Search Results (326)

Self-forgiveness is identified as a contributor to psychological wellbeing and may serve as a mechanism through which religiosity supports mental health. There is a dearth of research on wellbeing and the role of self-forgiveness in the English-speaking Caribbean. This preliminary study explored the relationship between religiosity, self-forgiveness, and wellbeing among persons with serious mental illness (SMI), a population largely overlooked in this context. A convenience sample of 362 out-patients receiving care in Trinidad and Tobago completed self-reported measures of self-forgiveness, the Religious Commitment Inventory, and Havard’s Flourishing Measure. Inferential statistics examined group differences in religiosity and wellbeing, and predictive relationships among key variables. Among persons with SMI, higher religiosity was significantly associated with greater wellbeing (p < 0.0001). Additionally, there was greater wellbeing among those who reported a propensity to self-forgive compared to those who did not (p < 0.0001). Self-forgiveness explained a significant part of the relationship between religiosity and wellbeing. Furthermore, among the non-highly religious, self-forgiveness was also significantly associated with greater wellbeing (p < 0.001). Our findings suggest that self-forgiveness may mediate the link between religiosity and wellbeing, highlighting its potential as a therapeutic coping mechanism for individuals with serious mental illness. This study adds to the growing literature on religious coping in mental health and underscores the need for further research to clarify the mediating role of self-forgiveness. Full article

Federated learning (FL) struggles under non-IID client data when local models drift toward conflicting optima, impairing global convergence and performance. We introduce entropy-regularized federated optimization (ERFO), a lightweight client-side modification that augments each local objective with a Shannon entropy penalty on the per-parameter update distribution. ERFO requires no additional communication, adds a single-scalar hyperparameter $\lambda$, and integrates seamlessly into any FedAvg-style training loop. We derive a closed-form gradient for the entropy regularizer and provide convergence guarantees: under $\mu$-strong convexity and L-smoothness, ERFO achieves the same $O(1/T)$ (or linear) rates as FedAvg (with only $O\left(\lambda \right)$ bias for fixed $\lambda$ and exact convergence when ${\lambda }_t\to 0$); in the non-convex case, we prove stationary-point convergence at $O(1/T)$. Empirically, on five-client non-IID splits of the UNSW-NB15 intrusion-detection dataset, ERFO yields a +1.6 pp gain in accuracy and +0.008 in macro-F1 over FedAvg with markedly smoother dynamics. On a three-of-five split of PneumoniaMNIST, a fixed $\lambda$ matches or exceeds FedAvg, FedProx, and SCAFFOLD—achieving 90.3% accuracy and 0.878 macro-F1—while preserving rapid, stable learning. ERFO’s gradient-only design is model-agnostic, making it broadly applicable across tasks. Full article

This article explores the themes of repentance and conversion in Augustine’s Notes on Job. Despite its fragmentary and often improvisational character, Augustine’s theological vision in the Notes presents Job as an exemplum of ongoing conversion. Though not portrayed as a sinner, Job undergoes spiritual transformation, embodying the human need for continual repentance and deeper understanding of God. This treatment aligns with Augustine’s depiction of other biblical figures, such as St. Paul and the Prodigal Son, whose stories serve as models of conversion. By closely examining the rhetorical and theological function of Job in the Notes, the article suggests that Augustine’s portrayal was meant to guide readers on the path toward salvation. In doing so, it contributes to a broader understanding of how Augustine constructs conversion narratives within his biblical commentaries. Full article

The white snook (Centropomus viridis) is an emerging aquaculture species with high market acceptance, exhibiting catadromous and protandric hermaphroditic characteristics in adulthood. This study aimed to preliminarily characterize certain hematological and biochemical parameters, as well as blood cell morphology, for identifying possible variations between sexes maintained under aquaculture recirculating system (RAS) conditions. The white snook broodstock was anesthetized with clove oil, and biometric values, as well as sex classification, were measured. Then, blood samples were collected from 14 females (7132 ± 1610 g) and 20 males (2200 ± 0.963 g) via caudal vessel puncture to analyze selected hematological parameters, blood biochemistry, and cellular morphology. Fulton’s condition factor (K) showed no differences between sexes, indicating a healthy fish status. Females showed significantly higher serum cholesterol, glucose, and triglyceride levels than males. Also, hematocrit (HCT) and mean corpuscular volume (MCV) were elevated in females. No sex-related differences were observed in red or white cell counts or in blood cell dimensions. Morphological characterization identified erythrocytes, thrombocytes, and three types of leukocytes: lymphocytes (small and large lymphocytes), neutrophils, and monocytes, with no eosinophils or basophils detected in either sex. These findings provide fundamental reference values for the hematological and biochemical profiles of C. viridis broodstock in captivity and highlight sex-specific differences relevant for reproductive and health monitoring. However, it should be considered that the sample size used to establish reference ranges for the species is small, so it is recommended to implement a monitoring plan for this and other broodstocks of this emerging species. Full article

Potatoes are highly sensitive to drought, particularly during tuber initiation. This study aimed to evaluate the effectiveness of film-forming (Vapor Gard [VG]) and metabolic (abscisic acid [ABA]) antitranspirants in mitigating drought stress and reducing tuber physiological disorders in four potato varieties. Two experiments examined the effects of VG and ABA antitranspirants on drought-stressed potato plants of four varieties (Challenger, Markies, Nectar, and Russet Burbank) grown in pots in a polytunnel (semi-controlled environment). Experiment 1 imposed severe drought by withholding irrigation until 70% of the available water content was depleted (reaching 15–17% volumetric water content within ~15 days), while Experiment 2 featured gradual drought stress from tuber initiation, with the soil volumetric water content declining to <10% over 30 days. Antitranspirants were applied at the start of the tuber initiation and two weeks later to assess their impact on the soil volumetric water content, stomatal conductance, relative water content, yield, and tuber physiological disorders. Drought significantly reduced the soil and plant water status, tuber yield, and quality across both experiments, with more severe effects observed in Experiment 1. VG and ABA had repeatable effects in both experiments and in all varieties, reducing water stress by preventing a large reduction in the relative water content during the tuber initiation and bulking stages. Both antitranspirants improved the tuber appearance by reducing the tuber skin disorder of russeting in the susceptible Challenger variety in both experiments, with VG being more effective than ABA. Beneficial reductions in the effects of drought from antitranspirants were also recorded in the volumetric water content, stomatal conductance, yield, and jelly end rot but not consistently in all varieties and in both experiments. The results show that antitranspirants have the potential to minimise water stress in droughted potatoes and subsequently reduce the physiological disorder of russeting and improve the tuber appearance of the Challenger variety. Full article

A killed, whole-cell vaccine was produced to induce immunity in dogs against leptospirosis. The vaccine, containing serovar Copenhageni, was produced and administered to 12 beagle dogs at both 8 and 12 weeks of age. Ten unvaccinated dogs of the same age group served as the control group. A live, virulent inoculum of Leptospira (1.52 × 10⁹–4.40 × 10⁹ leptospires per dog) was used to challenge the dogs at 2 weeks (Study 1) and 14 months (Study 2) post-booster vaccination. At regular intervals, pre- and post-challenge (PC), the microscopic agglutination test (MAT) was performed to measure antibody titers. Leptospiremia and leptospiruria were determined via culture, and the cytokine, biochemical, and pathological profiles of vaccinates and controls were also assessed. A high antibody response was measurable after booster administration. In Study 1 (onset of immunity), acute leptospirosis was observed in five (100%) out of five unvaccinated dogs. In contrast, no acute clinical leptospirosis developed in vaccinated dogs, except in one (20%) dog with mild clinical signs. In Study 2 (duration of immunity), mild clinical signs were observed in two (40%) of the control dogs, while all vaccinated dogs remained clinically normal. The incidence of leptospiruria and leptospiremia PC was lower in the vaccinated dogs compared to the unvaccinated group. Severe thrombocytopenia occurred in 100% (5/5) of the unvaccinated dogs in Study 1 that exhibited acute severe leptospirosis, whereas 80% (4/5) of the unvaccinated dogs in Study 2 showed mild to moderate thrombocytopenia 3 days after challenge. Four out of five unvaccinated dogs (80%) in Study 1 exhibited icteric tissues and hemorrhages in the lungs and mucosal surfaces of the stomach and intestines. A high IL-10 to TNF-α ratio, observed in the control group of both studies, and severe thrombocytopenia observed in the control group of Study 1, indicative of acute leptospiral disease, were detected. The vaccine prevented acute clinical leptospirosis and reduced the renal carrier state in beagle dogs, and further investigation is required using a larger sample size. Full article

Introduction: Federated Learning (FL) is a distributed machine learning paradigm where a global model is collaboratively trained across multiple decentralized clients without exchanging raw data. This is especially important in sensor networks and edge intelligence, where data privacy, bandwidth constraints, and data locality are paramount. Traditional FL methods like FedAvg struggle with highly heterogeneous (non-IID) client data, which is common in these settings. Background: Traditional FL aggregation methods, such as FedAvg, weigh client updates primarily by dataset size, potentially overlooking the informativeness or diversity of each client’s contribution. These limitations are especially pronounced in sensor networks and IoT environments, where clients may hold sparse, unbalanced, or single-modality data. Methods: We propose FedEmerge, an entropy-guided aggregation approach that adjusts each client’s impact on the global model based on the information entropy of its local data distribution. This formulation introduces a principled way to quantify and reward data diversity, enabling an emergent collective learning dynamic in which globally informative updates drive convergence. Unlike existing methods that weigh updates by sample count or heuristics, FedEmerge prioritizes clients with more representative, high-entropy data. The FedEmerge algorithm is presented with full mathematical detail, and we prove its convergence under the Polyak–Łojasiewicz (PL) condition. Results: Theoretical analysis shows that FedEmerge achieves linear convergence to the optimal model under standard assumptions (smoothness and PL condition), similar to centralized gradient descent. Empirically, FedEmerge improves global model accuracy and convergence speed on highly skewed non-IID benchmarks, and it reduces performance disparities among clients compared to FedAvg. Evaluations on CIFAR-10 (non-IID), Federated EMNIST, and Shakespeare datasets confirm its effectiveness in practical edge-learning settings. Conclusions: This entropy-guided federated strategy demonstrates that weighting client updates by data diversity enhances learning outcomes in heterogeneous networks. The approach preserves privacy like standard FL and adds minimal computation overhead, making it a practical solution for real-world federated systems. Full article

Personalized Federated Learning (PFL) seeks to train client-specific models across distributed data silos with heterogeneous distributions. We introduce Sign-Entropy Regularization (SER), a novel entropy-based regularization technique that penalizes excessive directional variability in client-local optimization. Motivated by Descartes’ Rule of Signs, we hypothesize that frequent sign changes in gradient trajectories reflect complexity in the local loss landscape. By minimizing the entropy of gradient sign patterns during local updates, SER encourages smoother optimization paths, improves convergence stability, and enhances personalization. We formally define a differentiable sign-entropy objective over the gradient sign distribution and integrate it into standard federated optimization frameworks, including FedAvg and FedProx. The regularizer is computed efficiently and applied post hoc per local round. Extensive experiments on three benchmark datasets (FEMNIST, Shakespeare, and CIFAR-10) show that SER improves both average and worst-case client accuracy, reduces variance across clients, accelerates convergence, and smooths the local loss surface as measured by Hessian trace and spectral norm. We also present a sensitivity analysis of the regularization strength $\rho$ and discuss the potential for client-adaptive variants. Comparative evaluations against state-of-the-art methods (e.g., Ditto, pFedMe, momentum-based variants, Entropy-SGD) highlight that SER introduces an orthogonal and scalable mechanism for personalization. Theoretically, we frame SER as an information-theoretic and geometric regularizer that stabilizes learning dynamics without requiring dual-model structures or communication modifications. This work opens avenues for trajectory-based regularization and hybrid entropy-guided optimization in federated and resource-constrained learning settings. Full article

This review aims to explore the role of immunotherapeutic strategies—primarily intravenous immunoglobulin (IVIG), plasma exchange (PLEX), and selected immunomodulatory agents—in the treatment of neurological and psychiatric disorders with suspected or confirmed autoimmune mechanisms. A central focus is placed on understanding the immunopathology of these conditions through the identification and characterization of disease-associated autoantibodies. Disorders such as autoimmune encephalitis, myasthenia gravis, limbic epilepsy, neuropsychiatric systemic lupus erythematosus (NPSLE), and certain forms of schizophrenia have shown clinical responses to immunotherapy, suggesting an underlying autoimmune basis in a subset of patients. The review also highlights the diagnostic relevance of detecting autoantibodies targeting neuronal receptors, such as NMDA and AMPA receptors, or neuromuscular junction components, as biomarkers that guide therapeutic decisions. Furthermore, we synthesize findings from published randomized controlled trials (RCTs) that have validated the efficacy of IVIG and PLEX in specific diseases, such as Guillain–Barré syndrome, and myasthenia gravis. Emerging clinical evidence supports expanding these treatments to other conditions where autoimmunity is implicated. By integrating immunological insights with clinical trial data, this review offers a comprehensive perspective on how immunotherapies may be tailored to target autoimmune contributors to neuropsychiatric disease. Full article

SARS-CoV-2 is a highly contagious virus that infects humans, wildlife, domesticated and farmed animals. An increase in SARS-CoV-2 variants and human–animal interactions could have implications for the global maintenance and perpetuation of the virus. This study aimed to detect SARS-CoV-2 infection in dogs and cats living in households with and without SARS-CoV-2-positive owners by real-time reverse transcription polymerase chain reaction (qRT-PCR) in Grenada. This cross-sectional study was conducted from August 2020 to April 2022 from dogs (139) and cats (22) in households (96) in Grenada. Nasopharyngeal and oropharyngeal swabs were taken from the dogs and cats to detect SARS-CoV-2. qRT-PCR tests were performed targeting the E and RdRP genes, respectively. Notably, 12% (17/139) of dogs and 23% (5/22) of cats tested positive for SARS-CoV-2. The positive animals were found in 17 (18%) households, all with at least one positive individual. No positive cases of pets were detected in households without infected humans. A statistically significant association (p < 0.0001) was observed between humans with SARS-CoV2 and their pets. Phylogenetic tree analysis showed evidence of a relationship between the Grenadian SARS-CoV-2 E gene and other SARS-CoV-2 E gene sequences available in the NCBI database. This study confirmed the concurrent SARS-CoV-2 human/companion animal infection from households in Grenada. Humans and pet animals were positive synchronously; however, the direction of transmission from pets to humans or vice versa remains unknown. This study suggests that pets could play a role in the maintenance, transmission, and prolongation of infection of human-adapted pathogens such as SARS-CoV-2. Full article

Natural flood management strategies are increasingly recognized as sustainable alternatives to conventional engineered flood control measures. Among these, leaky dams, also known as woody debris dams or log dams, have emerged as effective nature-based solutions for mitigating flood risks while preserving essential ecosystem services. This review traces the historical evolution of leaky dams from ancient water management practices to contemporary applications, highlighting their development and adaptation over time. It presents a comparative examination of leaky dams and conventional flood control structures, outlining their respective strengths and limitations across ecological, hydrological, and economic dimensions. The review also introduces a conceptual classification of leaky dams into naturally occurring, engineered, hybrid, and movable systems, showing how each form aligns with varying catchment characteristics and management objectives. By synthesizing foundational knowledge and strategic insights, this paper establishes a theoretical and contextual framework for understanding leaky dams as distinct yet complementary tools in integrated flood management, laying the groundwork for further technical evaluations. The findings offer valuable insights for end users by highlighting the potential of leaky dams as integral components of sustainable flood management systems, elucidating their roles in mitigating flood risks, enhancing water retention, and supporting ecosystem resilience. Full article

Leaky dams have become essential nature-based solutions for flood management, providing sustainable alternatives to traditional engineered flood control methods. This review delves into the mechanisms by which leaky dams operate, including the regulation of water flow through velocity reduction and distribution across floodplains, effective sediment trapping and soil quality enhancement, and the facilitation of groundwater recharge and water table stabilization. These structures not only mitigate peak flood flows and reduce erosion but also contribute to enhanced biodiversity by creating diverse aquatic habitats and maintaining ecological connectivity. The effectiveness of leaky dams is assessed through various performance metrics, demonstrating significant reductions in peak flows, improved sediment management, and increased groundwater levels, which collectively enhance ecosystem resilience and water quality. However, the implementation of leaky dams presents several technical challenges, such as design complexity, hydrological variability, maintenance requirements, and socio-economic factors like land use conflicts and economic viability. Additionally, while leaky dams offer numerous environmental benefits, potential negative impacts include habitat disruption, sediment accumulation, and alterations in water quality, which necessitate careful planning and adaptive management strategies. Emerging trends in leaky dam development focus on the integration of smart technologies, such as real-time monitoring systems and artificial intelligence, to optimize performance and resilience against climate-induced extreme weather events. Advances in modeling and monitoring technologies are facilitating the effective design and implementation of leaky dam networks, promoting their incorporation into comprehensive watershed management frameworks. This review highlights the significant potential of leaky dams as integral components of sustainable flood management systems, advocating for their broader adoption alongside conventional engineering solutions to achieve resilient and ecologically balanced water management. Full article

This study extends the Theory of Planned Behavior (TPB) to examine the factors influencing the employees’ intentions to save energy in the workplace (INSER), incorporating organizational culture (ORGCULT) and environmental knowledge (ENVKNOW) as mediating variables. Structural equation modeling (SEM) of survey data reveals that attitudes toward energy saving (ATESs) and perceived behavioral control (PERBCON) significantly predict INSER, while subjective norms (SUBNORMS) do not exert a direct effect. ORGCULT emerges as a strong mediator, highlighting its role in translating pro-environmental attitudes into actionable intentions. In contrast, ENVKNOW does not mediate the examined relationships, challenging the assumption that knowledge alone is sufficient to drive energy-saving behavior. These findings suggest that organizational commitment and leadership engagement exert a greater influence than peer norms or informational efforts in shaping sustainable workplace behaviors. From a practical perspective, the study underscores the importance of cultivating a sustainability-oriented organizational culture, implementing structural supports, and employing behavioral interventions beyond traditional awareness campaigns. Theoretically, it refines the TPB by illustrating that institutional factors may precede normative pressures in professional settings. Overall, the research contributes to the corporate sustainability literature by advocating for leadership-driven engagement strategies and policy-level interventions to promote long-term energy efficiency. Full article

The study aims to assess future streamflow forecasts in the Godavari basin of India under climate change scenarios. The primary objective of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) was to evaluate future streamflow forecasts across different catchments in the Godavari basin, India, with an emphasis on understanding the impacts of climate change. This study employed both conceptual and machine learning models to assess how changing precipitation patterns and temperature variations influence streamflow dynamics. Seven satellite precipitation products CMORPH, Princeton Global Forcing (PGF), Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), Infrared Precipitation with Stations (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN-CDR) were evaluated in a gridded precipitation evaluation over the Godavari River basin. Results of Multi-Source Weighted-Ensemble Precipitation (MSWEP) had a Nash–Sutcliffe efficiency (NSE), coefficient of determination (R²), and root mean square error (RMSE) of 0.806, 0.831, and 56.734 mm/mon, whereas the Tropical Rainfall Measuring Mission had 0.768, 0.846, and 57.413 mm, respectively. MSWEP had the highest accuracy, the lowest false alarm ratio, and the highest Peirce’s skill score (0.844, 0.571, and 0.462). Correlation and pairwise correlation attribution approaches were used to assess the input parameters, which included a two-day lag of streamflow, maximum and minimum temperatures, and several precipitation datasets (IMD, EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). CMIP6 datasets that had been adjusted for bias were used in the modeling process. R, NSE, RMSE, and R² assessed the model’s effectiveness. RF and M5P performed well when using CMIP6 datasets as input. RF demonstrated adequate performance in testing (0.4 < NSE < 0.50 and 0.5 < R² < 0.6) and extremely good performance in training (0.75 < NSE < 1 and 0.7 < R < 1). Likewise, M5P demonstrated good performance in both training and testing (0.4 < NSE < 0.50 and 0.5 < R² < 0.6). While RF was the best performer for both datasets, Indian Meteorological Department outperformed all CMIP6 datasets in streamflow modeling. Using the Indian Meteorological Department gridded precipitation, RF’s NSE, R, R², and RMSE values during training were 0.95, 0.979, 0.937, and 30.805 m³/s. The test results were 0.681, 0.91, 0.828, and 41.237 m³/s. Additionally, the Multi-Layer Perceptron (MLP) model demonstrated consistent performance across both the training and assessment phases, reinforcing the reliability of machine learning approaches in climate-informed hydrological forecasting. This study underscores the significance of incorporating climate change projections into hydrological modeling to enhance water resource management and adaptation strategies in the Godavari basin and similar regions facing climate-induced hydrological shifts. Full article