Search Results (73,073)

This study examines the adoption of a low-cost model to support digital transformation in small- and medium-sized industrial enterprises (SMEs) within the context of Industry 4.0. In light of the need to increase operational efficiency while simultaneously reducing expenditure, it becomes a priority to employ innovative and cost-effective solutions. To evaluate this impact, the research applies the PICO (Population, Intervention, Comparison, Outcome) methodology, systematically assessing how the proposed model influences digital transformation and operational efficiency. Drawing on a case study, the findings demonstrate that implementing the low-cost model leads to significant cost reductions, gains in operational efficiency, and an acceleration of digital transformation in industrial organizations. The results indicate that the approach not only optimizes internal processes but also contributes to lowering the organization’s overall costs. The conclusions confirm the hypotheses, showing that the model achieves a balance between technological advancement and economic efficiency. The study provides relevant insights into the potential of technologies to simultaneously drive operational efficiency and digital transformation within the framework of Industry 4.0, offering an innovative pathway for companies seeking to digitalize while controlling costs. This research strengthens the existing body of knowledge on the synergy between digital transformation, cost efficiency, and operational performance in industrial settings. Full article

Grey systems theory has provided a change in paradigm related to how numbers and their mathematics are perceived. By including various levels of knowledge associated with the variables, the theory has succeeded in modelling systems characterised by incomplete or partially known information. Among the methods offered by the grey systems theory, the grey clustering approach offers a distinct perspective on clustering methodology by allowing researchers to define degrees of importance for the variables included in the analysis. Despite its expanding use across disciplines, a comprehensive synthesis of grey clustering research is lacking. In this context, this study aims to provide a comprehensive and structured overview of the research field associated with grey clustering and its applications, rather than the more rhetorical formulation previously included. By using a PRISMA approach, a dataset containing papers related to grey clustering is extracted from the Clarivate Web of Science database and analysed through bibliometric tools and further enhanced by providing thematic maps and topics discovery through the use of Latent Dirichlet Allocation (LDA) and BERTopic analyses. The final dataset includes 318 articles, and their examination allows for a detailed assessment of publication trends, thematic structures, and methodological directions. The annual scientific production showcased an increase of 10.78%, while the thematic analysis revealed key themes related to performance management, risk assessment, evaluation models for enhancing organisational performance, urban and regional planning, civil engineering, industrial engineering and automation, and risk evaluation for health-related issues. Additionally, a detailed review of the most-cited papers has been performed to highlight the role of grey clustering in various research fields. Full article

Epilepsy is a prevalent neurological disorder where reliable seizure tracking is essential for patient care. Existing documentation often relies on self-reports, which are unreliable, creating a need for objective, wearable-based solutions. Prior work has shown that Electrocardiography (ECG)-based seizure detection is feasible but limited by small datasets. This study addresses this issue by evaluating Matrix Profile, MADRID, and TimeVQVAE-AD on SeizeIT2, the largest open wearable-ECG dataset with 11,640 recording hours and 886 annotated seizures. Using standardized preprocessing and clinically motivated windows, we benchmarked sensitivity, false-alarm rate (FAR), and a Harmonic Mean Score integrating both metrics. Across methods, TimeVQVAE-AD achieved the highest sensitivity, while MADRID produced the lowest FAR, illustrating the trade-off between detecting seizures and minimizing spurious alerts. Our findings show ECG anomaly detection on SeizeIT2 can reach clinically meaningful sensitivity while highlighting the sensitivity–false alarm trade-off. By releasing reproducible benchmarks and code, this work establishes the first open baseline and enables future research on personalization and clinical applicability. Full article

Doppler weather radars play a pivotal role in meteorology, providing critical data for monitoring severe weather phenomena, such as thunderstorms. However, Doppler velocity measurements are subjected to aliasing errors when the true velocity exceeds the radar’s maximum detection velocity, compromising the accuracy of velocity data. Effective dealiasing techniques are essential to correct these errors and improve data, leading to reliable data assimilation and therefore improved numerical weather prediction (NWP) as well as nowcasting applications. In this study, an attempt is made to present a comparative study of four dealiasing algorithms—convolution-, expansion-, amplitude correction-, and sine-based algorithms—to assess their effectiveness in processing Doppler radar velocity data. The study aims to evaluate these algorithms based on their ability to correct aliasing errors, their computational efficiency, and their practical applicability in real-world meteorological scenarios. Through an experimental evaluation, the performance of each algorithm is analyzed. Results indicate varying degrees of effectiveness among the algorithms, highlighting their respective strengths and limitations in dealing with the velocity aliasing of radar data. It was found that the Amplitude Correction and Convolution algorithms outperformed the others in correcting aliasing. A combined multi-algorithm approach achieved the highest overall accuracy when compared to manually corrected reference data and other algorithms. This research contributes to advancing the understanding of radar data processing techniques and provides insights into optimizing dealiasing strategies for enhanced meteorological forecasting and nowcasting, as well as severe weather prediction. Full article

Background/Objectives: Physician burnout and mental health issues are widespread, with over 50% experiencing burnout and nearly 25% suffering from depression, trends that have worsened since 2018. High-demand specialties like urology face additional stressors, including increasing workloads and technological changes. Gender disparities further exacerbate these challenges, with female urologists reporting higher burnout and work–life balance struggles. To evaluate perceptions of work–life balance, career satisfaction, and workplace experiences among urologists worldwide, and to provide potential strategies to improve physician well-being, promote gender equity, and support the sustainability of urology. Methods: A web-based, cross-sectional survey was conducted from March to June 2025, involving urologists, residents, and fellows globally. The 30-item questionnaire covered demographics, working conditions, work–life balance, and gender-related workplace issues. Data were analyzed using descriptive statistics stratified by gender, age, role, and region. Results: We received replies from 390 doctors in urology. Work-related stress was reported by 87.4% (340). A total of 17.7% (69) felt their career progression to be fully compatible with their personal life, while 42.3% (165) perceived a significant imbalance. Female urologists experienced higher perceptions of inequality in career and work–life opportunities. Over 50% expressed willingness to reduce workload for family reasons, highlighting systemic barriers. Burnout was most prevalent among younger urologists (<50 years), with persistent gender disparities across regions. Conclusions: Work–life imbalance and burnout remain major concerns for urologists globally, especially among female and early-career physicians. Addressing these issues requires institutional policies promoting flexibility, gender equity, and targeted support. Further research is needed to develop effective interventions to sustain a resilient urological workforce. Full article

Oats are regaining interest because of their nutritional and agro-environmental benefits. Hence, research into increasing oat productivity through sustainable agronomic practices has become increasingly important, especially as new varieties are developed and weather patterns become more unpredictable. The paper presents the effects of the cropping system (six-field crop rotation, continuous cropping since 1968), variety (two per six-year period), chemical crop protection (control, herbicide, herbicide plus fungicide), and study year, on spring oat grain yields for two six-year crop rotation cycles (2011–2016, 2017–2022) of a long-term experiment in Poland. The cropping system was the most influential factor. Studies confirmed that growing oats in crop rotation ensures higher productivity than continuous cropping and sustains satisfactory yields in Polish conditions despite yearly weather variability. The cultivated varieties differed in yield levels and degree of yield reduction in response to continuous oat cropping. Only during the 2011–2016 cycle was a decreasing trend in yields observed as continuous cropping was prolonged. Oats grown in crop rotation rarely benefited from chemical protection against weeds and pathogens. In continuous cropping, herbicide and fungicide treatments typically did not mitigate oat yield losses associated with the system, exacerbating them in the 2017–2022 cycle. Among the evaluated agronomic practices, the six-field crop rotation system proved the most reliable yield-enhancing strategy, whereas chemical protection rarely improved oat performance. In individual years, contradictory reactions of the two cultivated varieties to cropping systems and crop protection levels were often noted. Full article

This study rigorously evaluated the integration of energy-harvesting systems within electric vehicles to prolong battery service life. A laboratory-scale system was configured utilizing a scale electric vehicle with a 12.6 V lithium-polymer (Li-Po) battery alongside an automated control platform to precisely estimate the real-time State of Charge (SoC) through monitoring of current, voltage, and temperature of the vehicle battery under three distinct driving conditions: (A) constant velocity at 30 km/h, (B) variable velocities exhibiting a sawtooth profile, and (C) random speed variations. Wind energy was harvested employing Savonius rotor microturbines, with assessments conducted on efficiency losses and drag coefficients to determine the net power yield for each operational profile, which was found to be marginally positive. Considering the energy consumption of electric vehicles based on 2017 U.S. EPA fuel economy data, the maximal recovered energy corresponded to 0.0833% of auxiliary system demand, while the minimal recovery was 0.0398%. These results substantiated the necessity for continued research into sustainable energy management frameworks for electric vehicles. They emphasized the critical importance of optimizing the incorporation of renewable energy technologies to mitigate the environmental ramifications of the transportation sector. Full article

In the background of sustainable development in the construction industry, recycled cement mortar (RCM) has emerged as a research hotspot due to its eco-friendly features, where mechanical properties serve as critical indicators for evaluating its engineering applicability. This study proposes an artificial neural network (ANN) model optimized by intelligent algorithms, including the GWO (grey wolf optimizer), PSO (particle swarm optimization), and a GA (genetic algorithm), to predict the compressive strength of recycled mortar. By integrating experimental and prediction data, we establish a comprehensive database with eight input variables, including the water–cement ratio (W/C), cement–sand ratio (C/S), fly ash content (FA), aggregate replacement rate (ARR), and curing age. The predictive performance of neural network models with different database sizes (database 1: experimental data of RCM; database 2: experimental data of RCM and ordinary mortar; database 3: model prediction data of RCM, experimental data of RCM, and ordinary mortar) is analyzed. The results show that the intelligent optimization algorithms significantly enhance the predictive performance of the ANN model. Among them, the PSO-ANN model demonstrates optimal performance, with R² = 0.92, MSE = 0.007, and MAE = 0.0632, followed by the GA-ANN model and the GWO-ANN model. SHAP analysis reveals that the W/C, C/S, and curing age are the key variables influencing the compression strength. Furthermore, the size of the dataset does not significantly influence the computation time for the above models but is primarily governed by the complexity of the optimization algorithms. This study provides an efficient data-driven method for the mix design of RCM and a theoretical support for its engineering applications. Full article

Background: Tuberculosis (TB) remains a critical global health concern, exacerbated by the emergence of multidrug-resistant and extensively drug-resistant strains of Mycobacterium tuberculosis. In the search for novel therapeutic agents, naphthoquinones have garnered interest due to their diverse mechanisms of action and potent antimycobacterial activity. In this study, we report the design, synthesis, and biological evaluation of a novel series of eleven naphthoquinone-based derivatives (compounds 22–32), developed through a molecular hybridization strategy targeting shikimate kinase (Mtb-SK) an essential enzyme present exclusively in M. tuberculosis. Methods: The compounds were synthesized via a straightforward and efficient synthetic route, and preliminary screening identified five molecules with significant anti-TB activity. Notably, compound 26, 4-(4-ethoxyphenyl) amino) Naphthalene-1,2-dione, exhibited a minimum inhibitory concentration (MIC) of 21.33 µM, comparable to ethambutol and substantially more potent than pyrazinamide. Results: Molecular docking studies indicated that all active compounds interact favorably within the shikimate binding pocket of Mtb-SK, following the proposed mechanism of action. Additionally, ongoing cytotoxicity assays in HepG2 cells aim to assess the selectivity of these derivatives. Conclusions: These findings support the potential of this new class of naphthoquinones as promising scaffolds for the development of anti-TB agents, contributing to the growing body of research focused on new chemotherapeutic options against tuberculosis. Full article

Salt-sensitive hypertension (SSH) is an important and common subtype of hypertension, whose pathogenesis involves multi-level regulation, including the central nervous system (CNS), metabolic stress, and epigenetics. Dietary bioactive compounds have emerged as a research hotspot for SSH intervention due to their safety and multi-target effects. Although existing studies have focused on the CNS regulation of SSH or the role of individual dietary components, there is a lack of comprehensive analysis integrating multiple mechanisms, systematically summarizing multiple compounds, and incorporating a clinical translation perspective. This review first outlines the mechanisms of CNS pathways, endoplasmic reticulum (ER) stress, mitochondrial dysfunction, and epigenetic modifications in SSH. Then, it systematically reviews the mechanisms of action and preclinical and clinical research progress of bioactive compounds, including capsaicin, taurine, gamma-aminobutyric acid, tea, and anthocyanins in SSH. In summary, this review systematically clarifies the complex regulatory network of SSH and the intervention potential of dietary bioactive compounds from an integrated perspective, innovatively proposes a precise dietary intervention framework, and fills the research gaps in the integration of multiple mechanisms and systematic evaluation of compounds in existing studies. This framework not only provides a new integrated perspective for the basic research of SSH but also offers key references for clinical dietary guidance, functional food development, and the formulation of targeted intervention strategies. Full article

The global expansion of healthcare services has made medical waste management an increasingly critical and complex issue. Medical wastes require specialized management due to their high infection risk, potential for environmental pollution, and adverse effects on public health. The correct collection, transportation, and final disposal are vital for protecting environmental health and ensuring the safety of hospital personnel and the community. Numerous disposal methods exist. Selecting the appropriate one, however, is a multi-dimensional decision-making problem, necessitating the simultaneous evaluation of various conflicting criteria. Adana, one of Turkey’s largest provinces, generates significant medical waste volumes due to its dense population and developed health infrastructure. Therefore, choosing the most suitable disposal method for hospitals in Adana is crucial for establishing an effective and sustainable waste management system. Making this decision using traditional methods is difficult. The multitude of criteria prevents any single method from being optimal across all aspects. This complexity mandates the use of Multi-Criteria Decision-Making (MCDM) methodologies. In this study, MCDM methods were applied, based on expert opinions, to select the disposal method at a university hospital in Adana. The research examined twelve criteria and four alternatives. The CRITIC (Criteria Importance Through Intercriteria Correlation) method was employed to objectively weigh the criteria. For the rigorous evaluation and ranking of the alternatives, three robust MCDM methods were utilized: PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and EDAS (Evaluation based on Distance from Average Solution). The final results conclusively identified incineration as the most appropriate disposal method for the hospital. Full article

Taro (Colocasia esculenta (L.) Schott) is a nutritionally important and climate-resilient crop with high potential for enhancing food security. Despite its significance, taro remains underutilized and excluded from major agricultural policies in Rwanda, resulting in low national yields. This gap hinders evidence-based planning and limits the crop contribution to resilience amidst population growth and climate change. By taking Rwanda as an example, a worldwide top 10 taro-producing country but still facing food insecurity issues, this study conducted a nationwide land suitability assessment to identify optimal areas for taro cultivation and quantify the production gap. The Fuzzy Analytic Hierarchy Process (AHP) model was integrated with GIS, where climatic, topographic, and a remotely sensed soil dataset were weighted and combined to generate a composite suitability index. Results revealed that 22.8% of Rwanda's land is highly suitable (S1) and 55.7% is moderately suitable (S2) for taro cultivation. Within agricultural land, 30.2% is highly suitable, of which a significant portion (28.7%) remains largely underutilized, especially in the Eastern province. The national production gap was estimated at 32.4%, with over half of the districts exceeding 30%. The study highlights the importance of aligning taro cultivation with biophysical suitability and integrating spatial planning into national agricultural policies. The developed suitability map provides a critical decision-support tool for policymakers, agricultural planners, and extension services. By promoting sustainable taro production, improving farmer livelihoods and food security in Rwanda, it provides a global model for sustainable development for developing countries and advances research on orphan crops such as taro. The methodology offers a replicable framework for evaluating underutilized crops globally, contributing to sustainable agricultural diversification and food security. Full article

The present study evaluates the quality of charcoal produced from Quercus scytophylla Liebm. in Guerrero, Mexico, using a portable metal oven, namely, the Guadiana Valley Experimental Field (CEVAG) type. A $2\times 3$ factorial design was employed to analyse the influence of wood heterogeneity (sapwood vs. heartwood) and position within the oven (low, medium, high) on the yield and physicochemical properties of the charcoal. The mean yield of the process was found to be 20.0–26.7%. The characteristics of six properties were determined: moisture content, volatile matter, ash content, fixed carbon, basic density, and calorific value. The charcoal exhibited a low moisture content (1.49–3.56%) and ash content (2.18–2.52%), meeting international standards. Volatile matter was higher in heartwood (22%). Fixed carbon (73.73–74.05%) was close to the optimal parameters of international standards. The calorific value exhibited marked variations in accordance with the position during the process of carbonisation, with elevated values observed in the lower section (6751–7508 cal g⁻¹). The basic density of the wood was higher in the sapwood, with a maximum value of 0.57 g cm⁻³ observed in the upper section. A positive linear relationship was identified between the basic density and calorific value, although the coefficient of determination was small ($R^2=0.67$) and therefore inconclusive. The analysis showed the type of relationship that can be established between these two variables. The upper part of the kiln exhibited the optimal physicochemical properties, with the levels deemed acceptable. The utilisation of this oak for charcoal production fosters sustainable forest management and engenders direct economic benefits for rural communities. In conclusion, the research provides a viable technical model for sustainable wood energy production in forestry regions and underscores the need to evaluate other timber species with this potential. Full article

As urban architecture continues to emphasize integration with natural environments, the concept of waterfront buildings and blue–green spaces has been widely applied in the site selection of large urban structures. While existing research has extensively explored architectural types such as waterfront landscapes and sports venues, systematic studies on waterfront concert halls, as an important category of cultural architecture, remain limited. Specifically, the interaction mechanisms between such halls and their aquatic environments, as well as their impact on users’ psychological satisfaction, have not been thoroughly investigated. This study takes waterfront concert halls as representative cultural buildings and examines 1267 users from 12 typical waterfront concert halls across eight cities in China. A theoretical model was constructed with water visibility, water accessibility, water interactivity, and water integration as independent variables, biophilia and a sense of nature’s presence as parallel mediators, and user satisfaction as the dependent variable. Data were analyzed using covariance based structural equation modeling CB-SEM. The findings reveal that (1) water visibility, water accessibility, and water integration positively influence user satisfaction; (2) biophilia mediates the relationship between water visibility, water accessibility, water interactivity, water integration, and user satisfaction; (3) a sense of nature’s presence also mediates the relationship between these water-related variables and user satisfaction. This study empirically demonstrates the dual pathway psychological mechanism through which water elements influence user satisfaction, providing a new perspective for the design of waterfront cultural architecture. The research suggests that architects can enhance users’ biophilic instincts and sense of nature’s presence through specific design strategies, such as strengthening water visibility, optimizing waterfront circulation, and enriching water interaction experiences. These findings offer theoretical support for shifting contemporary architectural practice from physical space creation to environmental well-being promotion, while also establishing a practical foundation for developing human-centered evaluation systems for built environments. Full article

This research focuses on developing and optimizing mixed matrix membranes (MMMs) by incorporating graphene oxide (GO) into a polysulfone (PSF) matrix to enhance the separation performance of CO₂ and CH₄. The morphology and gas separation performance of the MMMs were systematically characterized. The incorporation of GO enhanced gas permeation and CO₂/CH₄ selectivity, as evaluated using a gas permeation setup. Notably, the PSF/GO-0.3 wt.% membrane exhibited superior performance, achieving a CO₂ permeability of 21.63 Barrer, among the highest reported for PSF-based MMMs. Additionally, the membrane demonstrated a CO₂/CH₄ selectivity of 14.32, highlighting its effectiveness in distinguishing between the two gases, which is essential for carbon capture and natural gas decontamination applications. The uniform distribution of GO within the polymer matrix contributed to the membrane’s enhanced performance. Furthermore, the MMMs exhibited outstanding resistance to CO₂ plasticization, with the PSF/GO-0.3 wt.% membrane maintaining its performance at pressures up to 10 bar, a significant improvement over the pristine PSF membrane, which failed at 4 bar. The improved plasticization resistance is ascribed to the reinforcing effect of GO, which stabilizes the polymer matrix, minimizing CO₂-induced swelling. The PSF/GO-0.3 wt.% membrane exhibited exceptional CO₂ permeability, selectivity, and plasticization resistance, making it a viable alternative for industrial gas separation applications and outperforming previously reported PSF-based MMMs. Full article