Search Results (298)

Recent breakthroughs in large language models have made it feasible to effectively summarize cross-lingual dialogue information, proving essential for the global communication context. However, existing methodologies encounter difficulties in maintaining factual consistency across multiple dialogue exchanges and lack clear explanations of the summarization process. This paper presents a novel factually consistent prompting technology with large language models to address these challenges in cross-lingual dialogue summarization. First, we propose a factual replacement mechanism to enhance information analysis by incorporating noise information into summarization candidates. We adopt a self-guidance framework to enforce factual consistency, enhancing information flow tracking in cross-lingual hybrid dialogue scenarios with the assistance of GPT-based models. Furthermore, we introduce a view-aware chain-of-thought-driven architecture to improve the interpretability and transparency of the cross-lingual dialogue summarization process. Comprehensive experimental evaluations on cross-lingual summarization tasks, spanning English, French, Spanish, Russian, Chinese, and Arabic, and hybrid cross-lingual tasks substantiate that the proposed model achieves superior performance relative to state-of-the-art baselines. Full article

Digital transformation has become important for SMEs that want better control, transparency, and coordinated operations. Yet, many studies treat digital tools in isolation and do not explain how AI and big data capabilities, together with process integration, drive governance outcomes. This gap limits a clear understanding of how digital transformation supports governance performance in SMEs. This study examines how digital transformation (DT) influences digital governance performance (DGP) in SMEs, with AI and big data analytical capability (AIBDAC) and process integration capability (PIC) as mediators. The research is grounded in the Resource-Based View, Dynamic Capabilities Theory, and the Technology Organization Environment framework. Data were collected from SMEs across five regions of Saudi Arabia using cluster and purposive sampling to target employees and managers involved in digital, analytical, and process integration work. A total of 396 valid responses were included in the analysis. Partial Least Squares Structural Equation Modelling (PLS SEM) was used to assess the measurement model, test the hypothesized paths, and evaluate mediation and moderation effects. The findings show that DT, AIBDAC, PIC, and top management support (TMS) have significant direct effects on DGP. AIBDAC and PIC act as key mediators, fully transmitting the effects of digital innovation capability and strategic readiness and partially mediating the effects of DT and TMS. Multi-group analysis shows that small and medium-large firms rely on different capability combinations. The study contributes by explaining how SMEs strengthen governance through capability development and offers practical guidance for improving governance through digital transformation. Full article

In complex driving environments, drivers must continuously detect and respond to critical visual information such as traffic signs and pedestrians. However, important targets may sometimes be overlooked due to high cognitive load during driving. Therefore, visual attention prompt methods have been proposed to guide drivers’ gaze toward relevant targets. A visual attention prompt method is a visual cue presented in a key area in a user’s field of view to draw his/her visual attention. This study evaluates the short-term performance of five visual attention prompt methods (Point, Arrow, Blur, Dusk, and ModAF) in a driving simulator and compares their performance between novice and proficient drivers. Eye-tracking data and multiple analyses are used to examine whether the influence of these methods could be maintained after they are disabled and to clarify drivers’ response patterns across methods in consideration with their driving proficiency. The results indicate that visual attention prompt methods could induce a short-term transfer effect, as drivers still tend to fixate on target traffic signs earlier after the methods are disabled, and the elapsed-time analysis estimates that this effect lasts about 84.35 s. Overall, the Point, Arrow, and Dusk methods show relatively stronger performance with significant reductions in the elapsed time to fixate on the traffic sign. The clustering analysis further shows that drivers’ response patterns are not uniform, with two clusters for novice drivers and three clusters for proficient drivers. The results suggest that most novice drivers tend to benefit from explicit non-directional visual cues that enhance target salience, such as the Point method, whereas proficient drivers are more likely to benefit from explicit directional visual cues that provide clear directional guidance, such as the Arrow method. These findings suggest that visual attention prompt methods may be useful for developing driver training strategies tailored to different levels of driving proficiency, helping drivers maintain more effective visual attention allocation during driving and potentially contributing to improved driving safety. Full article

Background: Artificial intelligence (AI) is increasingly integrated into healthcare systems worldwide, including mental health services. While AI holds promise for improving efficiency and addressing workforce shortages, its role in psychiatry remains complex due to the central importance of empathy, clinical judgment, and ethical responsibility. Understanding clinicians’ perceptions is essential for guiding responsible AI implementation, particularly in culturally specific settings such as Saudi Arabia. Material and Methods: A cross-sectional survey was conducted among psychiatrists and family medicine physicians in Saudi Arabia between October and December 2025. The survey questionnaire was adapted from previously published instruments to assess perceptions of AI’s impact on mental health professions, the likelihood that AI could fully replace clinicians in ten core psychiatric tasks, expected timelines for replacement, and views on the balance between AI’s benefits and risks. Descriptive statistics, subgroup comparisons, and multivariable linear regression were used to analyze factors associated with higher perceived AI replacement likelihood. Results: A total of 100 physicians participated (mean age, 43.3 ± 8.9 years; 47% female). Most respondents anticipated that AI would lead to slight (45.0%) or substantial (43.0%) changes in professional roles. Perceptions varied by task: administrative tasks were most replaceable (clinical documentation, 4.03 ± 0.95; 79% likely), diagnostic/assessment tasks showed mixed perceptions (40–58%), high-risk diagnostics (suicidal/homicidal thoughts) were largely resistant (2.73–2.82; 8–30%), and relational tasks including empathetic care were least replaceable (24% likely). Physicians currently using AI tools reported significantly higher AI replacement likelihood scores, a finding that remained significant after adjustment. Overall, 64.0% of participants believed that the benefits of AI in mental health care outweighed its potential risks. Conclusions: Mental health professionals in Saudi Arabia largely view AI as a supportive tool rather than a replacement for clinicians. Clear boundaries remain around tasks requiring empathy and ethical judgment. These findings underscore the need for culturally sensitive, clinician-led, and ethically grounded AI integration strategies that strengthen, rather than undermine, the human foundations of mental health care. Full article

To support farmers in their transition towards sustainable agriculture, sub-Saharan Africa needs a more effective extension. Thus, effective improvements based on a clear view of current and desired extension capacity are necessary. As in the past, mostly one-sided studies have been conducted. This paper proposes a more integral approach based on both characteristics and viewpoints of both farmers and extension workers. Capacity to provide effective extension and advisory services (EAS), or extension capacity, is defined and analyzed with mixed-research methods using data from 471 Ugandan dairy farmers, from three distinct production systems and 67 extension workers. Extension capacity is determined by farmers’ satisfaction, the frequency of delivering EAS to farmers, and the perceptions of both farmers and extension workers on the use of appropriate methods to deliver EAS. Results revealed moderate satisfaction across production systems, with a pronounced negative effect of long working experiences on the frequency of delivery. Positively influencing factors for delivery frequency are intrinsic motivation and the number of in-service trainings received by extension workers. On-farm demonstrations, individual farm visits, the use of contact farmers, and farmer training are perceived as the four most effective delivery methods among dairy farmers in Uganda. Given the moderate farmer satisfaction, low frequency of delivery, and slight mismatch between the perceived effective delivery methods and those being used, the study concludes that the current extension capacity remains low. However, low-hanging fruits for improvement include increasing in-service training opportunities, employing extension workers on contractual basis and motivating extension workers. Full article

Background/Objectives: A persistent mismatch between organ supply and transplant demand affects healthcare systems worldwide, particularly in underdeveloped and transitional systems. Intensive care units (ICUs) represent the primary setting for donor identification following brain death, placing intensive care physicians at the center of organ donation pathways. This nationwide cross-sectional survey aimed to evaluate Romanian intensivists’ knowledge, attitudes, and reported clinical practices regarding brain death determination, communication with families, and system-level barriers to organ donation, to identify modifiable factors relevant to transplant policy development. Methods: A prospective, nationwide, questionnaire-based survey was conducted among intensive care physicians in Romania. The structured questionnaire explored their knowledge and attitudes regarding brain death, communication with families, involvement in donation processes, ethical perceptions, and views on the organization of the transplant system. The survey was distributed through the Romanian Society of Anesthesia and Intensive Care, and descriptive exploratory analyses were performed. Results: A total of 117 ICU physicians participated (mean age 41.0 ± 9.9 years). Although 84.6% agreed with the current brain death diagnostic criteria, and 83% considered the protocol sufficiently clear. The mean number of brain-dead patients managed annually was 8.25 ± 12.90. 69.3% of respondents perceived communication competencies as insufficient. 77.8% considered family consent decisive in donation decisions, while 87% supported the establishment of a national donor registry and 77% favored a donor card system. Organ procurement was reported as a priority in only 38.5% of ICUs. Institutional prioritization of organ procurement and structured training was inconsistent. Conclusions: This nationwide survey identifies key educational, organizational, and systemic barriers limiting organ donation performance in Romania. Targeted training, improved communication strategies, integration of donation pathways into routine intensive care practice, and the adoption of national consent instruments represent essential clinical and policy priorities for low-performing transplant systems. Full article

In high-density urban cores, pedestrian safety perception is shaped not only by street physical environments but also by pronounced spatial heterogeneity. However, existing studies often rely on global regression or small-sample surveys, making it difficult to simultaneously reveal city-scale regularities and localized mechanisms. Taking Futian District, Shenzhen, as a case study, this study develops an integrated analytical framework that combines street-view imagery, machine learning, and multiscale geographically weighted regression (MGWR) to measure pedestrian safety perception at the city scale and to unpack its spatial mechanisms. The results show that model explanatory power improves markedly after accounting for spatial non-stationarity, indicating strong context dependence in the formation of pedestrian safety perception. MGWR further reveals clear multiscale differentiation across streetscape visual elements: greenery-related elements (e.g., tree and plant) exhibit near-global and consistently positive effects, whereas traffic exposure and interface-related elements (e.g., car, road, and wall) operate more locally, with both the direction and magnitude of their effects varying substantially with neighborhood structure and traffic contexts. These findings suggest that the impacts of individual street elements on pedestrian safety perception are not universally transferable and should be interpreted within a spatial-scale and contextual framework. By integrating machine learning-based prediction with MGWR-based spatial interpretation, this study enables both efficient city-scale measurement and multiscale mechanism identification of pedestrian safety perception, providing empirical support for safety perception-oriented street planning and fine-grained urban design. Full article

This study proposes a clinically driven hybrid segmentation framework for dense breast tissue analysis in mammographic images, addressing persistent challenges associated with intensity inhomogeneity, low-contrast, and complex tumor morphology. The framework integrates Multiplicative Intrinsic Component Optimization (MICO_2D) for bias field correction, followed by a distance-regularized multiphase Vese–Chan level-set model for coarse global tumor segmentation. To achieve precise boundary delineation, a localized refinement stage is employed using Localized Active Contours (LAC) with Local Image Fitting (LIF) energy, supported by Gaussian regularization to ensure smooth and coherent boundaries in regions with ambiguous tissue transitions. Building upon the refined semantic tumor mask, the framework further incorporates a panoptic-style tumor instance segmentation stage, enabling the decomposition of connected tumor regions into distinct anatomical instances, which were evaluated on both MIAS and INBreast mammography datasets to demonstrate generalizability. This extension facilitates detailed structural analysis of tumor multiplicity and spatial organization, enhancing interpretability beyond conventional pixel wise segmentation. Experiments conducted on Cranio-Caudal (CC) and Medio-Lateral Oblique (MLO) mammographic views demonstrate competitive performance relative to baseline U-Net and advanced deep learning fusion architectures, including multi-scale and multi-view networks, while offering improved interpretability and robustness. Quantitative evaluation using overlap-related metrics shows strong spatial agreement between predicted and reference segmentations, with per-image Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) distributions reported to ensure reproducibility. Descriptive per-image analysis, supported by bootstrap-based confidence intervals and paired comparisons, indicates consistent performance improvements across images. Robustness analysis under realistic perturbations, including noise, contrast degradation, blur, and rotation, demonstrates stable performance across varying imaging conditions. Furthermore, feature space visualizations using t-SNE and UMAP reveal clear separability between cancerous and non-cancerous tissue regions, highlighting the discriminative capability of the proposed framework. Overall, the results demonstrate the effectiveness, robustness, and clinical motivation of this hybrid panoptic framework for comprehensive dense breast tumor analysis in mammography, while emphasizing reproducibility and conservative statistical assessment. Full article

Clouds are essential for regulating the hydrological cycle and Earth’s radiation budget, and their fluctuations over the Tibetan Plateau (TP) have a significant effect on both regional climate dynamics and global atmospheric circulation. Using ground-based Vaisala CL51 ceilometer data and Fengyun-4A (FY-4A) satellite observations from October 2020 to June 2022, this study examines cloud occurrence frequency (COF), cloud vertical structure (including cloud base height (CBH), cloud top height (CTH), and cloud layer stratification), and related macroscopic properties over the Large High Altitude Air Shower Observatory (LHAASO). CL51 and FY-4A had cloud occurrence rates of 43.7% and 37.7%, respectively, over the observation period, with a strong correlation coefficient of 0.82. Given the impact of clouds on Cherenkov light observations by the LHAASO Wide Field of view Cherenkov Telescope Array (WFCTA), we specifically evaluated the cloud occurrence during the operational periods of the LHAASO-WFCTA, finding rates of 34.2% (CL51) and 28.0% (FY-4A), with the lowest rates occurring in the early morning. Due to monsoonal moisture inflow and dry northeasterly winds, seasonal COF changes showed clear peaks in summer (78.8%) and minima in winter (24.8%). Seasonal differences existed in the diurnal COF patterns, with nocturnal prominence in summer/autumn and daytime dominance in spring/winter. The CBH showed daily oscillations, peaking at 18:00 (local solar time) and troughing at 08:00 (local solar time), with seasonal CBH minima in summer/autumn and maxima in spring/winter. Low- and mid-level clouds predominated, with clear diurnal cycles: low- and mid-level clouds rose from morning until midday, while high-level clouds appeared after dusk. Vertical cloud structures were predominantly single-layered (81%), with multi-layered complexity peaking in the summer due to convective activity. The CTH distributions showed unimodal patterns in the fall and winter (1.5–3 km), while in the summer, they showed multimodal extents (up to 12 km). These results improve LHAASO-WFCTA observational scheduling, enhance climate model parameterizations, and deepen our understanding of the dynamics of the TP cloud. Full article

From the perspective of the circular economy and minimization of environmental pollution, recycling plastics is key for transforming polymeric waste streams (PWSs) towards reusable and, if possible, upgraded, value-added products. The low homogeneity of PWSs, even when sorted, complicates sampling, analytical characterization, processability, and quality assurance of the whole circular process. Therefore, sampling, sample preparation, and analysis methodologies that yield results accurate and representative enough to describe the contents and the safety of the bulk while being cost-effective are crucial. In this context, an experimental “model waste” approach was conceptualized to reliably assess and optimize sampling and sample preparation strategies towards specific goals, i.e., identifying and precisely quantifying different polymer types and non-polymeric contaminants (such as brominated flame retardants, BFR) along with establishing a correlation of the sample preparation steps with low deviation values between replicates. The results indicated that cryogenic grinding better preserved additive content, minimizing its degradation, i.e., 461 ± 17 ppm determined via HPLC-MS when the nominal concentration was 500 ppm. On the other hand, melt-based homogenization significantly improved homogeneity and hence reproducibility/variability of analytical results (RSD), albeit at the risk of partial additive thermal degradation (up to 70% reduction in BFR content). The current experimental approach allows a clear understanding of plastic waste characteristics in view of demonstrating analytical limits of detection (LoD), reliable verification of compliance with certain concentrations of unwanted contaminants, and eventually robust evaluation of the applied recycling scheme efficiency. Full article

Background: The sustentaculum screw plays a crucial role in achieving stable osteosynthesis for intra-articular calcaneal fractures, particularly when using minimally invasive or percutaneous techniques. Accurate placement of the screw within the sustentaculum tali is technically demanding due to the complex anatomy and the limited intraoperative visualization provided by standard fluoroscopic views. Methods: Patients were positioned in a standardized lateral decubitus position. Beginning with a standard lateral fluoroscopic view, the C-arm was tilted approximately 25° to align the central beam with the plane of the lower ankle joint. This adjustment enables clear visualization of the borders of the sustentaculum tali and allows precise definition of the target point for guide-wire insertion. To evaluate whether this technique improves screw positioning, two groups were compared: one using the described fluoroscopic view and a control group using conventional imaging alone. Results: Screw placement accuracy was significantly higher in the group using the dedicated fluoroscopic view compared with the control group. Conclusions: With meticulous preoperative planning, standardized positioning, and the use of a dedicated fluoroscopic setting—referred to as the “sustentaculum view”—accurate and safe screw placement can be achieved with significantly higher accuracy than with conventional imaging alone. Full article

Financial time series often show periods during which market index values or asset prices increase or decrease monotonically. These events are known as price runs, uninterrupted trends, or simply runs. By identifying such runs in the daily DJIA and IPC indices from 2 January 1990 to 17 October 2025, we construct their associated returns to obtain a non-arbitrary sample of multi-scale returns, which we call trend returns (TReturns). The timescale of each multi-scale return is determined by the exponentially distributed duration of its corresponding run. We empirically show that the distribution of these coarse-grained returns exhibits distinctive statistical properties: the central region displays an exponential decay, likely resulting from the exponential distribution of trend durations, while the tails follow a power-law decay. This combination of exponential central behavior and asymptotic power-law decay has also been observed in other complex systems, and our findings provide additional evidence of its natural emergence. We also explore the informational properties of multi-scale returns using three measures: Shannon entropy, permutation entropy, and compression-based complexity. We find that Shannon entropy increases with coarse-graining, indicating a wider range of values; permutation entropy drops sharply, revealing underlying temporal patterns; and compression ratios improve, reflecting suppressed randomness. Overall, these findings suggest that constructing TReturns filters out microscopic noise, reveals structured temporal patterns, and provides a complementary and clear view of market behavior. Full article

With the continuous expansion of high-density urban forms, residents’ opportunities for daily contact with natural environments have been increasingly reduced, making the equity of urban green space allocation a critical challenge for sustainable urban development. Existing studies have largely focused on green space quantity or accessibility at single time points, lacking systematic investigations into the spatiotemporal evolution of green space exposure (GSE) and its equity from the perspective of residents’ actual environmental experiences. GSE refers to the integrated level of residents’ contact with urban green spaces during daily activities across multiple dimensions, including visual exposure, physical accessibility, and spatial distribution, emphasizing the relationship between green space provision and lived environmental experience. Based on this framework, this study takes the central urban area of Hangzhou as the study area and integrates multi-temporal remote sensing imagery with large-scale street view data. A deep learning–based approach is developed to identify green space exposure, combined with spatial statistical methods and equity measurement models to systematically analyze the spatiotemporal patterns and evolution of GSE and its equity from 2013 to 2023. The results show that (1) GSE in Hangzhou increased significantly over the study period, with accessibility exhibiting the most pronounced improvement. However, these improvements were mainly concentrated in peripheral areas, while changes in the urban core remained relatively limited, revealing clear spatial heterogeneity. (2) Although overall GSE equity showed a gradual improvement, pronounced mismatches between low exposure and high demand persisted in densely populated areas, particularly in older urban districts and parts of newly developed residential areas. (3) The spatial patterns and evolutionary trajectories of equity varied significantly across different GSE dimensions. Composite inequity characterized by “low visibility–low accessibility” formed stable clusters within the urban core. This study further explores the mechanisms underlying green space exposure inequity from the perspectives of urban renewal patterns, land-use intensity, and population concentration. By constructing a multi-dimensional and temporally explicit analytical framework for assessing GSE equity, this research provides empirical evidence and decision-making references for refined green space management and inclusive, sustainable urban planning in high-density cities. Full article

Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were improved, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations. Full article

Background: Radspherin^® is a novel α-emitting radiopharmaceutical administered intraperitoneally following complete cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) for peritoneal metastases. It delivers short-range radiation aimed at eliminating residual microscopic disease. This qualitative study explored how participants with colorectal cancer experienced participating in an early-phase clinical trial involving CRS-HIPEC followed by Radspherin^®. Materials and Methods: Semi-structured interviews were conducted with ten participants enrolled in a phase 1/2a trial involving CRS-HIPEC and intraperitoneal Radspherin^®. The analysis was guided by a phenomenological and interpretive approach using reflexive thematic analysis. Results: Participants expressed a strong sense of motivation and hope tied specifically to receiving Radspherin^®, which they perceived as an opportunity to improve their prognosis. Many also viewed participation as a contribution to future cancer research. None attributed complications or side effects to Radspherin^®. Clear and supportive verbal communication from healthcare professionals was highly valued, while the written information was described as overwhelming. Despite fears of recurrence, most participants remained optimistic about regaining a meaningful life. While experiences with Radspherin^® were largely positive, participants also described pain, fatigue, and prolonged recovery related to CRS-HIPEC, including ongoing functional and psychosocial challenges. Conclusions: Participants associated Radspherin^® with hope and a therapeutic benefit but did not link it to their adverse events. Their willingness to participate in experimental treatment was shaped by trust in clinicians, clear communication, and a desire for extended survival. However, the burden of CRS-HIPEC-related side effects underscores the importance of tailored follow-up and support. Full article