Search Results (6,593)

Understanding the intricate interrelationships among ecosystem services (ESs) is fundamental to advancing sustainable ecological management. This study focuses on the Taihu Basin and examines five representative ESs, including water yield (WY), carbon sequestration (CS), soil retention (SR), habitat quality (HQ), and crop production (CP), for the years 2000, 2010, and 2020. Spatial distribution characteristics and spatiotemporal dynamics were quantified through the combined application of the InVEST model, a food production model, and ArcGIS. Spearman correlation analysis and K-means clustering were then applied to characterize trade-offs and synergies among ESs and to delineate ecosystem service bundles at multiple spatial scales, including 1 km × 1 km grids, 10 km × 10 km grids, and the county level, while GeoDetector was used to identify the associated driving mechanisms. The results indicated that (1) between 2000 and 2020, the spatial distribution pattern of the ESs in the Taihu Basin underwent significant changes, with WY and SR increasing by 48.97% and 51.89%, respectively, while HQ, CS, and CP decreased by 17.2%, 15.5%, and 47.6%. (2) From an overall perspective of trade-offs and synergies, the interactions among ESs shifted from trade-offs (r < 0) to synergies (r > 0) as the scale increased. From the perspective of the spatial characteristics of trade-offs and synergies, the intensity of these interactions varied significantly with increasing scale, but the trend remained relatively stable. (3) The Taihu Basin can be categorized into six ES bundles (ESBs). ESB 1, ESB 3, ESB 4, and ESB 5 have relatively stable ES structures, whereas ESBs 2 and 6 display significant variations. (4) The primary factors influencing ESs vary significantly across different spatial scales, with land use/land cover (LULC) and the proportions of arable land, forestland, and buildings exhibiting strong explanatory power. This highlights the critical role of coupled natural and anthropogenic processes in shaping the spatial patterns of ESs. This study considers the spatiotemporal variation and scale dependence of ecosystem services, providing management recommendations tailored to different regions and spatial scales, and offering a scientific basis for regional ecological planning and watershed governance. Full article

Controlled Environment Agriculture (CEA) has become a key driver of vertical farming (VF), offering innovative solutions for the sustainable production of ornamental plants in urban environments with limited arable land. This review examines recent advances in VF technologies and their applications in foliage and flowering ornamental plant production. The literature indicates that precise environmental control, including optimized LED lighting spectra, hydroponic and aeroponic nutrient delivery, and automated climate regulation, can significantly enhance plant growth, morphological characteristics, color intensity, and overall market quality of ornamental species. In addition, VF systems demonstrate substantial reductions in water consumption, pesticide use, and land requirements compared with conventional cultivation methods. However, several challenges remain, including high-energy demand, economic feasibility, and the need for crop-specific environmental optimization for different ornamental species. This review synthesizes current research on VF systems, highlights the integration of emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), and data-driven management tools, and evaluates their potential to improve production efficiency and sustainability in ornamental horticulture. Overall, vertical farming represents a promising approach for high-quality ornamental plant production, although further research is required to optimize energy efficiency and cultivation protocols for diverse ornamental crops. Full article

This work presents a space-oriented extension of an existing research program focused on developing innovative approaches and design solutions for rotorcraft. The study builds upon recent research conducted at the Military University of Technology, where new methods for main rotor optimization using parametric modeling were developed. The primary objective of this research is to investigate the feasibility of designing a rotorcraft capable of operating in the Martian environment. The proposed vehicle is intended to perform vertical takeoff, flight, and landing; sustain at least two hours of continuous operation; and transport a pilot with either a passenger or an equivalent payload of 100 kg. Additionally, the rotorcraft should be capable of being restored to an airworthy condition after each mission and prepared for reuse while maintaining its operational capabilities. Preliminary performance analyses were conducted based on Martian atmospheric conditions. Analytical models implemented in dedicated computational tools were used to estimate rotor dimensions, performance, and trim requirements. Several rotor configurations were evaluated to assess the feasibility of manned flight with an additional payload under extraterrestrial conditions. The results identify key limitations, risks, and technological challenges, while also highlighting potential design opportunities. The study culminates in a conceptual design proposal for a future Martian rotorcraft mission. The findings demonstrate the applicability of the proposed methodology and provide a foundation for further research and development in planetary rotorcraft systems. Full article

This study targets key challenges in ameliorating the plow-layer soil of coastal saline soils. A field experiment under a wheat–maize rotation was established with six treatments: CK, control with no organic inputs; A1, 45 t ha⁻¹ organic manure; A2, 45 t ha⁻¹ organic manure + microbial inoculant; A3, 45 t ha⁻¹ organic manure + microbial inoculant + plastic-film mulching; A4, 90 t ha⁻¹ organic manure; and A5, 135 t ha⁻¹ organic manure. By applying high rates of organic manure alone or in combination with microbial inoculation and mulching, we aimed to strengthen soil water–salt regulation, improve plow-layer soil quality, and ultimately promote crop growth and yield formation. We further quantified treatment-induced shifts in soil physicochemical properties and linked them to crop growth and yield responses. The results indicated that, compared with CK, plow-layer soil organic carbon increased by 45.56% and 107.91% under A3 and A4, respectively, while soil salinity decreased by 70.57% and 67.42%. All manure-based treatments increased yield relative to CK, with the highest yields achieved under A3 and A4: wheat yield reached 7628.16 and 7888.01 kg ha⁻¹, and maize yield reached 8828.29 and 8716.01 kg ha⁻¹, respectively. Overall, high-rate organic manure—especially when integrated with microbial inoculation and plastic mulching—substantially enhanced soil fertility while alleviating salinity stress, resulting in an integrated “fertility build-up–salinity reduction–yield enhancement” amelioration effect. This technology package offers a feasible pathway for improving coastal saline farmland and stabilizing productivity under rotation systems, with strong potential for further on-farm demonstration and wider adoption. Full article

Straw burning is a major source of regional air pollution. However, its reliable remote sensing detection faces problems in distinguishing agricultural fires from non-agricultural thermal anomalies, adequately leveraging burning seasonality, and overcoming the scarcity of pixel-level annotations. To comprehensively address these issues, this study proposes an end-to-end framework for straw burning identification that integrates spatio-temporal weighting and semi-supervised learning. The framework introduces a data-driven spatial weight optimization method to automatically learn discriminative weights for diverse land cover types (e.g., farmland, industry), replacing subjective empirical settings. Furthermore, a temporal weighting model, developed using Kernel Density Estimation, dynamically adjusts classification confidence according to historical burning seasonality, enhancing recall during peak seasons while suppressing off-season false positives. Finally, an adapted Dual-Backbone Dynamic Mutual Training (DB-DMT) strategy collaboratively leverages both limited labeled (24.5%) and abundant unlabeled (75.5%) high-resolution imagery, significantly improving model generalization in label-scarce scenarios. Validation across five representative regions of China demonstrated the framework’s superior performance, achieving a semantic segmentation mean Intersection over Union (mIoU) improvement of 3.33% (to 71.92%) and increasing precision in Henan from 95.21% to 97.71%. Crucially, the framework effectively reduced the off-season false positive rate (FPR) from 5.14% to a mere 0.23% in highly industrialized regions like Tianjin. By systematically mitigating both spatial geolocation bias and seasonal phenology confusion, our approach offers a robust and scalable solution for straw burning monitoring and a transferable paradigm for other environmental remote sensing applications. Full article

The Copernicus Sentinel-2 (S2) data are a crucial resource for urban policymakers in land-cover classification, offering a freely accessible alternative to expensive commercial data sources. While medium spatial resolution often limits the applicability of data-intensive machine learning approaches, the Geographic Object-Based Image Analysis (GEOBIA) framework could be an effective, operational alternative for urban land-cover classification using S2 data. This study applies the Geographic Object-Based Image Analysis (GEOBIA) approach to classify land cover in Hue, Vietnam, using Sentinel-2 data processed through the eCognition interface. The study’s findings emphasize the potential of GEOBIA and S2 data in enhancing decision-making processes for city authorities, ensuring better resource allocation, environmental protection, and infrastructure development. The results indicate that the method performs reliably for mesoscale and spatially continuous classes, such as vegetation and built-up surfaces, while accuracy is lower for small or spectrally heterogeneous features, particularly shallow water bodies and fragmented rice paddies, due to mixed-pixel effects inherent in 10–20 m resolution imagery. The results demonstrate an Overall Accuracy (OA) of 91%, highlighting the method’s effectiveness in extracting and classifying urban land-cover classes. This study demonstrates a replicable model for urban land monitoring that can be adapted across various geographic contexts. Furthermore, this approach fosters a more data-driven governance model, where urban expansion and land-use changes can be monitored in real time, allowing for proactive interventions. With urbanization accelerating worldwide, particularly in rapidly developing regions, such a cost-effective and accessible classification method can significantly aid in achieving long-term urban sustainability. The findings illustrate the relevance of GEOBIA as a feasible tool for supporting data-driven urban governance, enabling systematic tracking of land-use change, informed infrastructure planning, and sustainable urban management in both developed and rapidly urbanizing regions. Full article

Global Navigation Satellite Systems (GNSS), benefiting from global coverage, all-weather operation, high precision, and high temporal resolution, have progressively become a key technology in natural hazard monitoring and early warning systems. This paper adopts a hybrid review strategy that integrates scientometric analysis with a systematic review to examine the development trajectory, research hotspots, and technological evolution of GNSS applications in natural hazard studies based on the existing literature. From a technological perspective, three core capabilities of GNSS in hazard monitoring are identified: high-precision, multi-scale deformation sensing; multi-sphere environmental sensing based on signals of opportunity; and real-time monitoring supporting rapid early warning and emergency response. The paper further reviews the development of GNSS in conjunction with multi-sensor collaborative observation and its integration with data-driven methods such as machine learning. Representative applications of GNSS and its integrated techniques are summarized across major hazard types, including earthquakes, tsunamis, landslides, land subsidence, hydrometeorological hazards, and volcanic activity, and further discussions are provided on methodological considerations, the commonalities and differences in GNSS applications across different hazards, and future development directions. The review demonstrates that GNSS applications in natural hazard research are evolving from single-source deformation monitoring toward multi-source integration, intelligent sensing, and operational early warning support systems. This work provides a reference for the further development of GNSS technologies in natural hazard monitoring and risk mitigation. Full article

This study presents a novel data-driven framework for developing airport-specific landing policies and procedures from historical successful-landing data. The proposed process, termed the Airport-Dependent Landing Procedure (ADLP), is motivated by the fact that airports rely on uniquely tailored approach charts reflecting local operational constraints and environmental conditions. While existing approach charts and landing procedures are primarily designed based on expert knowledge, safety margins, and regulatory conventions, the authors argue that data science and data mining techniques offer a complementary and empirically grounded methodology for extracting operationally meaningful structures directly from historical landing data. In this work, we construct a probabilistic three-dimensional environment from real-world aircraft approach trajectories, capturing spatiotemporal relationships under varying atmospheric conditions during approach. The proposed methodology integrates Adversarial Inverse Reinforcement Learning (AIRL) with Recurrent Proximal Policy Optimization (R-PPO) to establish a foundation for automated landing without pilot intervention. AIRL infers reward functions that are consistent with behaviors exhibited in prior successful landings. Subsequently, R-PPO is employed to learn control policies that satisfy safety constraints related to airspeed, sink rate, and runway alignment. Application of the proposed framework to real approach trajectories at Guam International Airport demonstrates the efficiency and effectiveness of the methodology. Full article

This paper investigates the application of Transit-Oriented Development (TOD) principles to the retrofitting of historic Gulf urban cores through a comparative analysis of Souq Waqif (Doha) and Qasr Al Hokm (Riyadh). The research employs field observation, thematic mapping, and qualitative diagnosis using the Integrated Modification Methodology (IMM) to assess compactness, intricacy, and connectivity within walkable station catchments. The findings indicate that Souq Waqif has a highly compact and intricate historic core with robust pedestrian activity, yet exhibits discontinuities at its periphery, such as car-dominated streets, fragmented green spaces, and weak connections between the metro station, parks, and adjacent blocks. In Qasr Al Hokm, the analysis affirms the value of its fine-grained historic fabric and civic landmarks, but also identifies deficiencies in shading, last-mile connectivity, and land-use balance surrounding the new metro station. Drawing on lessons from Souq Waqif, the paper proposes a TOD-oriented urban design framework for Qasr Al Hokm, emphasizing shaded pedestrian corridors, active ground floors, intermodal hubs, and heritage-compatible mixed-use intensification. This comparative approach demonstrates how TOD can foster more livable, accessible, and climate-responsive historic cores in Gulf cities, while maintaining respect for local identity and governance structures. Full article

In the modular construction of liquefied natural gas (LNG) plants and receiving terminals, transport beams are critical components that enable modular mobility. However, these beams are susceptible to large deformations due to complex loads during land and sea transportation. Traditional monitoring methods (i.e., strain gauge and deflection meters) often suffer from low efficiency and poor accuracy and may disrupt operational continuity in real-time monitoring systems. This paper presents a non-contact, real-time deformation detection system for LNG modular transport beams based on digital image technology, which integrates a high-resolution camera with a real-time software framework to remotely monitor structural integrity. An experiment was conducted on a full-scale support column-transport beam frame with specialized connection joints designed for rapid assembly. Five digital image correlation (DIC) detection regions (5 cm × 5 cm) were established on box-shaped beam sleeves, column sleeves, and the end plates of the beam–column joints. In addition, displacement gauges were installed at the same DIC locations. The experimental results demonstrate that the DIC measurements show good agreement with traditional measurement methods, verifying the applicability of the proposed system for large-scale LNG engineering structures. Full article

Land is the fundamental basis for maintaining agricultural production and ensuring food security. The task of managing and sustainably utilizing land resources has always been a priority for every country in the world. The study used GIS-MEC technology to integrate data from seven types of single-factor maps to construct a soil quality map with 47 land units (including eight land units with an area >100 ha, 29 land units with an area from 10 to 100 ha, and 10 land units with an area <10 ha). In addition, by combining soil quality maps and the nutritional needs of different crops, an assessment of land suitability for six major crops was conducted, and three key crops were selected for focused development: rice, vegetables, and flowers. The application of GIS in soil quality management is in line with the current trends of digital transformation and integrated data management in Vietnam and around the world. However, this method has several limitations that need to be considered when applying it, such as dependence on expert expertise, high demands on input data and verification of output results, and limitations in analyzing trends and analyzing social, non-linear factors. Full article

Additive manufacturing (AM) has emerged as an enabling technology for producing lightweight and geometrically complex components in aerospace applications. This study investigates alternative manufacturing routes for a critical servo bracket used in a Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) platform, aiming to comparatively evaluate their environmental, economic, and operational performance within a life cycle perspective. The servo bracket was manufactured using CNC milling, Selective Laser Melting (SLM), and Metal Extrusion Additive Manufacturing (MEX/M) and the three routes were assessed using Life Cycle Assessment (LCA), Life Cycle Cost (LCC), and process-based indicators, including production time and production process. The results indicate that CNC milling exhibits the highest carbon footprint per part (156.3 kg CO₂-eq.), mainly due to aluminium chip waste, whereas electricity consumption is the dominant contributor in SLM. Production times were 8.9 h for CNC, 52.7 h for SLM, and 71.6 h for MEX/M. From an economic perspective, CNC provides the lowest unit cost, while SLM is associated with the highest cost due to machine depreciation. Overall, the findings highlight distinct trade-offs between conventional and metal additive manufacturing routes and provide a life cycle-based decision framework for selecting suitable manufacturing strategies for VTOL UAV structural components. Full article

Local governments are key actors in driving urban renewal. To implement urban renewal initiatives, in-depth research into their policy backgrounds, institutional characteristics, and governance logic is essential. Traditional policy analysis often neglects the value dimension, which undermines the effectiveness of embedding informal institutional values. To complement existing research, this study examines 50 urban renewal policy documents issued in Guangzhou between 1978 and 2025. Using content analysis and grounded theory methods, this study incorporates the value dimension into the traditional “supply–demand–environment” policy analysis framework to examine local governments’ policy preferences in urban renewal, and to interpret its governance logic from the perspective of Williams’ four-level framework. The findings are as follows: (1) Guangzhou’s urban renewal has formed a policy system centered on supply-side policies, supported by environmental policy improvements, with value embedding, demand-driven measures, and multi-dimensional guidance as supplementary components. Local governments show a distinct preference for supply-oriented policy tools. (2) Guangzhou’s urban renewal policies present a pyramid structure with resource allocation at the core and governance structure as the foundation. The policies focus on the optimal allocation of land resources, collaborative actions among government, market, and society, the deep integration of public values, the clarification of property rights rules, and the application of digital technologies. (3) The governance logic of urban renewal forms a four-tier progressive closed-loop: from value anchoring to rule linkage, then to multi-stakeholder collaboration, and finally to factor empowerment, establishing a systematic governance mechanism that balances people-centricity and efficiency. Accordingly, urban renewal should prioritize value embedding and cultural preservation, balance investment in physical assets and human capital, optimize governance structures and policy mixes, coordinate the roles of an effective market and a capable government, improve supply–demand matching and the efficiency of resource allocation, and adjust the complementarity and applicability of policy tools. Full article

This paper presents a lightweight hyper-network architecture for cross-scale multi-task object classification, addressing the critical challenge of gradient interference in joint learning scenarios. We propose a HyperConv module integrated into a slim ResNet-12 backbone, which dynamically generates task-adaptive 3 × 3 convolutional kernels from compact two-dimensional latent vectors. This design allows explicit control over gradient flows for different tasks with minimal parameter overhead (only 3.2% additional parameters). Our framework incorporates adversarial regularization via a Gradient Reversal Layer (GRL) and dynamic task-weight scheduling to mitigate gradient conflicts across domains. Experiments on both natural image datasets (Mini-ImageNet and CIFAR-100) and remote sensing benchmarks (EuroSat and UCMerced_LandUse) demonstrate statistically significant improvements over conventional shared-parameter baselines. The proposed method effectively reduces negative transfer, enhances feature representation, and offers a practical solution for on-device multi-task learning in resource-constrained remote sensing applications such as UAVs and edge satellites. Full article

Macroalgae represent a promising third-generation feedstock for biorefinery due to their high biomass productivity and non-reliance on arable land. However, their complex cell wall structure poses a significant barrier to efficient bioconversion. This review integrates current pretreatment methods, including physical, chemical, biological, and combined approaches, with a focus on their mechanisms, effectiveness, and limitations. Furthermore, it explores the conversion of pretreated macroalgal biomass into bioenergy and biochemicals, such as bioethanol, organic acid and polyhydroxyalkanoate, via microbial fermentation. The review also examines the application of genetic editing tools (e.g., CRISPR-Cas systems) for the targeted modification of macroalgae to improve their inherent characteristics for biorefinery, such as reducing biomass recalcitrance or increasing the content of target carbohydrates. Finally, future perspectives on technological innovations and integrated industrial chains of macroalgal biorefinery are discussed. This review serves as a systematic reference for deepening the understanding of macroalgal cell wall deconstruction processes and supports the development of efficient and environmentally benign pretreatment strategies to advance macroalgal biorefinery toward industrialization. Full article