Search Results (5,142)

Handwriting is a fundamental fine motor skill in early childhood development, yet between 10% and 30% of school-age children experience significant difficulties in its acquisition. Existing automated assessment approaches predominantly classify whether the correct character was produced, rather than evaluating the morphological quality of the stroke itself—the level at which handwriting difficulties are believed to manifest. This article addresses this gap by presenting the design, implementation, and technical evaluation of an interactive social robot shaped like a capybara, developed to support Spanish-language handwriting learning in preschool children through stroke-level, rather than character-level, assessment. The system integrates a Raspberry Pi 5, a 15.6-inch touchscreen, and a stroke morphological comparison algorithm implemented with the Open Source Computer Vision Library. The evaluation engine performs preprocessing, region-of-interest masking, and pixel-level coverage analysis based on the standard recall formulation, translated into 1-to-5-star multimodal feedback. A controlled technical evaluation of 360 trials, conducted by four trained adult evaluators, yielded an overall recognition rate of 82.78% (95% CI: 78.54–86.33%) and a mean response time of 0.68 s, well below the threshold identified in the literature as critical for sustaining engagement in preschool children. Recognition was statistically equivalent across character categories (p = 0.547) but differed markedly across stroke-quality levels (p < 0.001), evidencing the formative sensitivity of the algorithm. Exploratory observations in two Peruvian preschools indicated operational stability and children’s spontaneous engagement with the system. These results position the prototype as a technically validated, replicable foundation—based on general-purpose embedded hardware—for future pedagogically oriented research on child–robot interaction in handwriting instruction. Full article

Parental emotional intelligence (EI) has been associated with children’s socio-emotional development, yet less is known about how parental EI is associated with naturally occurring configurations of parenting practices. This cross-sectional study investigated two complementary statistical indirect associations connecting parental EI to preschoolers’ social competence using a sample of 13,398 Chinese families: a variable-centered pathway via positive and negative parenting practices, and a person-centered pathway via distinct parenting profiles. Exploratory latent profile analysis yielded four sample-specific parenting configurations: high-positive/low-negative (27.49%), near-average (55.19%), low-positive/elevated-negative (9.16%), and mixed slightly positive/high-negative (8.16%). Parental EI exhibited a positive concurrent association with children’s overall social competence. Positive and negative parenting statistically accounted for part of this bivariate association at the variable level, while parenting profile membership provided complementary person-centered information about differences in child social competence. Family climate was incorporated as an auxiliary contextual correlate and was not framed as a core predictor, mediator, or moderator within the focal model. Given the cross-sectional single-wave design and caregiver-reported data for all key constructs, all observed patterns should only be interpreted as correlational associations, rather than empirical support for causal mediation. Full article

This preliminary study aims to compare homeschooling in Greece and the UK in terms of experiences, arithmetical attainments, parental motivations, implementation practices, and support structures. For this purpose, we employed a multiple case study design, enrolling a total of five Greek and fifteen British homeschooling families. Results suggested that the Greek parents primarily chose homeschooling in response to their child’s severe and complex medical needs and reported limited access to support systems. British parents provided more varied motivations, higher engagement with educational resources, and stronger integration into informal support networks. The sample of UK children demonstrated higher arithmetical attainment than the Greek sample. Cultural, structural, and regulatory factors appear to shape homeschooling experiences differently across the Greek and UK educational systems. Differences also emerged in terms of daily routines, access to support, and parental confidence. Results are discussed in relation to educational provision and policy development. Full article

Background/Objectives: Nurses are frequently exposed to emotionally demanding situations, placing them at risk of adverse psychological outcomes such as secondary traumatic stress (STS) and burnout. Personality traits, particularly perfectionism, may influence these outcomes. Submissive compassion, characterized by caring behavior motivated by a desire for approval or avoidance of rejection, may play an explanatory role in these relationships. Methods: This study examined whether submissive compassion was statistically associated with the relationship between perfectionism and professional quality of life (ProQOL) among hospital nurses, while accounting for potential confounding and moderating factors. A cross-sectional online survey was conducted among ward nurses working in hospitals in Japan. Participants completed measures of perfectionism, submissive compassion, and ProQOL. Data from 218 nurses were analyzed using structural equation modeling. Results: Socially prescribed perfectionism was positively associated with submissive compassion and STS. Submissive compassion was significantly associated with the indirect relationship between socially prescribed perfectionism and STS. Age moderated the association between submissive compassion and STS, with stronger indirect effects observed among older nurses. Additionally, self-oriented perfectionism showed a relationship with stress-related outcomes, whereas socially prescribed perfectionism was associated with burnout. Conclusions: The findings suggest that submissive compassion may contribute to the association between perfectionism and adverse psychological outcomes among nurses. These results underscore the potential importance of maladaptive compassion motives in understanding how perfectionism relates to STS in this population and may help inform interventions aimed at supporting nurses’ psychological well-being. Full article

The use of artificial intelligence chatbots that mirror human interaction and emotional closeness has given rise to new forms of crime. Traditional online grooming is generally conceptualized as an offence in which a human perpetrator plans, initiates, and executes criminal conduct. However, the increasing involvement of artificial intelligence has introduced novel and complex scenarios. AI systems may either autonomously engage in conduct that facilitates the sexual exploitation of children or serve as tools that enhance, automate, or scale offenders’ activities. These developments challenge the traditional understanding of the offence and expose significant gaps in existing legal frameworks. Consequently, current regulatory approaches may prove inadequate to address the evolving nature of AI-assisted online grooming and associated forms of child sexual exploitation. This study investigates the case of grooming via social media using AI chatbots and discusses whether the current criminal legislation is sufficient to address this offence. Through a legal comparative method, this study examines the legal rules in the European Union, the United Kingdom, the United States, and China, focusing on the elements of criminal acts and criminal intent and the consideration of the liability of platform operators, developers, and deployers of AI systems. The study also discusses the problem of intermediary liability rules and less mature AI governance policies to tackle the fragmented and hidden nature of algorithmic actions. The study concludes that existing criminal law frameworks face significant challenges in addressing AI-assisted grooming, particularly regarding criminal intent, foreseeability, and liability allocation. The fragmentation of responsibility among offenders, platforms, and AI developers creates regulatory and enforcement gaps in the law. Accordingly, this study advocates for a risk-based liability framework, enhanced platform accountability, greater algorithmic transparency, and stronger child-centered safeguards. Full article

Spent cathode carbon (SCC), a hazardous waste generated during primary aluminium production, contains valuable graphitic carbon resources but remains difficult to recycle because carbon purification is governed by complex interactions among multiple leaching parameters. Conventional process optimization relies on extensive laboratory experimentation, resulting in high chemical consumption, energy use, and development costs. This study presents an explainable machine learning framework for cleaner and more resource-efficient carbon purification from SCC under limited-data conditions. Six machine learning algorithms (GBDT, CatBoost, XGBoost, LightGBM, Random Forest, and Decision Tree) were evaluated using experimental data from alkaline leaching. The LightGBM model was systematically optimized by combining Response Surface Method (RSM), Orthogonal Experimental Design (OED), and local parameter optimization methods. The optimized model (min_child_samples = 2, num_leaves = 32, n_estimators = 500, and learning_rate = 0.5) achieved an R² of 0.8015, RMSE of 0.9163, and MAE of 0.6599. SHAP analysis identified initial alkali concentration, liquid–solid ratio, stirring rate, and leaching time as the dominant factors controlling carbon purification, whereas temperature had a comparatively smaller influence within the investigated operating range. The results indicate that improving reagent utilization and hydrodynamic conditions offers greater potential for enhancing carbon purification than increasing thermal input alone. By integrating statistical experimental design with explainable machine learning, this study establishes an efficient, interpretable, and transferable decision-support framework for optimizing hazardous waste recycling and other resource recovery processes under limited-data conditions, thereby supporting cleaner production and circular economy practices. Full article

Numerous studies have examined the contribution of polycyclic aromatic hydrocarbons (PAHs) to soil contamination, resulting from legacy oil extraction and storage activities in the energy sector, and the human health risks. In this research, a human health risk assessment (HHRA) was performed on a site contaminated with crude oil, incorporating spatial analysis to identify hotspots of carcinogenic and non-carcinogenic risk from PAH-contaminated soil. In total, eight soil sampling locations were considered. The HHRA was conducted using a software tool developed by the National University of Science and Technology POLIETHNICA of Bucharest according to the United States Environmental Protection Agency (US EPA) and American Society for Testing and Materials (ASTM International) standards. The results obtained in the current assessment were validated against the Risk-net software 3.2 (an Italian tool for risk assessment validated by the ISPRA). The assessment considered a residential scenario (adult and child receptors) using four different exposure pathways: soil ingestion, dermal contact, vapor inhalation (due to the volatilization process) and groundwater ingestion (because of the leaching process). The carcinogenic risk (CR = 1 × 10⁻⁵) and total hazard index (THI < 1) in the studied area were above the threshold values for both adults’ and children’s receptors. The highest concern is given to the process of PAHs leaching from soil to groundwater. The highest carcinogenic concern is benzo(a)pyrene for children’s exposure via groundwater ingestion (CR = 3.53 × 10⁻⁵), while the highest toxic effect is registered for the contaminant naphthalene (THI = 6.22) for the same receptor and exposure pathway. Full article

Immunotherapy delivers a fundamental shift in the treatment of multiple cancers, offering hope for patients with cancer. Unlike conventional therapies, it leverages the patient’s immune system to precisely identify and destroy cancer cells. The remarkable journey of immunotherapy’s has revolutionized oncology and paved the way for the development of modern immunotherapies. Several approaches are applied for immunotherapy, including immune checkpoint inhibitors, adoptive cell therapies, cancer vaccines, tumor microenvironment reprogramming, monoclonal antibodies, cytokine therapies, oncolytic virus therapy and combination strategies. Various immunotherapies are being used for different types of cancers like skin, lung, bladder, kidney, liver, breast and blood cancers. Despite providing an effective cancer treatment, immunotherapies face some challenges in their clinical implementation. To overcome these limitations, future directions emphasize the development of personalized and combination therapies, the incorporation of AI and a good delivery system. The main aim of this review is to give an overview of the journey of evolution, different approaches, limitations, advances and future development for immunotherapy in cancer treatment. Full article

Child development monitoring by nurses enables the identification of alterations and early interventions. This study analyzed the clinical applicability of a terminology subset for monitoring the development of children aged zero to three years during nursing consultations. This is a cross-sectional, observational study that analyzed the applicability of a terminology subset. Three stages of the clinical applicability analysis method for terminology subsets were followed: development of the data collection instrument; systematic observation of 46 consultations in two Brazilian municipalities; and data organization and analysis. Nursing diagnoses, outcomes, and interventions observed at least once during a consultation were considered applicable. Of the analyzed subset, 64.7% of the diagnoses/outcomes and 21.3% of the interventions were observed during consultations and are applicable to clinical practice. Notably, the diagnosis “Child development, effective” was identified in 93% of the consultations. The data suggest the applicability of the subset in specific Brazilian primary healthcare contexts, predominantly with healthy children. Full article

Background: The prevalence of overweight and obesity in Chinese school-aged children has continued to rise, posing significant health risks. Rational nutrition knowledge and dietary behaviors are important for obesity prevention and control. The ‘Know Your Food’ campaign aimed to promote nutritious diets and empower informed food choices among Chinese children and young people. This study aims to assess both the implementation and effectiveness of the campaign in primary school settings. Methods: The monitoring framework was designed using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework and consisted of a series of social media advocacy and school-based nutrition sessions. A monitoring system based on the RE-AIM model was developed to assess the impact of the campaign. Observational data on reach, effectiveness, adoption, implementation, and maintenance were collected through media tracking, self-reported questionnaires, and focus group discussions. Results: Between 13 May and 15 June 2022, the campaign gained 77.13 million views and 291,029 interactions from social media. The campaign garnered positive feedback as it reached a wide audience through traditional media, social media and school sessions. 94.7% of the respondents were willing to share campaign information. After the session, more than two thirds of the students reported behavior change, with 70.0% reporting eating five vegetables a day, 67.7% reporting drinking fewer sugary drinks, 74.0% reporting replacing snacks with fruits/nuts, 76.9% reporting doing exercises, and 78.0% reporting getting sufficient sleep. Conclusions: The ‘Know Your Food’ campaign was able to reach and engage the target audience, increasing nutrition knowledge, and influencing behavior change within the short observation window. The findings exemplify the implementation and effectiveness of this campaign and highlight the importance of using a comprehensive assessment framework to assess such nutrition campaigns. Full article

Education for sustainability should connect planetary environmental change with local place, cultural meaning, and feasible collective action. Yet inclusive arts-based ocean-literacy education remains underexamined, especially for children from migrant backgrounds. This study examines Gyeongju Sea Rescue Team: Protecting the Sea through Minhwa, a five-session programme for elementary-school children from Koryoin and other migrant-background families in South Korea. A provenance-separated dual-strand design combined hybrid thematic analysis of programme documents and non-identifying artwork images with a fully synthetic 100-case corpus used only to audit a future artwork-elicitation protocol and codebook. The documentary strand identified four provisional mechanisms: the ecological reinterpretation of auspicious symbols, localisation through Gyeongju’s maritime heritage, multimodal participation across linguistic differences, and collective stewardship. The synthetic audit did not validate these mechanisms or programme outcomes. Instead, participant-correction and self-action prompts generated more clearly codable material on agency, cultural identity, multilingual meaning-making, and intercultural integration. Two designed perturbation passes yielded a quadratic-weighted kappa of 0.726 as a within-corpus stability diagnostic rather than human intercoder reliability. Diagnostic cases exposed adult projection, assimilationist interpretation, pressure to disclose private meanings, and advice without self-agency. The article proposes a provisional MIOL framework and four safeguards: child-led interpretation, reciprocal cultural exchange, appropriate consent and permissions, and continuity from symbolic expression to sustained action. Synthetic outputs concern method development only and do not represent children’s experiences or programme effectiveness. Full article

Companion animals have increasingly become central to human emotional life, yet the literature linking living with companion animals, human–animal interaction, and mental health remains fragmented. This study mapped global research trends on companion animals and mental health from 2000 to 2024 using bibliographic records retrieved from the Web of Science Core Collection (WoSCC). A final dataset of 930 peer-reviewed articles was analyzed with Bibliometrix (RStudio), CiteSpace, and VOSviewer to characterize publication growth, national and institutional collaboration, influential authors and journals, co-cited references, keyword clusters, and citation bursts. Results show a strong nonlinear increase in output after 2015, with the United States as the dominant contributor and Australia and the WoSCC country node labelled England (distinct from Scotland) as important international bridges. Core knowledge clusters center on human–animal interaction, animal-assisted intervention, older adults, child/adolescent development, loneliness, attachment, and post-traumatic stress. Recent burst terms and references indicate a shift from general companion-animal caregiving and physiological stress buffering toward intervention design, vulnerable populations, and pandemic-era mental health. Overall, the field has matured into an interdisciplinary research area with growing methodological sophistication, but future work should strengthen cross-cultural evidence, longitudinal designs, standardized outcome measures, and explicit assessment of animal welfare. Full article

Transjugular intrahepatic portosystemic shunt (TIPS) is an established treatment for complications of portal hypertension, but hepatic encephalopathy (HE), liver dysfunction, rebleeding, and mortality remain difficult to predict in otherwise eligible candidates. However, predicting post-TIPS outcomes remains challenging using conventional risk scores such as MELD 3.0 and Child–Turcotte–Pugh. This narrative review critically evaluates how clinical, biochemical, procedural, conventional imaging, handcrafted radiomics, and deep-learning features can be integrated for personalized post-TIPS risk prediction, supplemented by backward and forward reference checking. Thirty-two original post-TIPS studies met the core inclusion criteria: 18 focused primarily on clinical, biochemical, hemodynamic, microbiome, or procedural predictors and 14 on imaging, body composition, radiomics, or multimodal models. AI, ML, and radiomics models, by the aim of logistic regression, tree-based ensembles, support vector machines, artificial neural networks, and hybrid deep-learning models, able to capture non-linear interactions, have consistently demonstrated improved predictive performance compared with conventional scores, particularly for HE, with reported incidences of approximately 20–47%, mortality, and liver dysfunction. Their advantage lies in the ability to model complex, non-linear relationships and integrate heterogeneous data sources, including laboratory parameters, ammonia levels, hemodynamic variables, and imaging-derived features. Radiomics and deep learning approaches further enhance predictive accuracy. CT is currently the principal imaging substrate, while direct post-TIPS radiomics evidence for MRI and ultrasound remains sparse. Studies of liver and spleen morphology, portal-vein geometry, muscle and adipose tissue, and radiomic texture suggest incremental information beyond conventional scores, particularly when clinical and imaging features are combined; however, negative volumetric findings show that additional image features do not automatically improve prediction. However, most studies are retrospective, single-center, and lack external validation and no validated transformer-based or other sequence model has yet been established for post-TIPS outcomes. Standardization issues in radiomics and limited model interpretability remain significant barriers. Future directions should lead to prospective multicenter cohort validation, increasing sample sizes, harmonized imaging and endpoint definitions, locked external validation with recalibration, and the development of clinically interpretable tools to make it easier to identify those patients suitable for TIPS and their post-procedural management. Full article

Climate change and increasing socio-environmental pressures are intensifying the occurrence of compound and cascading hazards, highlighting the need for systematic approaches to reconstruct and analyze multi-hazard interactions from historical evidence. However, historical inventories are typically event-based, unevenly documented, and lack information on non-events, limiting their direct use for probabilistic interpretation. This study presents a reproducible evidence-mining framework that transforms a historical multi-hazard inventory into a hierarchical process–hazard–location dataset while explicitly distinguishing documented event-tree relationships from exploratory within-catalog associations. Applied to a historical multi-hazard inventory of Tenerife (Canary Islands), the framework reconstructs documented parent–child hazard relationships, identifies recurrent compound motifs, and formalizes association screening through transparent statistical procedures, including contingency-table analysis, uncertainty intervals, effect-size estimates, exact significance testing with false-discovery-rate control, and Bayesian sensitivity analysis. A quantitative assessment of documentation density reveals a strong increase in recorded evidence through time, demonstrating that temporal variations in the inventory primarily reflect changes in documentation rather than changes in hazard occurrence. Consequently, all derived association metrics and screening thresholds are interpreted as hypothesis-generating evidence rather than predictive or causal estimates. The proposed framework provides a transparent and reproducible basis for extracting structured knowledge from historical multi-hazard inventories, supporting research prioritization and future probabilistic developments while avoiding overinterpretation of heterogeneous historical records. Full article

As a crucial period for self and identity development, early adolescence may give rise to emotional issues due to inadequate methods of self-expression. Therefore, the development of self-expression during early adolescence may play a significant role in interpersonal relationships. “Assertion” refers to the ability to express one’s thoughts and emotions appropriately while simultaneously respecting those of others; thus, assertiveness training (AT) focuses on appropriate expression while also respecting others. Previous studies have shown AT programs’ effects over 1 to 6 months, but the minimum number of effective sessions has not been determined. Thus, this single-group study without a control group examined pre–post changes in 102 5th-grade students following three 45 min AT sessions on self-expression, requesting favors, and politely declining requests. Changes in assertiveness and mental health were assessed using the TK-Style Communication Skills and the Strength and Difficulties Questionnaire (SDQ). Results of available case analyses (depending on the outcome) showed a significant increase in assertiveness scores and decreases in both nonassertiveness scores and SDQ total difficulties scores. Overall, three short AT sessions were associated with favorable pre–post changes in assertiveness and mental health-related outcomes; however, the absence of a control group limits the causal interpretation. Full article