Search Results (18,494)

Prosocial behavior—children’s capacity to empathize, share, and cooperate—is essential for healthy emotional and social development. While family context plays a critical role in fostering these behaviors, the influence of overall family functioning remains underexplored. This study examines the association between perceived family functioning and prosocial behavior in school-aged children. A cross-sectional design was employed with a sample of 127 parents of children aged 6 to 12 years. Family functioning was assessed using the General Functioning Subscale of the McMaster Family Assessment Device (FAD), and child prosocial behavior was measured via the parent-reported Strengths and Difficulties Questionnaire (SDQ). Analyses included Pearson correlations, linear regression, and group comparisons by gender, education, employment status, and subjective socioeconomic status. Higher family functioning (i.e., lower FAD scores) significantly predicted higher prosocial behavior scores (β = −1.48, R² = 0.12, p < 0.001). A significant difference in prosocial behavior was found between socioeconomic groups, with children from high-income families scoring significantly higher than those from middle-income families. However, family functioning accounted for a greater proportion of the variance than socioeconomic status. These findings underscore the role of family relational quality in shaping children’s social development and highlight its importance in early interventions. Full article

The manufacturing supply chain has been exposed to natural disasters and geopolitical risks whose impacts, such as disruptions in the supply of materials and parts, can be devastating. In recent years, the data space has become more widely implemented, and it is expected to be used as a platform for widespread collaboration between companies. This article discusses how companies participating in the manufacturing supply chain cooperate to recover from disruption and mitigate risks using a data space platform and a flexible manufacturing system. Employing enterprise architecture modeling, we explore a comprehensive strategy for enhancing the resilience of a data space-based manufacturing supply chain. The proposed strategy adopts a comprehensive approach to addressing physical security and cybersecurity risks from a security perspective. By combining enterprise architecture modeling with the Unified Architecture Framework and conducting a scenario-based simulation, we discovered that an alternative manufacturing process with a flexible method in the data space can be a key security control measure for mitigating the risk associated with parts supply. The results of the alternative manufacturing simulation show that flexible manufacturing using BJT and MIM methods elicits better performance in terms of parts production volume and cost compared with conventional methods. The proposed method and the findings of this study contribute to consolidating a profound understanding of security and the mitigation of disruptive situations in a data space-based manufacturing supply chain. Full article

Sentiment analysis is a cornerstone in many contextual data analyses, from opinion mining to public discussion analysis. Gender bias is one of the well-known issues in sentiment analysis models, which can produce different results for the same text depending on the gender it refers to. This gender bias leads to further bias in other text analyses that use such sentiment analysis models. This study reviews existing solutions to reduce gender bias in sentiment analysis and proposes a new method to address this issue. The proposed method offers more practical flexibility as it focuses on sentiment estimation rather than model training. Furthermore, it provides a quantitative measure to investigate the gender bias in sentiment analysis results. The performance of the proposed method across five sentiment analysis models is presented using texts containing gender-specific words. The proposed method is applied to a set of social media posts related to Morocco’s 2023 earthquake to estimate the gender-unbiased sentiment of the posts and evaluate the gender-unbiasedness of five different sentiment analysis models in this context. The result shows that, although the sentiments estimated with different models are very different, the gender bias in none of the models is drastically large. Full article

The proliferation of small, agile unmanned aerial vehicles (UAVs) has exposed the limits of single-sensor surveillance in cluttered airspace. We propose an Internet of Things-enabled integrated sensing and communication (IoT-ISAC) framework that converts cellular base stations into cooperative, edge-intelligent sensing nodes. Within a four-layer design—terminal, edge, IoT platform, and cloud—stations exchange raw echoes and low-level features in real time, while adaptive beam registration and cross-correlation timing mitigate spatial and temporal misalignments. A hybrid processing pipeline first produces coarse data-level estimates and then applies symbol-level refinements, sustaining rapid response without sacrificing precision. Simulation evaluations using multi-band ISAC waveforms confirm high detection reliability, sub-frame latency, and energy-aware operation in dense urban clutter, adverse weather, and multi-target scenarios. Preliminary hardware tests validate the feasibility of the proposed signal processing approach. Simulation analysis demonstrates detection accuracy of 85–90% under optimal conditions with processing latency of 15–25 ms and potential energy efficiency improvement of 10–20% through cooperative operation, pending real-world validation. By extending coverage, suppressing blind zones, and supporting dynamic surveillance of fast-moving UAVs, the proposed system provides a scalable path toward smart city air safety networks, cooperative autonomous navigation aids, and other remote-sensing applications that require agile, coordinated situational awareness. Full article

Non-cooperative games in normal form are specified by a player set, sets of player strategies, and payoff functions. Cooperative games, meanwhile, are specified by a player set and a worth function that maps coalitions of players to payoffs they can feasibly achieve. Although these games study distinct aspects of social behavior, this paper proposes a novel attempt at relating the two models. In particular, cooperative games may be represented by a non-cooperative game in which players can freely sign binding agreements to form coalitions. These coalitions inherit a joint strategy set and seek to maximize collective payoffs. When these coalitions play against one another, the equilibrium payoffs for each coalition coincide with what is predicted by the worth function. This paper proves sufficient conditions under which cooperative games can be represented by non-cooperative games. This paper finds that all strictly superadditive partition function form (PFF) games can be represented under Nash equilibrium (NE) and rationalizability; that all weakly superadditive characteristic function form (CFF) games can be represented under NE; and that all weakly superadditive PFF games can be represented under trembling hand perfect equilibrium (THPE). Full article

China has adopted extensive restoration practices to improve ecosystem function. The efficiency of these restoration efforts remains unclear, which may hinder the supply of ecosystem services (ESs). In this context, this study first employed InVEST models to clarify spatio-temporal changes in five key ESs. The static and dynamic efficiencies of ecosystem service production in 130 cities from 2015 to 2021 in the Yangtze River Economic Belt (YREB) were then measured using the Super-SBM-Malmquist model, with ESs considered as outputs. The results indicated that water conservation (WC), water purification (WP), and soil retention (SR) exhibited overall declining trends, decreasing by 28.32%, 3.22%, and 10.00%, respectively, while carbon storage (CS) and habitat quality (HQ) remained steady. More than 70% of studied cities exhibited static efficiency levels below 50%, which were attributed to inefficient utilization of labor, capital, and technology. Significant spatial heterogeneity was observed, with high-efficiency cities mainly located in mountainous areas and low-efficiency cities concentrated in flat regions. The downward trend in dynamic efficiency has been reversed from a 39.02% decline in 2015–2018 to a 38.31% increase in 2018–2021, despite being adversely affected by technological regression. Finally, several policy implications are proposed, including optimizing resource allocation, introducing advanced technology and setting the intercity cooperation and complementarity mechanisms. Full article

There are no recorded cases of catheter-induced aortic dissection in pediatric patients. We report a unique case of a pediatric patient with Down syndrome who developed a long-standing dissecting aortic aneurysm. The patient underwent successful stent insertion 10 years after experiencing difficulty retrieving an embolized patent ductus arteriosus device. The Down syndrome presented a complex clinical scenario, making diagnosis challenging due to a lack of cooperation and uncertainty about when the dissection occurred, as symptoms like pain were not reported. Though rare in children, it is vital to recognize procedures such as percutaneous closure of patent ductus arteriosus followed by device retrieval as potential risk factors for aortic dissections in the pediatric population. Full article

With the aim of addressing the power quality problem associated with voltage fluctuation of multiple electric vehicles and renewable energy generation equipment integration into the AC micro-grid, a multi-agent system-based model-free adaptive constrained control method is proposed in this paper. First, a novel hierarchical control structure is developed. Therein, the upper-level cooperative controller is designed based on the directed graph and droop control strategy, enabling efficient power distribution among multiple electric vehicles. For the lower-level voltage controller, a model-free adaptive constrained control strategy is designed, incorporating a pseudo-partial derivative-based output observer, and an anti-windup compensator is designed to solve the voltage fluctuation problem, which achieves precise tracking of each electric spring output voltage. Finally, the effectiveness and superiority of the proposed control strategy is verified by the MATLAB/Simulink platform under scenarios of grid-side voltage fluctuations and load variations. Full article

Taiwan accounts for 90% of the total oolong tea production and enjoys a good global reputation for its quality. In recent years, oolong tea from neighboring countries has been imported into Taiwan and sold as Taiwanese oolong at high prices. This study aimed to rapidly classify oolong tea from four geographical origins (Taiwan, Vietnam, China, and Indonesia) using an electronic nose (E-nose) combined with machine learning. Color measurements were also conducted to support the classification. The electronic nose (E-nose) was utilized to analyze the aroma profiles of tea samples. To classify the samples, five machine learning models—linear discriminant analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), and random forest (RF)—were developed using 70% of the dataset for training and tested on the remaining 30%. Gray relational analysis (GRA) was applied to measure the relationship between sensor responses and reference tea origins. Multivariate analysis of variance (MANOVA) indicated a statistically significant effect of tea origin on color parameters, as confirmed by both Pillai’s trace and Wilks’ Lambda (Λ) tests (p = 0.000 < 0.05). Among the tested models, LDA and ANN achieved the highest overall classification accuracy (98.33%), with ANN outperforming in the discrimination of Taiwanese oolong tea, achieving 98.89% accuracy. GRA presented higher gray relational grade (GRG) values for Taiwanese tea samples compared to other origins and identified sensors S4, S6, and S14 as the dominant contributors. In conclusion, the E-nose combined with machine learning provides a rapid, non-destructive, and effective approach for geographical origin classification of oolong tea. Full article

The increasing integration of autonomous driving technologies into heavy-duty road transport requires a clear understanding of how these systems affect professional drivers’ working time, vehicle utilization, and regulatory compliance. This study develops a model-based comparative analysis to assess the cooperation between human drivers and autonomous trucks at SAE Levels 3 and 4. Using EU Regulation (EC) No 561/2006 as a legal framework, single-driver, double-driver, and ego vehicle scenarios were simulated to evaluate changes in working time classification and vehicle movement. The results indicate that Level 3 automation enables up to 13.25 h of daily vehicle movement while complying with working time regulations, compared with the 10-h limit for conventional operation. Level 4 automation further extends the effective movement time to 14.25 h in double-crew configurations, offering opportunities for increased efficiency without violating labor codes. The novelty of this work lies in the quantitative modeling of human–machine collaboration in professional transport under real regulatory constraints. These findings provide a foundation for regulatory updates, tachograph adaptation to AI-driven vehicles, and the design of hybrid driver roles. Future research will focus on validating these models in real-world transport operations and assessing the implications of Level 5 autonomy for logistics networks and labor markets. Full article

Antimicrobial resistance (AMR) has reached a critical situation globally, prompting urgent national responses to this escalating crisis, including the prioritization of novel antibiotic research. In 2016, Japan initiated a national AMR action plan that promoted appropriate antibiotic use in the country and encouraged a national environment conducive to mitigation measures. However, tackling AMR remains difficult. From an epidemiological perspective, this challenge now extends beyond severe infections, impacting common community-acquired infections, including uncomplicated urinary tract infections (uUTls) and gonorrhea. In uUTIs, the rising prevalence of extended-spectrum β-lactamase-producing and fluoroquinolone-resistant Escherichia coli diminishes the effectiveness of current, routinely used oral antibiotics, necessitating an exploration into innovative solutions. Similarly, the growing resistance of Neisseria gonorrhoeae to antibiotics such as azithromycin raises concerns about the efficacy of current therapeutic options for gonorrhea, which is a highly prevalent sexually transmitted infection. In Japan, since the removal of azithromycin as the recommended first-line treatment, there are no oral first-line antibiotics available to treat gonorrhea. Therefore, novel oral antibiotics are urgently needed for both serious and commonly occurring community-acquired infections. This narrative review discusses the limited availability of novel antibiotics in Japan, the distinctive features of the Japanese antibiotic repertoire and AMR epidemiology, and potential alternative oral treatments for community-acquired infections, including uUTIs and gonorrhea. Japan has been making significant advances toward tackling the AMR crisis through an updated national action plan, AMR policy changes, and innovative approaches to developing novel antibiotics. Substantial international cooperation and the engagement of diverse industry sectors are essential to address the pressing issue of AMR. Full article

The efficacy of Lagerstroemia speciosa (banaba) leaf extract (BLE), policosanol (POL), and their combination (BLE+POL) was evaluated in zebrafish (Danio rerio) against high cholesterol (HC)- and galactose (HG)-induced metabolic stress and organ toxicity. After 12 weeks of dietary intervention, BLE+POL significantly reduced HC+HG-augmented weight gain and improved hepatic and nephromegaly. Compared with BLE or POL alone, the combined intake of BLE+POL more effectively alleviated dyslipidemia and blood glucose levels. Likewise, BLE+POL effectively reduced blood malondialdehyde (MDA), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) levels and boosted plasma sulfhydryl content, ferric ion reduction ability (FRA), and paraoxonase (PON) activity. Histological outcomes suggest that BLE+POL has higher efficacy than either BLE or POL in mitigating HC+HG-induced fatty liver changes, hepatic inflammation, kidney senescence, and reactive oxygen species (ROS) production. Consistently, BLE+POL augmented the spermatozoa counts in the testes, enhanced mature vitellogenic oocytes in ovaries, and protected them from the HC+HG-induced oxidative stress. Compared with either BLE or POL, a combined intake of BLE+POL displayed a superior effect in inhibiting the apoptosis and accumulation of lipid peroxidation species 4-hyrdoxynonenal (4-HNE) in the brain. A combined intake of BLE+POL exhibited a pronounced impact than the BLE and POL alone and can be utilized as an effective formulation to counteract the HC+HG-induced events. Full article

Electron beam physical vapor deposited (EB-PVD) thermal barrier coatings (TBCs) are widely used to protect the hot section parts of aircraft engine turbines due to its uniform columnar microstructure and high strain tolerance. The microstructure and thermal shock life of 7 wt% Y₂O₃ stabilized zirconia (YSZ) coatings produced by EB-PVD were investigated as a function of oxygen gas flow during deposition. The surface and cross-section microstructure of EB-PVD YSZ coatings were highly influenced by the oxygen gas flow. When the oxygen gas flow is less than 60 sccm, a sandwich is formed between the bond coat (BC) layer and the YSZ layer, which significantly reduces the thermal shock life of the coating. Full article

The rapid development of the global transportation industry has led to increased carbon dioxide emissions, intensifying the pressure to reduce these emissions. On the basis of constructing a global carbon emission analysis framework for the transportation industry, this study used panel data on carbon emissions from the transportation industry in 140 countries or regions for a long-term time series from 1971 to 2018. The standard deviation ellipse, Gini coefficient, and Moran’s I index were used to characterize the spatial patterns of carbon emissions in the global transportation industry. The factors influencing carbon emissions from the global transportation industry were analyzed using quantile regression. The main findings are as follows: (1) From the distribution pattern, the total carbon emissions from the global transportation industry showed a significant upward trend, and the spatial polarization characteristics were particularly significant. (2) The Gini coefficient of global carbon emissions from the transportation industry showed a significant downward trend, characterizing a more balanced spatial distribution. (3) From the perspective of correlation patterns, the spatial distribution of carbon emissions from the global transportation industry was positively correlated. (4) Regarding influencing factors, population size had a significant role in promoting carbon emissions from the transportation industry, and the difference was not apparent. The influence of affluence on carbon emissions was basically in line with the characteristics of the Kuznets curve, technological advances had a significant negative influence on carbon emissions, and participation in the global value chain had a significant influence on carbon emissions from countries or regions with high carbon emissions. In conclusion, it is necessary to enhance international cooperation on carbon emission management in the global transportation industry and adopt differentiated policy measures. For instance, we should accelerate the construction of a multimodal transport system, increase the promotion and support for new energy heavy-duty trucks, implement policies such as priority road rights for new energy heavy-duty trucks and reduce tolls on expressways, and deepen the integration of transportation and energy. Full article

Activated carbon (AC) and biochar (BC) are porous substances derived from any carbonous material known to be highly effective adsorbents, making them valuable for removing pollutants like heavy metals. This study evaluated and compared the potential of AC and BC produced from Sargassum sp. by chemical activation and pyrolysis process for heavy metal removal, specifically Co²⁺ ions, to commercial AC (COMAC). Various techniques were employed to characterize these samples including FTIR, zeta potential, and surface area. Additionally, considering parameters such as pH, initial solution concentration, and the effect of AC/BC dose were investigated. The adsorption isotherm was also assessed. The results showed that a strong dependence of the adsorption capacity on pH was observed with optimal performance at ~6.8. Additionally, the optimal initial solution concentration was determined to be ~2 mmol/L. According to the Langmuir isotherm model, AC derived-Sargassum sp. exhibited maximum uptakes of 468.97 mg/g, higher than COMAC and BC. The experiment at different adsorbent dosages revealed that AC from Sargassum sp. outperformed other samples, with adsorption capacity observed at 94.94% as the dosage increased. Full article