Search Results (18,644)

Urban green spaces are essential for promoting human health and well-being, especially in cities facing increasing noise pollution and ecological stress. This study investigates the effects of audio-visual interaction on restorative outcomes across three soundscape types (park, residential, and street), focusing on the compensatory role of positive visual stimuli in low-quality soundscape environments. Thirty-two university students participated in a controlled evaluation using soundscapes and corresponding visual materials derived from 30 urban green spaces. A two-way repeated measures ANOVA revealed significant main effects of soundscape type and modality (auditory vs. audio-visual), as well as a significant interaction between these factors. Audio-visual conditions consistently outperformed auditory conditions, with the strongest restorative effects observed in noisy street soundscapes when paired with positive visual stimuli. Further analysis highlighted that visual cleanliness and structural clarity significantly enhanced restorative outcomes in challenging environments. These findings align with existing theories of sensory integration and extend their application to large-scale urban settings. This study shows that multi-sensory optimization can mitigate urban environmental stressors, supporting healthier, more resilient, and sustainable urban environments. Future research should explore long-term and cross-cultural applications to inform evidence-based urban planning and public health policies. Full article

While the ‘smart city’ concept is central to urban innovation, promising enhanced efficiency and livability, this paper interrogates a critical paradox: can cities be ‘smart’ yet ‘unlivable’? Existing indices, such as the IMD Smart City Index and the IESE Cities in Motion Index, while standard references, tend to prioritize technological and economic metrics, potentially failing to fully capture urban quality of life and sustainability. This study presents a preliminary attempt, based on an analysis of scientific literature, to critically examine current smart city indicators and propose a set of alternative indicators more representative of quality of life (QoL) and livability. The objective is not to overturn the rankings of cities like Zurich (high-ranking) and Athens (low-ranking), but to explore how a livability-focused approach, using more representative QoL indicators, might narrow the perceived gap between them, thereby highlighting diverse dimensions of urban performance. This research critically evaluates current smart city rankings. It aims to determine if livability-based indicators, supported by scientific literature, can provide a more balanced view of urban performance. This paper details how these alternative indicators were chosen, justifying their relevance to QoL with scientific support, and maps them to established smart city verticals (Smart Mobility, Smart Environment, Smart Governance, Smart Living, Smart People, Smart Economy). Finally, it outlines future research directions to further develop and validate this human-centric approach. Full article

Smart contracts are central to blockchain ecosystems, yet their development remains technically demanding, error-prone, and tied to platform-specific programming languages. This paper introduces SCEditor-Web, a web-based modeling environment that combines model-driven engineering (MDE) with generative artificial intelligence (Gen-AI) to simplify contract design and code generation. Developers specify the structural and behavioral aspects of smart contracts through a domain-specific visual language grounded in a formal metamodel. The resulting contract model is exported as structured JSON and transformed into executable, platform-specific code using large language models (LLMs) guided by a tailored prompt engineering process. A prototype implementation was evaluated on Solidity contracts as a proof of concept, using representative use cases. Experiments with state-of-the-art LLMs assessed the generated contracts for compilability, semantic alignment with the contract model, and overall code quality. Results indicate that the visual-to-code workflow reduces manual effort, mitigates common programming errors, and supports developers with varying levels of expertise. The contributions include an abstract smart contract metamodel, a structured prompt generation pipeline, and a web-based platform that bridges high-level modeling with practical multi-language code synthesis. Together, these elements advance the integration of MDE and LLMs, demonstrating a step toward more accessible and reliable smart contract engineering. Full article

Using the “Policy Pressure-Innovation Alignment-Performance Transformation” theory, this paper looks at how ESG ratings, green innovation, and corporate dual-organizational performance are linked. This study uses a multi-period Difference-in-Differences (DID) model in conjunction with a conditional mediation effect model to examine how ESG ratings causally influence substantive green innovation, which in turn improves corporate financial and environmental performance. Regression results show that corporate ESG ratings have a big effect on the performance of both organizations. ESG ratings have a bigger effect on financial performance, while ESG scores have a bigger effect on environmental performance. Looking at the sub-dimensions shows that policy ratings have immediate effects on environmental performance and delayed effects on financial performance. The conclusion that the internalization response of corporate environmental costs is timely, while the market revaluation has a delayed transmission effect, holds true after being tested through parallel trend analysis and synthetic DID testing. More research shows that differences in ESG ratings hurt financial performance but help environmental performance. This means that differences in ESG ratings may lead to more real green innovation activities, which have a direct effect on the environment and, in the end, lead to bigger improvements in environmental performance. The moderating effect test shows that being aware of the environment makes substantive green innovation more focused on quality by making people feel responsible for their actions. Also, environmental management leads to more corporate green patents, which has resource displacement effects and makes green patent innovations less effective. Heterogeneity analysis shows that state-owned businesses use their institutional advantages to improve the “quality-quantity” of substantive green innovation, which helps their corporate green development performance. Declining businesses push for green innovation to fix problems that are already there, but mature businesses don’t like ESG rating policies because they are stuck in their ways, which stops them from making real progress in green innovation. This paper ends with micro-level evidence and theoretical support to solve the “greenwashing” problem of ESG and come up with “harmonious coexistence” policy combinations that work for businesses. Full article

This study constructs a conceptual model based on the relationship between corporate social responsibility (CSR) and organizational resilience based on stakeholder theory, resource dependence theory, information asymmetry theory, and signaling theory, and it uses the panel data of Shanghai and Shenzhen A-share listed enterprises in the period of 2010–2021 to conduct empirical research. The results show that (1) corporate social responsibility helps to reduce financial volatility and promote performance growth, which, in turn, contributes to organizational resilience; (2) CSR shapes the enhancement of organizational resilience mainly through three aspects: improving the corporate information environment, easing corporate financing constraints, and improving technological innovation; (3) the effect of CSR on organizational resilience varies according to the degree of board diversity within the enterprise and the degree of regional marketization outside the enterprise, and the enhancement effect of CSR on organizational resilience is more pronounced when the degree of board diversity and the degree of regional marketization are higher. This study provides theoretical support for CSR-enabled organizational resilience in the era of high-quality development, as well as suggestions for strengthening the level of organizational resilience. Full article

Identifying tobacco smoke exposure in indoor environments is critical for public health, especially in vulnerable populations. In this study, we developed and validated a machine learning model to detect smoking households based on indoor air quality (IAQ) data collected using low-cost sensors. A dataset of 129 homes in Spain and Austria was analyzed, with variables including PM_2.5, PM₁, CO₂, temperature, humidity, and total VOCs. The final model, based on the XGBoost algorithm, achieved near-perfect household-level classification (100% accuracy in the test set and AUC = 0.96 in external validation). Analysis of PM_2.5 temporal profiles in representative households helped interpret model performance and highlighted cases where model predictions revealed inconsistencies in self-reported smoking status. These findings support the use of sensor-based approaches for behavioral inference and exposure assessment in residential settings. The proposed method could be extended to other indoor pollution sources and may contribute to risk communication, health-oriented interventions, and policy development, provided that ethical principles such as transparency and informed consent are upheld. Full article

This study investigates the firm- and country-level determinants that influence the extent of financial disclosure under International Financial Reporting Standards (IFRS) in selected Latin American Organisation for Economic Co-operation and Development (OECD) members or countries in the accession process in the period under analysis. Using a sample of 168 publicly listed companies from Argentina, Chile, Colombia, Mexico, and Peru, we construct a self-developed disclosure index based on compliance with International Accounting Standards IAS 16 (Property, Plant and Equipment) and IAS 2 (Inventories). These standards were selected due to their relevance across a broad range of sectors in emerging markets. Drawing on agency theory, stakeholder theory, institutional theory, signaling theory, and legitimacy theory, we examine how internal firm characteristics, macroeconomic performance, and institutional quality impact disclosure practices. Our empirical findings show that firm size, leverage, Gross Domestic Product (GDP) growth, and shareholder protection have a positive and statistically significant influence on the level of IFRS disclosure. However, not all institutional variables are equally effective, highlighting the complex interplay between regulatory environments and corporate reporting behavior in developing countries. The study contributes to the ongoing debate on the applicability and effectiveness of IFRS in emerging economies by offering evidence from underexplored Latin American markets and emphasizing the need for context-specific policy and regulatory interventions. Full article

Symmetry is an essential characteristic in both solution spaces and cloud task scheduling loads, as it reflects a structural balance that can be exploited to enhance algorithmic efficiency and robustness. In recent years, with the rapid development of 6G networks, the number of tasks requiring computation in the cloud has surged, prompting an increasing number of researchers to focus on how to efficiently schedule these tasks to idle computing nodes at low cost to enhance system resource utilization. However, developing reliable and cost-effective scheduling schemes for cloud computing tasks in real-world environments remains a significant challenge. This paper proposes a method for cloud computing task scheduling in real-world environments using an improved dhole optimization algorithm (IDOA). First, we enhance the quality of the initial population by employing a uniform distribution initialization method based on the Sobol sequence. Subsequently, we further improve the algorithm’s search capabilities using a sine elite population search method based on adaptive factors, enabling it to more effectively explore promising solution spaces. Additionally, we propose a random mirror perturbation boundary control method to better address individual boundary violations and enhance the algorithm’s robustness. By explicitly leveraging symmetry characteristics, the proposed algorithm maintains balanced exploration and exploitation, thereby improving convergence stability and scheduling fairness. To evaluate the effectiveness of the proposed algorithm, we compare it with nine other algorithms using the IEEE CEC2017 test set and assess the differences through statistical analysis. Experimental results demonstrate that the IDOA exhibits significant advantages. Finally, to verify its applicability in real-world scenarios, we applied IDOA to cloud computing task scheduling problems in actual environments, achieving excellent results and successfully completing cloud computing task scheduling planning. Full article

This study introduces the Machine Learning (ML)-Driven Pattern Synthesis for Digital Forensics in Synthetic Log Analysis (ML-PSDFA) framework to address critical gaps in digital forensics, including the reliance on real-world data, limited pattern diversity, and forensic integration challenges. A key innovation is the introduction of a novel temporal forensics loss ${\mathcal{L}}_{\mathrm{TFL}}$ in the Synthetic Attack Pattern Generator (SAPG), which enhances the preservation of temporal sequences in synthetic logs that are crucial for forensic analysis. The framework employs the SAPG with hybrid seed data (UNSW-NB15 and CICIDS2017) to create 500,000 synthetic log entries using Google Colab, achieving a realism score of 0.96, a temporal consistency score of 0.90, and an entropy of 4.0. The methodology employs a three-layer architecture that integrates data generation, pattern analysis, and forensic training, utilizing TimeGAN, XGBoost classification with hyperparameter tuning via Optuna, and reinforcement learning (RL) to optimize the extraction of evidence. Due to enhanced synthetic data quality and advanced modeling, the results exhibit an average classification precision of 98.5% (best fold 98.7%) 98.5% (best fold 98.7%), outperforming previously reported approaches. Feature importance analysis highlights timestamps (0.40) and event types (0.30), while the RL workflow reduces false positives by 17% over 1000 episodes, aligning with RL benchmarks. The temporal forensics loss improves the realism score from 0.92 to 0.96 and introduces a temporal consistency score of 0.90, demonstrating enhanced forensic relevance. This work presents a scalable and accessible training platform for legally constrained environments, as well as a novel RL-based evidence extraction method. Limitations include a lack of real-system validation and resource constraints. Future work will explore dynamic reward tuning and simulated benchmarks to enhance precision and generalizability. Full article

The transition toward sustainable production requires engineering and science education to adopt active, interdisciplinary, and practice-oriented teaching strategies. This article presents a comparative analysis of two educational initiatives implemented in Panama aimed at fostering sustainability competencies at the university and secondary school levels. The first initiative, developed at the Technological University of Panama, integrates project-based learning and circular economy principles into an extracurricular module focused on production planning, sustainable design, and quality management. Students created prototypes using recycled HDPE and additive manufacturing technologies within a simulated startup environment. The second initiative, carried out in two public secondary schools, applied project- and challenge-based learning through the Design Thinking framework, supporting teachers and students in addressing real-world sustainability challenges. Both programs emphasize hands-on learning, creativity, and iterative development, embedding environmental awareness and innovation in both formal and informal educational settings. The article identifies key opportunities and challenges in implementing active methodologies for sustainability education. Challenges such as limited infrastructure and rigid schedules were identified, along with lessons learned for future implementation. Students connected local issues to global goals like the SDGs and saw themselves as agents of change. These initiatives offer practical models for advancing sustainability education through innovation and interdisciplinary collaboration. Full article

In the Internet of Vehicles (IoV), zero-day attacks pose a significant security threat. These attacks are characterized by unknown patterns and limited sample availability. Traditional anomaly detection methods often fail because they rely on oversimplified assumptions, hindering their ability to model complex normal IoV behavior. This limitation results in low detection accuracy and high false alarm rates. To overcome these challenges, we propose a novel zero-day attack detection framework based on Feature-Enhanced Inverse Mapping (IM-ZDD). The framework introduces a two-stage process. In the first stage, a feature enhancement module mitigates data scarcity by employing an innovative multi-generator, multi-discriminator Conditional GAN (CGAN) with dynamic focusing loss to generate a large-scale, high-quality synthetic normal dataset characterized by sharply defined feature boundaries. In the second stage, a learning-based inverse mapping module is trained exclusively on this synthetic data. Through adversarial training, the module learns a precise inverse mapping function, thereby establishing a compact and expressive representation of normal behavior. During detection, samples that cannot be effectively mapped are identified as attacks. Experimental results on the F2MD platform show IM-ZDD achieves superior accuracy and a low false alarm rate, yielding an average AUC of 98.25% and F1-Score of 96.41%, surpassing state-of-the-art methods by up to 4.4 and 10.8 percentage points. Moreover, with a median detection latency of only 3 ms, the framework meets real-time requirements, providing a robust solution for zero-day attack detection in data-scarce IoV environments. Full article

Plant seedlings are sensitive to cultivation environment factors and highly susceptible to pathogenic infections under adverse conditions such as inappropriate light environment. In this study, five kinds of LED lighting sources with different red (R) and blue (B) light combinations were set up: R10B0, R7B3, R5B5, R2B8 and R0B10 (with R:B ratios of 10:0, 7:3, 5:5, 2:8 and 0:10, respectively) to explore their effects on tomato seedlings’ growth, AsA-GSH cycle, endogenous hormones, and resistance to Phytophthora infestans, providing a basis for factory seedling light-quality selection. The results showed that with the increase in the proportion of blue light in the composite light, the growth indicators, photosynthetic characteristic parameters and enzyme activities of tomato seedlings generally increased. The contents of AsA, reduced glutathione, and oxidized glutathione all reached the maximum under high-proportion blue-light treatments (R2B8 and R0B10). The high-blue-light groups (R2B8 and R0B10) had the highest AsA and glutathione contents. The red–blue combinations reduced inhibitory ABA and increased growth-promoting hormones (e.g., melatonin), while monochromatic light increased ABA to inhibit growth. After inoculation with P. infestans, the apoplastic glucose content was the highest under the red–blue-combined treatments (R5B5 and R2B8), while the total glucose content in leaves was the highest under the combined light R2B8 treatment. In conclusion, high-proportion blue-light treatment can greatly promote the photosynthetic process of tomato, enhance the AsA-GSH cycle, and achieve the best effect in improving the resistance of tomatoes to P. infestans. Given these, the optimal light environment setting was R:B = 2:8. Full article

Ceratitis capitata (Wiedemann) or medfly is a polyphagous pest of fruit crops worldwide. The Asian-native larval parasitoid Diachasmimorpha longicaudata (Ashmead) is mass-reared at the San Juan Biofactory and is currently released for medfly control in Argentina. Information on parasitoid survival, reproduction, and population growth parameters is critical for optimizing the mass-rearing process and successfully achieving large-scale release. This study provides a first-time insight into the demography of two population lines of D. longicaudata: one mass-reared on medfly larvae of the Vienna-8 temperature-sensitive lethal genetic sexing strain and the other on larvae of the wild biparental medfly strain. The aim was to compare both parasitoid populations to improve mass-rearing quality and to assess performance on medfly in a semi-arid environment, typical of Argentina’s central-western fruit-growing region. Tests were performed under laboratory and non-controlled environmental conditions in semi-field cages during three seasons. Dl_(Cc-bip) females exhibited higher reproductive potential than did Dl_(Cc-tsl) females under lab conditions. However, both Dl_(Cc-bip) and Dl_(Cc-tsl) were found to be similar high-quality females with high population growth rates in warm–temperate seasons, i.e., late spring and summer. Dl_(Cc-bip) females were only able to sustain low reproductive rates in early autumn, a colder season. These results are useful for improving the parasitoid mass production at the San Juan Biofactory and redesigning parasitoid release schedules in Argentina’s irrigated, semi-arid, fruit-growing regions. Full article

Background: Repeat induced abortion (defined as ≥two lifetime procedures) is becoming more common worldwide, yet its independent influence on women’s psychological health remains contested, particularly in settings where access to modern contraception is restricted. Objectives: This review sought to quantify the burden of depression, anxiety, stress, and generic quality of life (QoL) among women with repeat abortions and to determine how barriers to contraceptive access alter those outcomes. Methods: Following the preregistered PRISMA-2020 protocol, PubMed, Embase and Scopus were searched from inception to 31 June 2025. Results: Eight eligible studies comprising approximately 262,000 participants (individual sample sizes up to 79,609) revealed wide variation in psychological morbidity. Prevalence of clinically significant symptoms ranged from 5.5% to 24.8% for depression, 8.3% to 31.2% for anxiety, and 18.8% to 27% for perceived stress; frequent mental distress affected 12.3% of women in neutral policy environments but rose to 21.9% under highly restrictive abortion legislation. Having three or more abortions, compared with none or one, increased the odds of depressive symptoms by roughly one-third (pooled OR ≈ 1.37, 95% CI 1.13–1.67). Contextual factors exerted comparable or stronger effects: abortions sought for socioeconomic reasons elevated depression odds by 34%, unwanted disclosure of the abortion episode increased depressive scores by 0.62 standard deviations, and low partner support raised them by 0.67 SD. At the structural level, every standard deviation improvement in a state’s reproductive rights index reduced frequent mental distress odds by 5%, whereas enactment of a near-total legal ban produced an absolute increase of 6.8 percentage points. QoL outcomes were less frequently reported; where measured, denied or heavily delayed abortions were associated with a 0.41-unit decrement on a seven-point life satisfaction scale. Conclusions: Psychological morbidity after abortion clusters where legal hostility, financial hardship, or interpersonal coercion constrain contraceptive autonomy while, in comparison, the mere number of procedures is a weaker predictor. Interventions that integrate stigma-free mental health support with confidential, affordable, and rights-based contraception are essential to protect well-being in women who experience repeat abortions. Full article

Northern Thailand experiences annual haze events with fine particulate matter (PM2.5) exceeding standards, posing risks to schoolchildren. This cross-sectional study (Chiang Mai, 2024) evaluated respiratory impacts among primary school children aged 8–12 years. Daily mean PM2.5 concentrations were obtained from a single fixed-site monitoring station (36T) located within 2 km of the spirometry site. Among 93 children with acceptable spirometry, 52% exhibited restrictive, 18% obstructive, and 30% had normal function. After adjustment for BMI, males had significantly lower odds of any pulmonary abnormality than females (AOR = 0.084; 95% CI 0.017–0.417; p = 0.002). The mean FEV₁/FVC ratio was normal (86.30 ± 13.07%), whereas mean FVC, FEV₁, and PEF were significantly below predicted values, indicating a predominantly restrictive pattern. This predominance likely reflects cumulative exposure to biomass-burning related PM2.5 during the haze season, infiltration of outdoor PM2.5 into indoor environments alongside indoor sources, and the vulnerability of developing lungs in children’s factors that reduce lung volumes while largely preserving the FEV₁/FVC ratio. The exposure assessment provides pragmatic, proximity-based estimates but is limited by reliance on one station and one season, which may not capture spatial or temporal variability. These findings highlight sex-based susceptibility and support stronger air quality protections for children. Full article