Search Results (606)

Generation Z, a cohort defined by digital connectivity, sensitivity to social influence, and environmental awareness, has attracted considerable scholarly attention in sustainable consumption research. Yet a persistent gap between their expressed pro-sustainability attitudes and actual purchasing decisions remains well-documented. This study examines whether Gen Z characteristics help bridge that gap by directly influencing sustainable purchase behavior and by moderating the role of purchase intention in that process. A quantitative design was employed using survey responses from 302 Gen Z consumers. The findings suggest that while Gen Z characteristics significantly predicted actual sustainable purchasing and purchase intention exerted a positive direct effect, the interaction between the two was negative and statistically significant. Conditional effects analysis further revealed that the influence of generational characteristics on purchasing behavior is stronger at lower levels of purchase intention and progressively weaker as intention increases. These results suggest that traits such as digital responsiveness, social embeddedness, and environmental orientation do not merely reinforce existing intentions but appear to compensate for their absence, activating sustainability-aligned behavior even when motivational commitment is limited. The study repositions the intention–behavior gap among Gen Z as something modulated by generational characteristics that drive purchasing behavior when intention alone falls short. Full article

Background/Objectives: This study explored parents’ perspectives regarding digital media use in children and adolescents with neurodevelopmental disorders (NDs) and examined how these views vary according to family and clinical characteristics. Methods: Data were collected from an Italian survey involving 352 families. Items assessed the perceived effects of digital devices on child development and parenting, awareness of screen time guidelines, and use of time- and content-limiting tools. Quantitative analyses were complemented by a reflexive thematic analysis of open-ended responses describing how digital media influenced parenting. Results: Parents expressed divergent attitudes towards digital media, with broadly similar proportions reporting positive, neutral, and negative views regarding both child development and parenting. More favourable views were associated with greater perceived benefits for children and were more frequent among parents of children with more severe functional disabilities. About half had discussed screen use with health professionals, and most were aware of existing guidelines. Thematic analysis identified six themes related to digital parenting: educational means (digital devices as tools for communication, learning, and socialisation), entertainment (screens as a source of leisure or behavioural management), reward (digital media used as reinforcement), screen time as a “necessity” (technology as an integral and sometimes rehabilitative part of daily life), negative effects on the child (concerns about detachment, reduced social interaction, and mood dysregulation), and parental behaviour and attitudes (reflecting the emotional burden of regulation and broader beliefs about digital media). Conclusions: Parents of children with NDs navigate digital media use through a complex balance of perceived risks and benefits. Findings highlight the need for family-centred guidance and assistive technology approaches that promote digital inclusion while addressing parental stress and regulatory challenges. Full article

Background: Megaprosthetic reconstruction is the standard of care for limb salvage after tumor resection around the knee, but the full burden of unplanned revision surgery is rarely reported as a structured composite outcome. We evaluated 241 consecutive patients over 21 years at a tertiary orthopedic oncology center. Methods: This retrospective cohort included 241 patients (160 distal femur, 78 proximal tibia, three combined) treated between 2003 and 2024. Revision-free survival (RFS, composite of any unplanned revision or amputation) and amputation-free survival were estimated by Kaplan–Meier analysis; independent predictors were identified by Cox regression. A pre-specified major-event composite (amputation, implant removal, or recurrence resection) was used for sensitivity analysis. Results: Mean age was 34.9 ± 19.5 years; mean follow-up was 120.2 months. Negative resection margin (R0) was achieved in 85.5% (206/241). Unplanned revision was required in 25 patients (10.4%); overall limb salvage was 92.9%. Five-year RFS was 73.8% (distal femur) vs. 65.0% (proximal tibia; p = 0.084), and 5-year limb salvage was 88.9% vs. 84.3% (p = 0.081). Surgical margin was strongly associated with outcome: 5-year RFS 75.4% (R0) vs. 48.7% (R1/R2; p < 0.001); 5-year limb salvage 90.6% vs. 71.5% (p = 0.003). On exploratory multivariate Cox analysis, proximal tibia site and positive margin were associated with worse revision-free survival; within the proximal tibia subgroup, absence of gastrocnemius flap coverage was also associated with worse outcome (interpreted with caution given the small flap subgroup, n = 11, and limited event count). Conclusions: In this single-center series, megaprosthetic reconstruction around the knee achieved acceptable revision-free survival and limb salvage. Surgical margin status was the strongest independent predictor of both endpoints, reinforcing the well-established importance of oncologic margin quality and site-specific soft tissue strategies. Full article

Green finance (GF) is increasingly seen as an important policy tool for promoting sustainable urban development; however, its role in facilitating the green transformation of urban land remains insufficiently understood, particularly from the perspectives of land use efficiency and spatial interactions. This study takes China’s Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment and employs a spatial difference-in-differences framework to examine whether and how GF affects urban land green use efficiency (LGUE). The results indicate that GF significantly improves LGUE in pilot cities, and this finding remains robust across a range of alternative specifications and robustness checks. The mechanism analysis further suggests that GF enhances LGUE primarily by optimizing resource allocation, promoting green innovation, and strengthening information disclosure. In addition, digital development is found to reinforce the positive effects of GF. Compared with existing studies, this paper integrates mechanism analysis with spatial econometric methods to provide a more comprehensive understanding of both the transmission channels and spatial spillover effects of GF. In particular, it provides new evidence on geographically constrained negative spillover effects across cities. The results further indicate that such spillover effects are most pronounced within a 250 km radius, suggesting that GF induces localized inter-city competition and resource reallocation. This finding offers empirical support for understanding the effects of GF from a spatial competition perspective. This study highlights the necessity of coordinating regional policy design to mitigate spatial spillover effects and improve the overall effectiveness of green finance policies. Full article

This study examines how Syrian migrants in Turkey—who generally have temporary protection status—adapt to their new environment, focusing on their sense of belonging, social acceptance, and social interaction. In this research, acculturation is considered not only as a one-way adaptation process but also as a multidimensional and mutually evaluated process that emerges through various variables such as the relationships migrants establish with the host society, their intercultural communication experiences, and their daily life practices. The study, conducted using a qualitative research design, is based on data obtained from in-depth interviews with semi-structured questions conducted with 20 Syrian migrants who have resided in various cities in Turkey for at least 5 years. The data emerging from the interviews were analyzed using descriptive-thematic analysis. The findings reveal that positive social contact and interaction within the social structure reinforce the sense of belonging; conversely, discrimination, exposure to exclusion, and legal uncertainty negatively affect acculturation processes. The study contributes to the literature by providing a context-sensitive analysis of acculturation, emphasizing the role of social interaction, belonging, and social acceptance in shaping migrants’ experiences. Full article

The rapid expansion of the Internet of Things (IoT) has increased the risk of large-scale Distributed Denial-of-Service (DDoS) attacks. In high-availability IoT environments, the operational costs of false positives and false negatives are asymmetric, whereas conventional deep learning models usually optimize static accuracy-based objectives. To address this, we propose CDRL-QNN, a cost-aware and chaos-driven reinforcement learning quantum neural network framework in which a parameterized quantum circuit serves as the action-value function approximator within a Deep Q-Network (DQN) agent. The framework incorporates asymmetric operational penalties through both the reward function and sample-wise weighted Bellman optimization, while a logistic-map-based deterministic perturbation mechanism is used to promote exploration under constrained quantum-circuit training conditions. Evaluated on a computationally constrained balanced subset of the CIC-DDoS2019 dataset, the proposed framework reduced false negatives from 49 to 33 without increasing false positives, improving recall from 0.9673 to 0.9780 and F1-score from 0.9738 to 0.9793 while lowering operational cost. These findings suggest that hybrid quantum representations can be integrated into cost-sensitive reinforcement learning pipelines for IoT intrusion detection under constrained experimental conditions. Full article

Interregional renewable-dominant power systems are increasingly constrained by limited transmission capacity and insufficient support capability in weak-grid areas. This paper proposes a coordinated planning method for transmission expansion and grid-forming energy storage. A two-stage stochastic mixed-integer optimization model is developed in which candidate corridor expansion and grid-forming storage siting and sizing are jointly determined in the planning stage, while conventional generation dispatch, renewable accommodation, storage charging and discharging, and interregional power flows are optimized in the operation stage under multiple load and renewable scenarios. Planning-level support constraints and operational support availability constraints are introduced to represent the structural and operational support roles of grid-forming storage in weak-grid areas. Case studies show that the optimal investment structure combines reinforcement of key transmission corridors with grid-forming storage deployment at critical nodes. Compared with the baseline scheme, the coordinated scheme reduces the annual total cost from CNY 6.842 billion to CNY 6.078 billion, decreases annual renewable curtailment from 426 thousand MWh to 121 thousand MWh, reduces annual unserved energy from 12.8 thousand MWh to 0.5 thousand MWh, and changes the planning-level support margins of weak-grid regions from negative to positive. Additional tail-risk stress tests under high-load and low-renewable conditions further show that the coordinated scheme preserves lower unserved energy and positive support margins. The results indicate that transmission expansion mainly improves interregional resource allocation, whereas grid-forming storage mainly enhances local support capability and operational flexibility. The twn o resources therefore exhibit strong complementarity across both spatial and temporal dimensions. The proposed method provides systematic decision support for renewable energy delivery, backbone grid reinforcement, and grid-forming storage planning. Full article

The bond strength of corroded reinforced concrete (CRC) structures is critical for structural safety and long-term durability. However, the corrosion-induced bond degradation process is influenced by multiple, coupled factors and exhibits complex, nonlinear behavior, making it difficult for traditional theoretical models to provide accurate predictions. To address this challenge, this study proposes a novel, unified prediction framework based on machine learning techniques. A total of 391 experimental datasets were collected and compiled, covering key parameters including bond strength, reinforcing bar diameter, yield strength, concrete cover thickness, concrete compressive strength, mass loss rate due to corrosion, and the presence of stirrups. Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms were employed to develop predictive models for bond strength. Model training and testing were performed using 10-fold cross-validation. Furthermore, the SHapley Additive exPlanations (SHAP) approach was introduced to enhance model interpretability and quantitatively assess the influence of each input feature, revealing that mass loss rate and bar diameter are the dominant factors. This study effectively bridges the research gap between high-precision black-box algorithms and the need for physical interpretability in engineering. The results demonstrate that (1) the proposed XGBoost model significantly outperforms traditional empirical formulations, achieving a high coefficient of determination (R² = 0.893) and a much lower coefficient of variation (25.85%) on the testing set, and (2) the SHAP analysis reveals that the machine learning predictions are highly consistent with established physical mechanisms, successfully capturing the negative impact of splitting tensile stresses caused by rust expansion and the positive confinement effect of stirrups. Overall, the proposed models demonstrate superior accuracy, robustness, and generalization capability, providing an effective tool and theoretical basis for evaluating bond behavior and designing durable CRC structures with broad engineering applicability. Full article

This study examines the determinants of creative goods exports within a multidimensional openness framework that integrates trade, cultural, and political openness. Its importance stems from the growing strategic role of the creative economy in international trade and sustainable development, particularly under conditions of deglobalization pressures, re-globalization, and geoeconomic fragmentation. While previous empirical research has largely treated openness dimensions separately, this study argues that their interaction may offer a more accurate explanation of creative export performance. Using unbalanced panel data for 13 countries from the MENA region over the period 2002–2022, the study applies a Panel ARDL model estimated through the Pooled Mean Group (PMG) approach to identify both short-run dynamics and long-run equilibrium relationships. The analysis focuses on whether political openness reinforces the effects of cultural and trade openness on creative goods exports. The findings reveal a stable long-run relationship among the variables. Cultural openness and political openness exert positive and significant long-run effects on creative goods exports, whereas trade openness does not appear significant in isolation. The interaction between cultural and political openness is positive and significant, indicating that institutional openness enhances the export benefits of cultural integration. By contrast, the interaction between trade and political openness is negative in the long run, suggesting diminishing marginal gains under stronger institutional integration. Overall, the study highlights that the sustainability of creative exports relies more on the interplay between cultural connectivity and institutional quality than solely on trade liberalization. Full article

The construction industry is one of the most hazardous occupational sectors globally, with persistently high rates of worker injuries and fatalities. This study examined the association between safety motivation and safety climate among construction workers, addressing a critical gap in understanding their bidirectional relationship. A cross-sectional survey was administered to 922 construction workers across multiple commercial projects within a single U.S. state, yielding 383 valid responses (41.5% response rate). The survey instrument measured safety motivation types (intrinsic, extrinsic, and negative) and multiple safety climate dimensions, including leadership and communication, safety procedures and training, peer support, recognition, and equipment availability. The results revealed that safety motivation demonstrated a significant positive correlation with overall safety climate (r = 0.467, p < 0.01), with leadership and communication showing the strongest association (r = 0.514, p < 0.01). Analysis of motivation types indicated that negative motivation (fear of accidents) predominated (41%), followed by extrinsic (34%) and intrinsic motivations (25%). The findings support a reciprocal relationship wherein safety motivation and safety climate mutually reinforce one another, influencing safety performance and outcomes. The study highlights the need for safety interventions that simultaneously address organizational climate factors and diverse individual motivational pathways to improve safety performance in the construction industry. Full article

Cash dividends, as a tangible form of monetary distribution, serve as a fundamental mechanism for remunerating investors for their capital commitments. Beyond manifesting a firm’s commitment to fulfilling its social responsibilities toward shareholders, such distributions potentially shape corporate deliberations regarding accountability toward a broader spectrum of stakeholders. Drawing on behavioral explanations of corporate decision-making, this study examines the association between cash dividend payouts and environmental protection tax burdens among Chinese A-share listed companies from 2018 to 2023. The empirical results indicate a significant and robust negative association between corporate cash dividend payouts and environmental protection tax burdens. Mechanism analysis suggests that this cross-domain behavioral consistency is primarily channeled through the proactive fulfillment of corporate environmental responsibilities. Further inquiry reveals that both government environmental subsidies and media coverage exert positive moderating effects on this relationship. Notably, this observed negative association is particularly pronounced in firms characterized by lower executive environmental awareness, those operating in regions with lenient environmental regulations, companies navigating economic downturns, and those situated within low-pollution industries. This research provides novel evidence for the “governance complementarity” hypothesis, suggesting that financial accountability and environmental stewardship are mutually reinforcing rather than mutually exclusive. Furthermore, it offers a pioneering micro-behavioral perspective on how firms in emerging economies can harmonize shareholder wealth distribution with green transition objectives. Full article

Industrial Internet of Things (IIoT) and SCADA-connected networks are increasingly vulnerable to Distributed Denial of Service (DDoS) attacks, which can disrupt time-sensitive industrial processes and compromise operational continuity. Effective mitigation requires accurate and low-latency attack detection at the network edge, where industrial gateways operate under strict constraints in computation, memory, and energy. This study investigates Deep Reinforcement Learning (DRL) for real-time binary DDoS detection and proposes a detector based on Proximal Policy Optimisation (PPO) for deployment in resource-constrained IIoT environments. Four DRL agents, namely Deep Q-Network (DQN), Double DQN, Dueling DQN, and PPO, are trained and evaluated within a unified experimental pipeline incorporating automatic label mapping, numerical feature selection, robust scaling, and class balancing. Experiments are conducted on three representative benchmark datasets: CIC-DDoS2019, Edge-IIoTset, and CICIoT23. Performance is assessed using accuracy, precision, recall, F1-score, false positive rate, false negative rate, and CPU inference latency. The reward function is asymmetric: +1 for correct classification, −1 for false positive, and −2 for false negative, penalising missed attacks more heavily for IIoT safety. The results show that PPO provides a competitive accuracy–latency tradeoff across all three datasets, achieving the highest mean accuracy of 97.65% and ranking first on CIC-DDoS2019 with a score of 95.92%, while remaining competitive on Edge-IIoTset (99.11%) and CICIoT23 (97.92%). PPO also converges faster than the value-based baselines. Inference latency is below 0.8 ms per sample on a standard CPU (Intel i7-11800H), confirming real-time feasibility. To support practical deployment, the trained PPO policies are exported to ONNX format (≈9 KB per model), enabling lightweight and PyTorch-independent inference on industrial edge gateways. Full article

Multilingual learning is key in natural language processing, but is challenged by the transfer–interference trade-off, where positive transfer benefits certain languages, while negative interference affects others. Prior methods, including linguistic-based and embedding-based language clustering, have attempted to address this; yet, they remain constrained by their static design and lack of task-specific feedback. In this study, we propose a novel computational strategy inspired by molecular design that constructs molecules with targeted properties. Languages are modeled as nodes in an undirected graph, with edges representing the transfer strength. This language molecule is optimized via Reinforcement Learning to adjust edge connections and weights to enhance positive transfer and minimize interference, where graph clustering is applied, and clusters are then evaluated on the Named Entity Recognition and POS tagging tasks using 25 languages from the WikiANN dataset and 12 typologically diverse languages from the UDPOS dataset. Compared to linguistic and embedding-based language clustering baselines, our method yields substantial improvements, especially for low-resource languages, with some showing over 35% increase in F1 score, while high-resource languages benefit moderately, confirming reduced transfer–interference trade-off. Our atom–language model offers a novel path for multilingual learning, inspired by molecular principles from physical sciences. Full article

Sentiment analysis of online game reviews has increasingly become important in understanding player experiences and supporting data-driven game development. However, research in this domain has continuously faced two unresolved challenges: (1) the extreme imbalance between positive and negative feedback, and (2) the inefficiency of existing feature-weighting schemes in capturing sentiment signals embedded in informal gaming discourses. Prior works demonstrated that negative feedback—though a few in number are highly influential—usually contain richer emotional content and longer textual structures; yet, prevailing classification models often perform poorly for these minorities (i.e., negative feedback). Numerous studies explored multimodal imbalance issues, class imbalance in cross-lingual ABSA (Aspect-Based Sentiment Analysis), reinforcement-learning-based architectures for imbalanced extraction tasks, and oversampling strategies like SMOTE (Synthetic Minority Over-sampling Technique) variants. Few investigations specifically addressed imbalanced sentiment classification in the contexts of online game reviews, where user-generated content exhibits unique lexical, structural, and emotional characteristics. To address these gaps, this study integrated TF-IDF (Term Frequency-Inverse Document Frequency), VADER (Valence Aware Dictionary and Sentiment Reasoner) lexicon features, and IGM (Inverse Gravity Moment) weightings with advanced oversampling methods such as ADASYN (Adaptive Synthetic Sampling Approach for Imbalanced Learning) and Borderline-SMOTE to improve the detection of minority sentiment classes. Ensemble models, including XGBoost (Extreme Gradient Boosting) and LightGBM (Light Gradient-Boosting Machine), were further employed to enhance the robustness of imbalance. Using a large-scale dataset of Steam game reviews, the proposed framework demonstrated substantial improvement in identifying negative sentiments, addressing a critical limitation in the existing computational game-analysis literature, and advancing the modeling for detecting the emotion-rich but imbalance-prone user feedback. Full article

Background: People living with HIV (PLWH) constitute a vulnerable population during the COVID-19 pandemic; however, it remains uncertain whether long-term suppressive antiretroviral therapy (ART) restores sufficient immune competence to support robust hybrid immunity. While vaccination followed by breakthrough infection—termed hybrid immunity—typically elicits potent humoral responses in immunocompetent individuals, the functional quality and breadth of these responses against evolving Omicron subvariants remain poorly characterized in PLWH. This study aimed to assess functional antibody responses, including neutralizing activity and Fc effector functions, in vaccinated and unvaccinated PLWH who experienced breakthrough infection with Omicron subvariants BA.4/5 or BF.7. Methods: We enrolled three cohorts between December 5 and December 20, 2022: 25 HIV-negative individuals with breakthrough infection (BTI-HC), 20 ART-experienced PLWH with breakthrough infection following three-dose COVID-19 vaccination (BTI-HIV), and 10 ART-experienced PLWH with primary infection without prior vaccination (PI-HIV). All HIV-positive participants were receiving suppressive ART with regimens based on non-nucleoside reverse transcriptase inhibitors or integrase strand transfer inhibitors for a median of 3.4 years. We measured receptor-binding domain (RBD)-specific IgG, neutralizing antibody titers against ancestral D614G, Delta, BA.1, BA.4/5, BF.7, XDV, KP.2, and KP.3 variants, and antibody-dependent cellular cytotoxicity (ADCC) responses. Results: Despite lower absolute CD4⁺ T cell counts, BTI-HIV participants mounted RBD-binding IgG, neutralizing antibody, and ADCC responses that were comparable to BTI-HC and significantly exceeded PI-HIV across all tested variants. Both breakthrough infection cohorts exhibited immunological imprinting, with higher neutralizing titers against ancestral D614G than infecting BA.4/5 or BF.7 variants. Emerging variants XDV, KP.2, and KP.3 demonstrated substantial neutralization escape in all groups. PI-HIV showed markedly diminished neutralization breadth and failed to generate enough responses against all tested Omicron strains. Conclusions: Suppressive ART enables PLWH to mount hybrid immunity—conferred by vaccination followed by BF.7 or BA.4/5 breakthrough infection—with neutralizing and ADCC responses comparable to HIV-negative individuals, and significantly exceeding those of unvaccinated PLWH with primary infection. This underscores the critical role of vaccination in establishing effective hybrid immunity in this population. However, we observed immunological imprinting, with higher titers against ancestral strains than against infecting variants, and substantial escape by emerging sublineages XDV, KP.2, and KP.3 across all groups. These findings support prioritizing updated variant-containing vaccines for HIV-positive populations and reinforce the essential role of vaccination in this vulnerable group. Full article