Search Results (1,067)

The recent surge in the popularity of brachycephalic dog breeds has raised concerns about their predisposition to serious health issues linked to breed-specific morphological traits. This study examined the demographic characteristics, motivations, and awareness of owners regarding welfare issues in four brachycephalic breeds (French Bulldogs, Bulldogs, Pugs, Boston Terriers). Methods: A total of 497 owners of brachycephalic dogs examined over six years at an Italian university veterinary hospital were considered; a subset of 75 owners completed a structured questionnaire. Based on responses to a key multiple-choice question about the main reason for breed choice, owners were classified into three groups: trend-driven (aesthetics/fashion), value-oriented (intelligence/behavior), and indeterminate. Results: Gender distribution did not differ significantly compared to the overall population, but brachycephalic owners were significantly younger (p < 0.001). Value-oriented owners were significantly more likely (p < 0.01) to consult a veterinarian before acquisition and showed better understanding of typical respiratory issues, which did not affect their purchasing decision. Trend-driven owners were more influenced by public figures (p < 0.05) and less engaged in preventive care. Conclusion: Our findings highlight the need for pre-acquisition veterinary counseling. Veterinarians can also assist breeders by promoting awareness of the ethical risks of selecting extreme traits in dogs. Full article

The Moroccan garment industry faces challenges in scheduling small order batches, often hindered by traditional product family-based methods that increase downtime by 15–20%. This study proposes a clustering-based scheduling approach, grouping garments by technological times rather than product families to reduce changeovers and downtime by 30–35%. A case study in a Moroccan factory with 50–100-unit batches showed a 20% lead time reduction and a 15% productivity boost. Using methods like K-Means, the approach enhances planning flexibility and resource use. This methodology offers a scalable solution for optimizing production and maintaining competitiveness in fast fashion markets. Full article

Neural Architecture search (NAS) aims to automate the design process of Deep Neural Networks, reducing the Deep Learning (DL) expertise required and avoiding a trial-and-error process. Nonetheless, one of the main drawbacks of NAS is the high consumption of computational resources. Consequently, efficient evaluation methods (EEMs) to assess the quality of candidate architectures are an open research problem. This work tests various EEMs in the Deep Genetic Algorithm (DeepGA), including early stopping, population memory, and training-free proxies. The Fashion MNIST, CIFAR-10, and CIFAR-100 datasets were used for experimentation. The results show that population memory has a valuable impact on avoiding repeated evaluations. Additionally, early stopping achieved competitive performance while significantly reducing the computational cost of the search process. The training-free configurations using the Logsynflow and Linear Regions proxies, as well as a combination of both, were only partially competitive but dramatically reduced the search time. Finally, a comparison of the architectures and hyperparameters obtained with the different algorithm configurations is presented. The training-free search processes resulted in deeper architectures with more fully connected layers and skip connections than the ones obtained with accuracy-guided search configurations. Full article

In the world of globalization and increasing business competition, innovation has become a significant component of the sustainability of an organization. One of the important components affecting an organization’s ability to innovate is human resource management (HRM). This study analyzes how HRM practices relate to banking sector sustainability, testing theoretical pathways through organizational innovation and human capital as potential mediators. SPSS v25 was used to analyze data collected from 207 banking sector employees. The results demonstrate that the human capital of an organization can be increased by the practices of human resource management, which stimulates organizational innovation in the same fashion. This study also shows that human capital is a partial mediator of the relationship between human resource management practices and organizational innovation, highlighting its importance for converting human resource management activities into innovative results. Considering these results, banks are advised to implement complete human resource management strategies that combine operational efficiency with workforce capacity development to create a dynamic banking environment allowing for continued innovation. The proposed mediation model based on empirical data contributes to the literature and provides insights for banking institutions, which can use human capital to drive innovation in difficult situations. Full article

This study addresses the long-standing difficulty of predicting the remaining useful life (RUL) of rolling bearings from highly non-stationary vibration signals by proposing WaveAtten, a symmetry-aware deep learning framework. First, mirror-symmetric and bi-orthogonal Daubechies wavelet filters are applied to decompose each raw signal into multi-scale approximation/detail pairs, explicitly preserving the left–right symmetry that characterizes periodic mechanical responses while isolating asymmetric transient faults. Next, a bidirectional sparse-attention module reinforces this structural symmetry by selecting query–key pairs in a forward/backward balanced fashion, allowing the network to weight homologous spectral patterns and suppress non-symmetric noise. Finally, the symmetry-enhanced features—augmented with temperature and other auxiliary sensor data—are fed into a long short-term memory (LSTM) network that models the symmetric progression of degradation over time. Experiments on the IEEE PHM2012 bearing dataset showed that WaveAtten achieved superior mean squared error, mean absolute error, and R² scores compared with both classical signal-processing pipelines and state-of-the-art deep models, while ablation revealed a 6–8% performance drop when the symmetry-oriented components were removed. By systematically exploiting the intrinsic symmetry of vibration phenomena, WaveAtten offers a robust and efficient route to RUL prediction, paving the way for intelligent, condition-based maintenance of industrial machinery. Full article

The emergence of antifungal-resistant Aspergillus fumigatus (A. fumigatus) became a serious public health concern, underscoring the need for new effective antifungal agents. Here, we present a strategy based on the in situ generation of radical species that are toxic to the pathogen. The synthesis of an alkoxyamine linked to a peptide substrate recognized by A. fumigatus-secreted dipeptidyl peptidase is described. Kinetic experiments show a stable prodrug prior to enzymatic activation. Ensuing peptide cleavage and spontaneous homolysis resulted in the generation of a stable nitroxide and a reactive alkyl radical moiety. Next, the exposure of A. fumigatus spores to the prodrug lead to pathogen growth inhibition in a compound concentration-dependent fashion (e.g., 42% inhibition at 10 µg/L). Importantly, the designed alkoxyamine inhibited not only the growth of a clinical voriconazole-susceptible A. fumigatus strain, but also the growth of a strain resistant to this azole. To determine the antifungal importance of the reactive alkyl radical, its substitution with a non-radical structure did not prevent A. fumigatus growth. Furthermore, the introduction of succinic group in the peptide substrate resulted in the loss of alkoxyamine antifungal properties. Our work reports a novel chemical strategy for antifungal therapy against A. fumigatus based on the pathogen enzyme-mediated generation of toxic radicals. Significantly, these findings are timely since they could overcome the emerged resistance to conventional drugs that are known to target defined pathogen biologic mechanisms such as ergosterol synthesis. Full article

Background/Objective: Hypertrophic cardiomyopathy (HCM) often presents later in the disease course, with frequent misdiagnoses and population-level underdiagnoses. Underserved patients may have even greater diagnostic delays. We aimed to test the hypothesis in a retrospective cohort that artificial intelligence analysis of ECGs (AI-ECG) could have afforded the opportunity for earlier diagnosis of HCM in one health system. Methods: We collected all available ECGs from patients referred to an HCM Center of Excellence over 15 years, both before and after HCM diagnosis. We applied AI-ECG to each ECG in a blinded fashion to predict the probability of HCM. We calculated the time between each patient’s AI-ECG diagnosis and clinical diagnosis. We examined the sensitivity and specificity of AI-ECG for all patients, and by septal subtype and genetic test result. Results: 3499 ECGs were analyzed in 404 patients (age 56 ± 18 years, 52% female). AI-ECG correctly identified HCM in 155 patients with a sensitivity of 67%, specificity of 95%, positive predictive value of 94%, and a negative predictive value of 69%. The AUC was similar using mean probability from all ECGs for each patient (AUC 0.91 [0.88, 0.94]) or using probability from the first ECG (AUC 0.91 [0.87,0.93]). AI-ECG diagnosed 27 patients over 1 year before clinical diagnosis, and up to 16.3 years early. Black patients were more likely than White patients to have an AI-ECG diagnosis before a clinical diagnosis (p = 0.005). Conclusions: AI-ECG offers the potential for advanced HCM diagnosis. Differences in identification timing between subgroups highlight inequities in current care and show the potential of AI-ECG for the greatest benefit in underserved ethnic groups. Full article

Micro gas turbines serve small-scale generation where swift response and low emissions are highly valued, and they are commonly fuelled by natural gas. True to their ‘micro’ designation, their size is indeed compact; however, a noteworthy portion of the enclosure is devoted to power electronics components. This article considers whether these components can be made even smaller by substituting their conventional silicon switches with switches fashioned from silicon carbide. The wider bandgap of silicon carbide permits stronger electric fields and reliable operation at higher temperatures, which together promise lower switching losses, less heat, and simpler cooling arrangements. This study rests on a simple volumetric model. Two data sets feed the model. First come the manufacturer specifications for a pair of converter modules (one silicon, the other silicon carbide) with identical operation ratings. Second are the operating data and dimensions of a commercial 100 kW micro gas turbine. The model splits the converter into two parts: the semiconductor package and its cooling hardware. It then applies scaling factors that capture the higher density of silicon carbide and its lower switching losses. Lower switching losses reduce generated heat, so heatsinks, fans, or coolant channels can be slimmer. Together these effects shrink the cooling section and, therefore, the entire converter. The findings show that a micro gas turbine inverter built with silicon carbide occupies about one fifth less space and delivers more than a quarter higher power density than its silicon counterpart. Full article

In addition to the use of reference instruments, low-cost sensors (LCSs) are becoming increasingly popular for air quality monitoring both indoors and outdoors. These sensors provide real-time measurements of pollutants and facilitate better spatial and temporal coverage. However, these simpler devices are typically characterised by lower accuracy and precision and can be more sensitive to the environmental conditions than the reference instruments. It is therefore crucial to characterise the applicability and limitations of these instruments, for which a possible solution is their comparison with reference measurements in real-world conditions. To this end, a measurement campaign has been carried out to evaluate the PM_2.5 readings of several low- and medium-cost air quality instruments of different types and categories (IQAir AirVisual Pro, TSI DustTrak™ II Aerosol Monitor 8532, Xiaomi Mijia Air Detector, and Xiaomi Smartmi PM_2.5 Air Detector). A GRIMM EDM180 instrument was used as the reference. This campaign took place in Budapest, Hungary, from 12 November to 15 December 2020, during typically humid and foggy weather conditions, when the air pollution level was high due to the increased anthropogenic emissions, including wood burning for heating purposes. The results indicate that the individual sensors tracked the dynamics of PM_2.5 concentration changes well (in a linear fashion), but the readings deviated from the reference measurements to varying degrees. Even though the AirVisual sensors performed generally well (0.85 < R² < 0.93), the accuracy of the units showed inconsistency (13–93%) with typical overestimation, and their readings were significantly affected by elevated relative humidity levels and by temperature. Despite the overall overestimation of PM_2.5 by the Xiaomi sensors, they also exhibited strong correlation coefficients with the reference, with R² values of 0.88 and 0.94. TSI sensors exhibited slight underestimations with high explained variance (R² = 0.93–0.94) and good accuracy. The results indicated that despite the inherent bias, the low-cost sensors are capable of capturing the temporal variability of PM_2.5, thus providing relevant information. After simple and multiple linear regression-based correction, the low-cost sensors provided acceptable results. The results indicate that sensor data correction is a necessary prerequisite for the usability of the instruments. The ensemble method is a reasonable alternative for more accurate estimations of PM_2.5. Full article

Background/Objectives: Schizophrenia Spectrum Disorders (SSDs) carry a debilitating burden of disease which, even after pharmacological and psychological treatment are optimized, remains difficult to fully target. New online-delivered and user-led interventions may provide an appropriate, cost-effective answer to this problem. This study aims to retrieve the currently gathered findings on the efficacy of these interventions across several outcomes, such as symptom severity, social cognition, functioning and others. Methods: A systematic review of the current available literature was conducted. Of 29 potentially relevant articles, 26 were included and assigned at least one of four intervention types: Web-Based Therapy (WBT), Web-Based Psycho-Education (WBP), Online Peer Support (OPS) and Prompt-Based Intervention (PBI). Results: The findings were grouped based on outcome. Of 24 studies evaluating the effects of symptom severity, 14 have achieved statistically significant results, and 10 have not. WBT (such as online-delivered Cognitive Behavioral Therapy, Acceptance and Commitment Therapy, social cognition training and Mindfulness Training) seemed to be the most effective at targeting symptoms. Of 14 studies evaluating functioning, seven achieved significant results, four involving a form of social or neurocognitive training, suggesting a potential pathway towards functional improvements through interventions targeting cognition and motivation. Regarding social cognition, all seven studies measuring the effects of an intervention on this outcome produced significant results, indicating that this outcome lends itself well to remote, online administration. This may be linked with the nature of social cognition exercises, as they are commonly administered through a digital medium (such as pictures, videos and auditory exercises), a delivery method that suits the online-user led model very well. Conclusions: Online user-led interventions show promise as a new way to tackle functional deficits in SSD patients and achieve these improvements through targeting social cognition, a hard-to-reach component of the burden of SSDs which seems to be successfully targetable in a remote, user-led fashion. Symptomatic improvements can also be achievable, through the combination of these interventions with treatment as usual. Full article

E. coli attaches to, and forms biofilms on various surfaces, including latex and polystyrene, contributing to nosocomial spread. E. coli responds to both exogenous and endogenous insulin, which induces behavioral changes. Human insulin, a quorum signal surrogate for microbial insulin, may affect the ability of E. coli to interact with latex and polystyrene in the presence of various sugars. E. coli ATCC 25923 was grown in peptone (1%) yeast nitrogen base broth to either the logarithmic or stationary growth phase. Adherence to latex was determined using 6 × 6 mm latex squares placed in a suspension of washed cells (10³ CFU/mL; 30 min; 37 °C) in buffer containing insulin at 2, 20, and 200 µU/mL (Humulin^® R; Lilly) with and without mannose, galactose, fructose, sorbose, arabinose, xylose, lactose, maltose, melibiose, glucose-6-phosphate, glucose-1-phosphate, and glucosamine at concentrations reported to affect behavioral response. Attachment levels to latex were determined by the press plate method. Biofilm levels were measured in a similar fashion but with overnight cultures in flat bottom uncoated polystyrene plates. Controls were media, insulin, sugar, or buffer alone. Glucose served as the positive control. Overall, the stationary phase cells’ adherence to latex was greater, regardless of the test condition, than was measured for the logarithmic phase cells. The effect of insulin on adherence to latex was insulin and sugar concentration dependent. The addition of insulin (200 µU/mL) resulted in a significantly (p < 0.05) increased adherence to latex and biofilm formation on polystyrene compared with sugar alone for 12 of the 13 sugars tested with stationary phase bacteria and 10 of the 13 sugars tested with logarithmic phase bacteria. Adherence in response to sorbose was the only sugar tested that was unaffected by insulin. These findings show that insulin enhances E. coli’s association with materials in common usage in medical environments in a nutrition-dependent manner. Full article

This study focuses on the shift to a circular economy (CE) in the textile, apparel, and fashion (TAF) sectors, which generate tons of waste annually. Thus, embracing CE practices is essential for contributing to UN Sustainable Development Goals. This study employs a mixed-methods approach, integrating PRISMA for systematic literature selection, BERTopic modeling and AI-driven SDG mapping, and case study analysis to explore emerging CE themes, implemented circular practices, and systemic barriers. Machine-learning-based SDG mapping reveals strong linkages to SDG 9 and SDG 12, emphasizing technological advancements, industrial collaborations, and circular business models. Moderately explored SDGs, namely, SDG 8, SDG 6, and SDG 7, highlight research on labor conditions, water conservation, and clean energy integration. Reviewing 655 peer-reviewed papers, the BERTopic modeling extracted six key themes, including sustainable recycling, circular business models, and consumer engagement, whereas case studies highlighted regulatory frameworks, stakeholder collaboration, and financial incentives as critical enablers. The findings advance institutional theory by demonstrating how certifications, material standards, and regulations drive CE adoption, reinforcing SDG 12 and SDG 16. The natural resource-based view is extended by showing that technological resources alone are insufficiently aligned with SDG 9. Using the Antecedents–Decisions–Outcomes framework, this study presents a structured, AI-driven roadmap for scaling CE in the TAF industry, addressing systemic barriers, and supporting global sustainability goals, highlighting how multistakeholder collaboration, digital traceability, and inclusive governance can improve the impact of CE. The results recommend that CE strategies should be aligned with net-zero targets, carbon credit systems, and block-chain-based supply chains. Full article

Antibiotics are indispensable in medical patient care, yet they may elicit off-target effects, particularly by affecting mitochondrial function. This study investigates three commonly used antibiotics, gentamicin, ciprofloxacin, and amoxicillin, for their direct effects on mitochondrial respiration and membrane potential. Using high-resolution respirometry, we show that gentamicin and ciprofloxacin markedly increase mitochondrial leak respiration in permeabilized human embryonic kidney cells, suggesting alterations in the mitochondrial inner membrane. This is supported by a gentamicin-induced decrease in mitochondrial membrane potential. Especially gentamicin, but also ciprofloxacin, dose- and time-dependently inhibit oxidative phosphorylation and the mitochondrial electron transfer capacity, pronouncedly in the NADH-linked but also in the succinate-linked pathway. Furthermore, gentamicin decreases Complex IV (CIV) activity in a time-dependent fashion. In contrast, amoxicillin has no significant effect on mitochondrial respiration. These findings emphasize the importance of evaluating the potential direct toxicity of antibiotics on mitochondria, as they are most critical off-target sites. High-resolution respirometry provides a powerful approach to characterize such effects early in the drug development process. Full article

The fashion industry is undergoing significant transformation through blockchain technology, which enhances product traceability, authenticity, and transactional transparency. This study explores blockchain’s potential to revolutionize the fashion supply chain by enabling detailed monitoring from design and manufacturing to certification, quality control, storage, transportation, and delivery. To assess customers’ readiness to adopt these authenticated products, an innovative model for fashion product traceability and authenticity based on blockchain was proposed. Since the adoption of blockchain models relies on widespread user involvement, it is crucial to examine the factors that motivate individuals to take part. To this end, an acceptance study was conducted using the modified UTAUT2 (Unified Theory of Acceptance and Use of Technology) framework, with data analyzed using SMART PLS software. The results indicate that the proposed blockchain model can improve transparency, authenticity, and customer trust in fashion products. Furthermore, the findings identify expected effort, perceived efficiency, and social influence as key factors influencing blockchain adoption in the fashion industry. These insights show the importance of targeted education and customer engagement strategies for successful implementations of blockchain technology in the fashion industry. Full article

Recycling in the construction industry is a necessity, not just a fashionable trend in scientific research. The use of coal slag aggregates in concrete means a significant reduction in environmental footprint and should be a priority. For these reasons, this study presents tests of the physical and mechanical properties of high-performance concrete (HPC) with coal slag (CS) used as a replacement for natural coarse aggregate in the amounts of 10%, 20%, and 30% after a long curing time. The investigation determined the porosity, water absorption, density, compressive strength, flexural strength, tensile splitting strength, modulus of elasticity, and ultrasonic pulse velocity (UPV), and analyzed HPC microstructure at 28, 56 days, as well as 2 years of maturation. The use of coal slag resulted in significant increases in compressive strength, flexural strength, and tensile splitting strength compared to reference concrete. However, for HPC with CS, a slight decrease in the elastic modulus and UPV was obtained. The SEM analysis showed a very good adhesion of the cement paste to the slag aggregate. In general, research shows that it is possible to obtain durable high-performance concrete with a 30% replacement of natural aggregate by coal slag. Full article