Search Results (13,068)

This paper presents the SMALLDERS project, a European initiative aimed at transforming smallholders’ business models through an innovative technological platform. The platform functions as an e-marketplace that connects small farmers directly with consumers while simultaneously promoting environmental sustainability and collaboration across the agri-food value chain. The study evaluates the platform’s commercial viability and acceptance through a mixed-methods approach, incorporating qualitative and quantitative data. Research methods include focus group sessions, interviews with key stakeholders—such as transport companies, large distributors, and public administrations—and a consumer survey assessing intentions and attitudes toward the e-marketplace. Results indicate limited overall consumer readiness to adopt the platform; however, 48.6% of respondents expressed willingness to use it provided competitive prices and personal benefits are assured. Smallholders regard e-commerce as a promising opportunity, yet they face significant barriers, including limited resources, low digital literacy, and logistical constraints. Stakeholders generally view the platform positively, emphasizing that its success depends on achieving a critical mass of business volume. To foster adoption, SMALLDERS proposes three business models for smallholders: sustainable, cooperative, and technological. The platform includes a user-friendly feature to assist smallholders in transitioning among these models, complemented by training and support services designed to encourage more resilient and innovative agricultural practices. Full article

Artificial intelligence (AI) applications are everywhere, including within churches and other faith communities. Churches are complex systems, and the incorporation of new people and things over the lifetime of these communities changes them. New technology, in general, and AI tools in specific are no exception. In Christian ministry, innovation is no stranger: witness how quickly churches adopted online worship during the COVID-19 pandemic. Given that AI is already present in the complex system of churches, care must be taken to assess the opportunities and threats associated with its intentional adoption before doing so. This report advocates for ministry to make use of an approach borrowed from scientific exploration for the evaluation of opportunities and threats and for managing the process of introducing and managing new elements into a complex system: systems engineering (SE) principles and processes. SE methodology enables scientific exploration in environments that are difficult to explore due to danger, cost or technological immaturity by rigorously managing the development, introduction and life cycle of technology that will be used in the complex system of exploration. Such an approach would be promising for increasing the safety of a technology that comes with some inherent danger, easing its integration into church operation, mission, formation and worship and in turn allowing ministry leaders agency in determining the future of their relationship with artificial intelligence. Full article

International organizations have highlighted the importance of consistent and reliable environment, social and governance (ESG) disclosure and metrics to inform business strategy and investment decisions. Greater corporate disclosure is a positive signal to investors who prioritize sustainable investment. In this study, economic and climate sentiment are extracted from the integrated and sustainability reports of the top 40 corporates listed on the Johannesburg Stock Exchange, employing domain-specific natural language processing. The intention is to clarify the complex interactions between climate risk, corporate disclosures, financial performance and investor sentiment. The study provides valuable insights to regulators, accounting professionals and investors on the current state of disclosures and future actions required in South Africa. A time series analysis of the sentiment scores indicates a noticeable change in the corporates’ disclosures from climate-related risks in the earlier years to climate-related opportunities in recent years, specifically in the banking and mining sectors. The trends are less pronounced in sectors with good ESG ratings. An exploratory regression study reveals that climate and economic sentiments contain information that explain stock price movements over the longer term. The results have important implications for asset allocation and offer an interesting direction for future research. Monitoring the sentiment may provide early-warning signals of systemic risk, which is important to regulators given the impact on financial stability. Full article

Background/Objectives: Anosognosia for hemiplegia (AHP) is a multifaceted syndrome in which stroke survivors fail to recognize motor impairments. Although AHP has significant clinical implications, rehabilitation strategies have remained fragmented and underexplored. This systematic review aimed to critically evaluate rehabilitation interventions for AHP published between 2006 and 2025, categorize intervention types, and assess clinical outcomes to inform future research and practice. Methods: A structured search was conducted in the PubMed and PsycINFO databases on 31 March 2025, using predefined keywords related to stroke, anosognosia, and rehabilitation. The eligible studies included randomized controlled trials, case–control studies, and case studies. Following title, abstract, and full-text screening, nine studies focusing on rehabilitation interventions for AHP were selected and analyzed. Results: The interventions reviewed included sensorimotor recalibration techniques, neuromodulatory approaches, error-based cognitive training, and self-observation in video replay strategies. Interventions emphasizing motor intention monitoring, error correction, and self-observation were more consistently associated with durable improvements in motor awareness than neglect-based spatial interventions were. However, many studies were limited by small sample sizes and a lack of standardized outcome measures. Assessment methodologies vary widely, highlighting the need for multidimensional theory-driven evaluation tools. Conclusions: Effective rehabilitation for AHP requires strategies targeting disrupted self-monitoring and agency mechanisms, rather than spatial realignment alone. The video self-observation and error-based learning paradigms show particular promise. Future research should focus on controlled trials, longitudinal tracking, and the integration of individualized, mechanism-specific rehabilitation models to optimize outcomes for stroke survivors with AHP. Full article

Objectives: Acute drug poisoning represents a significant public health issue among the pediatric population. The aim of this study was to evaluate the characteristics of drug poisoning in children and adolescents in the Vojvodina region from 2018 to 2023. Methods: In a retrospective observational study, 82 patients with confirmed drug poisoning were included, and data was collected regarding demographic characteristics, clinical manifestations, types of drugs involved, and the therapeutic interventions administered. The severity of poisonings was evaluated using the Poisoning Severity Score, and toxicological analysis was performed using gas chromatography–mass spectrometry. Results: The results indicated that poisonings were most prevalent in adolescent girls (72%), with 78% of cases resulting from intentional poisoning, while unintentional poisoning was more common in children. Benzodiazepines, antipsychotics, and analgesics were the primary drugs causing these poisoning incidents. The majority of patients (78%) experienced mild clinical symptoms, whereas 9% of pediatric patients suffered from severe poisoning, related to complications such as aspiration pneumonia and acute renal failure. Addressing pediatric drug poisoning in Vojvodina requires an increased focus on preventive strategies, including parental education and appropriate psychosocial support for the youth. Conclusions: Through collaborative efforts among healthcare providers, educators, and policymakers, prevention, treatment, and support mechanisms can be enhanced to combat this pressing public health challenge. Full article

(1) Background: This study aimed to identify baseline demographic, clinical, and psychosocial predictors of treatment response in adolescents with anorexia nervosa (AN) undergoing an intensive 20-week enhanced cognitive behavioral therapy (CBT-E) program, which included inpatient and day patient phases. Treatment outcomes were assessed at the end of intensive treatment (EOIT) and at a 20-week follow-up. (2) Methods: A prospective cohort of 68 adolescents under the age of 16 consecutively admitted to intensive CBT-E was evaluated. Baseline measures included body mass index (BMI)-for-age percentiles, percentage of expected body weight (%EBW), eating disorder psychopathology (EDE-Q), general psychopathology, and functional impairment. (3) Results: Of those who began treatment, 83.4% completed the program and 70.2% were available for follow-up assessment. Based on intent-to-treat analysis, 94.1% achieved a “good BMI outcome” and 73.5% met criteria for “full response” at EOIT. At follow-up, 64.7% maintained a good BMI and 55.9% sustained a full response. Completers’ analysis indicated that baseline body weight, clinical impairment, general psychopathology, and weight regain influenced treatment outcomes. However, no baseline demographic or clinical variables predicted treatment completion or outcome at either time point at intention-to-treat analysis, except that younger age at admission was linked to higher eating disorder psychopathology at follow-up. (4) Conclusions: In treatment completers, certain baseline clinical factors and weight regain influenced outcomes, while in the full sample, younger age predicted greater residual psychopathology at follow-up. These findings, if confirmed, emphasize the need for early intervention, focused support for weight regain, and potential adaptations of CBT-E for early adolescents. Full article

Customers’ repurchase intention is a key driver of sustained profitability for companies. However, the influencing factors of customers’ repurchase intention and their direct mechanisms of action are not yet clear. This study empirically examines the relationships among intrinsic quality, perceptual quality, brand equity, customer satisfaction, online word-of-mouth, and customers’ repurchase intention using a structural equation model. A customer repurchase intention model is constructed with customer satisfaction as the mediating variable and online word-of-mouth as the moderating variable. The empirical results show that intrinsic quality, perceptual quality, and brand equity have a significant impact on customer satisfaction. Notably, intrinsic quality exerts an indirect effect on customers’ repurchase intention primarily through customer satisfaction, with no significant direct impact, while perceptual quality and brand equity have significant direct effects on repurchase intention. Customer satisfaction plays a partial mediating role between intrinsic quality, perceptual quality, brand equity, and repurchase intention. Online word-of-mouth has a significant moderating effect on the relationship between customer satisfaction and customers’ repurchase intention. These findings provide actionable insights for smartphone enterprises to optimize product development, brand management, and online reputation strategies, thereby enhancing customer loyalty and market competitiveness. Full article

Direct purchasing offers consumers the advantage of fresher, higher-quality food, often at lower prices, with greater transparency in terms of origin and production methods, which creates trust and enables more environmentally conscious choices. On the other hand, direct selling empowers farmers by cutting out middlemen and improving their income. As a climate-friendly option, direct selling reduces transport emissions, supports environmentally friendly agricultural practises and strengthens the resilience of the food system. This paper examines the factors that influence consumer purchase intentions for direct sales in the context of climate change. The study was conducted with a sample of 313 direct sales consumers at on- and off-farm locations in Croatia (Istria and Primorsko-goranska County). The sample consists mainly of women with higher education and over 45 years of age. The exploratory factor analysis revealed three factors: (1) attitude towards climate change, (2) influence of direct sales on climate change, and (3) intention to buy in direct sales. A hierarchical regression analysis revealed a positive and significant influence of the factors' attitude towards climate change and the influence of direct selling on climate change on the purchase intention in direct selling. The results suggest that direct selling consumers are aware of climate change and are inclined to choose sustainable behaviours, such as direct selling, to mitigate climate change. Full article

This study extended the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating affective constructs (innovativeness, optimism, and hedonic motivation) to examine user adoption of autonomous bus (AB) in China, where government-supported deployment creates unique adoption dynamics. Analyzing 313 responses, collected via stratified sampling using SmartPLS 4.0, we identified innovativeness as the dominant driver (total effect, β = 0.347), directly influencing behavioral intention (β = 0.164*) and indirectly shaping optimism (β = 0.692*), effort expectancy (β = 0.347*), and hedonic motivation (β = 0.681*). Our findings highlight contextual influences in public service systems. Performance expectancy (β = 0.153*) exerts a stronger effect than hedonic or social factors (H6/H3 rejected), while optimism demonstrates a dual scaffolding effect (OPT→EE, β = 0.189*; OPT→PE, β = 0.401*), reflecting a “calculative optimism” pattern where users balance technological interest with pragmatic utility evaluation in policy-supported deployment contexts. From a practical perspective, these findings suggest targeting high-innovativeness users through incentive programs, emphasizing system reliability over ease of use, and implementing adapted designs. This study contributes to the literature both theoretically, by validating the hierarchical role of innovativeness in UTAUT, and practically, by offering actionable strategies for China’s ongoing AB deployment initiative, including ISO-standardized UX and policy tools such as municipal Innovator Badges. Full article

With the global surge in patent filings, accurately evaluating similarity between patent documents has become increasingly critical. Traditional similarity assessment methods—primarily based on unimodal inputs such as text or bibliographic data—often fall short due to the complexity of legal language and the semantic ambiguity that is inherent in technical writing. To address these limitations, this study introduces a novel multimodal patent similarity evaluation framework that integrates weak AI techniques and conceptual similarity analysis of patent drawings. This approach leverages a domain-specific pre-trained language model optimized for patent texts, statistical correlation analysis between textual and bibliographic information, and a rule-based classification strategy. These components, rooted in weak AI methodology, significantly enhance classification precision. Furthermore, the study introduces the concept of conceptual similarity—as distinct from visual similarity—in the analysis of patent drawings, demonstrating its superior ability to capture the underlying technological intent. An empirical evaluation was conducted on 9613 patents in the manipulator technology domain, yielding 668,010 document pairs. Stepwise experiments demonstrated a 13.84% improvement in classification precision. Citation-based similarity assessment further confirmed the superiority of the proposed multimodal approach over existing methods. The findings underscore the potential of the proposed framework to improve prior art searches, patent examination accuracy, and R&D planning. Full article

Background: Enterotoxigenic Bacteroides fragilis (ETBF) carries the bft toxin gene, which influences the host immune response and inflammatory pathways and promotes colorectal cancer (CRC). This study investigated the potential role of ETBF in CRC liver metastasis. Methods: We reviewed the records of 226 consecutive patients who underwent curative-intent (R0) resection of CRC liver metastases. ETBF DNA in fresh-frozen metastasis specimens was quantified using droplet digital PCR (ddPCR). Patients were grouped into very-low (≤80%; N = 178), low (80–90%; N = 24), and high (>90%; N = 24) ETBF-DNA groups. Three tissue cores per specimen were stained for CD8, CD4, CD20, FOXP3, CD68, and CD163, and immune-cell densities were measured digitally (cells/mm²). Results: ETBF DNA was detected in 219 of 226 lesions (96.9%). The densities of cytotoxic CD8⁺ T-cells, effector CD4⁺ T-cells, CD20⁺ B-cells, and CD163⁺ macrophages did not differ significantly by ETBF-DNA group (P_trend all > 0.12). FOXP3⁺ regulatory T-cells (Tregs) decreased (P_trend = 0.010), and CD68⁺ macrophages increased (P_trend = 0.020) as ETBF-DNA levels increased. ETBF-DNA levels in CRC liver metastases were not associated with disease-free survival or overall survival or serum C-reactive protein levels. Conclusions: ETBF was present in almost all CRC liver metastases. Higher ETBF levels were associated with a tumor-immune microenvironment enriched in CD68⁺ macrophages and deficient in FOXP3⁺ Tregs, suggesting that ETBF facilitates immune evasion without loss of effector lymphocytes. Although ETBF-DNA levels did not predict survival in this single-center cohort, the potential role of ETBF in immune remodeling and as a candidate biomarker and therapeutic target in metastatic CRC warrants further study. Full article

Intention prediction is essential for enabling intuitive and adaptive control in upper-limb exoskeletons, especially in dynamic industrial environments. However, the suitability of different cues, sensors, and computational models for real-world industrial applications remains unclear. This systematic review, conducted according to PRISMA guidelines, analyzes 29 studies published between 2007 and 2024 that investigate intention prediction in active exoskeletons. Most studies rely on motion capture (14) and electromyography (14) to estimate joint torque or trajectories, predicting from 450 ms before to 660 ms after motion onset. Approaches include model-based and model-free regression, as well as classification methods, but vary significantly in complexity, sensor setups, and evaluation procedures. Only a subset evaluates usability or support effectiveness, often under laboratory conditions with small, non-representative participant groups. Based on these insights, we outline recommendations for robust and adaptable intention prediction tailored to industrial task requirements. We propose four generalized support modes to guide sensor selection and control strategies in practical applications. Future research should leverage wearable sensors, integrate cognitive and contextual cues, and adopt transfer learning, federated learning, or LLM-based feedback mechanisms. Additionally, studies should prioritize real-world validation, diverse participant samples, and comprehensive evaluation metrics to support scalable, acceptable deployment of exoskeletons in industrial settings. Full article

Background and Objectives: Acute poisoning is a frequent cause of pediatric emergency department visits and represents a significant public health issue, with children particularly vulnerable due to developmental and behavioral factors. This study aimed to characterize the clinical and demographic profiles of pediatric patients presenting with acute intoxication over a ten-year period (2014–2023) and to assess the impact of the SARS-CoV-2 pandemic on patterns of pediatric poisoning. Materials and Methods: A retrospective cohort study was conducted at the Fondazione Policlinico A. Gemelli in Rome, including all patients under 18 years presenting with acute intoxication between September 2014 and June 2023. Data were extracted from electronic medical records and categorized by age group (0–5, 6–11, and 12–18 years) and by three pandemic-related periods: Pre-COVID-19 (to March 2020), COVID-19 (March 2020–June 2021), and Post-COVID-19 (June 2021 onwards). Statistical analyses included chi-squared tests and Cramér’s V for effect size. Results: Of 794 PED admissions for acute intoxication, 64.5% involved children aged 0–5 years, and 24.9% involved adolescents. Most events occurred at home (63.2%) and were accidental (76.4%), with voluntary intoxications exclusively among adolescents. Drug ingestion was the leading cause (39.3%), followed by solvents (17.8%) and alcohol (7.8%), with alcohol-related cases rising markedly post-pandemic. Statistically significant associations were found between the pandemic period and both age group and intentionality of poisoning (p < 0.00001), but not gender. Voluntary and recreational intoxications increased during and after the pandemic, particularly among adolescents. Conclusions: Pediatric acute poisoning displays a bimodal age distribution, with accidental exposures predominant in young children, and intentional cases predominant in adolescents. The COVID-19 pandemic was associated with an increase in adolescent voluntary and recreational intoxications. These findings underscore the need for targeted prevention strategies addressing both household safety for young children and mental health and substance use interventions for adolescents. Full article

Background: Emotion regulation (ER) plays a vital role in mental health, spanning mood, anxiety, and personality disorders. Cognitive reappraisal (CR) is one of the most common ER strategies and depends on prefrontal brain areas, but its success varies, and its neural basis is not fully clear. Interest is growing in using transcranial direct current stimulation (tDCS) to support ER, yet most studies have focused only on the dorsolateral prefrontal cortex (dlPFC) and used simple tasks. Objective: This study explored whether tDCS applied to either the dlPFC or the ventromedial prefrontal cortex (vmPFC) could shape autonomic responses during CR while people watched emotionally engaging film clips. Methods: Participants were randomly assigned to receive either active or sham tDCS over the dlPFC or vmPFC. While stimulated, they used CR strategies (positive reappraisal, fictional reappraisal, or distancing) to manage their reactions to negative film scenes. Heart rate (HR), skin conductance (SC), and respiratory rate (RR) were tracked throughout as physiological indicators. Results: Active dlPFC tDCS combined with CR led to significantly greater reductions in HR toward the end of emotional exposure, compared to sham or non-CR conditions. dlPFC stimulation also lowered HR even without explicit CR, pointing to possible effects on automatic regulation. vmPFC effects were inconsistent, and no reliable effects were observed for SC or RR. Conclusions: These results suggest that tDCS effects on autonomic ER depend on the brain region and timing. dlPFC stimulation may strengthen both intentional and automatic emotion regulation, especially when combined with reappraisal, highlighting the value of realistic emotional tasks in neuromodulation studies. Full article

Exoskeleton robots function as augmentation systems that establish mechanical couplings with the human body, substantially enhancing the wearer’s biomechanical capabilities through assistive torques. We introduce a lumbar spine-assisted exoskeleton design based on Variable-Stiffness Pneumatic Artificial Muscles (VSPAM) and develop a dynamic adaptation mechanism bridging the pneumatic drive module with human kinematic intent to facilitate human–robot cooperative control. For kinematic intent resolution, we propose a multimodal fusion architecture integrating the VGG16 convolutional network with Long Short-Term Memory (LSTM) networks. By incorporating self-attention mechanisms, we construct a fine-grained relational inference module that leverages multi-head attention weight matrices to capture global spatio-temporal feature dependencies, overcoming local feature constraints inherent in traditional algorithms. We further employ cross-attention mechanisms to achieve deep fusion of visual and kinematic features, establishing aligned intermodal correspondence to mitigate unimodal perception limitations. Experimental validation demonstrates 96.1% ± 1.2% motion classification accuracy, offering a novel technical solution for rehabilitation robotics and industrial assistance. Full article