Search Results (416)

A facile and versatile strategy to obtain NPs@ZnAlO nanocomposite materials, comprising controlled-size nanoparticles (NPs) within a ZnAlO matrix is reported. The mono-(Au, Ni) and bimetallic (Ni-Au) NPs serving as an active phase were prepared by the polyol-alkaline method, while the ZnAlO support was obtained via the thermal decomposition of its corresponding layered double hydroxide (LDH) precursors. X-ray diffraction (XRD) patterns confirmed the successful fabrication of the nanocomposites, including the synthesis of the metallic NPs, the formation of LDH-like structure, and the subsequent transformation to ZnO phase upon LDH calcination. The obtained nanostructures confirmed the nanoplate-like morphology inherited from the original LDH precursors, which tended to aggregate after the addition of gold NPs. According to the UV-Vis spectroscopy, loading NPs onto the ZnAlO support enhanced the light absorption and reduced the band gap energy. ATR-DRIFT spectroscopy, H₂-TPR measurements, and XPS analysis provided information about the functional groups, surface composition, and reducibility of the materials. The catalytic performance of the developed nanostructures was evaluated by the photodegradation of bisphenol A (BPA), under simulated solar irradiation. The conversion of BPA over the bimetallic Ni-Au@ZnAlO reached up to 95% after 180 min of irradiation, exceeding the monometallic Ni@ZnAlO and Au@ZnAlO catalysts. Its enhanced activity was correlated with good dispersion of the bimetals, narrower band gap, and efficient charge carrier separation of the photo-induced e⁻/h⁺ pairs. Full article

Enhancing farmers’ digital literacy is both an inevitable requirement for adapting to the digital age and an important measure for promoting the sustainable development of livestock and poultry manure resource utilization. This study surveyed and obtained data from 1047 farm households in Ningxia and Gansu, two provinces in China that have long implemented livestock manure resource utilization policies, from December 2023 to January 2024, and employed the binary probit model to analyze how digital literacy influences farmers’ willingness to adopt two livestock manure resource utilization modes, as well as to analyze the moderating role of three policy regulations. This paper also explores the heterogeneous results in different village forms and income groups. The results are as follows: (1) Digital literacy significantly and positively impacts farmers’ willingness to adopt both the “household collection” mode and the “livestock community” mode. For every one-unit increase in a farmer’s digital literacy, the probability of farmers’ willingness to adopt the “household collection” mode rises by 22 percentage points, and the probability of farmers’ willingness to adopt the “livestock community” mode rises by 19.8 percentage points. After endogeneity tests and robustness checks, the conclusion still holds. (2) Mechanism analysis results indicate that guiding policy and incentive policy have a positive moderation effect on the link between digital literacy and the willingness to adopt the “household collection” mode. Meanwhile, incentive policy also positively moderates the relationship between digital literacy and the willingness to adopt the “livestock community” mode. (3) Heterogeneity analysis results show that the positive effect of digital literacy on farmers’ willingness to adopt two livestock manure resource utilization modes is stronger in “tight-knit society” rural areas and in low-income households. (4) In further discussion, we find that digital literacy removes the information barriers for farmers, facilitating the conversion of willingness into behavior. The value of this study is as follows: this paper provides new insights for the promotion of livestock and poultry manure resource utilization policies in countries and regions similar to the development process of northwest China. Therefore, enhancing farmers’ digital literacy in a targeted way, strengthening the promotion of grassroots policies on livestock manure resource utilization, formulating diversified ecological compensation schemes, and establishing limited supervision and penalty rules can boost farmers’ willingness to adopt manure resource utilization models. Full article

To enhance agricultural soil health and soil organic carbon (SOC) sequestration, it is important to accurately measure SOC. The aim of this study was to compare common methods for measuring SOC in soils in order to determine the most effective approach among different agricultural land types. The measurement methods of loss-on-ignition (LOI), automated dry combustion (Dumas), and real-time near-infrared spectroscopy (NIRS) were compared. A total of 95 soil core samples, ranging in clay and calcareous content, were collected across a range of agricultural land types from forty-eight fields across five farms in the Southwest of England. There were similar and positive correlations between all three methods for measuring SOC (ranging from r = 0.549 to 0.579; all p < 0.001). On average, permanent grass fields had higher SOC content (6.6%) than arable and temporary ley fields (4.6% and 4.5%, respectively), with the difference of 2% indicating a higher carbon storage potential in permanent grassland fields. Newly predicted conversion equations of linear regression were developed among the three measurement methods according to all the fields and land types. The correlation of the conversation equations among the three methods in permanent grass fields was strong and significant compared to those in both arable and temporary ley fields. The analysed results could help understand soil carbon management and maximise sequestration. Moreover, the approach of using real-time NIRS analysis with a rechargeable portable NIRS soil device can offer a convenient and cost-saving alternative for monitoring preliminary SOC changes timely on or offsite without personnel risks from the high-temperature furnace and chemical reagent adopted in the LOI and Dumas processes, respectively, at the laboratory. Therefore, the study suggests that faster, lower-cost, and safer methods like NIRS for analysing initial SOC measurements are now available to provide similar SOC results as traditional soil analysis methods of the LOI and Dumas. Further studies on assessing SOC levels in different farm locations, land, and soil types across seasons using NIRS will improve benchmarked SOC data for farm stakeholders in making evidence-informed agricultural practices. Full article

To enhance social interaction of residents living with dementia and their (in)formal carers in nursing homes, we examined the effects of a digital, person-centred, Photo-Activity (PA) versus a conversation activity (control). An explorative randomized controlled trial was conducted in 81 resident-informal carer (IC) dyads and 51 formal carers (FC) with three measurements (pre/post-test, 2-week follow-up). Intervention effects were tested using Mann–Whitney U’s, and ANCOVA’s with pre-test scores as covariates. Interaction effects were examined between dementia severity (DS; less/more) and condition (PA/control). A post-test effect was observed in social interaction (INTERACT-subscale: Mood [p = 0.037, ηp² = 0.07]), with PA residents showing better mood than controls. Residents with less DS showed more positive effects of PA than residents with more DS (interaction effects: INTERACT-subscales Mood [p = 0.017, ηp² = 0.092], Stimulation Level [p = 0.011, ηp² = 0.106], and Need for Prompting [p = 0.013, ηp² = 0.099]). Higher QUALIDEM Positive Affect scores were observed in the PA group, post-test (p = 0.025, ηp² = 0.082), and follow-up (p = 0.042, d = 0.39). PA FC showed less empathy (IRI; p = 0.006, ηp² = 0.185;) than controls, but reported getting to know the residents better (p = 0.035, r = 0.299). PA improved mood and positive affect of residents with dementia and led to FC knowing the residents better. Less empathy was observed in FC providing PA, requiring further investigation. Full article

Pesticide exposure gradients between occupational, para-occupational, and general populations remain poorly characterized in North African agricultural contexts. This study evaluates urinary pesticide levels among farmers, indirectly exposed individuals, and a control group in Morocco’s Fez-Meknes region. A cross-sectional survey measured pesticide concentrations using LC-MS/MS in urine samples collected from 154 adults residing in both rural and urban areas. A questionnaire was used to gather information from participants regarding factors that may elevate the risk of pesticide exposure. The results revealed that farmers exhibited the highest concentrations of pesticides in their urine, including compounds classified as Ia/Ib by the World Health Organization. Indirectly exposed individuals showed moderate levels of contamination, with notable detections such as dichlofluanid (22.13 µg/L), while the control group had residual traces of neonicotinoids, notably imidacloprid (2.05 µg/L). Multivariate analyses revealed several sociodemographic factors significantly associated with increased pesticide exposure. The main risk factors identified included low education, residence in an agricultural area, and the consumption of untreated water (wells/rivers). Conversely, wearing personal protective equipment was associated with reduced urinary concentrations. This study highlights intense occupational exposure among farmers, secondary environmental contamination among residents living near treated areas, and the widespread dispersion of pesticide residues into urban areas. Full article

Corporate environmental claims often neglect the substantial ecological impact of land-use changes. This case study examines the spatial dimension of retail-driven land-use transformation by analyzing supermarket expansion in the Veneto region (northern Italy), with a focus on a large grocery retailer. We evaluated its corporate environmental claims by assessing land consumption patterns from 1983 to 2024 using Geographic Information Systems (GIS). The GIS-based methodology involved geocoding 113 Points of Sale (POS—individual retail outlets), performing photo-interpretation of historical aerial imagery, and classifying land-cover types prior to construction. We applied spatial metrics such as total converted surface area, land-cover class frequency across eight categories (e.g., agricultural, herbaceous, arboreal), and the average linear distance between afforestation sites and POS developed on previously rural land. Our findings reveal that 65.97% of the total land converted for Points of Sale development occurred in rural areas, primarily agricultural and herbaceous lands. These landscapes play a critical role in supporting urban biodiversity and providing essential ecosystem services, which are increasingly threatened by unchecked land conversion. While the corporate sustainability reports and marketing strategies emphasize afforestation efforts under their “We Love Nature” initiative, our spatial analysis uncovers no evidence of actual land-use conversion. Additionally, reforestation activities are located an average of 40.75 km from converted sites, undermining their role as effective compensatory measures. These findings raise concerns about selective disclosure and greenwashing, driving the need for more comprehensive and transparent corporate sustainability reporting. The study argues for stronger policy frameworks to incentivize urban regeneration over greenfield development and calls for the integration of land-use data into corporate sustainability disclosures. By combining geospatial methods with content analysis, the research offers new insights into the intersection of land use, business practices, and environmental sustainability in climate-vulnerable regions. Full article

Fully printable carbon-based multiporous layered electrode perovskite solar cells (MPLE−PSCs) are close to being commercialized due to their excellent stability, their ability to easily be scaled up, and their amenability to mass production via non-vacuum fabrication processes. To improve their efficiency, it is important that detailed studies of the morphologies of mesoporous electrodes be carried out. In this study, we prepared five types of ZrO₂ spacer layers for MPLE−PSCs, and the morphology of ZrO₂ and device performance were evaluated using a scanning electron microscope, nitrogen adsorption/desorption measurements, electrode resistance measurements, UV-visible light reflectance measurements, and current density–voltage measurements. The results reveal that the adequate specific surface area and pore size distribution of mesoporous ZrO₂ provided high insulation ability when used as spacers between electrodes and light absorbance, resulting in a 10.92% photoelectric conversion efficiency with a 23.22 mA cm⁻² short-circuit current density. This information can serve as a guideline for designing morphologies useful for producing high-efficiency devices. Full article

Background: Data accessibility remains a significant barrier in healthcare AI due to privacy constraints and logistical challenges. Synthetic data, which mimics real patient information while remaining both realistic and non-identifiable, offers a promising solution. Large Language Models (LLMs) create new opportunities to generate high-fidelity clinical conversations between patients and physicians. However, the value of this synthetic data depends on careful evaluation of its realism, accuracy, and practical relevance. Objective: To assess the performance of four leading LLMs: ChatGPT 4.5, ChatGPT 4o, Claude 3.7 Sonnet, and Gemini Pro 2.5 in generating synthetic transcripts of patient–physician interactions in plastic surgery scenarios. Methods: Each model generated transcripts for ten plastic surgery scenarios. Transcripts were independently evaluated by three clinically trained raters using a seven-criterion rubric: Medical Accuracy, Realism, Persona Consistency, Fidelity, Empathy, Relevancy, and Usability. Raters were blinded to the model identity to reduce bias. Each was rated on a 5-point Likert scale, yielding 840 total evaluations. Descriptive statistics were computed, and a two-way repeated measures ANOVA was used to test for differences across models and metrics. In addition, transcripts were analyzed using automated linguistic and content-based metrics. Results: All models achieved strong performance, with mean ratings exceeding 4.5 across all criteria. Gemini 2.5 Pro received mean scores (5.00 ± 0.00) in Medical Accuracy, Realism, Persona Consistency, Relevancy, and Usability. Claude 3.7 Sonnet matched the scores in Persona Consistency and Relevancy and led in Empathy (4.96 ± 0.18). ChatGPT 4.5 also achieved perfect scores in Relevancy, with high scores in Empathy (4.93 ± 0.25) and Usability (4.96 ± 0.18). ChatGPT 4o demonstrated consistently strong but slightly lower performance across most dimensions. ANOVA revealed no statistically significant differences across models (F(3, 6) = 0.85, p = 0.52). Automated analysis showed substantial variation in transcript length, style, and content richness: Gemini 2.5 Pro generated the longest and most emotionally expressive dialogues, while ChatGPT 4o produced the shortest and most concise outputs. Conclusions: Leading LLMs can generate medically accurate, emotionally appropriate synthetic dialogues suitable for educational and research use. Despite high performance, demographic homogeneity in generated patients highlights the need for improved diversity and bias mitigation in model outputs. These findings support the cautious, context-aware integration of LLM-generated dialogues into medical training, simulation, and research. Full article

This paper addresses the problem of information extraction using an AI-powered chatbot. The problem concerns searching and extracting relevant information from large databases in response to a human user’s query. Expanding the traditional discrete search problem well known in operations research, this problem introduces two players; the first player—an AI chatbot such as ChatGPT 4.0—sequentially scans available datasets to find an appropriate answer to a given query, while the second—a human user—conducts a dialogue with the chatbot and evaluates its answers in each round of the dialogue. The goal of an AI-powered chatbot is to provide maximally useful and accurate information. During a natural language conversation between a human user and an AI, the human user can modify and refine queries until s/he is satisfied with the chatbot’s output. We analyze two key characteristics of human–AI interaction: search reliability and efficiency. Search reliability is defined as the ability of a robot to understand user queries and provide correct answers; it is measured by the frequency (probability) of correct answers. Search efficiency of a chatbot indicates how accurate and relevant the information returned by the chatbot is; it is measured by the satisfaction level a human user receives for a correct answer. An AI chatbot must perform a sequence of scans over the given databases and continue searching until the human user declares, in some round, that the target has been found. Assuming that the chatbot is not completely reliable, each database may have to be scanned infinitely often; in this case, the objective of the problem is to determine a search policy for finding the optimal sequence of chatbot scans that maximizes the expected user satisfaction over an infinite time horizon. Along with these results, we found a counterintuitive relationship between AI chatbot reliability and search performance: under sufficiently general conditions, a less reliable AI chatbot may have higher expected search efficiency; this phenomenon aligns with other well-known “more-for-less” paradoxes. Finally, we discussed the underlying mechanism of this paradox. Full article

Background/Objectives: Burnout and anger are prevalent among healthcare professionals in high-stress environments, particularly in addiction services. This study explores the relationship between burnout and anger among 124 operators working in public addiction services (SERD) in Calabria and Sicily. The objective is to assess how different anger dimensions contribute to burnout and identify protective factors that could inform targeted interventions. Methods: The sample consisted of 58 men and 66 women, with a mean age of 39.2 years (SD = 9.8), ranging from 25 to 59 years old. Burnout was measured using the Maslach Burnout Inventory (MBI), assessing emotional exhaustion, depersonalization, and personal accomplishment. Anger was evaluated through the State-Trait Anger Expression Inventory-2 (STAXI-2), examining trait anger, state anger, anger expression (anger-in, anger-out), and anger control. A cross-sectional design was used, with correlation and regression analyses controlling for gender and years of service. Results: High levels of burnout, particularly emotional exhaustion and depersonalization, were found. Emotional exhaustion correlated strongly with trait anger, indicating that individuals with a chronic predisposition to anger are more vulnerable to burnout. Suppression of anger (anger-in) significantly predicted depersonalization, exacerbating emotional disengagement from patients. Conversely, anger control acted as a protective factor, helping maintain a sense of personal accomplishment. Conclusions: These findings underscore the importance of emotional regulation in mitigating burnout among addiction service workers. Interventions such as emotional regulation training and anger management programs could help reduce psychological distress and promote resilience. Workplace strategies that support emotional well-being may improve both staff retention and patient care quality. Further research should explore longitudinal trends and intervention effectiveness. Full article

High efficiency and high power density are key targets in modern power conversion. Operating power converters at high switching frequencies enables the use of smaller passive components, which, in turn, facilitate achieving high power density. However, the concurrent increase in switching frequency and power density leads to efficiency and overheating issues. Soft switching techniques are typically employed to minimize switching losses and significantly improve efficiency by reducing power losses. However, the hysteresis behavior of the power electronics devices’ output capacitance, C_OSS, is the cause of regrettable losses in Super-Junction (SJ) MOSFETs, SiC MOSFETs, and GaN HEMTs, which are usually adopted in soft switching-based conversion schemes. This paper reviews the techniques for measuring hysteresis traces and power losses, as well as the understanding of the phenomenon to identify current research trends and open problems. A few studies have reported that GaN HEMTs tend to exhibit the lowest hysteresis losses, while Si superjunction (SJ) MOSFETs often show the highest. However, this conclusion cannot be generalized by comparing the results from different works because they are typically made across devices with different (when the information is reported) breakdown voltages, on-state resistances, die sizes, and test conditions. Moreover, some recent investigations using advanced TCAD simulations have demonstrated that newer Si-SJ MOSFETs employing trench-filling epitaxial growth can achieve significantly reduced hysteresis losses. Similarly, while multiple studies confirm that hysteresis losses increase with increasing dv/dt and decreasing temperature, the extent of this dependence varies significantly with device structure and test methodology. This difficulty in obtaining a general conclusion is due to the lack of proper figures of merit that account for hysteresis losses, making it problematic to evaluate the suitability of different devices in resonant converters. This problem highlights the primary current challenge, which is the development of a standard and automated method for characterizing C_OSS hysteresis. Consequently, significant research effort must be invested in addressing this main challenge and the other challenges described in this study to enable power electronics researchers and practitioners to develop resonant converters properly. Full article

The COVID-19 pandemic was a particularly challenging time for the construction industry as it experienced significant disruptions to operations, affecting various stakeholders. With various national and international health agencies promoting preventive measures, the construction industry struggled with the implementation of these measures due to the unique nature of the work involved in construction. This study aimed to highlight the ways in which stakeholders in the construction industry interacted and responded to the prescribed preventive measures through social media analysis. Using model-based clustering and structural topic modelling, this study provided insights into the prevalent discussion topics in social media around prevention measures in construction. In addition, sentiment analysis demonstrated interesting polarisation around the topic areas. Four prevalent topics that encapsulated the entirety of the social media data were identified, with two of the topics showing an upward trend, as expected, while the other two topics showed a contrasting downward trend. These findings offer practical value for construction managers and policymakers by revealing the effectiveness of different communication strategies and identifying areas where prevention measures faced resistance or acceptance. The sentiment polarisation patterns (50% positive, 40% negative) provide actionable insights for developing more targeted engagement approaches, while the topic evolution trends inform the timing and focus of safety communications. Construction organisations can leverage these insights to improve workplace safety protocols and enhance stakeholder buy-in for future health initiatives. This study lays the foundation for future studies to investigate the connections between the prevalent prevention and the interrelated dynamics within the conversation regarding COVID-19 prevention strategies in the construction sector. Full article

In the context of vessel monitoring, the accuracy of vessel speed measurements is contingent on the availability of AIS data. However, the absence, failure, or signal congestion of AIS devices may lead to delays and inaccuracies in the speed information. To address this challenge, this paper proposes a vessel speed detection method based on target detection and tracking to acquire vessel speed in real time. The proposed methodology involves the establishment of a mapping relationship between image coordinates and four real-world coordinates, ensuring precise conversion from pixel velocity to physical velocity. Subsequently, a frame difference method combined with a multi-frame averaging strategy calculates the vessel speed. Furthermore, an advanced M-YOLOv11 detection model is introduced to enhance the detection performance in different vessel shapes and complex environments, thus ensuring the accuracy of speed information is further improved. The experimental results demonstrate that M-YOLOv11 exhibits a significant performance enhancement, with a 13.95% improvement in the average precision metric over the baseline model. Over 60% of the measured vessel speed measurement errors are less than 0.5 knots, with an overall average error below 0.45 knots. These findings substantiate the efficacy and superiority of the proposed method in practical applications. Full article

Background: The World Health Organization (WHO) defines stillbirth as the loss of a fetus after 28 weeks of gestation. Annually, approximately 2 million stillbirths occur worldwide. Projections indicate that by 2030, this figure could rise to nearly 15.9 million, with half of these stillbirths expected to take place in Sub-Saharan Africa. In the global literature, causes include placental complications, birth defects, and maternal health issues, though often the cause is unknown. Stillbirths have significant emotional and financial impacts on families. Methods: The process involves using chi-square tests to identify candidate covariates for model building. The relative risk (RR) measures the association between variables using the sample data of 1435 mothers collected retrospectively. Since these tests are independent, covariates might be interrelated. The unadjusted RR from the bivariate analysis is then refined using stepwise logistic regression, guided by the Akaike Information Criterion (AIC), to select the best subset of covariates among the candidate variables. The logistic model’s regression coefficients provide the adjusted RR (aRR), indicating the strength of the association between a factor and stillbirth. Results: The model fit results reveal that heavy bleeding in the second or third trimester increases stillbirth risk by 4.69 times. Other factors, such as water breaking early in the third trimester (aRR = 3.22), severe back pain (aRR = 2.61), and conditions like anemia (aRR = 2.45) and malaria (aRR = 2.74), also heightened the risk. Further, mothers with a history of hypertension faced a 3.89-times-greater risk, while multifetal pregnancies increased risk by over 6 times. Conversely, proper mental and physical relaxation could reduce stillbirth risk by over 60%. Additionally, mothers aged 20 to 35 had a 40% lower risk than younger or older mothers. Conclusions: This research study identifies the significant predictors for forecasting stillbirth in pregnant women, and the results could help in the development of health monitoring strategies during pregnancy to reduce stillbirth risks. The research findings further support the importance of targeted interventions for high-risk groups. Full article

Patient-reported health data, especially patient-reported outcomes measures, are vital for improving clinical care but are often limited by memory bias, cognitive load, and inflexible questionnaires. Patients prefer conversational symptom reporting, highlighting the need for robust methods in symptom extraction and conversational intelligence. This study presents a weakly-supervised pipeline for training and evaluating medical Named Entity Recognition (NER) models across eight languages, with a focus on low-resource settings. A merged English medical corpus, annotated using the Stanza i2b2 model, was translated into German, Greek, Spanish, Italian, Portuguese, Polish, and Slovenian, preserving the entity annotations medical problems, diagnostic tests, and treatments. Data augmentation addressed the class imbalance, and the fine-tuned BERT-based models outperformed baselines consistently. The English model achieved the highest F1 score (80.07%), followed by German (78.70%), Spanish (77.61%), Portuguese (77.21%), Slovenian (75.72%), Italian (75.60%), Polish (75.56%), and Greek (69.10%). Compared to the existing baselines, our models demonstrated notable performance gains, particularly in English, Spanish, and Italian. This research underscores the feasibility and effectiveness of weakly-supervised multilingual approaches for medical entity extraction, contributing to improved information access in clinical narratives—especially in under-resourced languages. Full article