Search Results (712)

Marine sources contribute approximately 2% of global energy-related CO₂ emissions, with the shipping industry accounting for 87% of this total, making it the fifth-largest emitter globally. Environmental regulations by the International Maritime Organization (IMO), such as the MARPOL (International Convention for the Prevention of Pollution from Ships) treaty, have driven the exploration of alternative green energy solutions, including nuclear-powered ships. These ships offer advantages like long operational periods without refueling and increased cargo space, with around 200 reactors already in use on naval vessels worldwide. Among advanced reactor concepts, the molten salt reactor (MSR) is particularly suited for marine applications due to its inherent safety features, compact design, high energy density, and potential to mitigate nuclear waste and proliferation concerns. However, MSR systems face significant challenges, including tritium production, corrosion issues, and complex behavior of volatile fission products. Understanding the impact of marine-induced motion on the thermal–hydraulic behavior of MSRs is crucial, as it can lead to transient design basis accident scenarios. Furthermore, the adoption of MSR technology in the shipping industry requires overcoming regulatory hurdles and achieving global consensus on safety and environmental standards. This review assesses the current progress, challenges, and technological readiness of MSRs for marine applications, highlighting future research directions. The overall technology readiness level (TRL) of MSRs is currently at 3. Achieving TRL 6 is essential for progress, with individual components needing TRLs of 4–8 for a demonstration reactor. Community Readiness Levels (CRLs) must also be addressed, focusing on public acceptance, safety, sustainability, and alignment with decarbonization goals. Full article

Sub-national governments (SNGs) in developing countries have increasingly explored green municipal bonds as a financing tool, driven by promises of lower borrowing costs, enhanced reputation, and support for sustainable economic development. This study aims to critically examine these claims by analysing the actual costs and complexities associated with green municipal bonds (GMBs). The research involves a comparative analysis of traditional municipal bonds and GMBs, focusing on the financial and operational challenges faced by SNGs. Detailed case studies from Mexico City and Cape Town are used to illustrate the practical implications of issuing GMBs. The findings reveal that, despite similar or slightly lower interest rates, GMBs often entail higher issuance costs due to the need for certification, monitoring, and reporting of green projects. These additional costs, coupled with the necessity of hiring external consultants, make GMBs more expensive and difficult to replicate after initial transactions. Furthermore, the study highlights that the demand from investors has not met initial expectations. While green-aligned investors show a higher appetite for GMBs, they constitute only a small portion of the finance market. Traditional investors, primarily focused on financial returns, are less inclined to invest in GMBs solely based on their green label. Consequently, many SNGs are increasingly turning to traditional “vanilla” municipal bonds as a more viable option for financing green initiatives. This shift underscores the practical challenges of GMB issuance in developing countries and calls for a strategic approach to balance the use of GMBs with other financing options to promote sustainable economic development. Full article

The establishment of urban ventilation corridors (UVCs) aims to mitigate the urban heat island effect. While most studies focus on the construction and assessment of the environmental benefit of UVCs, they often overlook the analysis of UVCs’ topological features. This research integrates multi-source data including 3D urban buildings, historical meteorological observations, high-resolution remote sensing, and land use planning, combined with multiple models, including geographic information system spatial analysis, circuit theory, and complex networks. Based on an assessment of urban ventilation potential, the circuit model was applied to extract UVCs aligned with the prevailing wind direction for both summer and winter seasons. Complex network modeling was employed to analyze the topological features of the ventilation network. From the analytical results, a multi-level wind corridor system for Chengdu was quantitatively developed. The results indicate that the city’s overall ventilation resistance is high, with notable spatial clustering, and the southeastern region faces substantial ventilation obstructions. A total of 143 critical ventilation nodes were identified, with the number of air inlets and outlets in summer being significantly fewer than in winter. However, the cooling effect of ventilation corridors in the prevailing summer wind direction is superior to that in winter. The ventilation network comprises 16 communities with distinct ventilation characteristics, exhibiting moderate connectivity, lacking small-world properties, and showing congestion and instability. Full article

Natural reserves, such as the Santurbán Moor in Colombia, are ecologically important but face significant threats from activities like mining and agriculture. Preserving biodiversity in these ecosystems is essential for maintaining ecological balance and promoting sustainable tourism practices. Identifying plant species in these reserves accurately is challenging due to environmental variability and species similarities, complicating conservation efforts and educational tourism promotion. This study aims to create and assess a mobile application based on deep learning, called FloraBan, to autonomously identify plant species in natural reserves, enhancing biodiversity conservation and encouraging sustainable and educational tourism practices. The application employs the EfficientNet Lite4 model, trained on a comprehensive dataset of plant images taken in various field conditions. Designed to work offline, the application is particularly useful in remote areas. The model evaluation revealed an accuracy exceeding 90% in classifying plant images. FloraBan was effective under various lighting conditions and complex backgrounds, offering detailed information about each species, including scientific name, family, and conservation status. The ability to function without internet connectivity is a significant benefit, especially in isolated regions like natural reserves. FloraBan represents a notable improvement in the field of automated plant identification, supporting botanical research and efforts to preserve biodiversity in the Santurbán Moor. Additionally, it encourages educational and responsible tourism practices, which align with sustainability goals, providing a useful tool for both tourists and conservationists. Full article

In scenarios where global navigation satellite systems (GNSSs) and radio navigation systems are denied, vision-based autonomous landing (VAL) for fixed-wing unmanned aerial vehicles (UAVs) becomes essential. Accurate and real-time runway detection in VAL is vital for providing precise positional and orientational guidance. However, existing research faces significant challenges, including insufficient accuracy, inadequate real-time performance, poor robustness, and high susceptibility to disturbances. To address these challenges, this paper introduces a novel single-stage, anchor-free, and decoupled vision-based runway detection framework, referred to as YOLO-RWY. First, an enhanced data augmentation (EDA) module is incorporated to perform various augmentations, enriching image diversity, and introducing perturbations that improve generalization and safety. Second, a large separable kernel attention (LSKA) module is integrated into the backbone structure to provide a lightweight attention mechanism with a broad receptive field, enhancing feature representation. Third, the neck structure is reorganized as a bidirectional feature pyramid network (BiFPN) module with skip connections and attention allocation, enabling efficient multi-scale and across-stage feature fusion. Finally, the regression loss and task-aligned learning (TAL) assigner are optimized using efficient intersection over union (EIoU) to improve localization evaluation, resulting in faster and more accurate convergence. Comprehensive experiments demonstrate that YOLO-RWY achieves ${\mathrm{AP}}_{50:95}$ scores of 0.760, 0.611, and 0.413 on synthetic, real nominal, and real edge test sets of the landing approach runway detection (LARD) dataset, respectively. Deployment experiments on an edge device show that YOLO-RWY achieves an inference speed of 154.4 FPS under FP32 quantization with an image size of 640. The results indicate that the proposed YOLO-RWY model possesses strong generalization and real-time capabilities, enabling accurate runway detection in complex and challenging visual environments, and providing support for the onboard VAL systems of fixed-wing UAVs. Full article

Background: In many urban areas, 80% to 90% of pollutant emissions are generated by road traffic, particularly from heavy vehicles. With the anticipated surge in e-commerce logistics, the need for effective urban mobility control measures has become urgent, focusing on traffic restrictions and efficient enforcement tools. This work introduces Log-ON, a multi-stakeholder information system designed to facilitate the implementation and management of sustainable traffic restrictions. Methods: The proposed system was developed through extensive literature reviews, expert consultations, and feedback from logistics fleet managers. User-centered mock-ups were created for various stakeholders, including the public, regulatory authorities, logistics operators, and enforcement agencies, ensuring that the system effectively addresses a diverse set of needs. Results: By taking into account a wide range of influencing factors, Log-ON functions as a decision-support tool designed to optimize access restrictions for vehicles, particularly heavy vehicles, in urban environments. Conclusions: Log-ON’s adoption promises significant improvements in urban mobility by reducing traffic-related pollution and fostering healthier, cleaner cities. However, traffic restrictions could increase delivery costs, potentially disrupting logistics operations. To address this, the development of new business models for last-mile delivery is essential, ensuring that sustainable traffic management strategies align with the economic challenges faced by logistics providers. Full article

This exploratory study investigates changes in the autonomic cardiac system of young analog astronauts in a hostile, confined, and isolated environment. It uses linear and nonlinear indices of heart rate variability (HRV) during a Mars analog mission to assess how HRV varies under day and night stressors. This study is guided by the hypothesis that significant HRV changes occur based on adaptation days, aiming to offer insights into autonomic nervous system (ANS) adaptation to environmental stressors. Over five days in August 2022, five analog astronauts faced adverse conditions in the Mojave Desert, simulating Martian conditions. Electrocardiograms were recorded daily for five minutes during morning and evening sessions to extract short-term RR time series. HRV parameters were analyzed using both time- and frequency-domain indices and nonlinear measures. Significant differences in HRV parameters across days highlight the mission environment’s impact on autonomic cardiac function. Morning measurements showed significant changes in average RR intervals and heart rate, indicating ANS adaptation. Nonlinear indices such as detrended fluctuation analysis and approximate entropy also showed significant differences, reflecting shifts in autonomic function. The Borg scale indicated reduced perceived exertion over time, aligning with HRV changes. Increased vagal activity during Mars analog adaptation under confinement/isolation may be crucial for cardiovascular adaptation and survival in future space flights. Full article

The transition to sustainability in agriculture faces significant challenges, especially to balance environmental goals with the practical demands of food production. This paper examines two different case studies that reveal the complexities of agricultural regulation. The first case focuses on the valorization of agri-food residual biomasses, highlighting the potential to transform food waste into valuable bioproducts such as bioenergy and biofertilizers. Despite the clear environmental and economic benefits, the absence of specific European regulations hinders the widespread adoption of these practices. Without clear rules for achieving “end-of-waste” status, the development and marketing of bio-based products remain restricted. The second case study examines the European Union’s unsuccessful effort to implement the Sustainable Use of Pesticides Regulation (SUR), which aimed to reduce pesticide use by 50% by 2030. Although the regulation sought to align agricultural practices with the EU’s Green Deal, it triggered widespread protests from farmers concerned about the potential economic losses and decreased productivity. These two cases, one showing under-regulation and the other over-regulation, highlight the need for balanced and practical regulatory frameworks that promote sustainability without imposing unrealistic demands on stakeholders. This paper ends with recommendations to harmonize regulations across Europe, ensuring that both innovation in agricultural waste management and practical pesticide reduction strategies are implemented in a way that supports farmers and producers, minimizing economic disruptions and encouraging sustainable agricultural practices. Full article

The acquisition of deictic verbs is a significant milestone in language development. This complex process requires an understanding of the interplay between the personal pronouns “I/you” and deictic verbs. Although demonstrating the cognitive processes associated with deictic shifting through data is valuable, research issues regarding data accuracy and the spatial arrangement of the self and other remain unresolved. This pilot study aimed to quantitatively measure the body movements of Japanese-speaking children during their utterances of “come/go”. Twelve typically developing children aged 6–7 participated in this study. Multiple scenarios were set up where the researcher presented phrases using “come/go” with deictic gestures, such as moving one’s upper body forward or backward, and the participant replied with “come/go”. When performing a role, the researcher sat face-to-face or side-by-side with the participant, depending on the type of question–response. It is possible that there is a learning process whereby verbal responses using “come/go” align with corresponding body movements in the specific question type. This process is deeply involved in the development of perspective-taking abilities. Future research with relatively large samples and cross-cultural comparisons is warranted to deepen the understanding of this linguistic acquisition process and its implications. Full article

Olive cultivation, an icon of Mediterranean agriculture, economy, and cultural heritage, faces significant challenges due to climate change and soil degradation. Climate projections indicate that altered precipitation patterns, rising temperatures, and increased frequency of extreme weather events will adversely affect olive tree growth, fruit quality, and yield. This review provides a novel perspective on addressing these challenges through both long-term and short-term adaptation strategies, emphasizing innovative products, advanced technologies, and practical solutions that must work synergistically and be tailored to regional conditions. Long-term practices refer to proactive strategies for enduring climate resilience, including cover cropping, mulching, soil amendments, and breeding programs which enhance soil health, improve water retention, and increase the trees’ resilience. Short-term strategies focus on immediate impacts, offering immediate stress relief and enhanced plant physiological responses, including optimized irrigation systems, pruning management, particle coating films, biostimulants, and plant growth regulators. The review underscores the importance of aligning agricultural practices with sustainability goals and evolving environmental policies and the education of farmers and policymakers. By integrating adaptive practices and technological advancements, the olive sector can better address climate challenges, contribute to global food security, and advance environmental sustainability. Full article

Chromosome alignment on the metaphase plate is a conserved phenomenon and is an essential function for correct chromosome segregation for many organisms. Organisms with naturally-occurring trivalent chromosomes provide a useful system for understanding how chromosome alignment is evolutionarily regulated, as they align on the spindle with one kinetochore facing one pole and two facing the opposite pole. We studied chromosome alignment in a praying mantid that has not been previously studied chromosomally, the giant shield mantis Rhombodera megaera. R. megaera has a chromosome number of 2n = 27 in males. Males have X₁, X₂, and Y chromosomes that combine to form a trivalent in meiosis I. Using live-cell imaging of spermatocytes in meiosis I, we document that sex trivalent Y chromosomes associate with one spindle pole and the two X chromosomes associate with the opposing spindle pole. Sex trivalents congress alongside autosomes, align with them on the metaphase I plate, and then the component chromosomes segregate alongside autosomes in anaphase I. Immunofluorescence imaging and quantification of brightness of kinetochore–microtubule bundles suggest that the X₁ and X₂ kinetochores are associated with fewer microtubules than the Y kinetochore, likely explaining the alignment of the sex trivalent at the spindle equator with autosomes. These observations in R. megaera support the evolutionary significance of the metaphase alignment of chromosomes and provide part of the explanation for how this alignment is achieved. Full article

Intelligent mobile robots have been gradually used in various fields, including logistics, healthcare, service, and maintenance. Path planning is a crucial aspect of intelligent mobile robot research, which aims to empower robots to create optimal trajectories within complex and dynamic environments autonomously. This study introduces an improved A* algorithm to address the challenges faced by the preliminary A* pathfinding algorithm, which include limited efficiency, inadequate robustness, and excessive node traversal. Firstly, the node storage structure is optimized using a minimum heap to decrease node traversal time. In addition, the heuristic function is improved by adding an adaptive weight function and a turn penalty function. The original 8-neighbor is expanded to a 16-neighbor within the search strategy, followed by the elimination of invalid search neighbor to refine it into a new 8-neighbor according to the principle of symmetry, thereby enhancing the directionality of the A* algorithm and improving search efficiency. Furthermore, a bidirectional search mechanism is implemented to further reduce search time. Finally, trajectory optimization is performed on the planned paths using path node elimination and cubic Bezier curves, which aligns the optimized paths more closely with the kinematic constraints of the robot derivable trajectories. In simulation experiments on grid maps of different sizes, it was demonstrated that the proposed improved A* algorithm outperforms the preliminary A* Algorithm in various metrics, such as search efficiency, node traversal count, path length, and inflection points. The improved algorithm provides substantial value for practical applications by efficiently planning optimal paths in complex environments and ensuring robot drivability. Full article

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods. Full article

Identifying inter-well connectivity is crucial for optimizing reservoir development and facilitating informed adjustments. While current engineering methods are effective, they are often prohibitively expensive due to the complex nature of reservoir conditions. In contrast, methods that utilize historical production data to identify inter-well connectivity offer faster and more cost-effective alternatives. However, when faced with incomplete dynamic data—such as long-term shut-ins and data gaps—these methods may yield substantial errors in correlation results. To address this issue, we have developed an unsupervised machine learning algorithm that integrates sparse inverse covariance estimation with affinity propagation clustering to map and analyze dynamic oil field data. This methodology enables the extraction of inter-well topological structures, facilitating the automatic clustering of producers and the quantitative identification of connectivity between injectors and producers. To mitigate errors associated with sparse production data, our approach employs sparse inverse covariance estimation for preprocessing the production performance data of the wells. This preprocessing step enhances the robustness and accuracy of subsequent clustering and connectivity analyses. The algorithm’s stability and reliability were rigorously evaluated using long-term tracer test results from a test block in an actual reservoir, covering a span of over a decade. The results of the algorithm were compared with those of the tracer test to evaluate its accuracy, precision rate, recall rate, and correlation. The clustering results indicate that wells with similar characteristics and production systems are automatically grouped into distinct clusters, reflecting the underlying geological understanding. The algorithm successfully divided the test block into four macro-regions, consistent with geological interpretations. Furthermore, the algorithm effectively identified the inter-well connectivity between injectors and producers, with connectivity magnitudes aligning closely with actual tracer test data. Overall, the algorithm achieved a precision rate of 79.17%, a recall rate of 90.48%, and an accuracy of 91.07%. This congruence validates the algorithm’s effectiveness in the quantitative analysis of inter-well connectivity and demonstrates significant potential for enhancing the accuracy and efficiency of inter-well connectivity identification. Full article

Sustainable digital transformation in SMEs not only simplifies operations and increases efficiency but also fosters innovation, facilitates data-driven decision-making, and minimizes negative environmental and social impacts, paving the way for a sustainable competitive advantage in the digital age. However, SMEs still face significant barriers to implementing this transformation. In addition to limited financial resources, a critical obstacle is the lack of specific and comprehensible roadmaps. Thus, this paper aims to analyze existing roadmaps for sustainable digital transformation in SMEs. A systematic literature review was conducted using the PRISMA and Methodi Ordinatio methods, with data collection and analysis performed using the NVivo 12 and VOSviewer software (version 1.6.20). Content analysis was used to assess how these papers align with the study’s context. The analysis identified whether the roadmaps address aspects related to the triple bottom line (TBL), their specificity to SMEs, the principles of digital transformation they incorporate, and the aspect of digital maturity. The findings indicate that none of the current literature provides a fully developed roadmap for sustainable digital transformation in SMEs. Consequently, future studies are suggested to develop a comprehensive roadmap that addresses these issues. Finally, this study highlights the main gaps and opportunities in each studied roadmap and formulates 14 research questions divided into six categories for future investigation. Addressing these questions can help close the current gaps in the literature and provide SMEs with robust, adaptable, and sustainable digital transformation strategies. Full article