Search Results (6,601)

Background/Objectives: Exclusive breastfeeding offers optimal benefits for infant nutrition and health and increases maternal involvement, bonding and interactions. This study aimed to explore breastfeeding practices among mothers in Romania and identify risk factors associated with low exclusive breastfeeding rates. Methods: A cross-sectional online survey was conducted between September and December 2025, targeting mothers in Romania via social media platforms. The questionnaire, developed specifically for this study, collected data on sociodemographic characteristics, birth and neonatology variables, hospital practices, feeding intentions, community influences, and breastfeeding outcomes. Responses were analyzed using Fisher’s exact tests and multivariable logistic regression. Results: A total of 357 complete questionnaires were analyzed. Cesarean section was the most frequent mode of delivery (54.6%), while immediate mother–infant contact after birth was reported by only 35.6% of mothers, and breastfeeding initiation within the first hour occurred in 10.6% of cases. Overall, 49.3% of mothers reported exclusive breastfeeding, 35.3% mixed feeding, and 15.4% exclusive formula feeding. Women who delivered in private hospitals reported earlier mother–infant contact, more frequent encouragement to initiate breastfeeding, and earlier breastfeeding initiation compared with those delivering in public hospitals. Preterm birth was associated with delayed breastfeeding initiation, reduced rooming-in, and lower rates of exclusive breastfeeding up to six months. In multivariable logistic regression, rooming-in was independently associated with higher odds of exclusive breastfeeding (aOR = 2.798, 95% CI: 1.779–4.401), while lack of lactation support was associated with lower odds (aOR = 0.546, 95% CI: 0.302–0.987). No significant associations were observed for timing of initial maternal–infant contact (aOR = 1.084, 95% CI: 0.679–1.733) or encouragement from medical staff to initiate breastfeeding (aOR = 1.207, 95% CI: 0.721–2.020). Conclusions: Our study highlights current breastfeeding practices and associated hospital factors in Romania. However, significant challenges remain in supporting and encouraging mothers to optimally feed their infants. Additional investment and bold policy action are needed to promote and support breastfeeding from the first hour of life, for both term and preterm infants, in all maternity hospitals in Romania. Full article

Deploying highly generalizable deepfake detection systems on resource-constrained edge devices poses a significant technical challenge for conventional end-to-end large models that rely heavily on computational resources. Extracting multi-source physical prior features is a viable approach under limited computational power; however, in few-shot scenarios, the dimensional mismatch of heterogeneous features is prone to causing downstream classifiers to overfit. To mitigate this bottleneck, this paper proposes a “static feature extraction–central normalization alignment–independent downstream decision” decoupled detection system for few-shot cross-domain tasks on edge devices. The front end of the system constructs an 856-dimensional comprehensive feature reservoir, and a lightweight residual normalization adapter $g_{\varphi }$ is introduced as the central support module. This module explicitly compresses the intra-class variance of heterogeneous features, providing a smoothly aligned manifold base for downstream classifiers. Experimental results indicate that this decoupled architecture demonstrates consistent stability in few-shot ($K=10$) cross-domain evaluations. When encountering intra-family cross-domain shifts and cross-mechanism distribution shifts from diffusion models, the accuracy reaches 84.9% and 76.1%, respectively. Compared to representative end-to-end meta-learning baselines (e.g., MAML), the relative error rate is reduced by over 30%. Furthermore, after completing the asynchronous offline pre-processing (approximately 897 ms) at the front end, a single-image online classification query requires only 7.7 ms under a simulated single-core CPU constraint, satisfying the low-latency requirements for lightweight deployment on edge devices. Finally, combined with empirical observations, this paper discusses the performance boundaries of the architecture in cross-mechanism metric mismatch scenarios, providing a low-barrier, robust engineering defense scheme for resource-constrained environments. Full article

In persistent patrol and online task discovery in environments with obstacles, unmanned aerial vehicle (UAV) swarms are constrained by limited battery capacity and frequent recharging disrupts patrol continuity. In comparison, unmanned ground vehicle (UGV) fleets have higher endurance and payload capacity and can serve as mobile charging platforms while executing ground-service tasks. In such collaborative scenarios, UAVs patrol along a coverage path and discover tasks online, whereas UGVs execute discovered ground tasks and provide mobile charging support. To cope with rendezvous uncertainty due to obstacle-induced detours and inefficient usage of UGV time during charging, this study proposes an energy-constrained UAV-UGV coordination framework based on adaptive anticipatory rendezvous and time-window scheduling. In particular, the adaptive anticipatory rendezvous module handles anticipatory rendezvous planning, while the time-window scheduling module models the post-rendezvous charging stage as a schedulable time window for opportunistic ground-task insertion. Simulations demonstrate that the proposed framework consistently reduces system energy consumption, completion time, and the number of emergency landings compared with three representative baselines. Moreover, a UAV-UGV prototype with AprilTag-based visual landing and post-landing mechanical correction is developed to validate the engineering feasibility of the key closed-loop process. Full article

Background: Obesity is a major public health concern associated with adverse physical and psychological outcomes, including body shame (BS) and reduced self-esteem (SE). Lifestyle interventions incorporating dietary and exercise components, such as time-restricted eating (TRE), mindful eating, and structured physical activity, have shown promise; however, evidence on their combined effects within scalable, web-based formats remains limited. Objective: This study aimed to evaluate the effectiveness of a multi-component, web-based lifestyle intervention integrating TRE, mindful eating, and structured online exercise on body composition and psychological outcomes in young adults. Methods: In this pilot randomized controlled trial, 42 healthy young adults (age: 20.4 ± 1.6 years) were allocated to either an intervention group (n = 28) or a control group (n = 14). The intervention group followed an integrated program combining TRE, mindful eating principles, and guided online exercise sessions, while the control group received standard dietary and physical activity recommendations. Outcomes included body composition, anthropometric measures, BS (Weight- and Body-Related Shame and Guilt Scale), SE (Rosenberg Self-Esteem Scale), and eating behavior (Three-Factor Eating Questionnaire). Results: Significant group × time interactions were observed for body fat percentage (p < 0.001), fat mass (p = 0.001), and body mass (p = 0.025), with the intervention group demonstrating greater reductions compared with controls. BS scores significantly decreased in the intervention group, whereas no significant between-group differences were observed for SE (p > 0.05). Dietary adherence appeared higher than exercise adherence over the intervention period. Conclusions: A multi-component, web-based lifestyle intervention integrating TRE, mindful eating, and structured exercise may improve body composition and reduce BS in young adults. However, changes in SE were not observed over the short term. These findings support the feasibility of scalable digital lifestyle interventions, while highlighting the need for longer-term studies to clarify psychological outcomes. Full article

Background/Objectives: Medical chatbots are increasingly integrated into healthcare to facilitate patient communication, often under the assumption that they reduce stigma and foster the disclosure of sensitive information. However, empirical support for this effect remains inconsistent. Drawing on online disinhibition theory, this study introduces the concept of machine-mediated disinhibition (MMD) to examine whether chatbot consultations elicit greater disclosure than human-mediated or face-to-face interactions. Methods: A scenario-based, between-subjects experiment (n = 373) compared three modes: face-to-face, human-through-computer, and chatbot. Results: Results revealed no evidence of increased disinhibition in the chatbot condition. Conversely, participants were significantly less willing to disclose sensitive health information to chatbots than to humans. Conclusions: These findings suggest that in high-stakes healthcare contexts, trust-related concerns override disinhibition effects, leading to avoidance rather than openness. This study challenges the prevailing assumption that AI agents inherently facilitate disclosure and highlights the critical need for further research on trust in AI-mediated medical communication. Full article

Climate change is increasingly recognized as a source of psychological distress, yet the prevalence, predictors, and behavioral implications of climate anxiety remain unevenly understood. This study examines climate anxiety, its key drivers, and associated behavioral responses in a multi-country sample of adults. A cross-sectional online survey was conducted across 21 countries using the Climate Change Anxiety Scale (CCAS), alongside measures of awareness, coping strategies, social support, and food-related behaviors, including food waste reduction, increased plant-based food consumption, and home or community gardening. Analyses included descriptive statistics, exploratory factor analysis (EFA), and multivariable regression. Given the uneven country-level representation, results are reported as pooled patterns with a few exploratory cross-country comparisons. Climate anxiety was widely reported, with over 60% of participants indicating that climate challenges were emotionally overwhelming. Regression analyses showed that climate awareness and frequency of climate-related thinking were positively associated with higher anxiety, although the effect sizes were small and explanatory power was limited (R² = 0.055). EFA identified two related dimensions: cognitive concern about future impacts and affective distress. Climate anxiety across countries showed modest variation (2.44–3.23) and no statistically significant differences, despite variation in awareness. A gap between concern and climate action was evident: only 39.1% reported environmentally motivated dietary changes. Cost, limited availability, and lack of information were the main barriers to climate action, and only 24.4% reported frequent social support. These findings indicate that climate anxiety is shaped by both psychological and structural factors, and that reducing it requires not only increasing awareness but also enabling conditions that support meaningful climate action. Full article

Australia’s ban on social media for under-16s, introduced in December 2025, made it the first country worldwide to implement a nationwide prohibition on major platforms for adolescents. This narrative literature review compares Australia’s age-based restriction with international approaches to protecting young people from online risks. The review synthesized 26 academic studies and 15 grey literature sources (policy documents, legislation, and official reports published between 2015 and 2025). It employed Bronfenbrenner’s bioecological systems theory to examine effects across family, platform, institutional, and broader socio-legal contexts. Three key themes emerged: (A) Empirical findings on age-threshold policies remain inconclusive and context-dependent. While unregulated use relates to psychological vulnerabilities, structured and intentional engagement can promote social connection, identity exploration, and support access, especially for marginalized youth. (B) Global responses vary, favoring alternatives like parental consent, platform duty-of-care obligations, and screen-time control measures. (C) Balanced, sustainable harm reduction depends on combining parental involvement, platform accountability, and digital literacy education. Overall, while Australia’s precautionary approach addresses legitimate developmental and public health concerns, its effectiveness seems limited by enforcement challenges, risks of digital exclusion, and potential human rights issues. Bronfenbrenner’s framework underscores the need for coordinated governance across interconnected systems to lessen online harm. Full article

Background: Mucopolysaccharidosis type II (MPS II; Hunter syndrome) is a rare X-linked lysosomal storage disorder caused by pathogenic variants in the iduronate-2-sulfatase gene, leading to progressive multisystem involvement. Although international management guidelines exist, challenges in their implementation across different healthcare systems remain insufficiently addressed. This study aimed to establish a national expert consensus in Türkiye on the treatment and management of MPS II, aligning local practice with international standards. Methods: A modified Delphi methodology was conducted using two rounds of online surveys supported by three steering committee meetings. The process involved 10 experienced clinicians and a scientific committee of six professors. Based on international guidelines and country-specific clinical challenges, 72 consensus statements and 84 exploratory questions were developed. Statements achieving ≥ 80% agreement were accepted as consensus. Results: Consensus supported initiating enzyme replacement therapy (ERT) in both severe and attenuated MPS II, guided by functional and cognitive status. Severe cognitive impairment was not considered an exclusion criterion for ERT, given its somatic benefits. Experts agreed on continuing ERT into adulthood with individualized discontinuation decisions. Routine evaluations every 6–12 months, including respiratory, cardiac, and neurocognitive assessments, were recommended. Additional consensus areas included individualized premedication strategies, structured transition to adult care, selective home infusion, annual patient-reported outcome assessments, and the establishment of a national MPS II registry. Hematopoietic stem cell transplantation was not endorsed. Conclusions: This Delphi study demonstrates strong expert consensus on MPS II management in Türkiye, providing a practical framework to guide clinical practice, support alignment with international recommendations, and inform future policy and research priorities. Full article

Family and parental education are increasingly recognized as key levers for sustainable development and family well-being. This study examines whether an online parental intervention program focused on strengthening parental self-efficacy can improve parents’ relationship satisfaction and couple satisfaction. A sample of 50 Romanian parents with below-average levels of parental self-efficacy and relationship satisfaction was randomly assigned to an experimental group and a control group. Participants in the experimental group attended the “Confident Parents” program over three months, while the control group received no structured intervention. Pre- and post-test data were collected using standardized measures of parental self-efficacy, couple satisfaction, and relationship satisfaction. Data analysis combined non-parametric Wilcoxon signed-rank tests with linear regression and moderation analysis. The results showed significant pre–post improvements in parental self-efficacy, relationship satisfaction, and couple satisfaction in the experimental group, with no meaningful changes in the control group. Post-test, parental self-efficacy significantly predicted both relationship satisfaction and couple satisfaction, and moderation analyses indicated that this predictive relationship was stronger for parents in the intervention group. These findings suggest that parental education programs centered on self-efficacy can contribute to more satisfying couple and family relationships, supporting psychological well-being and the broader goals of sustainable family functioning. Full article

The contemporary media landscape has sustained a substantial transformation with the rise of AI-driven algorithmic platforms that enable activist organizations to produce and disseminate their own forms of political communication and campaigns. This study examines the YouTube channel of Code Pink, a prominent U.S.-based anti-war and social justice organization, to explore how activist media practices intersect with contemporary forms of journalism. Over a one-month period, video transcripts from the organization’s YouTube channel were analyzed using NVivo 15, employing a hybrid qualitative approach that combined inductive and deductive coding. Deductive codes were informed by sustained observation of the channel over one year (short and long videos on YouTube, TikTok, and X), supplemented by engagement with relevant news coverage, while inductive coding followed grounded theory principles, allowing themes to emerge directly from the transcripts. Large Language Models (LLMs) were employed as exploratory analytic tools to support AI-assisted qualitative analysis, complementing manual coding processes. The analysis focuses on how Code Pink frames political events and U.S. foreign policy through confrontational interviews, protest documentation, and the dissemination of commentary to online audiences. Findings suggest that the organization’s video content operates simultaneously as political activism, protest performance, and quasi-journalistic reporting. Activists frequently adopt journalistic techniques—including interviewing political figures, providing on-the-ground commentary, and framing narratives around public accountability—while also advancing explicit ideological positions that challenge dominant media narratives. The study highlights how platform-based activist media blurs the boundaries between journalism, advocacy, and political performance, contributing to the construction of alternative public narratives in the digital age. Full article

Glamping tourism has expanded rapidly as travelers increasingly seek nature-based experiences combined with comfort and privacy, particularly in the post-COVID-19 period. Online reviews provide a valuable source of insight into how guests perceive such experiential accommodation, yet large-scale, data-driven analyses of glamping sentiment remain limited. This study applies machine-learning techniques to classify customer sentiment expressed in online reviews of glamping sites in South Korea. A total of 3233 reviews were collected from ten leading glamping locations on Naver Map, cleaned, and translated from Korean to English. Sentiment labels (negative, neutral, and positive) were generated using VADER (Valence Aware Dictionary and sEntiment Reasoner), a lexicon-based sentiment scoring tool validated for short informal texts and the labeled corpus was subsequently used to train and evaluate six supervised classifiers. Six supervised classifiers—Naïve Bayes, k-Nearest Neighbors, Random Forest, Logistic Regression, Gradient Boosting, and Support Vector Machine (SVM)—were trained and evaluated through stratified ten-fold cross-validation using accuracy, AUC, F1-score, and Matthews Correlation Coefficient (MCC). Results indicate that SVM achieved the strongest overall discriminatory performance, particularly in identifying minority sentiment classes under substantial class imbalance. These findings suggest that automated sentiment classification holds practical potential for supporting evidence-based service monitoring and reputation management in glamping tourism, although further validation in operational settings is needed before deployment can be recommended. Full article

Multi-objective flexible job shop scheduling requires balancing conflicting objectives while supporting real-time decision-making in industrial environments. However, although traditional metaheuristics are effective for global search, their high computational cost limits their applicability in time-sensitive scenarios. To address this issue, this paper proposes dual-pool guided preference-conditioned graph reinforcement learning (DPG-GRL), an encoder–decoder framework for the multi-objective flexible job shop scheduling problem. In DPG-GRL, a graph attention network encoder extracts operation and machine-level representations from a heterogeneous graph, while the decoder is conditioned on a preference vector to generate scheduling solutions with different trade-offs using a single trained policy. To improve sample efficiency and training stability, a dual-pool guidance mechanism is introduced, in which an offline expert pool provides a stable behavioral prior for policy initialization and an online elite pool continuously replays high-quality trajectories to refine the policy. Experimental results show that DPG-GRL outperforms representative multi-objective evolutionary algorithms, including the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective evolutionary algorithm based on decomposition (MOEA/D), on synthetic instances, with more pronounced advantages in solution quality and inference efficiency as the problem scale grows. In addition, evaluations on public benchmark instances using a model trained only on the small synthetic setting demonstrate rapid Pareto-front approximation, high-quality solution sets, and promising generalization to unseen instances. These results indicate the potential of DPG-GRL for real-time production scheduling and energy-aware manufacturing. Full article

This study develops and tests an association-based model explaining how consumers interpret AI-enabled personalization in fashion e-commerce and how these interpretations relate to behavioral intentions. Integrating perspectives from Social Exchange Theory, the Antecedents of Trust Model, Self-Determination Theory, Psychological Contract Breach Theory, and Surveillance Capitalism, we examine the joint associations of perceived personalization, transparency, data control, and privacy concerns with brand trust, perceived surveillance, privacy violation perceptions, and purchase intention. Using PLS-SEM with data from 664 online shoppers, we find that personalization, transparency, and data control are each positively associated with brand trust, while personalization and privacy concerns are positively associated with surveillance perceptions. Brand trust is negatively associated with both surveillance and privacy violation perceptions, and privacy violation is negatively associated with purchase intention. Data control is directly associated with lower surveillance perceptions, whereas transparency operates indirectly through brand trust. Mediation analysis reveals that surveillance is associated with lower purchase intention only indirectly through privacy violation (full mediation), identifying perceived privacy violation as the central psychological pathway in the personalization-privacy paradox. Multi-group analysis identifies segment-level variations by gender and education: personalization is a stronger trust cue for men, while transparency is a stronger trust cue for women; trust buffers violation more strongly for higher-educated consumers. The results highlight a trust-first personalization strategy in which relevance must be paired with meaningful transparency and data-control features to mitigate surveillance and violation appraisals, supporting positive consumer outcomes in fashion e-commerce. Full article

Reference-driven humanoid motion tracking aims to reproduce a source motion on a target humanoid while preserving physical executability under actuation limits and changing contact conditions. The problem becomes particularly challenging for dynamic motions involving rapid support transitions, landing impacts, mixed hand–foot contacts, and moderate topology-preserving morphology variation. Existing pipelines often rely heavily on morphology-specific world-frame targets or treat balance and contact quality only indirectly during execution, which limits their reliability under dynamic contact variation. This paper presents a task and cost formulation for reference-driven humanoid tracking within the residual-based MuJoCo model predictive control (MPC) framework. The source motion is decomposed into a pelvis-centered canonical local reference, pelvis height and tilt references, and a pelvis-derived horizontal center-of-mass (CoM) velocity intent, and is tracked online with a zero moment point (ZMP)-aware contact-conditioned residual design including slip, penetration, posture, and control regularization. The formulation is compatible with standard MuJoCo MPC planners, and the evaluation is conducted under a shared iterative linear quadratic Gaussian (iLQG) setting on nominal and morphology-varied humanoids against tracking-only and two-stage inverse-kinematics (IK)-based baselines. The proposed formulation improves success rate, support quality, slip reduction, and progression accuracy, with the clearest gains on contact-sensitive motions; for example, success rate increases from 56.7% to 76.7% on Jump–Turn and from 46.7% to 70.0% on Cartwheel relative to the tracking-only MPC baseline. These results support the use of execution-oriented reference representation and contact-conditioned residual design for physically reliable reference-driven humanoid tracking. Full article

Massively Multiplayer Online Role-Playing Games (MMORPGs) increasingly rely on player-developed third-party tools to extend functionality and personalise gameplay, creating a complex software ecosystem that introduces both usability benefits and security risks. This study investigates whether such tools can be exploited as an attack vector for cybercrime by designing and implementing a proof-of-concept add-on within a widely deployed commercial MMORPG using its native scripting and application programming interface. The developed tool supports automated player discovery, chat capture, target inspection, and local data persistence, enabling a systematic evaluation of how cyber-assisted and cyber-dependent crimes could be facilitated within the game client. Empirical testing demonstrates that while the platform’s protected execution model and interface restrictions prevent direct credential theft and remote code execution, the add-on architecture allows extensive behavioural data collection and social-engineering-relevant monitoring, making several forms of cyber-enabled crime technically feasible. These findings show that MMORPG add-on frameworks represent a non-trivial socio-technical attack vector in next-generation online platforms, where security depends not only on code isolation, but also on how user-generated extensions interact with human behaviour. The results highlight the need for architecture-aware security controls and governance mechanisms to mitigate emerging threats in large-scale, extensible virtual environments. Full article