Search Results (1,870)

An in-depth understanding of the spatiotemporal heterogeneity of ecosystem service (ES) trade-offs and synergies, along with their driving factors, is crucial for formulating key ecological restoration strategies and effectively allocating ecological environmental resources in the Hulunbuir region. This study employed an integrated analytical approach combining the InVEST model, ArcGIS geospatial processing, R software environment, and Optimal Parameter Geographical Detector (OPGD). The spatiotemporal patterns and driving factors of the interaction of four major ES functions in Hulunbuir area from 2000 to 2020 were studied. The research findings are as follows: (1) carbon storage (CS) and soil conservation (SC) services in the Hulunbuir region mainly show a distribution pattern of high values in the central and northeast areas, with low values in the west and southeast. Water yield (WY) exhibits a distribution pattern characterized by high values in the central–western transition zone and southeast and low values in the west. For forage supply (FS), the overall pattern is higher in the west and lower in the east. (2) The trade-off relationships between CS and WY, CS and SC, and SC and WY are primarily concentrated in the western part of Hulunbuir, while the synergistic relationships are mainly observed in the central and eastern regions. In contrast, the trade-off relationships between CS and FS, as well as FS and WY, are predominantly located in the central and eastern parts of Hulunbuir, with the intensity of these trade-offs steadily increasing. The trade-off relationship between SC and FS is almost widespread throughout HulunBuir. (3) Fractional vegetation cover, mean annual precipitation, and land use type were the primary drivers affecting ESs. Among these factors, fractional vegetation cover demonstrates the highest explanatory power, with a q-value between 0.6 and 0.9. The slope and population density exhibit relatively weak explanatory power, with q-values ranging from 0.001 to 0.2. (4) The interactions between factors have a greater impact on the inter-relationships of ESs in the Hulunbuir region than individual factors alone. The research findings have facilitated the optimization and sustainable development of regional ES, providing a foundation for ecological conservation and restoration in Hulunbuir. Full article

This paper investigates the cultural, spiritual, and ecological use and value of fish by-products in the material practices of Alaska Native (Indigenous Peoples are the descendants of the populations who inhabited a geographical region at the time of colonisation and who retain some or all of their own social, economic, cultural, and political institutions. In this paper, I use the terms “Indigenous” and “Native” interchangeably. In some countries, one of these terms may be favoured over the other.) and Greenlandic Inuit women. It aims to uncover how fish remnants—skins, bones, bladders, vertebrae, and otoliths—were transformed through tanning, dyeing, and sewing into garments, containers, tools, oils, glues, and adornments, reflecting sustainable systems of knowledge production rooted in Arctic Indigenous lifeways. Drawing on interdisciplinary methods combining Indigenist research, ethnographic records, and sustainability studies, the research contextualises these practices within broader environmental, spiritual, and social frameworks. The findings demonstrate that fish-based technologies were not merely utilitarian but also carried symbolic meanings, linking wearers to ancestral spirits, animal kin, and the marine environment. These traditions persisted even after European contact and the introduction of glass trade beads, reflecting continuity and cultural adaptability. The paper contributes to academic discourse on Indigenous innovation and environmental humanities by offering a culturally grounded model of zero-waste practice and reciprocal ecology. It argues that such ancestral technologies are directly relevant to contemporary sustainability debates in fashion and material design. By documenting these underexamined histories, the study provides valuable insight into Indigenous resilience and offers a critical framework for integrating Indigenous knowledge systems into current sustainability practices. Full article

As large language models (LLMs) increasingly enter specialized domains, inference without external resources often leads to knowledge gaps, opaque reasoning, and hallucinations. To address these challenges in power equipment defect grading, we propose a zero-shot question-answering framework that requires no task-specific examples. Our system performs two-stage retrieval—first using a Sentence-BERT model fine-tuned on power equipment maintenance texts for coarse filtering, then combining TF-IDF and semantic re-ranking for fine-grained selection of the most relevant knowledge snippets. We embed both the user query and the retrieved evidence into a Chain-of-Thought (CoT) prompt, guiding the pre-trained LLM through multi-step reasoning with self-validation and without any model fine-tuning. Experimental results show that on a held-out test set of 218 inspection records, our method achieves a grading accuracy of 54.2%, which is 6.0 percentage points higher than the fine-tuned BERT baseline at 48.2%; an Explanation Coherence Score (ECS) of 4.2 compared to 3.1 for the baseline; a mean retrieval latency of 28.3 ms; and an average LLM inference time of 5.46 s. Ablation and sensitivity analyses demonstrate that a fine-stage retrieval pool size of k = 30 offers the optimal trade-off between accuracy and latency; human expert evaluation by six senior engineers yields average Usefulness and Trustworthiness scores of 4.1 and 4.3, respectively. Case studies across representative defect scenarios further highlight the system’s robust zero-shot performance. Full article

The pepper weevil Anthonomus eugenii is a devastating pest native to Central America that can cause severe damage to over 35 pepper varieties. Global trade in peppers has significantly increased the risk of its spread and expansion. Moreover, future climate change may add more uncertainty to its distribution, resulting in considerable ecological and economic damage globally. Therefore, we employed an ensemble model combining Random Forests and CLIMEX to predict the potential global distribution of A. eugenii in historical and future climate scenarios. The results indicated that the maximum temperature of the warmest month is an important variable affecting global A. eugenii distribution. Under the historical climate scenario, the potential global distribution of A. eugenii is concentrated in the Midwestern and Southern United States, Central America, the La Plata Plain, parts of the Brazilian Plateau, the Mediterranean and Black Sea coasts, sub-Saharan Africa, Northern and Southern China, Southern India, Indochina Peninsula, and coastal area in Eastern Australia. Under future climate scenarios, suitable areas in the Northern Hemisphere, including North America, Europe, and China, are projected to expand toward higher latitudes. In China, the number of highly suitable areas is expected to increase significantly, mainly in the south and north. Contrastingly, suitable areas in Central America, northern South America, the Brazilian Plateau, India, and the Indochina Peninsula will become less suitable. The total land area suitable for A. eugenii under historical and future low- and high-emission climate scenarios accounted for 73.12, 66.82, and 75.97% of the global land area (except for Antarctica), respectively. The high-suitability areas identified by both models decreased by 19.05 and 35.02% under low- and high-emission scenarios, respectively. Building on these findings, we inferred the future expansion trends of A. eugenii globally. Furthermore, we provide early warning of A. eugenii invasion and a scientific basis for its spread and outbreak, facilitating the development of effective quarantine and control measures. Full article

This research presents the development of a novel object detection algorithm designed to identify humans in natural outdoor environments using minimal computational resources. The proposed system, SARGAS, combines a custom convolutional neural network (CNN) classifier with MOG2 background subtraction and partial affine transformations for camera stabilization. A secondary CNN refines detections and reduces false positives. Unlike conventional supervised models, SARGAS is trained in a partially unsupervised manner, learning to recognize feature patterns without requiring labeled data. The algorithm achieved a recall of 93%, demonstrating strong detection capability even under challenging conditions. However, the overall accuracy reached 65%, due to a higher rate of false positives—an expected trade-off when maximizing recall. This bias is intentional, as missing a human target in search and rescue applications carries a higher cost than producing additional false detections. While supervised models, such as YOLOv5, perform well on data resembling their training sets, they exhibit significant performance degradation on previously unseen footage. In contrast, SARGAS generalizes more effectively, making it a promising candidate for real-world deployment in environments where labeled training data is limited or unavailable. The results establish a solid foundation for further improvements and suggest that unsupervised CNN-based approaches hold strong potential in object detection tasks. Full article

The purpose of this paper is to analyze how China’s outward foreign direct investment (OFDI), driven by different motivations, affects the bilateral global value chain (GVC) relationship between the home country (China) and host countries, evaluating both bilateral GVC trade value and relative GVC positions. Employing the OECD Trade in Value Added (TiVA) database combined with Chinese listed firm data, we found the following results: (1) Strategic asset-seeking OFDI strengthens the GVC relationship between China and host countries while enhancing China’s GVC position relative to host countries. (2) Efficiency-seeking OFDI increases the domestic value-added exported from host countries to China but does not improve China’s relative GVC position. (3) Natural resource-seeking OFDI enhances bilateral GVC trade volumes but has no significant impact on the relative GVC positions of China and host countries. (4) China’s OFDI, not driven by these motivations, generates a trade substitution effect between home and host countries. We also examined the heterogeneity of these effects. Our findings suggest that China’s OFDI fosters equitable and sustainable international cooperation, supports mutually beneficial GVC trade and host-country economic growth, and therefore, progresses toward Sustainable Development Goal (SDG) 8. Full article

Herbivore-induced plant volatiles (HIPVs) are well known for their roles in herbivore deterrence and attraction of natural enemies, but their direct impact on insect reproduction remains largely unexplored. In this study, we provide novel evidence that two representative HIPVs, 2-heptanol and α-cedrene, exert opposing effects on the reproduction of Chilo suppressalis, a major rice pest. While both volatiles repelled adults, α-cedrene unexpectedly enhanced oviposition, whereas 2-heptanol significantly suppressed egg laying. To examine these effects, we conducted oviposition assays, preoviposition and longevity tests, combined with qPCR and transcriptome analyses to explore underlying molecular responses. Mechanistically, α-cedrene upregulated Kr-h1, a gene linked to juvenile hormone signaling and vitellogenesis, promoting reproductive investment. Transcriptomic profiling revealed divergent molecular responses: α-cedrene activated reproductive pathways, whereas 2-heptanol induced stress- and immune-related genes, suggesting a trade-off between stress defense and reproduction. These findings demonstrate that HIPVs can exert compound-specific reproductive effects beyond repellency. This work fills a key knowledge gap and highlights the potential of HIPVs as precision tools in pest management strategies that exploit behavioral and physiological vulnerabilities beyond repellency. Full article

In the context of achieving low-carbon goals, building low-carbon energy systems is a crucial development direction and implementation pathway. Renewable energy is favored because of its clean characteristics, but the access may have an impact on the power grid. Microgrid technology provides an effective solution to this problem. Uncertainty exists in single microgrids, so multiple microgrids are introduced to improve system stability and robustness. Electric carbon trading and profit redistribution among multiple microgrids have been challenges. To promote energy commensurability among microgrids, expand the types of energy interactions, and improve the utilization rate of renewable energy, this paper proposes a cooperative operation optimization model of multi-microgrids based on the green certificate and carbon trading mechanism to promote local energy consumption and a low carbon economy. First, this paper introduces a carbon capture system (CCS) and power-to-gas (P2G) device in the microgrid and constructs a cogeneration operation model coupled with a power-to-gas carbon capture system. On this basis, a low-carbon operation model for multi-energy microgrids is proposed by combining the local carbon trading market, the stepped carbon trading mechanism, and the green certificate trading mechanism. Secondly, this paper establishes a cooperative game model for multiple microgrid electricity carbon trading based on the Nash negotiation theory after constructing the single microgrid model. Finally, the ADMM method and the asymmetric energy mapping contribution function are used for the solution. The case study uses a typical 24 h period as an example for the calculation. Case study analysis shows that, compared with the independent operation mode of microgrids, the total benefits of the entire system increased by 38,296.1 yuan and carbon emissions were reduced by 30,535 kg through the coordinated operation of electricity–carbon coupling. The arithmetic example verifies that the method proposed in this paper can effectively improve the economic benefits of each microgrid and reduce carbon emissions. Full article

Direct electrification and hydrogen utilization represent two key pathways for decarbonizing the glass industry, with their effectiveness subject to adequate furnace design and renewable energy availability. This study presents a techno-economic assessment for optimal solar energy integration in a representative 300 t/d oxyfuel container glass furnace with a specific energy consumption of 4.35 GJ/t. A mixed-integer linear programming formulation is developed to evaluate specific melting costs, carbon emissions, and renewable energy self-consumption and self-production rates across three scenarios: direct solar coupling, battery storage, and a hydrogen-based infrastructure. Battery storage achieves the greatest reductions in specific melting costs and emissions, whereas hydrogen integration minimizes electricity export to the grid. By incorporating capital investment considerations, the study quantifies the cost premiums and capacity requirements under varying decarbonization targets. A combination of 30 MW of solar plant and 9 MW of electric boosting enables the realization of around 30% carbon reduction while increasing total costs by 25%. Deeper decarbonization targets require more advanced systems, with batteries emerging as a cost-effective solution. These findings offer critical insights into the economic and environmental trade-offs, as well as the technical constraints associated with renewable energy adoption in the glass industry, providing a foundation for strategic energy and decarbonization planning. Full article

Many commercial wearable sensor systems typically rely on a single continuous cardiorespiratory sensing modality, photoplethysmography (PPG), which suffers from inherent biases (i.e., differences in skin tone) and noise (e.g., motion and pressure artifacts). In this research, we present a wearable device that provides robust estimates of cardiorespiratory variables by combining three physiological signals from the upper arm: multiwavelength PPG, single-sided electrocardiography (SS-ECG), and bioimpedance plethysmography (BioZ), along with an inertial measurement unit (IMU) providing 3-axis accelerometry and gyroscope information. We evaluated the multimodal device on 16 subjects by its ability to estimate heart rate (HR) and breathing rate (BR) in the presence of various static and dynamic noise sources (e.g., skin tone and motion). We proposed a hierarchical approach that considers the subject’s skin tone and signal quality to select the optimal sensing modality for estimating HR and BR. Our results indicate that, when estimating HR, there is a trade-off between accuracy and robustness, with SS-ECG providing the highest accuracy (low mean absolute error; MAE) but low reliability (higher rates of sensor failure), and PPG/BioZ having lower accuracy but higher reliability. When estimating BR, we find that fusing estimates from multiple modalities via ensemble bagged tree regression outperforms single-modality estimates. These results indicate that multimodal approaches to cardiorespiratory monitoring can overcome the accuracy–robustness trade-off that occurs when using single-modality approaches. Full article

Achieving synergies between government-led and market-based policy instruments is critical to advancing Energy–Environmental Productivity and Sustainable Urban Development. This study investigates the effects of China’s dual-pilot energy policies (New Energy Demonstration Cities (NEDCs) and Energy Consumption Permit Trading (ECPT)) on urban environmental productivity (UEP) across 279 prefecture-level cities from 2006 to 2023. Utilizing a Non-Radial Directional Distance Function (NDDF) approach, combined with Difference-in-Differences (DID) estimation and spatial econometric models, the analysis reveals that these synergistic policies significantly enhance both comprehensive and net measures of UEP. Mechanism analysis highlights the roles of industrial restructuring, technological innovation, and energy transition in driving these improvements, while heterogeneity analysis indicates varying effects across different city types. Spatial spillover analysis further demonstrates that policy impacts extend beyond targeted cities, contributing to broader regional gains in UEP. These findings offer important insights for the design of integrated energy and environmental policies and support progress toward key Sustainable Development Goals (SDG 7, SDG 11, and SDG 12). Full article

Tissue-engineered scaffolds, particularly composite scaffolds composed of polymers combined with ceramics, bioactive glasses, or nanomaterials, play a vital role in regenerative medicine by providing structural and biological support for tissue repair. As scaffold designs grow increasingly complex, the need for non-invasive imaging modalities capable of monitoring scaffold integration, degradation, and tissue regeneration in real-time has become critical. This review summarizes current non-invasive imaging techniques used to evaluate tissue-engineered constructs, including optical methods such as near-infrared fluorescence imaging (NIR), optical coherence tomography (OCT), and photoacoustic imaging (PAI); magnetic resonance imaging (MRI); X-ray-based approaches like computed tomography (CT); and ultrasound-based modalities. It discusses the unique advantages and limitations of each modality. Finally, the review identifies major challenges—including limited imaging depth, resolution trade-offs, and regulatory hurdles—and proposes future directions to enhance translational readiness and clinical adoption of imaging-guided tissue engineering (TE). Emerging prospects such as multimodal platforms and artificial intelligence (AI) assisted image analysis hold promise for improving precision, scalability, and clinical relevance in scaffold monitoring. Full article

A multi-objective genetic algorithm is tailored to optimize the design of a wavelength interleaver/deinterleaver device. An interleaver combines data streams from two physical channels into one. The deinterleaver does the opposite job. The WDM requirements for this device include channel spacing of 50 GHz, channel bandwidth of 20 GHz, free spectral range of 100 GHz, maximum channel dispersion of 30 ps/nm, and maximum crosstalk of −23 dB. The challenges for the optimization process include the lack of a closed-form expression for the device performance and the trade-off between the conflicting performance parameters. So, for this multi-objective problem, the proposed approach maneuvers to find a compromise between the performance parameters within a few minutes, saving the designer the laborious design process previously proposed in the literature, which relies on visually inspecting the Z-plane for the dynamics of the transmission poles and zeros. Designs of better performance are achieved, with fewer ring resonators, a channel dispersion as low as 1.6 ps/nm, and crosstalk as low as −30 dB. Full article

Unmanned Aerial Vehicles (UAVs) face significant challenges in global path planning within complex terrains, as traditional algorithms (e.g., A*, PSO, APF) struggle to balance computational efficiency, path optimality, and safety. This study proposes a Voronoi–A* fusion algorithm, combining Voronoi-vertex-based rapid trajectory generation with A* supplementary expansion for enhanced performance. First, an adaptive DEM layering strategy divides the terrain into horizontal planes based on obstacle density, reducing computational complexity while preserving 3D flexibility. The Voronoi vertices within each layer serve as a sparse waypoint network, with greedy heuristic prioritizing vertices that ensure safety margins, directional coherence, and goal proximity. For unresolved segments, A* performs localized searches to ensure complete connectivity. Finally, a line-segment interpolation search further optimizes the path to minimize both length and turning maneuvers. Simulations in mountainous environments demonstrate superior performance over traditional methods in terms of path planning success rates, path optimality, and computation. Our framework excels in real-time scenarios, such as disaster rescue and logistics, although it assumes static environments and trades slight path elongation for robustness. Future research should integrate dynamic obstacle avoidance and weather impact analysis to enhance adaptability in real-world conditions. Full article

Agricultural development is vital for East Africa’s economic growth, yet the region faces significant disparities and systemic barriers. A critical problem exists due to the lack of an integrated quantitative framework to systematically comparing agricultural capacities and facilitate optimal resource allocation, as existing studies often overlook combined internal and external factors. This study proposes a comprehensive multi-criteria decision-making (MCDM) model to assess, categorize, and strategically profile the agricultural development capacity of 18 East African countries. The method employed is an integrated Entropy-MAIRCA model, which objectively weighs six criteria (the food production index, arable land, production fluctuation, food export/import ratios, and the political stability index) and ranks countries by their distance from an ideal development state. The experiment applied this framework to 18 East African nations using official data. The results revealed significant differences, forming four distinct strategic groups: frontier, emerging, trade-dependent, and high risk. The food export index (C4) and production volatility (C3) were identified as the most influential criteria. This model’s contribution is providing a science-based, transparent decision support tool for designing sustainable agricultural policies, aiding investment planning, and promoting regional cooperation, while emphasizing the crucial role of institutional factors. Full article