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Islands and island communities are often perceived as homogenous in mainstream discourse. While many islands share characteristics, such as smallness or isolation, these are experienced differently across and within island contexts and intersect with spatial, socio-cultural, political, and economic landscapes. The concept of islandness is developed to both understand shared island characteristics and their differences across places, communities, and situations. This makes islandness highly relevant to discussions of green transitions as it highlights the need to examine the diverse, intersecting, and local realities that might interfere with green citizenship. However, analytical approaches to islandness are limited, with few spatial, scalable, and transferable frameworks available. This paper argues that spatial microsimulation offers a productive way to engage with islandness using the case of climate change and environmental attitudes across Iceland. We present the SimIceland model, developed within the EU-funded project PHOENIX: The Rise of Citizens’ Voices for a Greener Europe. The model is developed to better understand how Iceland’s citizens’ feel about climate change by taking socio-cultural, environmental, and different geographical administrative regions into account. Through a simple example of an analytical demonstration, we show how this model can support a deeper understanding of islandness in the specific context of climate attitudes in Iceland. Furthermore, we discuss how the model can contribute to public participation initiatives. The model and data are open access, and we conclude by inviting further developments and the use of spatial microsimulation to explore islandness, green citizenship, and participatory approaches to sustainability in island contexts. Full article

The Akkuyu Nuclear Power Plant (NPP) is framed as a flagship of Türkiye’s national low-carbon transition. This study examines how domestic economic actors perceive the project’s socio-economic and environmental impacts, and how those perceptions align with—or diverge from—official assessments and the United Nations Sustainable Development Goals. Using a qualitative phenomenological approach, the research draws on 28 semi-structured interviews with members of the Silifke Chamber of Commerce and Industry Council. This lens captures how locally embedded businesses read the project’s risks and rewards in real time. Four themes stand out. First, respondents see a clear economic uptick—but one that feels time-bound and vulnerable to the project cycle. Second, many feel excluded from decision-making; as a result, their support remains conditional rather than open-ended. Third, participants describe environmental signals as ambiguous, paired with genuine ecological concern. Fourth, skepticism about governance intertwines with sovereignty anxieties, particularly around foreign ownership and control. Overall, while short-term economic benefits are widely acknowledged, support is tempered by procedural exclusion, environmental worry, and distrust of foreign control. Conceptually, the study contributes to energy-justice scholarship by elevating sovereignty as an additional dimension of justice and by highlighting the link between being shut out of processes and perceiving higher environmental risk. Policy implications follow directly: create robust, domestic communication channels; strengthen participatory governance so local actors have a real voice; and embed nuclear projects within regional development strategies so economic gains are durable and broadly shared. Full article

The significant impact of road traffic accidents on public health requires clear and effective policies to combat them. However, public action can only be truly effective when supported by robust monitoring tools. This project aims to evaluate the effectiveness of a set of machine learning algorithms in predicting road accidents in Switzerland, utilizing open-access Confederation drive crash databases combined with environmental and socio-economic factors. Three different algorithms are tested: Logistic Regression Model (LRM), Random Forest with Ranger (RF), and Artificial Neural Network (ANN) with Keras. Among the predictive factors, road types are shown to be of high importance in all models. Regarding model performance, all the applied algorithms show a high level of accuracy, with all models achieving over 90%. The Random Forest algorithm, optimised using the Ranger application, exhibited the best performance, particularly in terms of specificity (0.88 compared to 0.34 and 0.40 for LRM and Keras, respectively) and negative predictive value (0.96 compared to 0.65 for LRM and 0.68 for Keras). These results suggest that this approach could support public policy for traffic management, if data collection and sharing activities are constantly carried out. Full article

Deep learning has emerged as a prominent focus in both academia and industry, with a wide range of models being applied across diverse domains. Fast and efficient model inference is essential for the practical deployment of deep learning models. Under specific hardware constraints, accelerating inference remains a key research challenge. Common techniques for model acceleration include quantization, pruning, and vectorization. Although quantization and pruning primarily reduce model precision or complexity to enhance efficiency, this paper concentrates on vectorization, a technique that accelerates models by increasing the parallelism of operator execution. Based on the open-source Buddy-MLIR project, this work implements vectorization optimizations for Matmul, Conv2d, and Max Pooling operations to improve inference performance. These optimizations are designed as compiler passes and integrated into the Buddy-MLIR framework, offering a general solution for vectorizing such operators. Two optimization approaches are proposed: general vectorization and adaptive vectorization. Compared to the standard MLIR lowering pipeline and the fully optimized LLVM backend, the proposed general and adaptive vectorization methods reduce the inference latency of LeNet-5 by $26.7\%$ and $37.3\%$, respectively. For the more complex ResNet-18 model, these methods achieve latency reductions of $79.9\%$ and $82.6\%$, respectively. Full article

In order to solve the problem that it is difficult to take into account the performance constraints between the core functions of insulation, current flow and arc extinguishing of high-voltage vacuum circuit breakers at the same time, this paper proposes a flexible control strategy for the motor operating mechanism of high-voltage vacuum circuit breakers. The relationship between the rotation angle of the motor and the linear displacement of the moving contact of the circuit breaker is analyzed, and the ideal dynamic curve is planned. The motor drive control device is designed, and the phase-shifted full-bridge circuit is used as the boost converter. The voltage and current double closed-loop sliding mode control strategy is used to simulate and verify the realization of multi-stage and stable boost. The experimental platform is built and the experiment is carried out. The results show that under the voltage conditions of 180 V and 150 V, the control range of closing speed and opening speed is increased by 31.7% and 25.9% respectively, and the speed tracking error is reduced by 51.2%. It is verified that the flexible control strategy can meet the ideal action curve of the operating mechanism, realize the precise control of the opening and closing process and expand the control range. The research provides a theoretical basis for the flexible control strategy of the high-voltage vacuum circuit breaker operating mechanism, and provides new ideas for the intelligent operation technology of power transmission and transformation projects. Full article

Root architecture and anatomy critically regulate maize nitrogen (N) acquisition, but their coordinated low-N response in N-efficient hybrids remains poorly understood. Elucidating this mechanism is essential for advancing root system regulation and breeding strategies aimed at enhancing N-use efficiency. In this study, six root architectures, twelve root anatomies, and six N-efficiency traits were evaluated in six maize hybrids and nine parental inbreds under sufficient (SN, 180 kg ha⁻¹) and low N (LN, 30 kg ha⁻¹), with transcriptome analysis of inbreds applied to uncover mechanisms. Hybrids were categorized as follows: EE (N-efficient under both N levels), SNE (N-efficient only under SN), and NN (inefficient under both N). Compared with other hybrids, EE developed a 6.0–15.7% narrower root opening angle (ROA), a 11.9–12.4% larger root projected area (RPA), 16.3–22.6% deeper roots (D_Wmax), and 22.6–37.1% more cortical aerenchyma (AA) under LN; SNE showed 9.49–19.51% lower RPA and higher LN-induced reductions in D_Wmax (8.84–17.09%); NN exhibited the largest ROA (60.75–64.48°) and LN-induced reductions in RPA (16.43%), D_Wmax (14.76%), and total projected structure length (11.28%). Correlation, principal component, and structural equation modeling analyses revealed significant root architecture–anatomy integration, and they collectively influence yield through traits such as D_Wmax, AA, and xylem vessel area (XVA) (r = −0.48–0.62, path coefficients: 0.19–0.27). Additionally, the EE and NN hybrids inherited and integrated the superior N-efficient root phenotypes from their parental inbred lines. Transcriptomic analysis identified eight root development genes, including GRMZM5G878558, whose expression correlated with both D_Wmax and AA (r = 0.61–0.73). These findings clarified that N-efficient maize achieved higher yield through coordinated root architecture–anatomy optimization involving associated genes, providing a theoretical foundation for N-efficiency-targeted root regulation and varietal selection. Full article

Accurate boundary detection is critical for autonomous trackless rubber-wheeled vehicles in underground coal mines, as it prevents lateral collisions with tunnel walls. Unlike open-road environments, underground tunnels suffer from poor illumination, water mist, and dust, which degrade visual imaging. To address these challenges, this paper proposes a navigable area computation for underground autonomous vehicles via ground feature and boundary processing, consisting of three core steps. First, a real-time point cloud correction process via pre-correction and dynamic update aligns ground point clouds with the LiDAR coordinate system to ensure parallelism. Second, corrected point clouds are projected onto a 2D grid map using a grid-based method, effectively mitigating the impact of ground unevenness on boundary extraction; third, an adaptive boundary completion method is designed to resolve boundary discontinuities in junctions and shunting chambers. Additionally, the method emphasizes continuous extraction of boundaries over extended periods by integrating temporal context, ensuring the continuity of boundary detection during vehicle operation. Experiments on real underground vehicle data validate that the method achieves accurate detection and consistent tracking of dual-sided boundaries across straight tunnels, curves, intersections, and shunting chambers, meeting the requirements of underground autonomous driving. This work provides a rule-based, real-time solution feasible under limited computing power, offering critical safety redundancy when deep learning methods fail in harsh underground environments. Full article

The spread of the invasive mosquitoes Aedes albopictus, Aedes aegypti, and Aedes japonicus in Spain represents an increasing public health risk due to their capacity to transmit arboviruses such as dengue, Zika, and chikungunya, among others. Traditional field entomological surveillance remains essential for tracking their spread, but it faces limitations in terms of cost, scalability, and labor intensity. Since 2014, the Mosquito Alert citizen-science project has enabled public participation in surveillance through the submission of geolocated images via a mobile app, which are identified using AI in combination with expert validation. While field surveillance provides high accuracy, citizen science offers low-cost, large-scale, real-time data collection aligned with open data management principles. It is particularly useful for detecting long-distance dispersal events and has contributed up to one-third of the municipal detections of invasive mosquito species since 2014. This study assesses the value of integrating both surveillance systems to capitalize on their complementary strengths while compensating for their weaknesses in the areas of taxonomic accuracy, scalability, spatial detection patterns, data curation and validation systems, geographic precision, interoperability, and real-time output. We present the listing of municipal detections of these species from 2004 to 2024, integrating data from both sources. Spain’s integrated approach demonstrates a pioneering model for cost-effective, scalable vector surveillance tailored to the dynamics of invasive species and emerging epidemiological threats. Full article

Recent advances in large language models and retrieval-augmented generation, a method that enhances language models by integrating retrieved external documents, have created opportunities to deploy AI in secure, offline environments. This study explores the feasibility of using locally hosted, open-weight large language models with integrated retrieval-augmented generation capabilities on CPU-only hardware for tasks such as question answering and summarization. The evaluation reflects typical constraints in environments like government offices, where internet access and GPU acceleration may be restricted. Four models were tested using LocalGPT, a privacy-focused retrieval-augmented generation framework, on two consumer-grade systems: a laptop and a workstation. A technical project management textbook served as the source material. Performance was assessed using BERTScore and METEOR metrics, along with latency and response timing. All models demonstrated strong performance in direct question answering, providing accurate responses despite limited computational resources. However, summarization tasks showed greater variability, with models sometimes producing vague or incomplete outputs. The analysis also showed that quantization and hardware differences affected response time more than output quality; this is a tradeoff that should be considered in potential use cases. This study does not aim to rank models but instead highlights practical considerations in deploying large language models locally. The findings suggest that secure, CPU-only deployments are viable for structured tasks like factual retrieval, although limitations remain for more generative applications such as summarization. This feasibility-focused evaluation provides guidance for organizations seeking to use local large language models under privacy and resource constraints and lays the groundwork for future research in secure, offline AI systems. Full article

Despite flies regularly visiting flowers, limited research has gone into their pollination ability on commercial crops. A national project in Australia aimed to identify fly species as potential managed pollinators for the horticultural industry and, in particular, avocado. This study investigated the ability of two calliphorids (Calliphora dubia and Calliphora vicina) and a syrphid (Eristalis tenax) fly species to pollinate Hass avocados in southwestern Australia. Four (4) field trials over three (3) years showed that each fly species (all found across Australia) was capable of pollinating Hass avocados when released into netted enclosures around multiple trees (12–26) during flowering. Trees enclosed with Eristalis tenax produced the highest fruit yield (18.0 kg/tree) outperforming trees pollinated by either C. dubia (11.6), managed honey bees in the open orchard (10.5) or C. vicina (6.8). Increasing fly numbers from 10,000 to 15,000 in the enclosures provided no additional pollination benefit. These results suggest that either E. tenax or C. dubia could be valuable managed pollinators for the avocado industry either with or without honey bees. Calliphora dubia was a significant pollinator during warmer flowering seasons and C. vicina was a useful pollinator during cold and wet flowering seasons. Full article

The Horizon 2020 EnerMaps project addresses the fragmentation and variable reliability of European energy datasets by developing a reproducible quality control (QC) framework aligned with FAIR principles. This research supports sustainability goals by enabling better decision making in energy management, resource optimization, and sustainable policy development. This study applies this framework to an initial inventory of 50 spatially referenced energy datasets, classifying them into three assessment levels and subjecting each level to progressively deeper checks: expert consultation, metadata verification against a customized “DataCite/schema.org” schema, documentation review, completeness analysis, consistency testing via simple linear regressions, comparative descriptive statistics, and community feedback preparation. The results show that all datasets are findable and accessible, yet critical FAIR attributes remain weak: 68% lack explicit licenses and 96% omit terms-of-use statements; methodology descriptions are present in 77% of cases, while quantitative accuracy information appears in only 43%. Completeness screening reveals that more than half of the datasets exhibit over 20% missing values in one or more key dimensions. Consistency analyses nevertheless indicate statistically significant correlations (p < 0.05) for the majority of paired comparisons, supporting basic reliability. By improving the FAIRness (Findable, Accessible, Interoperable, Reusable) of energy data, this study directly contributes to more effective sustainability assessments and interventions. The proposed QC workflow therefore provides a scalable route to improve the transparency, comparability, and reusability of heterogeneous energy data, and its adoption could accelerate open energy modelling and policy analysis across Europe. Full article

With the advancement of drone technology, the availability of different sensors has become more reliable and cost-effective for monitoring large open-pit mine project activities. Key advantages of drone technology, including low operational expenses, rapid revisit capabilities, deployment flexibility, and high precision, have established these systems as powerful instruments for monitoring open-pit mine areas. This paper aims to provide a comprehensive review of drone technology utilization in open-pit mine reclamation monitoring. Mining 4.0 has shown promise in open-pit mine monitoring for drone deployment for use in green mining practices. This review synthesizes current research on drone survey platforms, various sensor technologies, and their practical field applications within open-pit mines for mine reclamation monitoring. This review study aims to establish a robust framework for the monitoring and management of mine reclamation. This study will provide a technically reliable reference, advancing the knowledge and application of drone technology for reclamation monitoring and management. Full article

The absence of parental presence has a direct impact on the emotional stability and social routines of children, especially during extended periods of separation from their family environment, as in the case of daycare centers, hospitals, or when they remain alone at home. At the same time, the technology currently available to provide emotional support in these contexts remains limited. In response to the growing need for emotional support and companionship in child care, this project proposes the development of a multi-platform software architecture based on artificial intelligence (AI), designed to be integrated into humanoid robots that assist children between the ages of 6 and 14. The system enables daily verbal and non-verbal interactions intended to foster a sense of presence and personalized connection through conversations, games, and empathetic gestures. Built on the Robot Operating System (ROS), the software incorporates modular components for voice command processing, real-time facial expression generation, and joint movement control. These modules allow the robot to hold natural conversations, display dynamic facial expressions on its LCD (Liquid Crystal Display) screen, and synchronize gestures with spoken responses. Additionally, a graphical interface enhances the coherence between dialogue and movement, thereby improving the quality of human–robot interaction. Initial evaluations conducted in controlled environments assessed the system’s fluency, responsiveness, and expressive behavior. Subsequently, it was implemented in a pediatric hospital in Guayaquil, Ecuador, where it accompanied children during their recovery. It was observed that this type of artificial intelligence-based software, can significantly enhance the experience of children, opening promising opportunities for its application in clinical, educational, recreational, and other child-centered settings. Full article

This study presents a system model developed for collecting and analyzing spatial data on the project environment of transport infrastructure development in the post-war context, with a focus on supporting sustainable management and recovery planning. The model utilizes the OpenStreetMap Overpass Application Programming Interface (Overpass API) to extract structured geospatial information from OpenStreetMap (OSM), enabling efficient and accurate assessments of settlements affected by armed conflict. Python 3.11-based software modules were created to process OSM data, evaluate 17 relevant attributes of transport infrastructure objects, and visualize key characteristics for decision-makers. A case study was conducted on 23 Ukrainian settlements with partially damaged infrastructure, demonstrating how the proposed model facilitates timely and informed decisions for infrastructure redevelopment. By improving the accessibility and quality of spatial data, the model enhances the capacity for sustainable management of post-war transport infrastructure projects. To ensure the quality of spatial data obtained from OSM, a verification procedure was carried out by cross-checking with satellite images and official national geospatial data. The results showed an average deviation of ±4.4% in the length of road sections, confirming the reliability and accuracy of spatial objects obtained from OSM for use in transport infrastructure planning. The findings offer valuable insights for regional planners, public administrators, and policymakers involved in sustainable reconstruction and digital governance. Future research will focus on developing a comprehensive information system for identifying and prioritizing infrastructure development projects within defined administrative units such as municipalities and local communities. Full article

We investigate the longstanding question of whether compact Levi-flat hypersurfaces exist in the complex projective plane ${\mathbb{CP}}^2$. While the nonexistence of closed real-analytic Levi-flat hypersurfaces in ${\mathbb{CP}}^n$ for $n>2$ is well known, the case $n=2$ remains open. By combining techniques from the classification of homogeneous CR-manifolds with projective foliation geometry, we prove that no homogeneous Levi-flat hypersurfaces exist in ${\mathbb{CP}}^2$, thus partially resolving the problem under natural symmetry assumptions. Full article