Search Results (5,113)

Background/Objectives: Vedolizumab is a gut-selective anti-integrin monoclonal antibody approved for the treatment of inflammatory bowel disease (IBD). While clinical trials have demonstrated a favorable safety profile, real-world studies are essential for identifying rare adverse events (AEs) and evaluating post-marketing safety. This study assessed vedolizumab’s safety in a real-world cohort and supported the detection of potential safety signals. Methods: A retrospective chart review was conducted on adult IBD patients treated with vedolizumab at a tertiary center in the Republic of Serbia between October 2021 and August 2022. Data included demographics, AEs, and newly reported extraintestinal manifestations (EIMs). Exposure-adjusted incidence rates were calculated per 100 patient-years (PYs). Disproportionality analysis using the FDA Adverse Event Reporting System (FAERS) was performed to identify safety signals, employing reporting odds ratios (RORs) and proportional reporting ratios (PRRs) for AEs also observed in the cohort. Prior IBD therapies and reasons for discontinuation were evaluated. Results: A total of 107 patients (42.1% Crohn’s disease, 57.9% ulcerative colitis) were included, with a median vedolizumab exposure of 605 days. There were 92 AEs (56.51/100 PYs), most frequently infections (23.95/100 PYs), gastrointestinal disorders (4.30/100 PYs), and skin disorders (4.30/100 PYs). The most frequently reported preferred terms (PTs) included COVID-19, COVID-19 pneumonia, nephrolithiasis, and nasopharyngitis. Arthralgia (12.90/100 PYs) was the most frequent newly reported EIM. No discontinuations due to vedolizumab AEs occurred. FAERS analysis revealed potential signals for events not listed in prescribing information but observed in the cohort: nephrolithiasis, abdominal pain, diarrhea, malaise, cholangitis, gastrointestinal infection, blood pressure decreased, weight decreased, female genital tract fistula, respiratory symptom, and appendicectomy. Most patients had received three prior therapies, often stopping one due to AEs. Conclusions: Vedolizumab demonstrated a favorable safety profile in the IBD cohort. However, FAERS-identified signals, such as nephrolithiasis, gastrointestinal infections, and decreased blood pressure, warrant further investigation in larger, more diverse populations. Full article

Despite increasing interest in including indigenous and local people in forest restoration initiatives, their views on which species are most useful, or reasons behind not planting native tree species are often ignored. Focused on south-western Rwanda, this study addressed these knowledge gaps. We carried out 12 focus group discussions with village elders to determine the following: main benefits provided by native forests, the native species they prefer for different uses, and the main barriers to species’ cultivation. Then, considering other key information from the literature, we performed a ranking exercise to determine which native species had the greatest potential for large-scale tree planting initiatives. Our results show that native forests provide 17 benefits to local communities, some of which cannot be replaced by plantations with exotic species. Among the 26 tree species identified as most useful for timber, firewood, medicine and fodder, ten were ranked as with the greatest potential for restoration initiatives. Of these, two had not been included in recent experimental plantations using native species in Rwanda, and none were considered among the priority species for domestication in Africa. Overall, our study highlights the need to better connect the ecological and social dimension of forest reforestation initiatives in multiple contexts. Full article

This perspective paper explores the future potential of “conversational intelligence” by examining how Large Language Models (LLMs) could be combined with GRAPHYP’s network system to better understand human conversations and preferences. Using recent research and case studies, we propose a conceptual framework that could make AI reasoning transparent and traceable, allowing humans to see and understand how AI reaches its conclusions. We present the conceptual perspective of “Matching Game Preferences through Dialogical Large Language Models (D-LLMs),” a proposed system that would allow multiple users to share their different preferences through structured conversations. This approach envisions personalizing LLMs by embedding individual user preferences directly into how the model makes decisions. The proposed D-LLM framework would require three main components: (1) reasoning processes that could analyze different search experiences and guide performance, (2) classification systems that would identify user preference patterns, and (3) dialogue approaches that could help humans resolve conflicting information. This perspective framework aims to create an interpretable AI system where users could examine, understand, and combine the different human preferences that influence AI responses, detected through GRAPHYP’s search experience networks. The goal of this perspective is to envision AI systems that would not only provide answers but also show users how those answers were reached, making artificial intelligence more transparent and trustworthy for human decision-making. Full article

Agricultural workers—individuals employed for labor in agriculture—are at high risk of various negative health outcomes, with many impacted by both the existence of health disparities and stress. While the issue of farm stress and associated psychosocial health outcomes has been studied in the general agricultural population, research investigating these issues specifically within the agricultural worker population is sparse. This study presents data from the United States Western Region Agricultural Worker Stress Survey (N = 354), which gauged workers’ perceived stress levels, sources of stress, desired stress management topics, and preferred methods of receiving information and support services. Long working hours, working in extreme temperatures, and a lack of time emerged as the top three stressors. On average, workers across the Western region of the U.S. are experiencing a moderate level of stress, with younger workers reporting greater stressor pileup than their older counterparts. Retirement planning was cited as the most preferred stress management topic, regardless of demographic. Lastly, workers chose in-person counseling as the support modality that they would most likely utilize. This research provides a variety of stress management recommendations such as working with farm owners to increase the safety of their operation, investing in face-to-face counseling services, and utilizing community health workers as sources of support. Full article

Studying consumers’ willingness to pay (WTP) for sustainable certification labels and preferences in consumption is a relevant issue for policymakers. Several studies have revealed a positive WTP a premium price for many certified products. The aim of this paper is to assess an overview of Italian consumers’ WTP for eight different sustainable certification labels and to collect information about their consumption preferences and perceptions in consumption. Participants were selected by stratified simple random sampling, using regional distribution, gender, and age as stratification criteria, to obtain a representative sample of n = 3600. Eight ordered logit models were estimated to understand how consumer sociodemographic characteristics influence the price premium. The results show important differences in WTP among different certification labels; a higher WTP emerged for ethical certifications than for environmentally focused labels. Younger individuals; women; and those with higher education, income and life satisfaction, as well as consumers in southern regions, were significantly more willing to pay premiums for certified products. However, a key finding for policymakers is that the stated price premium consumers are willing to pay falls significantly short of the actual higher costs of these products in supermarkets. Furthermore, insights into consumer perceptions and preferences revealed that quality and origin are perceived as key price drivers, while method of production holds less importance. It also emerged that consumers primarily seek a balance between quality and price, with only a small segment prioritizing certified products. Full article

User requirements are the core driving force behind the iterative development of complex products. Their comprehensive collection, accurate interpretation, and effective integration directly affect design outcomes. However, current practices often depend heavily on single-source data and designer intuition, resulting in incomplete, biased, and fragile design decisions. Moreover, multi-source heterogeneous user requirements often exhibit inherent asymmetry and imbalance in both structure and contribution. To address these issues, this study proposes a symmetric and balanced optimization method for multi-source heterogeneous user requirements in complex product design. Multiple acquisition and analysis approaches are integrated to mitigate the limitations of single-source data by fusing complementary information and enabling balanced decision-making. Firstly, unstructured text data from online reviews are used to extract initial user requirements, and a topic analysis method is applied for modeling and clustering. Secondly, user interviews are analyzed using a fuzzy satisfaction analysis, while eye-tracking experiments capture physiological behavior to support correlation analysis between internal preferences and external behavior. Finally, a cooperative game-based model is introduced to optimize conflicts among data sources, ensuring fairness in decision-making. The method was validated using a case study of oxygen concentrators. The findings demonstrate improvements in both decision robustness and requirement representation. Full article

This study investigates whether audit tenure mandates—designed to curb managerial discretion—may unintentionally enable disguised opinion shopping. Specifically, it examines whether firms benefit from observed mandates that align with their unobservable preferences, despite appearing to comply with mandatory audit firm rotation or retention rules. A counterfactual framework is developed to estimate firms’ preference for switching or retention in the absence of regulation, allowing identification of strategic alignment under constraint. Empirical analysis using Korean data from 2000 to 2009 reveals that firms classified as disguised opinion shoppers are more likely to receive unmodified audit opinions and exhibit lower audit quality, as indicated by higher discretionary accruals and more frequent reporting irregularities. These effects are concentrated under mandatory retention and not observed under rotation, suggesting that forced auditor turnover weakens firms’ ability to secure favorable outcomes. Additional evidence shows that these firms are more likely to retain the same auditor after mandates expire, consistent with a reward-for-accommodation mechanism. Thus, this study not only provides empirical evidence that opinion shopping can persist under auditor tenure mandates, but also introduces a novel method for identifying such behavior when traditional signals—such as voluntary dismissals—are unavailable. These findings inform ongoing regulatory debates on the effectiveness of tenure-based reforms. Full article

Chinese Optical Character Recognition (OCR) technology is essential for digital transformation in Chinese regions, enabling automated document processing across various applications. However, Chinese OCR systems struggle with visually similar characters, where subtle stroke differences lead to systematic recognition errors that limit practical deployment accuracy. This study develops FLIP (Feedback Learning-based Intelligent Plugin), a lightweight post-processing plugin designed to improve Chinese OCR accuracy across different systems without external dependencies. The plugin operates through three core components as follows: UTF-8 encoding-based output parsing that converts OCR results into mathematical representations, error correction using information entropy and weighted similarity measures to identify and fix character-level errors, and adaptive feedback learning that optimizes parameters through user interactions. The approach functions entirely through mathematical calculations at the character encoding level, ensuring universal compatibility with existing OCR systems while effectively handling complex Chinese character similarities. The plugin’s modular design enables seamless integration without requiring modifications to existing OCR algorithms, while its feedback mechanism adapts to domain-specific terminology and user preferences. Experimental evaluation on 10,000 Chinese document images using four state-of-the-art OCR models demonstrates consistent improvements across all tested systems, with precision gains ranging from 1.17% to 10.37% and overall Chinese character recognition accuracy exceeding 98%. The best performing model achieved 99.42% precision, with ablation studies confirming that feedback learning contributes additional improvements from 0.45% to 4.66% across different OCR architectures. Full article

With increasing attention on DNA ancestry tests, scholars have explored how these tests inform modern understandings of race. Current research reveals the flaws and misinterpretations that arise when DNA tests, such as those offered by 23andMe and AncestryDNA, are used as a proxy for racial identity. While prominent in popular culture, the legitimacy and implications of these tests remain contested in the scholarly literature. Some researchers have explored how the increased availability of DNA tests affects how multiracial individuals identify and disclose their racial and ethnic identities, though this exploration remains limited. As discourse about mixed race identity and ancestry tests becomes more nuanced, I argue for the utility of using diunital perspectives, an expansive lens that resists either/or thinking, to complicate conversations about ancestry tests and multiraciality. This scholarly essay integrates personal narrative and a genealogical deconstruction of monoracialism to explore the question, “How can DNA tests contribute to the unlearning of monoracialism?” I share two personal vignettes to illustrate how these tests can reveal a preference for discrete racial categories. Drawing from Critical Race Theory, strategic essentialism, and diunital perspectives, I examine how DNA tests intersect with identity, family, and monoracialism, concluding with implications for disrupting monoracial logics. Full article

Background: Behavioural heterogeneity in obesity is increasingly recognised, but how specific dietary patterns, food preferences and physical activity vary between obesity classes remains poorly characterised. Methods: We analysed behavioural, dietary, and lifestyle data from 1366 adults attending a tertiary obesity clinic in Italy. Participants were stratified into five obesity classes defined by BMI. Age-adjusted regression models and chi-square tests with Bonferroni correction were used to examine associations between obesity severity and key behavioural outcomes, including food preferences, eating behaviours, physical activity, and self-reported sleep quality. Results: The prevalence of uncontrolled eating, skipping meals, and fast eating significantly increased with obesity severity after adjusting for age (all p < 0.05). Preference for yoghurt and legumes declined with increasing BMI, whereas preferences for meat and dairy remained stable. Age-adjusted sport participation decreased progressively, with significantly lower odds in Obesity I, II, and IIIA compared to the Overweight group. Sleep quality was highest among overweight participants and declined with obesity severity; night-time awakenings were most frequent in Obesity IIIB. Conclusions: Distinct behavioural and lifestyle traits, including lower sport participation, reduced preference for fibre-rich foods, and greater frequency of uncontrolled, fast, and irregular eating, showed overall trends across obesity classes. While these findings suggest the presence of behavioural phenotypes, their interpretation is limited by the cross-sectional design and the use of self-reported, non-validated measures. Future studies should incorporate objective assessments to inform targeted obesity interventions. Full article

Our study aimed to identify the overall and cluster-specific characteristics of Czech beef cattle breeding systems. We used data from an online survey to ascertain farmers’ preferences in breeding objectives. Considering various evaluation criteria and clustering approaches in 41 farms, three beef systems were defined according to herd size, management, marketing, breeding strategies and structures, and farmer age. Breeding values and performance were jointly used as the primary information in all three systems. Cow temperament and calf viability, maternal fertility and longevity, and animal health were found to be the most important traits. Cluster 1 represents pure-breeding farms that specialize in producing breeding animals. Farms in clusters 2 and 3 combined pure- and crossbreeding strategies with production, which was partially (cluster 2) and fully (cluster 3) diversified for all beef categories. Farms also prioritized calving performance and calf growth (clusters 1 and 2) and exterior traits (cluster 3). Production type scores significantly (p < 0.05) differed in clusters 3 (4.12) and 2 (3.25). The proportion of production, functional, and exterior trait categories was 12:37:51, with low variability among clusters (±1 to 2 percentage points). The inter-cluster comparison showed that specific characteristics were compatible with certain breeding goal trait preferences. Full article

This study estimates a willingness-to-pay (WTP) space mixed logit model to evaluate user valuations of travel time, safety, and comfort attributes associated with common access modes in Bangkok, including walking, motorcycle taxis, and localized minibuses. The model accounts for preference heterogeneity by specifying random parameters for travel time. Results indicate that users—exhibiting substantial variation in preferences—place higher value on reducing motorcycle taxi travel time, particularly in time-constrained contexts such as peak-hour commuting, whereas walking is more acceptable in less pressured settings. Safety and comfort attributes—such as helmet availability, smooth pavement, and seating—significantly influence access mode choice. Notably, the WTP for helmet availability is estimated at THB 8.04 per trip, equivalent to approximately 40% of the typical fare for station access, underscoring the importance of safety provision. Women exhibit stronger preferences for motorized access modes, reflecting heightened sensitivity to environmental and social conditions. This study represents one of the first applications of WTP-space modeling for valuing informal station access transport in Southeast Asia, offering context-specific and segment-level estimates. These findings support targeted interventions—including differentiated pricing, safety regulations, and service quality enhancements—to strengthen first-/last-mile connectivity. The results provide policy-relevant evidence to advance equitable and sustainable transport, particularly in rapidly urbanizing contexts aligned with SDG 11.2. Full article

Face recognition technology is a biometric technology, which is based on the identification or verification of facial features. Automatic face recognition is an active research field in the context of computer vision and artificial intelligence (AI) that is fundamental for a variety of real-time applications. In this research, the design and implementation of a face verification and identification system of a flexible, modular, secure, and scalable architecture is proposed. The proposed system incorporates several and various types of system components: (i) portable capabilities (mobile application and mixed reality [MR] glasses), (ii) enhanced monitoring and visualization via a user-friendly Web-based user interface (UI), and (iii) information sharing via middleware to other external systems. The experiments showed that such interconnected and complementary system components were able to perform robust and real-time results related to face identification and verification. Furthermore, to identify a proper model of high accuracy, robustness, and performance speed for face identification and verification tasks, a comprehensive evaluation of multiple face recognition pre-trained models (FaceNet, ArcFace, Dlib, and MobileNetV2) on a curated version of the ID vs. Spot dataset was performed. Among the models used, FaceNet emerged as a preferable choice for real-time tasks due to its balance between accuracy and inference speed for both face identification and verification tasks achieving AUC of 0.99, Rank-1 of 91.8%, Rank-5 of 95.8%, FNR of 2% and FAR of 0.1%, accuracy of 98.6%, and inference speed of 52 ms. Full article

Efficient object detection is crucial to real-time monitoring applications such as autonomous driving or security systems. Modern RGB cameras can produce high-resolution images for accurate object detection. However, increased resolution results in increased network latency and power consumption. To minimise this latency, Convolutional Neural Networks (CNNs) often have a resolution limitation, requiring images to be down-sampled before inference, causing significant information loss. Event-based cameras are neuromorphic vision sensors with high temporal resolution, low power consumption, and high dynamic range, making them preferable to regular RGB cameras in many situations. This project proposes the fusion of an event-based camera with an RGB camera to mitigate the trade-off between temporal resolution and accuracy, while minimising power consumption. The cameras are calibrated to create a multi-modal stereo vision system where pixel coordinates can be projected between the event and RGB camera image planes. This calibration is used to project bounding boxes detected by clustering of events into the RGB image plane, thereby cropping each RGB frame instead of down-sampling to meet the requirements of the CNN. Using the Common Objects in Context (COCO) dataset evaluator, the average precision (AP) for the bicycle class in RGB scenes improved from 21.08 to 57.38. Additionally, AP increased across all classes from 37.93 to 46.89. To reduce system latency, a novel object detection approach is proposed where the event camera acts as a region proposal network, and a classification algorithm is run on the proposed regions. This achieved a 78% improvement over baseline. Full article

In vitro meat presents a promising alternative to conventional meat production by addressing environmental and animal welfare concerns. However, broader market adoption depends on increasing consumer acceptance. Labels on product packaging have been shown to be effective in influencing consumer behavior in previous studies. This paper examines the impact of different front-of-package labels on German consumers’ choices regarding in vitro chicken meat, with the goal of identifying effective labeling strategies. To investigate this, an online choice experiment was conducted with 200 participants from Germany. In addition to the label, products varied in terms of price, origin, and calorie content. The data were analyzed using latent class analysis, which identified four distinct consumer segments characterized by their preferences, attitudes, and personal characteristics. The results were used to simulate market scenarios, evaluating the effectiveness of different labeling strategies for in vitro chicken meat. These insights provide a foundation for targeted marketing approaches that promote consumer acceptance and inform the introduction of in vitro meat products in Germany. Full article