Search Results (19,606)

While existing research highlights a positive association between computational thinking (CT) and self-regulation (SR) skills, limited attention has been given to the embodied and social processes within CT activities that support young children’s executive functions (EFs)—key components of SR. This study investigates how preschoolers develop basic and higher-order EFs, such as focused attention, inhibitory control, causal reasoning, and problem-solving, through their engagement with a tangible programming toy in teacher-guided small groups in a university-affiliated preschool. Informed by a we-syntonicity framework that integrates Papert’s concepts of body/ego syntonicity and Schutz’s “we-relationship”, we conducted a multimodal microanalysis of video-recorded group sessions. Our analysis focuses on two sessions, the “Obstacle Challenge” and “Conditionals”, featuring four excerpts. Findings reveal that children leverage bodily knowledge and empathy toward the toy—named Rapunzel—to sustain attention, manage impulses, reason about cause-effect, and collaborate on problem-solving. Three agents shape these processes: the toy, fostering collective engagement; the teacher, scaffolding learning and emotional regulation; and the children, coordinating actions and sharing affective responses. These findings challenge traditional views of SR as an individual cognitive activity, framing it instead as an embodied, social, and situated practice. This study underscores the importance of collaborative CT activities in fostering SR during early childhood. Full article

Aim: The study developed and validated AppER, an mHealth tool for informal caregivers of children with Duchenne Muscular Dystrophy, and examined differences between app users and non-users. Methods: Four phases were followed: (1) focus groups with experts and caregivers to identify care-related domains; (2) prototype development and validity testing (CVR, I-CVI, I-FVI) using the MARS scale; (3) implementation of the final app; and (4) psychosocial profiling of 88 caregivers (42 users and 46 non-users) measuring quality of life, dependency, somatic symptoms, and coping strategies. Results: AppER showed high content and face validity, surpassing reference thresholds. In the psychosocial analysis, users reported lower perceived quality of life than non-users, despite no significant differences in dependency, somatic symptoms, or coping strategies. Conclusions: Employment patterns differed: more users were dedicated to household tasks, while more non-users were self-employed, suggesting economic factors may affect app uptake and quality of life perceptions. Findings indicate AppER is a valid, well-rated support tool, and that caregivers with lower perceived quality of life may be more inclined to adopt digital health solutions, potentially to address complex care demands. Designing targeted digital interventions may be particularly valuable for those in less favorable socioeconomic contexts. Because of the small sample and between-group imbalances, results are exploratory and warrant confirmation in larger, balanced samples. Full article

Introduction: Radon is a radioactive noble gas formed from uranium decay in the Earth’s crust. The most significant isotope, 222Rn, emits alpha particles capable of damaging lung tissue and inducing cancer. Radon exposure is affected by geophysical and building characteristics and is recognized as a Group 1 carcinogen by the IARC. Despite regulatory thresholds (e.g., EURATOM standards), health risks remain. Various mitigation methods aim to reduce indoor radon exposure and its impact. Materials and Methods: This systematic review followed PRISMA guidelines. PubMed, Scopus, and Web of Science were searched up to 28 February 2025, using a defined string. Studies with original data on radon exposure and lung cancer risk or mitigation efficacy were included. Independent screening and quality assessment (Newcastle–Ottawa Scale) were conducted by multiple reviewers. Results: Of the 457 studies identified, 14 met the inclusion criteria. Eleven of these investigated the link between indoor radon and lung cancer risk, and three evaluated mitigation strategies. Radon levels were commonly measured using passive alpha track detectors. Levels varied depending on geographical location, season, building design and ventilation, these were higher in rural homes and during the colder months. Case–control studies consistently found an increased lung cancer risk with elevated radon exposure, especially among smokers. Effective mitigation methods included sub-slab depressurisation and balanced ventilation systems, which significantly reduced indoor radon concentrations. Adenocarcinoma was the most common lung cancer subtype in non-smokers, whereas squamous and small cell carcinomas were more prevalent in smokers exposed to radon. Discussion and Conclusions: This review confirms the robust association between indoor radon exposure and lung cancer. Risks persist even below regulatory limits and are amplified by smoking. While mitigation techniques are effective, their application remains uneven across regions. Stronger public education, building codes, and targeted interventions are needed, particularly in high-risk areas. To inform future prevention and policy, further research should seek to clarify radon’s molecular role in lung carcinogenesis, especially among non-smokers. Full article

Digital financial services have transformed consumer financial behavior, yet their effects on sustainable development outcomes remain poorly understood. This study examines how mobile financial services (MFS) usage influences financial behaviors across temporal dimensions and investigates the moderating role of financial literacy from a systemic sustainability perspective. Drawing on Construal Level Theory, Dual Process Theory, and Social Cognitive Theory, we analyze data from 21,757 U.S. adults from the 2021 National Financial Capability Study to explore relationships between MFS usage, financial literacy dimensions—objective knowledge (OK), subjective knowledge (SK), and perceived ability (PA)—and both short-term and long-term financial behaviors. The results reveal a dual temporal pattern: MFS usage negatively affects short-term behaviors, including spending control and emergency preparedness, while positively influencing long-term behaviors such as retirement planning and investment participation. Financial literacy dimensions demonstrate differential moderating effects, with OK providing protective benefits against short-term risks, while PA can paradoxically exacerbate these adverse short-term effects. These findings highlight complex implications for sustainable development, demonstrating how individual behaviors aggregate to influence systemic financial resilience and progress toward Sustainable Development Goals related to poverty reduction, economic growth, and inequality reduction. Policymakers should adopt behaviorally informed regulatory approaches that address temporal trade-offs. Educators should design digital-specific literacy programs emphasizing realistic risk assessment alongside confidence-building, thereby promoting sustainable financial behaviors in increasingly digital environments. Full article

This study aims to investigate the impact of digital transformation on corporate tax avoidance. In fact, this revolution has pervasively affected firms in different aspects and represents a significant opportunity to modernize their internal processes, bringing alongside a set of challenges that they must overcome. One hypothesis posits that digitalization enhances information transparency and internal control, reducing tax avoidance, while the other one suggests that the increase in digitalization leads to more complex and opaque transactions, leaving avenues for more aggressive tax strategies. This paper uses data of listed firms in the Casablanca Stock Exchange from 2020 to 2024, excluding the financial sector due to its specific tax regulation, leaving a final sample of 56 companies and 272 firm-year observations. It applies an OLS regression to assess the relation between the two variables, controlling for a set of firm and governance characteristics. The aim of the article is to address the scholarly debate by providing insights into an emerging economy where there is little research on the subject. The findings reveal that digital transformation contributes to the decrease in corporate tax avoidance in conjunction with governance variables like the presence of independent directors on the board and the duality of a CEO position, strongly supporting the first hypothesis. Notably, the OLS regression results show that an increase in digitalization by 1 point is associated with a decrease of 40.4755 in the book-tax differences, significant at the 5% level. The results provide high support for firms to invest in technologies in order to optimize their internal processes and improve their data quality; it also calls for tax authorities to strengthen their digital audit capacities and integrate data-driven tools to detect and interpret signals of potential tax-aggressive strategies. Full article

Molecular communication (MC) has emerged as a promising paradigm for nanoscale information exchange in Internet of Bio-Nano Things (IoBNT) environments, offering intrinsic biocompatibility and potential for real-time in vivo monitoring. This study proposes a cascaded MC channel framework for vascular stenosis detection, which integrates non-Newtonian blood rheology, bell-shaped constriction geometry, and adsorption–desorption dynamics. Path delay and path loss are introduced as quantitative metrics to characterize how structural narrowing and molecular interactions jointly affect signal propagation. On this basis, a peak response time-based delay inversion method is developed to estimate both the location and severity of stenosis. COMSOL 6.2 simulations demonstrate high spatial resolution and resilience to measurement noise across diverse vascular configurations. By linking nanoscale transport dynamics with system-level detection, the approach establishes a tractable pathway for the early identification of vascular anomalies. Beyond theoretical modeling, the framework underscores the translational potential of MC-based diagnostics. It provides a foundation for non-invasive vascular health monitoring in IoT-enabled biomedical systems with direct relevance to continuous screening and preventive cardiovascular care. Future in vitro and in vivo studies will be essential to validate feasibility and support integration with implantable or wearable biosensing devices, enabling real-time, personalized health management. Full article

User preference mining uses rating data, item content or comments to learn additional knowledge to support the prediction task. For the use of rating data, the usual approach is to take rating matrix as data source, and collaborative filtering as the algorithm to predict user preferences. Item content and comments are usually used in sentiment analysis or as auxiliary information for other algorithms. However, factors such as data sparsity, category diversity, and numerical processing requirements for aspect sentiment analysis affect model performance. This paper proposes a hybrid method, which uses the deep neural network as the basic structure, considers the complementarity of text and numeric data, and integrates the numeric and text embedding into the model. In the construction of text-based embedding, extracts the text summary of each text-based review, and uses the Doc2vec to convert the text summary into multi-dimensional vector. Experiments on two Amazon product datasets show that the proposed model consistently outperforms other baseline models, achieving an average reduction of 15.72% in RMSE, 24.13% in MAE, and 28.91% in MSE. These results confirm the effectiveness of our proposed method for learning user preferences. Full article

Traditional food products (TFPs) hold a significant place in the cultural and gastronomic identity of Vojvodina, and consumer interest in these products is continuously growing, positioning them among the most relevant research topics in the fields of hospitality and consumer behavior. The aim of this study was to examine how consumers’ socio-demographic characteristics influence their attitudes and perception of the quality and attributes of TFPs on hospitality and tourism market. The research was conducted on a sample of 507 adult respondents from the territory of the Autonomous Province of Vojvodina. Data were analyzed using descriptive statistics, nonparametric tests (Mann–Whitney U and Kruskal–Wallis) and ordinal logistic regression. The results indicate that age, gender, education level and place of residence significantly affect attitudes toward the quality, price, availability, and advantages of TFPs compared to industrial or imported products. Respondents from rural areas, those with lower education levels and lower incomes, show a greater tendency to consume traditional products. The main barriers to consumption were identified as high prices and insufficient information. The regression results showed that gender and place of residence were significant predictors of consumers’ tendency to choose dishes prepared with TFPs in hospitality establishments. The study highlights the need for tailored marketing and educational strategies to improve the accessibility, diversity, and visibility of TFPs on the market, with particular attention to their integration into the hospitality sector. However, the study is limited to the region of Vojvodina and relies on self-reported data, which may introduce response bias. Future research could explore comparative analyses across different regions or include qualitative insights into consumer motivations. Full article

Starvation in horses presents critical welfare, economic, and management challenges with underlying molecular mechanisms of metabolic modification and recovery left poorly defined. Prolonged caloric deprivation induces significant systemic shifts in carbohydrate, protein, and lipid metabolism, reflected in coordinated changes in tissue-specific gene expression. This review synthesizes current knowledge on equine metabolic responses to starvation, emphasizing pathways found through RNA sequencing (RNA-seq) and real-time quantitative polymerase chain reaction (RT-qPCR) studies. Molecular investigations using RNA-seq and RT-qPCR have provided insight into transcriptional reprogramming during starvation and subsequent refeeding. Shifts in gene expression reflect the metabolic transition from carbohydrate dependence to lipid use, suppression of anabolic signaling, and activation of proteolytic pathways. However, interpretation of these data requires caution, as factors such as post-mortem interval, tissue handling, and euthanasia methods particularly the use of sodium barbiturates can influence transcript stability and abundance, potentially confounding results. The literature shows that starvation-induced molecular changes are not uniform across tissues, with skeletal muscle, liver, and adipose tissue showing distinct transcriptional signatures and variable recovery patterns during refeeding. Cross-species comparisons with hibernation, caloric restriction, and cachexia models provide context for understanding these changes, though equine-specific studies remain limited. Identified gaps include the scarcity of longitudinal data, inconsistent tissue sampling protocols, and lack of standardized reference genes for transcriptomic analyses in horses. Addressing these limitations will improve the accuracy of molecular evaluations and enhance our ability to predict recovery trajectories. A more comprehensive understanding of systemic and tissue-specific responses to starvation will inform evidence-based rehabilitation strategies, reduce the risk of refeeding syndrome, and improve survival and welfare outcomes for affected horses. Full article

Objectives: To assess the incidence rate of anterior acute uveitis (AAU) in patients with longstanding axial spondyloarthritis (axSpA); to evaluate demographic and clinical characteristics associated with AAU development; and to determine the influence of AAU on bDMARD initiation and retention in this population. Methods: This two-timepoint cohort study analysed data from patients enrolled in the Spanish SpA registry REGISPONSER (2004–2007), who were re-evaluated 17 years later in the REGISPON-3 follow-up study (2021–2023). Information on the date of first AAU episode and bDMARD initiation was collected. Kaplan–Meier and Cox proportional hazards models were used to assess AAU incidence, predictors, and its association with time to bDMARD initiation and treatment retention. Results: A total of 299 patients with longstanding axSpA were included, of whom 33.4% experienced at least one episode of AAU, corresponding to an incidence rate of 1.15 per 100 person-years. The cumulative probability of a first episode of AAU increased with disease duration. The relative risk for developing a second episode after the first, compared to the overall risk of any episode in the total population, was 1.85 (95% CI: 1.34–2.57). In multivariable cox analysis, female sex and baseline enthesitis were independently associated with a higher risk of AAU. AAU did not significantly affect the likelihood of subsequent bDMARD initiation, with similar cumulative treatment probabilities in patients with and without AAU. Among treated patients, adalimumab was more frequently prescribed in those with a history of AAU. bDMARD retention rates at two and five years were comparable regardless of AAU status, suggesting that AAU was not associated with long-term treatment persistence. Conclusions: In patients with longstanding axSpA, the incidence of AAU increased steadily over time. However, the presence of AAU did not significantly influence bDMARD initiation or long-term retention in routine clinical practice. Full article

Background/Objectives: Infant emotional responses during complementary feeding offer key insights into early developmental processes and feeding behaviors. AI-driven facial emotion analysis presents a novel, objective method to quantify these subtle expressions, potentially informing interventions in early childhood nutrition. We aimed to investigate how maternal and infant traits influence infants’ emotional responses during complementary feeding using an automated facial analysis tool. Methods: This multi-center study involved 117 typically developing infants (6–11 months) and their mothers. Standardized feeding sessions were recorded, and OpenFace software quantified six emotions (surprise, sadness, fear, happiness, anger, disgust). Data were normalized and analyzed via Generalized Estimating Equations to identify associations with maternal BMI, education, work status, and infant age, sex, and complementary feeding initiation. Results: Emotional responses did not differ significantly across five food groups. Infants of mothers with BMI > 30 kg/m² showed greater surprise, while those whose mothers were well-educated and not working displayed more happiness. Older infants and those introduced to complementary feeding before six months exhibited higher levels of anger. Parental or infant food selectivity did not significantly affect responses. Conclusions: The findings indicate that maternal and infant demographic factors exert a more pronounced influence on infant emotional responses during complementary feeding than the type of food provided. These results highlight the importance of integrating broader psychosocial variables into early feeding practices and underscore the potential utility of AI-driven facial emotion analysis in advancing research on infant development. Full article

The construction industry faces persistent productivity challenges despite the widespread adoption of advanced digital technologies. This systematic review examines how digital technologies contribute to improving on-site labor productivity within the Architecture, Engineering, Construction, and Operations (AECOs) sector. Following the PRISMA methodology, 431 records were initially identified, with 28 high-quality articles ultimately selected for analysis through rigorous screening and snowballing techniques. The reviewed technologies include Building Information Modeling (BIM), photogrammetry, LiDAR, augmented reality (AR), global navigation satellite systems (GNSSs), radio frequency identification (RFID), and unmanned aerial vehicles (UAVs), which were categorized into three key areas: factors affecting productivity, modeling and evaluation, and productivity improvement methods. Findings highlight that these technologies collectively enhance resource allocation, reduce labor costs, and improve project scheduling through better coordination. Whilst digital technologies demonstrate substantial impact on construction productivity, further research is needed to quantify long-term benefits and address scalability challenges across different project contexts and organizational structures. Ultimately, the review concludes that digital technologies play a crucial role in enhancing construction productivity, highlighting the need for further research to assess long-term advantages and scalability across diverse construction environments. These technological advancements are essential for modernizing the industry and supporting sustainable growth in the digital transition era. Full article

Scene Text Recognition (STR) faces significant challenges under complex degradation conditions, such as distortion, occlusion, and semantic ambiguity. Most existing methods rely heavily on language priors for correction, but effectively constructing language rules remains a complex problem. This paper addresses two key challenges: (1) The over-correction behavior of language models, particularly on semantically deficient input, can result in both recognition errors and loss of critical information. (2) Character misalignment in visual features, which affects recognition accuracy. To address these problems, we propose a Deformable-Alignment-based Dual Correction Mechanism (DADCM) for STR. Our method includes the following key components: (1) We propose a visually guided and language-assisted correction strategy. A dynamic confidence threshold is used to control the degree of language model intervention. (2) We designed a visual backbone network called SCRTNet. The net enhances key text regions through a channel attention module (SENet) and applies deformable convolution (DCNv4) in deep layers to better model distorted or curved text. (3) We propose a deformable alignment module (DAM). The module combines Gumbel-Softmax-based anchor sampling and geometry-aware self-attention to improve character alignment. Experiments on multiple benchmark datasets demonstrate the superiority of our approach. Especially on the Union14M-Benchmark, where the recognition accuracy surpasses previous methods by 1.1%, 1.6%, 3.0%, and 1.3% on the Curved, Multi-Oriented, Contextless, and General subsets, respectively. Full article

As climate variability intensifies, its impacts are increasingly visible through disrupted agricultural systems and rising food insecurity, especially in climate-sensitive regions. This study explores the complex relationships between environmental stressors, such as rising temperatures, erratic rainfall, and soil degradation, with food insecurity outcomes in selected districts of Uttarakhand, India. Using the Fuzzy DEMATEL method, this study analyzes 19 stressors affecting the food supply chain and identifies the nine most influential factors. An Environmental Stressor Index (ESI) is constructed, integrating climatic, hydrological, and land-use dimensions. The ESI is applied to three districts—Rudraprayag, Udham Singh Nagar, and Almora—to assess their vulnerability. The results suggest that Rudraprayag faces high exposure to climate extremes (heatwaves, floods, and droughts) but benefits from a relatively stronger infrastructure. Udham Singh Nagar exhibits the highest overall vulnerability, driven by water stress, air pollution, and salinity, whereas Almora remains relatively less exposed, apart from moderate drought and connectivity stress. Simulations based on RCP 4.5 and RCP 8.5 scenarios indicate increasing stress across all regions, with Udham Singh Nagar consistently identified as the most vulnerable. Rudraprayag experiences increased stress under the RCP 8.5 scenario, while Almora is the least vulnerable, though still at risk from drought and pest outbreaks. By incorporating crop yield models into the ESI framework, this study advances a systems-level tool for assessing agricultural vulnerability to climate change. This research holds global relevance, as food supply chains in climate-sensitive regions such as Africa, Southeast Asia, and Latin America face similar compound stressors. Its novelty lies in integrating a Fuzzy DEMATEL-based Environmental Stressor Index with crop yield modeling. The findings highlight the urgent need for climate-informed food system planning and policies that integrate environmental and social vulnerabilities. Full article

Coating degradation monitoring is increasingly important in offshore industries, where protective layers ensure corrosion prevention and structural integrity. In this context, coating thickness estimation provides critical information. The ultrasound pulse-echo technique is widely used for non-destructive testing (NDT), but closely spaced acoustic interfaces often produce overlapping echoes, which complicates detection and accurate isolation of each layer’s thickness. In this study, analysis of the pulse-echo signal from a coated sample has shown that the front-coating reflection affects each main backwall echo differently; by comparing two consecutive backwall echoes, we can cancel the acquisition system’s impulse response and isolate the propagation path-related information between the echoes. This work introduces an ultrasound echo-based methodology for estimating coating thickness by first obtaining the impulse response of the test medium (reflectivity sequence) through a deconvolution model, developed using two consecutive backwall echoes. This is followed by an enhanced detection of coating layer thickness in the reflectivity function using a 1D convolutional neural network (1D-CNN) trained with synthetic signals obtained from finite-difference time-domain (FDTD) simulations with k-Wave MATLAB toolbox (v1.4.0). The proposed approach estimates the front-side coating thickness in steel samples coated on both sides, with coating layers ranging from $60\mathsf{\mu }$m to $740\mathsf{\mu }$m applied over 5 mm substrates and under varying coating and steel properties. The minimum detectable thickness corresponds to approximately $\mathit{\lambda }/5$ for an 8 MHz ultrasonic transducer. On synthetic signals, where the true coating thickness and speed of sound are known, the model achieves an accuracy of approximately $8\mathsf{\mu }$m. These findings highlight the strong potential of the model for reliably monitoring relative thickness changes across a wide range of coatings in real samples. Full article