.6% increase in assault hospitalizations (95% CI: 1.003, 1.008). In interaction models, eviction filing rates strengthened the positive associations between outlet density and hospitalizations for both assault and AUD. Reducing housing evictions may mitigate the impact of alcohol outlet density on AUD and assault. Future research, policy, and practice should explore opportunities for jointly addressing the alcohol environment and neighborhood housing conditions.
Jesuit leadership, grounded in Ignatian spirituality, emphasizes values such as cura personalis (care for the whole person), accompaniment, discernment, and magis (the pursuit of excellence), which together foster holistic human development and wellbeing. While these principles are widely applied across Jesuit educational, healthcare, and organizational settings, systematic evidence linking Ignatian leadership with outcomes of human flourishing remains limited. This study presents a systematic review of peer-reviewed literature published between 2020 and 2025 to examine how Ignatian spirituality functions as a formative foundation for Jesuit leadership and how these leadership practices contribute to wellbeing and human flourishing, with particular relevance to the Indian context. Following PRISMA 2009 guidelines, a comprehensive search was conducted across five databases-Scopus, Web of Science, PsycINFO, PubMed, and Google Scholar-resulting in the inclusion of forty-five eligible studies. The reviewed literature was appraised using the Mixed Methods Appraisal Tool and synthesized through thematic analysis. Findings reveal that Ignatian spirituality functions as a formative framework for leadership development, with practices of cura personalis and accompaniment enhancing emotional, psychological, and spiritual wellbeing, while magis and reflective discernment promote ethical excellence, resilience, and purposeful growth. Overall, the review demonstrates that Jesuit leadership supports integrative human flourishing by aligning ethical reflection, compassionate service, and institutional mission. The study highlights the need for future empirical research employing validated wellbeing measures to further examine Ignatian leadership constructs across diverse cultural and institutional contexts.
The study presents MT-PyraRisk, a multi-task learning framework that integrates pyramidal attention mechanisms for cross-border e-commerce risk prediction. The model processes multimodal time-series data through a shared feature extraction layer and task-specific prediction modules, supporting both regression tasks, such as operational risk and demand forecasting, and classification tasks, such as anomaly detection. Experiments were conducted on the Global E-commerce Dataset and the Kaggle Network Traffic Dataset using a sliding-window time-series setting, with the data divided into training, validation, and testing sets at a ratio of 70%, 15%, and 15%, respectively. The model was evaluated using MSE and MAE for regression tasks and Precision, Recall, and F1-score for classification and anomaly detection tasks. Compared with the strong baseline Timesformer, MT-PyraRisk reduced MSE by approximately 4.3% and improved F1-score and Recall by approximately 1.1% and 2.3%, respectively, on the Global E-commerce Dataset. On the Kaggle Network Traffic Dataset, MT-PyraRisk reduced MSE by approximately 5.1% and improved F1-score, Precision, and Recall by approximately 3.3%, 2.2%, and 4.5%, respectively. Ablation results further demonstrate the contribution of the pyramidal attention mechanism, task-specific prediction layers, and joint optimization module, as removing these components led to clear performance degradation. These results indicate that MT-PyraRisk can provide an effective multi-task modeling solution for operational risk forecasting, demand prediction, and anomaly detection in cross-border e-commerce risk management.
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Multiple sclerosis (MS) arises from an autoimmune response in which the immune system erroneously targets myelin autoantigens within the central nervous system, leading to myelin degradation and subsequent neurological dysfunction. Identifying myelin autoantigenic peptides (MAPs) is therefore critical for understanding MS pathogenesis and developing targeted therapies; however, conventional experimental approaches remain time-consuming and costly. Thus, computational methods that can perform in silico screening of T cell-specific MAP in MS (MAPMSs) using only peptide sequences are highly desirable. Existing computational methods primarily rely on a single modality, which often fails to capture key information of MAPMSs, leading to limited sequence representation and generalization ability. To address this limitation, we propose MIF-MAPMS, a novel multimodal information fusion framework that leverages multimodal information, including peptide format and SMILEs notation, for accurate MAPMS identification. This novel framework processes different modalities of compositional descriptors, molecular fingerprints, ESM-2 embeddings, and Mol2V embeddings using specific deep learning methods, leading to enriched MAPMS representation. Subsequently, the extracted embeddings are fused and passed through a multilayer perceptron (MLP), followed by a fully connected neural network for MAPMS identification. Both cross-validation and independent test results show that MIF-MAPMS attains significant improvements in MAPMS identification over the benchmark main and alternative datasets, with Matthew's correlation coefficient (MCC) of 0.931-0.968 and 0.812-0.928, providing 5.78%-8.04% and 1.22%-2.98% increases, respectively, compared to the existing method. Ablation studies further confirm the necessity of multimodal information fusion in improving MAPMS representation and the model's predictive performance. All codes and datasets are freely available online at https://github.com/lawankorn-m/MIF-MAPMS.
To improve energy efficiency and inhibit urease, this study developed an infrared physical field (IPF) method. This study integrates experimental approaches and computational simulations to elucidate the mechanism of urease inactivation under IPF treatment. The optimal treatment (130 °C-9 min) reduced the enzyme activity by 97.55% within the experimental range (90-130 °C, 1-9 min). Experimentally, circular dichroism showed a 25.36% increase in random coil content, and hydrophobicity assays showed a 1.23-fold increase in surface hydrophobicity. Molecular dynamics simulations predicted disruption of intra- and intermolecular hydrogen bonds (2.60% and 11.69%), van der Waals interactions (99.99%), and electrostatic interactions (96.80%). The computed ΔGbind became 98.98% more negative, matching the experimental 99.36-fold reduction in Kcat/Km. In summary, IPF at 130 °C-9 min effectively inactivated urease via significant disruption of conformational structures and catalytic function.
This study develops a mixed-integer linear programming model for a multi-product, multi-period vaccine supply chain network that incorporates injection centers and heterogeneous storage conditions, including cold and ultra-cold refrigeration. The model captures key operational decisions such as distribution center location, vaccine allocation, and storage configuration under capacity and demand constraints, while ensuring no shortage at injection centers. To efficiently solve large-scale instances, a tailored genetic algorithm (GA) is proposed. The main achievements of this research are as follows. First, the proposed model provides an integrated framework that simultaneously considers multi-vaccine characteristics and storage requirements within a three-tier supply chain. Second, computational experiments demonstrate that the GA achieves high-quality solutions with very small optimality gaps compared to exact solutions obtained by GAMS for small and medium-sized problems. Third, the results show that while exact methods become computationally inefficient or infeasible for large-scale instances, the GA remains robust and capable of producing near-optimal solutions within reasonable computational time. Finally, sensitivity analysis confirms the consistency and validity of the model, showing that total cost increases logically with demand and cost parameters. These findings highlight the effectiveness and scalability of the proposed approach and demonstrate its applicability as a decision-support tool for policymakers and healthcare planners in managing complex vaccine distribution systems, particularly during large-scale public health emergencies.
Assessing teacher competency in a reliable and multidimensional manner remains an open problem, largely because conventional evaluation instruments capture only a fraction of the behavioral repertoire that defines effective instruction. We tackle this challenge by developing an integrated framework that fuses heterogeneous classroom signals-video, audio, transcribed text, and physiological recordings-through modality-specific encoders coupled with a cross-modal attention mechanism. The attention module adaptively re-weights each data stream according to its diagnostic relevance for a given competency dimension, while a hierarchical temporal component jointly models short-term pedagogical adjustments and long-term professional growth trajectories. Competency scores are formulated as a continuous regression task (evaluated via RMSE and MAE) and simultaneously discretized into ordinal proficiency levels for classification-based evaluation (accuracy and F1-score), thereby addressing both assessment perspectives within a unified multi-task objective. A knowledge graph-enhanced recommendation engine then maps diagnosed competency gaps onto targeted training resources. Experiments conducted on multimodal recordings from 856 teachers across 15 schools demonstrate that our model reaches 0.834 classification accuracy and 0.312 RMSE, outperforming all baselines on each of the seven evaluation dimensions. The recommendation module attains 0.478 Precision@5, a 13.0% relative gain over the strongest knowledge-graph baseline. Ablation analyses confirm that every architectural component contributes measurably; removing temporal modeling alone reduces accuracy by 7.1 percentage points. Taken together, these results establish a closed-loop, interpretable pipeline from diagnostic assessment to actionable professional development pathways.
Chromosome spreading is a key step in karyotyping and fluorescence-based analyses, yet it often suffers from limited dispersion, chromatid overlap, and morphological distortion due to operator-dependent variability. Here, we introduce an anisotropic, nanowrinkled polydimethylsiloxane (PDMS) substrate that enhances chromosome spreading by tuning droplet impact dynamics via anisotropic wetting and splashing-assisted transport. The uniaxial wrinkle geometry promotes lateral redistribution of the fixative droplet, leading to broader chromosome dispersion, stronger directional spreading, and improved chromatid separation compared with flat substrates. High-resolution atomic force microscopy reveals subtle but reproducible nanoscale surface modulations on chromosomes deposited on wrinkled PDMS, consistent with partial transcription of the underlying wrinkle relief during adsorption. The characteristic spacing of these modulations (∼300-500 nm) matches the wrinkle periodicity and is interpreted here as substrate-coupled nanoscale conformability, rather than as direct evidence of intrinsic chromatin organization. Collectively, these findings establish nanowrinkled PDMS as a practical, tunable platform for controlled chromosome deposition and further indicate that engineered nanoscale interfaces can elicit nanoscale conformability of metaphase chromosomes during adsorption.
This study investigates the unintended effects of legal changes in shareholder litigation on employee injuries, revealing substantial consequences for employee welfare when restrictions are placed on shareholder litigation. Using a quasi-natural experiment involving the adoption of universal demand laws, we found that this reform, aimed at improving operational efficiency, is associated with a 28% increase in workplace injuries. This increase coincided with a marked surge in managerial focus on firm growth, without a commensurate increase in attention to safety. The association is stronger in lower income counties, highlighting a potential economic redistribution effect that concerns vulnerable employee groups. We also find that female-led firms exhibit minimal increases in injury rates after the adoption, in contrast to male-led firms, indicating that CEO gender moderates the relationship. These findings highlight how regulatory changes may be associated with unintended consequences for employees through shifts in managerial focus. We discuss the theoretical and policy implications of our findings. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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Sentiment analysis (SA) of natural language text has become as a powerful instrument for enhancing financial market predictions. Quarterly reports from companies, in particular, offer a rich source of data for sentiment analysis, providing key insights into a company's performance, strategic actions, and future prospects. These reports can significantly influence investor decisions regarding asset investments. Notwithstanding the potential, prior research has not investigated sentiment analysis concerning these resources in portfolio optimization. To fill this void, we propose an innovative three-stage approach to constructing stock portfolios. In the first stage, we perform sentiment analysis on companies' quarterly reports using the FinBERT model to assess the sentiment surrounding each company. In the second stage, we utilize a Long-Short-Term Memory (LSTM) model for forecasting future prices, which enables the calculation of expected returns and the covariance matrix. In final stage, we present a three-objective portfolio optimization model that incorporates risk, return, and sentiment-derived trend features. We solve this model using the Weighted Goal Programming (WGP) method. Our results indicate that the proposed model effectively supports portfolio optimization. Moreover, the model is implemented using data from companies that are part of the Dow Jones Industrial Average (DJIA), and findings demonstrate high accuracy, confirming the practical potential of the proposed approach.
The development of latent fingermarks on documents is a critical component of forensic investigations. There are several established techniques that allow for the development of fingermark deposits on this substrate type. Providing contextual information, however, such as the chronological sequence of fingermark deposition relative to printed information, is an area that is far less understood. This study builds upon previous work that explored the application of gelatin lifters to a document surface, followed by exposure to disulfur dinitride within the RECOVER™ system. The proof-of-concept study described herein utilises the same gelatin lifter medium, but alongside cyanoacrylate (superglue) fuming to provide a simpler, effective and accessible approach to determine the deposition order of fingermark deposits and printed toner. These findings highlight a potential practical innovation that integrates two commonly used forensic techniques and further expands on work to address one of the great challenges of forensic document and dactyloscopic examination.