Search Results (26,075)

Virtual AI-driven streamers have been gradually used in live commerce, and they may affect consumer impulsive purchase intention. Drawing on the stimulus–organism–response (S-O-R) model, this research examined consumer impulsive purchase intention in virtual AI streaming. Based on survey data from 411 predominantly young and educated virtual AI streaming users recruited through snowball sampling, we found that perceived responsiveness, perceived likeability, perceived expertise, and perceived anthropomorphism of virtual AI streamers are associated with trust and flow experience, both of which predict consumers’ impulsive purchase intentions. The fsQCA identified two paths that lead to impulsive purchase intention. The results imply that live streaming platforms need to engender consumers’ trust and flow experience in order to increase their impulsive purchase intention. Full article

Background/Objectives: Curcumin (CUR) is well known for its therapeutic properties, particularly attributed to its antioxidant and anti-inflammatory effects in managing chronic diseases such as arthritis. While CUR application for biomedical purposes is well known, the phytochemical has several restrictions given its poor water solubility, physicochemical instability, and low bioavailability. These limitations have led to innovative formulations, with nanocarriers emerging as a promising alternative. For this reason, this study aimed to address the potential advantages of associating CUR with nanocarrier systems in managing arthritis through a scoping review. Methods: A systematic literature search of preclinical (in vivo) and clinical studies was performed in PubMed, Scopus, and Web of Science (December 2024). General inclusion criteria include using CUR or natural derivatives in nano-based formulations for arthritis treatment. These elements lead to the question: “What is the impact of the association of CUR or derivatives in nanocarriers in treating arthritis?”. Results: From an initial 536 articles, 34 were selected for further analysis (31 preclinical investigations and three randomized clinical trials). Most studies used pure CUR (25/34), associated with organic (30/34) nanocarrier systems. Remarkably, nanoparticles (16/34) and nanoemulsions (5/34) were emphasized. The formulations were primarily presented in liquid form (23/34) and were generally administered to animal models through intra-articular injection (11/31). Complete Freund’s Adjuvant (CFA) was the most frequently utilized among the various models to induce arthritis-like joint damage. The findings indicate that associating CUR or its derivatives with nanocarrier systems enhances its pharmacological efficacy through controlled release and enhanced solubility, bioavailability, and stability. Moreover, the encapsulation of CUR showed better results in most cases than in its free form. Nonetheless, most studies were restricted to the preclinical model, not providing direct evidence in humans. Additionally, inadequate information and clarity presented considerable challenges for preclinical evidence, which was confirmed by SYRCLE’s bias detection tools. Conclusions: Hence, this scoping review highlights the anti-arthritic effects of CUR nanocarriers as a promising alternative for improved treatment. Full article

Sinus augmentation provides a well-established model for investigating the three-dimensional morphometry and macromolecular dynamics of bone regeneration, particularly when using biphasic calcium phosphate (BCP) graft substitutes. This case series included six biopsies from patients who underwent maxillary sinus augmentation using BCP granules composed of 30% hydroxyapatite (HA) and 70% β-tricalcium phosphate (β-TCP). Bone core biopsies were obtained at healing times of 6 months, 9 months, and 12 months. Histological evaluation yielded qualitative and quantitative insights into new bone distribution, while micro-computed tomography (micro-CT) and Raman microspectroscopy (RMS) were employed to assess the three-dimensional architecture and macromolecular composition of the regenerated bone. Micro-CT analysis revealed progressive maturation of the regenerated bone microstructure over time. At 6 months, the apical regenerated area exhibited a significantly higher mineralized volume fraction (58 ± 5%) compared to the basal native bone (44 ± 11%; p = 0.0170), as well as significantly reduced trabecular spacing (Tb.Sp: 187 ± 70 µm vs. 325 ± 96 µm; p = 0.0155) and degree of anisotropy (DA: 0.37 ± 0.05 vs. 0.73 ± 0.03; p < 0.0001). By 12 months, the mineralized volume fraction in the regenerated area (53 ± 5%) was statistically comparable to basal bone (44 ± 3%; p > 0.05), while Tb.Sp (211 ± 20 µm) and DA (0.23 ± 0.09) remained significantly lower (Tb.Sp: 395 ± 41 µm, p = 0.0041; DA: 0.46 ± 0.04, p = 0.0001), indicating continued structural remodelling and organization. Raman microspectroscopy further revealed dynamic macromolecular changes during healing. Characteristic β-TCP peaks (e.g., 1315, 1380, 1483 cm⁻¹) progressively diminished over time and were completely absent in the regenerated tissue at 12 months, contrasting with their partial presence at 6 months. Simultaneously, increased intensity of collagen-specific bands (e.g., Amide I at 1661 cm⁻¹, Amide III at 1250 cm⁻¹) and carbonate peaks (1065 cm⁻¹) reflected active matrix formation and mineralization. Overall, this case series provides qualitative and quantitative evidence that bone regeneration and integration of BCP granules in sinus augmentation continues beyond 6 months, with ongoing maturation observed up to 12 months post-grafting. Full article

The COVID-19 pandemic was a profoundly influential global occurrence in recent history, impacting daily life, economics, and healthcare systems for an extended period. The abundance of data has been essential in creating models to simulate and forecast the dissemination of infectious illnesses, aiding governments and health organizations in making educated decisions. This research primarily focuses on designing a control technique that incorporates the five most important inputs that impact the spread of COVID-19 on the Romanian territory. Quantitative analysis and data filtering are two crucial aspects to consider when developing a mathematical model. In this study the transfer function principle was used as the most accurate method for modeling the system, based on its superior fit demonstrated in a previous study. For the control strategy, a PI (Proportional-Integral) controller was designed to meet the requirements of the intended behavior. Finally, it is showed that for such complex models, the chosen control strategy, combined with fine tuning, led to very accurate results. Full article

Fileless malware is a type of malware that does not rely on executable files to persist or propagate. Unlike traditional file-based malware, fileless malware is more difficult to detect and remove, posing a significant threat to organizations. This paper introduces a novel hybrid analysis model that combines static and dynamic analysis techniques to identify fileless malware. Applied to four real-world and two custom-created fileless malware samples, the proposed model demonstrated its qualitative effectiveness in uncovering complex behaviors and evasion tactics, such as obfuscated macros, process injection, registry persistence, and covert network communications, which often bypass single-method analyses. While the analysis reveals the potential for significant damage to organizational reputation, resources, and operations, the paper also outlines a set of mitigation measures that cybersecurity professionals and researchers can adopt to protect users and organizations against threats posed by fileless malware. Overall, this research offers valuable insights and a novel analysis model to better address and understand fileless malware threats. Full article

Employee turnover remains a critical challenge for organizations, prompting an examination of how leadership approaches influence employees’ intentions to leave. This study investigates the impact of transformational leadership on turnover intention, focusing on emotional exhaustion and creativity as potential mediators. The study employs a quantitative design grounded in leadership and organizational psychology theory and surveys 182 professionals working in the information technology sector across Bosnia and Herzegovina, Croatia, Serbia, and Montenegro. Structural equation modeling reveals that transformational leadership reduces turnover intention by alleviating emotional exhaustion, highlighting the importance of psychological well-being in employee retention. While transformational leadership enhances employee creativity, creativity did not significantly mediate turnover intention in this context. These findings suggest that strategies that foster engagement and reduce burnout in knowledge-intensive industries can strengthen organizational commitment and improve retention. This study contributes to the understanding of behavioral mechanisms linking leadership to employee outcomes and offers actionable insights for modern organizations aiming to address turnover through supportive, empowering leadership practices. Additional mediators and contextual variables should be explored in further research. Full article

The growing need for rapid screening of adsorption energies in organic materials has driven substantial progress in developing various architectures of equivariant graph neural networks (eGNNs). This advancement has largely been enabled by the availability of extensive Density Functional Theory (DFT)-generated datasets, sufficiently large to train complex eGNN models effectively. However, certain material groups with significant industrial relevance, such as aromatic compounds, remain underrepresented in these large datasets. In this work, we aim to bridge the gap between limited, domain-specific DFT datasets and large-scale pretrained eGNNs. Our methodology involves creating a specialized dataset by segregating aromatic compounds after a targeted ensemble extraction process, then fine-tuning a pretrained model via approaches that include full retraining and systematically freezing specific network sections. We demonstrate that these approaches can yield accurate energy and force predictions with minimal domain-specific training data and computation. Additionally, we investigate the effects of augmenting training datasets with chemically related but out-of-domain groups. Our findings indicate that incorporating supplementary data that closely resembles the target domain, even if approximate, would enhance model performance on domain-specific tasks. Furthermore, we systematically freeze different sections of the pretrained models to elucidate the role each component plays during adaptation to new domains, revealing that relearning low-level representations is critical for effective domain transfer. Overall, this study contributes valuable insights and practical guidelines for efficiently adapting deep learning models for accurate adsorption energy predictions, significantly reducing reliance on extensive training datasets. Full article

Ternary choline chloride-based deep eutectic solvents (TDESs) exhibit unique physicochemical properties, including lower viscosities, lower melting points, higher thermal stabilities, and enhanced solvations compared to binary deep eutectic solvents (BDESs). Although BDESs have been widely studied, the addition of a third component in TDESs offers opportunities to further optimize their performance. This review aims to evaluate the physicochemical properties of TDESs and highlight their potential applications in sustainable industrial processes compared to BDESs. A comprehensive analysis of the existing literature was conducted, focusing on TDES properties, such as phase behavior, density, viscosity, pH, conductivity, and the effect of water, along with their applications in various fields. TDESs demonstrated superior physicochemical characteristics compared to BDESs, including improved solvation and thermal stability. Their applications in biomass conversion, CO₂ capture, heavy oil upgrading, refrigeration gases, and as solvents/catalysts in organic reactions show significant promise for enhancing process efficiency and sustainability. Despite their advantages, TDESs face challenges including limited predictive models, potential instability under certain conditions, and scalability hurdles. Overall, TDESs offer significant potential for advancing sustainable and efficient chemical processes for industrial applications. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article

Workplace violence (WV) is a ubiquitous risk in healthcare settings where it has been associated with physical and mental health problems. We aimed to investigate the relationship between the violence experienced by healthcare workers (HCWs) and the presence of eating disorders (EDs). During routine health surveillance, 1215 HCWs were questioned about their experience of WV and the short version of the Eating Disorder Examination Questionnaire (EDE-QS) was used to assess their eating behaviors. Sleep quality, stress, and the presence of common mental illnesses and metabolic disorders were also evaluated. HCWs who had experienced one or more assaults in the previous year had a significantly higher EDE score than their colleagues. In a multivariate model, WV doubled the risk of EDs (odds ratio 2.33, confidence intervals 95% 1.30; 4.18, p < 0.01). A very significant association was observed between common mental disorders and EDs (OR 1.13, CI 95% 1.04; 1.23, p < 0.01), while low sleep quality almost reached a significant level (OR 1.09, CI 95% 0.99; 1.20). The higher frequency of EDs among workers subjected to violence may result from maladaptive coping mechanisms used when stress and mental health problems caused by WV lead to compensatory overeating. However, reverse causation, where WV is induced by stigmatization, cannot be ruled out. Because of the considerable impact EDs have on physical and mental health, productivity, and patient care, healthcare organizations should adopt programs designed to prevent these disorders in HCWs. Full article

Hypothermia-related deaths present significant diagnostic challenges due to non-specific and often inconsistent autopsy findings. This study investigated the histological and immunohistochemical alterations associated with primary and secondary hypothermia in an experimental Rattus norvegicus model, focusing on the effects of benzodiazepine and alcohol ingestion. Twenty-one male rats were divided into three groups: control (K), benzodiazepine-treated (B), and alcohol-treated (A). After two weeks of substance administration, hypothermia was induced and multiple organ samples were analyzed. Histologically, renal tissue showed hydropic and vacuolar degeneration, congestion, and acute tubular injury across all groups, with no significant differences in E-cadherin expression. Lung samples revealed congestion, emphysema, and hemorrhage, with more pronounced vascular congestion in the alcohol and benzodiazepine groups. Cardiac tissue exhibited vacuolar degeneration and protein denaturation, particularly in substance-exposed animals. The spleen showed preserved architecture but increased erythrocyte infiltration and significantly elevated myeloperoxidase (MPO)-positive granulocytes in the intoxicated groups. Liver samples demonstrated congestion, focal necrosis, and subcapsular hemorrhage, especially in the alcohol group. Immunohistochemical analysis revealed statistically significant differences in MPO expression in both lung and spleen tissues, with the highest levels observed in the benzodiazepine group. Similarly, CK7 and CK20 expression in the gastroesophageal junction was significantly elevated in both alcohol- and benzodiazepine-treated animals compared to the controls. In contrast, E-cadherin expression in the kidney did not differ significantly among the groups. These findings suggest that specific histological and immunohistochemical patterns, particularly involving pulmonary, cardiac, hepatic, and splenic tissues, may help differentiate primary hypothermia from substance-related secondary hypothermia. The study underscores the value of integrating toxicological, histological, and molecular analyses to enhance the forensic assessment of hypothermia-related fatalities. Future research should aim to validate these markers in human autopsy series and explore additional molecular indicators to refine diagnostic accuracy in forensic pathology. Full article

Background: While several test-automation maturity models (e.g., CMMI, TMMi, TAIM) exist, none explicitly integrate ISO 9001-based quality management systems (QMS), leaving a gap for organizations that must align E2E test automation with formal quality assurance. Objective: This study proposes a test-automation maturity model (TAMM) that bridges E2E automation capability with ISO 9001/ISO 9004 self-assessment principles, and evaluates its reliability and practical impact in industry. Methods: TAMM comprises eight maturity dimensions, 39 requirements, and 429 checklist items. Three independent assessors applied the checklist to three software teams; inter-rater reliability was ensured via consensus review (Cohen’s κ = 0.75). Short-term remediation actions based on the checklist were implemented over six months and re-assessed. Synergy with the organization’s ISO 9001 QMS was analyzed using ISO 9004 self-check scores. Results: Within 6 months of remediation, mean TAMM score rose from 2.75 → 2.85. Inter-rater reliability is filled with Cohen’s κ = 0.75. Conclusions: The proposed TAMM delivers measurable, short-term maturity gains and complements ISO 9001-based QMS without introducing conflicting processes. Practitioners can use the checklist to identify actionable gaps, prioritize remediation, and quantify progress, while researchers may extend TAMM to other domains or automate scoring via repository mining. Full article

Monitoring water quality is crucial for achieving clean water and sanitation goals, particularly in remote areas. The project “Morbidity vs. Water Quality for Human Consumption in Tonosí: A Pilot Study” aimed to enhance water quality assessments in Panama using advanced analytical techniques to assess volatile organic compounds, heavy metals, and microbiological pathogens. To support this, the Technical Unit for Water Quality (UTECH) was established, featuring a novel mobile laboratory with cutting-edge technology for accurate testing, minimal chemical reagent use, reduced waste generation, and equipped with a solar-powered battery system. The aim of this paper is to explore the design, deployment, and impact of the UTECH. Furthermore, this study presents results from three sampling points in Tonosí, where several parameters exceeded regulatory limits, demonstrating the capabilities of the UTECH and highlighting the need for ongoing monitoring and intervention. The study also assesses the environmental, social, and economic impacts of the UTECH in alignment with the Sustainable Development Goals and national initiatives. Finally, a SWOT analysis illustrates the UTECH’s potential to improve water quality assessments in Panama while identifying areas for sustainable growth. The study showcases the successful integration of advanced mobile laboratory technologies into water quality monitoring, contributing to sustainable development in Panama and offering a replicable model for similar initiatives in other regions. Full article

With the global aging population on the rise, identifying environmental factors that modulate immunosenescence is critical for health interventions. While urban green spaces are known to confer health benefits, the long-term effects of forest exposure on immunosenescence remain unclear. This study investigated the differential impacts of urban forest versus urban environments on immunosenescence using a D-galactose-induced murine model. Mice were assigned to urban or forest environments for 8 weeks, with serum cytokines (IL-2, IL-6, TNF-α, IFN-γ), T-cell subsets, and organ indices analyzed. Forest environments exhibited significantly higher humidity and negative air ion concentrations alongside lower noise levels compared to urban settings. Aged forest-exposed mice showed attenuated immunosenescence markers, including significantly lower IL-6 levels (p < 0.01) and improved thymic indices, suggesting urban forest environments may mitigate immune decline. These findings highlight the potential of urban forests in promoting healthy aging, advocating for their integration into urban planning. Further human studies are warranted to translate these findings into public health strategies. Full article

Soil salinization in oasis areas of arid regions is recognized as a dynamic and multifaceted environmental threat influenced by both natural processes and human activities. In this study, 13 spatiotemporal predictors derived from field surveys and remote sensing are utilized to construct a spatial probabilistic model of salinization. A Bayesian Belief Network is integrated with spline interpolation in ArcGIS to map the likelihood of salinization, while Partial Least Squares Structural Equation Modeling (PLS-SEM) is applied to analyze the interactions among multiple drivers. The test results of this model indicate that its average sensitivity exceeds 80%, confirming its robustness. Salinization risk is categorized into degradation (35–79% probability), stability (0–58%), and improvement (0–48%) classes. Notably, 58.27% of the 1836.28 km² Keriya Oasis is found to have a 50–79% chance of degradation, whereas only 1.41% (25.91 km²) exceeds a 50% probability of remaining stable, and improvement probabilities are never observed to surpass 50%. Slope gradient and soil organic matter are identified by PLS-SEM as the strongest positive drivers of degradation, while higher population density and coarser soil textures are found to counteract this process. Spatially explicit probability maps are generated to provide critical spatiotemporal insights for sustainable oasis management, revealing the complex controls and limited recovery potential of soil salinization. Full article