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Social innovation in SMEs: examining the role of artificial intelligence and social and environmental sustainability
Full text linkThis study examines how environmental sustainability, social sustainability, and artificial intelligence (AI) interact to foster social innovation in small and medium-sized enterprises (SMEs). Drawing on Resource-Based Theory (RBT) and Institutional Theory, it explores how internal resources, and external pressures jointly shape social innovation outcomes. Using a large dataset of more than 12,000 European SMEs, the study investigates the individual and combined effects of these practices. The findings reveal differential impacts among the three drivers. Social sustainability emerges as the most significant predictor of social innovation, highlighting its central role in generating social value and promoting equity and inclusion. Environmental sustainability also exerts a positive influence, contributing to the development of innovations that address both ecological and social challenges. In contrast, AI plays a more indirect role by enhancing efficiency, resource optimisation, and the implementation of sustainability strategies. Theoretically, the study advances understanding of social innovation in SMEs by integrating internal (RBT) and external (institutional theory) perspectives. It shows that innovation arises from the alignment of firm capabilities with institutional expectations. Practically, it offers guidance for SMEs and policymakers on how to integrate AI and sustainability to improve competitiveness while contributing to societal well-being
Financial cooperation and regulation: along the belt and road
Full text linkThe book aims to examine the field of financial cooperation and regulation along the Belt and Road. By examining the relevant theories, investigating the different financial sectors, and analyzing the individual country case study, the book presents a full portrait of the topic area. The book has drawn from experiences of different jurisdictions, covering both developed countries and developing countries. Moreover, the book has a thorough and comprehensive study on different financial sectors, e.g. banking, bond, equity, Fintech, financial professionals, etc
SF-Net: spatial-frequency feature synthesis for semantic segmentation of High-Resolution Remote Sensing imagery
Full text linkPrecise semantic segmentation of High-Resolution Remote Sensing(HRRS) images is essential for robust environmental surveillance and detailed land use mapping. Despite substantial advances in deep learning, most conventional approaches focus on the spatial domain. This focus often neglects the rich textural and structural nuances found in the frequency domain, which reduces the representation of comprehensive data. Addressing this issue, we introduce SF-Net. This network synthesizes features across spatial and frequency domains, aiming for seamless and effective integration. The core of SF-Net employs a multiscale Convolutional Grouping Fusion Module (CGFM) to extract spatial features at varying resolutions. Following this, the Haar Wavelet Transform decomposes these features into distinct low-frequency components (structure) and high-frequency components (detail). Subsequently, a Mamba-enhanced Global Spatial Feature Extraction Module (GSFEM) reinforces low-frequency semantic information with global context, while a Spatial-Frequency Fusion Module (S-FFM) applies targeted attention to sharpen high-frequency details. Experimental results on the ISPRS Vaihingen, LoveDA, and Potsdam benchmarks confirm SF-Net's superior performance, achieving state-of-the-art mean Intersection over Union (mIoU) scores of 83.12%, 53.28%, and 83.35%, respectively, validating its effectiveness and superority
Prediction of nutritional impact of the mango sector in Côte d’Ivoire: past, present and future
Full text linkMango is an important tropical fruit, rich in essential micronutrients. In Côte d’Ivoire, the harvested fruit is either exported to international and regional markets, processed, or remains on the local market. Nutritional flows across value chains and their resulting nutritional impact are poorly understood. The purpose of this study was to estimate the past, present, and future nutritional impact of the mango value chain in Côte d’Ivoire. We adapted the NUTRI-P-LOSS methodology that predicts nutritional postharvest losses across crop value chains, to assess nutritional flows in the mango value chain. Mango production quantities were estimated, and fruits at harvest, during ripening, and after drying, were analysed for macro- and micronutrients. Findings indicate that mango domestic nutritional contribution is likely to increase in the future, helping address vitamin A and C deficiencies and anaemia related-issues that remain prevalent, particularly among vulnerable groups such as children under five, pregnant teenagers and women (i.e. meeting requirements for vitamin C, from approximatively 60,000 people in 2000, to 1 million people in 2050). Whilst vitamin A is concentrated in dried mango, vitamin C—the fresh fruit’s primary micronutrient—is lost in drying. Therefore, increased domestic consumption of dried mango would increase vitamin A from mango but not improve the overall nutritional impact of mango on the local population (vitamins A and C, iron). Our work leads to the development of a user-friendly tool that predicts the past, present, and future nutritional impact of crop production with mango in Côte d’Ivoire as an example
Effect of oceanographic variables and space-time factors on the density of Peruvian anchovy (Engraulis ringens): an approach with GAMLSS
No full textThis study examines how environmental and spatiotemporal factors modulate the acoustic density (area backscattering coefficient, sA) of the Peruvian anchoveta (Engraulis ringens), using data from 25 IMARPE surveys conducted between 2007–2017. Given the semicontinuous nature of sA (characterized by an excess of zeros and strong positive skewness) GAMLSS models with zero-adjusted Box–Cox t and Generalized Inverse Gaussian distributions (ZABCTo and ZAGIG) were fitted. These models allow sA to be analyzed in its original scale while jointly estimating the location (μ), scale (σ), skewness (ν), kurtosis (τ), and probability of absence (π0) parameters, thus providing a complete distributional characterization of aggregation processes. The results revealed a consistent spatial structure, with the highest densities occurring within 40 nm of the coast and between 6°–15°S, under salinities of 33.75–35.2 PSU, sea surface temperatures of 14–25 °C, and dissolved oxygen > 5 mL L⁻1. Modeling sA on its original scale uncovered nonlinear environmental thresholds, diel variability, and changes in spatial heterogeneity that are not detected when using Gaussian and Tweedie GAMs or GAMLSS models based on logarithmic transformations. Seasonal models further showed that upwelling intensity, ENSO-related anomalies, and short-term fluctuations influence not only the magnitude and dispersion of sA but also reflect the extreme densities that characterize anchoveta aggregation dynamics. By demonstrating that distributional modeling of sA without transformations preserves ecological information that is lost under traditional approaches, this study provides a robust statistical basis for interpreting aggregation responses in the highly dynamic upwelling system of the Humboldt Current
Predicting toxic species generation resulting from external cladding fires
Full text linkA CFD method to predict the generation of toxic gases produced by burning external cladding is presented. The model and its assumption have been validated using BS 8414 experimental data for two cladding systems with different aluminium composite materials (ACM). The predicted temperatures are in good agreement with the measured data and the predicted toxic gas concentrations follow the measured trends. The model is then used to assess tenability conditions in a target flat and the lobby five floors above the fire origin, in which toxic smoke penetrates the flat via a half-open kitchen window. Only the impact of cladding materials is considered. Simulations indicate that if the target flat occupants commence their evacuation shortly after alarm activation, they are likely to be able to pass through the lobby and safely evacuate. However, if they delay their evacuation by 21 minutes, they are likely to become incapacitated due to inhalation of toxic gases. The PIR contribution to the fractional incapacitating dose (FIN) is approximately half that from ACM PE in the target flat and varies from a third to a half in the target lobby. Furthermore, the HCN contribution to the FIN is minor (less than 5%)
Hedging climate and geopolitical risks in low- and lower-middle-income economies: exploring the role of well-known and novel safe-haven indices
Full text linkThis study is the first to comprehensively investigate whether transition and physical climate risks (CRs) and disaggregated geopolitical risks (GPRs) transmit shocks to financial markets in low- and lower-middle-income (LLMI) economies, and whether there exist assets that can diversify such risks. We considered five CR and GPR indices, nine LLMI equity indices representing sixteen economies worldwide, and six potential hedge and safe-haven candidates. Using the time-varying parameter vector autoregressive model, we uncover that LLMI equities are receivers of shocks from both CRs and GPRs, including when they are jointly included in the network. We then employ a recently developed safe-haven index (SHI) by Baur-Dimpfl-Pena, three international S&P Global indices representing green bonds, clean energy transitions, and Shari'ah-compliant assets, two novel commodity and cryptocurrency indices (CMI and CCI) that we developed, and dynamic conditional correlation-based hedge and safe-haven regressions. The hedge and strong safe-haven roles of the candidate indices are found to be heterogeneous: Overall, SHI outperforms others, followed by CMI and CCI; however, against CRs, the safe-haven role of the clean energy transition index (CETI) is dominant, followed by CMI and CCI, which offer comparable benefits. Results from this study highlight the need for investors to carefully optimize their LLMI equity portfolios in relation to CRs and GPRs, and for policymakers to reinforce regional cooperation to mitigate climate and geopolitical risks
A mixed-methods investigation of XR security warnings: lessons learned
No full textAs immersive XR environments become more prevalent, timely and effective security warnings are essential to protect users from cyberattacks that compromise performance and well-being. This paper investigates how users perceive and respond to in-headset alerts triggered during Denial-of-Service (DoS) attacks. We developed a real-time warning system and evaluated its effectiveness across three pilot studies (n = 46) in healthcare and industrial training scenarios. Using self-report measures (IDSQ, SAM) and behavioural categorization, we assessed alert comprehension, urgency perception, and user action. We distil three design lessons emphasizing the importance of visual salience, modality coordination, and urgency calibration. These findings offer practical guidance for designing effective XR security notifications that support user awareness and action during immersive threats
From mapping to action: Social Network Analysis as a strategic tool in cross-national community interventions
Full text linkCommunity interventions increasingly leverage Social Network Analysis (SNA) both to understand relational patterns and to facilitate structural changes within networks. Indeed, SNA serves not only as an analytical tool but also as a catalyst for reflection and change. Although SNA has been widely used as an intervention tool, its application in cross-national contexts remains underexplored. This study aims to address this research gap by investigating how SNA can contribute to cross-national community interventions. We use a case study approach based on a longitudinal analysis of the Assistance and Legal Program for Emigrant Support (ALPES) network, a cross-national project established at the Italian-French border. In this project, SNA has been used both as a diagnostic tool to map the information exchange network of third-sector organizations and as a strategic intervention strategy that produced behavioral changes in these organizations. Our results show that SNA functioned as both a translational monitoring tool and a catalytic intervention: network visualization prompted organizations to strategically alter their collaborative patterns and address structural gaps in migrant support services across borders. This demonstrates how network feedback processes can enhance inter-organizational collaboration in complex cross-national contexts
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