Search Results (226)

Amid intensifying geopolitical tensions and global uncertainties, regional economies face mounting pressures that threaten both stability and sustainability. Against this backdrop, building resilient regional systems has become a central issue in sustainable urban development. As a key driver of resilience, innovation has been central to China’s development agenda. Continuous and large-scale R&D investment has redirected focus from input expansion to efficiency improvement, positioning R&D efficiency at the heart of resilience-building. Under external shocks and uncertainty, can improvements in R&D efficiency enhance regional economic resilience? If so, which additional factors embedded in sustainable urban development planning can further amplify this effect? To address these questions, this study employs provincial panel data from 2000 to 2021 and integrates the SBM-DEA approach with an entropy-weighted resilience index for regression analysis. The results indicate that (1) R&D efficiency exerts a positive but limited impact on resilience, with an average increase of only 0.188 units, indicating that efficiency alone cannot generate resilient economies without institutional coordination; (2) human capital agglomeration and financial density strengthen this relationship, highlighting the need to integrate talent and financial strategies; (3) the positive effect is observed in eastern provinces but remains insignificant in central and western regions, revealing pronounced structural disparities that risk widening the resilience gap across regions rather than fostering balanced development; and (4) targeted government intervention effectively converts innovation efficiency into resilience gains, fostering coordinated and sustainable development. This study empirically demonstrates that improving R&D efficiency significantly enhances regional resilience in China and based on this evidence introduces the ICT Synergy Framework as a novel analytical lens for understanding how innovation, capital, and talent jointly drive resilience and sustainable development. The findings further suggest that targeted government intervention in R&D resource allocation can reinforce resilience, offering broader lessons for other developing economies. By integrating innovation outcomes with spatial and institutional planning, the study provides actionable insights for advancing sustainable urban development and coordinated regional growth. Full article

This study presents the development and evaluation of an Augmented Reality (AR)-based training system aimed at improving patient setup accuracy in radiation therapy. Leveraging Microsoft HoloLens 2, the system provides an immersive environment for medical staff to enhance their understanding of patient setup procedures. High-resolution 3D anatomical models were reconstructed from CT scans using 3D Slicer, while Luma AI was employed to rapidly capture complete body surface models. Due to limitations in each method—such as missing extremities or back surfaces—Blender was used to merge the models, improving completeness and anatomical fidelity. The AR application was developed in Unity, employing spatial anchors and 125 × 125 mm² QR code markers to stabilize and align virtual models in real space. System accuracy testing demonstrated that QR code tracking achieved millimeter-level variation, with an expanded uncertainty of ±2.74 mm. Training trials for setup showed larger deviations in the X (left–right), Y (up-down), and Z (front-back) axes at the centimeter scale. This meant that we were able to quantify the user’s patient setup skills. While QR code positioning was relatively stable, manual placement of markers and the absence of real-time verification contributed to these errors. The system offers a radiation-free and interactive platform for training, enhancing spatial awareness and procedural skills. Future work will focus on improving tracking stability, optimizing the workflow, and integrating real-time feedback to move toward clinical applicability. Full article

Fluctuations in precipitation usually affect the ecological environment and human socioeconomics through events such as floods and droughts, resulting in substantial economic losses. The high-resolution models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are vital for simulating precipitation patterns in China; however, significant uncertainties still exist. This study utilized the quantile mapping (QM) method to correct biases in nine high-resolution Earth System Models (ESMs) and then comprehensively evaluated their precipitation simulation capabilities over the Chinese mainland from 1985 to 2014. Based on the selected models, the Bayesian Model Averaging (BMA) method was used to integrate them to obtain the spatial–temporal variation in precipitation over the Chinese mainland. The results showed that the simulation performance of nine models for precipitation from 1985 to 2014 was significantly improved after the bias correction. Six out of the nine high-resolution ESMs were selected to generate the BMA ensemble model. For the BMA model, the precipitation trend and the locations of significant points were more closely aligned with the observational data in the summer than in other seasons. It overestimated precipitation in the spring and winter, while it underestimated it in the summer and autumn. Additionally, both the BMA model and the worst multi-model ensemble (WMME) model exhibited a negative mean bias in the summer, while they displayed a positive mean bias in the winter. And the BMA model outperformed the WMME model in terms of mean bias and bias range in both the summer and winter. Moreover, high-resolution models delivered precipitation simulations that more closely aligned with observational data compared to low-resolution models. Full article

In recent years, with the popularization of intelligent scene monitoring, sound source localization (SSL) has become a major means for indoor monitoring and target positioning. However, existing sound source localization solutions are difficult to extend to multi-source and three-dimensional scenarios. To address this, this paper proposes a three-dimensional sound source localization technology based on eight microphones. Specifically, the method employs a rectangular eight-microphone array and captures Direction-of-Arrival (DOA) information via the direct path relative transfer function (DP-RTF). It introduces spatial entropy to quantify the uncertainty caused by the exponentially growing DOA combinations as the number of sound sources increases, while further reducing the spatial entropy of sound source localization through geometric intersection. This solves the problem that traditional sound source localization methods cannot be applied to multi-source and three-dimensional scenarios. On the other hand, machine learning is used to eliminate coordinate deviations caused by DOA estimation errors of the direct path relative transfer function (DP-RTF) and deviations in microphone geometric parameters. Both simulation experiments and real-scene experiments show that the positioning error of the proposed method in three-dimensional scenarios is about 10.0 cm. Full article

In confronting escalating economic uncertainty, achieving a win–win situation for low-carbon transition and improved employment structure will contribute to economic recovery and sustainable growth but also contribute to building a community with a shared future for mankind. A critical issue for China’s economy and societal welfare, as well as a core component of sustainable development, concerns whether low-carbon economic transition influences employment skill structure. This study utilizes data from 30 provinces (municipalities and autonomous regions) in China from 2006 to 2021. Employing the entropy method, a low-carbon economic development level indicator system was constructed from four aspects: low-carbon output, low-carbon consumption, low-carbon resources, and low-carbon environment to measure the low-carbon economy and explore its direct and indirect effects on employment skill structure and spatial effects. The research findings indicate that low-carbon economies not only directly and significantly promote employment skill structure optimization but also indirectly generate promotional effects through pathways such as industrial structure adjustment, green innovation’s innovative effects, and factor substitution effects of increased pollution control investment. Among these, the indirect impact of industrial structure adjustment contributes most substantially. Low-carbon economies’ influence on employment skill structures exhibits spatial spillover effects, with neighboring regions’ low-carbon economies exerting positive spillover effects on local skill structures. Additionally, significant negative interdependence exists among regional employment skill structures. Based on the aforementioned research conclusions, the following recommendations are proposed: accelerate low-carbon economy development and employment skill structure enhancement in central and western regions to diminish regional disparities; encourage green innovation and promote traditional industry upgrading and transformation; formulate regional coordinated development plans, thereby strengthening the low-carbon economy’s optimizing role upon employment skills structure; and increase educational investment and strengthen labor skill training. Full article

Real-time coordination of heterogeneous multi-agent systems in dynamic and partially observable environments poses significant challenges. To address this, we propose a framework that integrates fuzzy inference systems with real-valued genetic algorithms to optimize decision-making under strict time constraints and sensory uncertainty. We evaluate the proposed method in the RoboCup Soccer Simulation 2D League, where 22 autonomous agents coordinate through a fuzzy-evaluated action sequence search. Spatial heuristics are encoded as fuzzy rules, and optimization based on genetic algorithms refines evaluation function parameters according to performance metrics such as number of shots, goal area entries, and scoring rates. The resulting control strategy remains interpretable; spatial heat maps reveal emergent behaviors such as coordinated positioning and ridgeline passing patterns near the penalty area. The experiments against established RoboCup teams, serving as benchmarks, demonstrate the competitive performance of our trained agents while enabling analyses of evolving decision structures and agent behaviors. Our method provides a transparent and adaptable framework for controlling heterogeneous agents in uncertain real-time environments, with broad applicability to robotics, autonomous systems, and distributed control systems. Full article

Amid an increasingly complex and uncertain global landscape, geopolitical tensions and frequent trade frictions have emerged as critical external risks threatening the economic stability and sustainable development of Chinese cities. Enhancing cities’ economic resilience has become a key challenge in advancing China’s high-quality development agenda. As a major national strategic initiative, the Belt and Road Initiative (BRI) is expected to offer new development opportunities and pathways for risk mitigation, particularly for cities situated along its domestic routes. This paper examines whether and how the BRI affects the economic resilience of these cities and further explores the moderating role of local governance capacity in policy implementation. To this end, an empirical strategy combining the entropy weighting method and the difference-in-differences (DID) approach is employed to systematically assess the impact of the BRI on urban economic resilience at the city level. The key findings are as follows: (1) The findings show that the BRI has an enhancing effect on the economic resilience of cities along the routes, but governance is very weak, and urban resilience improves by 0.0045 units on average. Our findings imply that, while the BRI appears to be on the correct path, enhanced governance is necessary to implement city-specific planning approaches effectively. (2) The results of the moderating effect indicate that local governance capacity significantly amplifies the impact of the BRI on urban economic resilience, underscoring the critical role of institutional strength in the policy transmission process. (3) The heterogeneity analysis reveals significant regional disparities in policy effectiveness: while the BRI significantly improves economic resilience in eastern and central cities, it exerts a suppressive effect in western regions. This divergence is closely associated with variations in local governance capacity. In contrast, cities with stronger governance capabilities are more likely to experience positive outcomes, as confirmed by the significant moderating effect of local governance capacity. This study contributes to the growing literature on the spatial implications of national development strategies by empirically examining how the BRI reshapes urban economic resilience across regions. It offers important policy insights for enhancing the spatial governance of cities, particularly in aligning strategic infrastructure investment with differentiated local capacities. The findings also provide a valuable reference for land-use planning and regional development policies aimed at building resilient urban systems under conditions of global uncertainty. Full article

Electric rope shovels play a critical role in open-pit mining, where their automation and operational efficiency directly affect productivity. This paper presents a LiDAR-based relative positioning method to determine the spatial relationship between the ERS and mining trucks. The method utilizes dynamic DBSCAN for noise removal and RANSAC for truck edge detection, enabling robust and accurate localization. Leveraging this positioning data, a time-optimal trajectory planning strategy is proposed specifically for autonomous swing motion during the unloading process. The planner incorporates velocity and acceleration constraints to ensure smooth and efficient movement, while obstacle avoidance mechanisms are introduced to enhance safety in constrained excavation environments. To execute the planned trajectory with high precision, a neural network-based sliding-mode controller is designed. An adaptive RBF network is integrated to improve adaptability to model uncertainties and external disturbances. Experimental results on a scaled-down prototype validate the effectiveness of the proposed positioning, planning, and control strategies in enabling accurate and autonomous swing operation for efficient unloading. Full article

Existing nighttime visibility enhancement methods primarily focus on improving overall brightness under low-light conditions. However, nighttime road images are also affected by glare, glow, and flare from complex light sources such as streetlights and headlights, making it challenging to suppress locally overexposed regions and recover fine details. To address these challenges, we propose a Nighttime Road Glare Suppression Network (NRGS-Net) for glare removal and detail restoration. Specifically, to handle diverse glare disturbances caused by the uncertainty in light source positions and shapes, we designed a gated positional attention (GPA) module that integrates positional encoding with local contextual information to guide the network in accurately locating and suppressing glare regions, thereby enhancing the visibility of affected areas. Furthermore, we introduced an improved Uformer backbone named LCAtransformer, in which the downsampling layers adopt efficient depthwise separable convolutions to reduce computational cost while preserving critical spatial information. The upsampling layers incorporate a residual PixelShuffle module to achieve effective restoration in glare-affected regions. Additionally, channel attention is introduced within the Local Context-Aware Feed-Forward Network (LCA-FFN) to enable adaptive adjustment of feature weights, effectively suppressing irrelevant and interfering features. To advance the research in nighttime glare suppression, we constructed and publicly released the Night Road Glare Dataset (NRGD) captured in real nighttime road scenarios, enriching the evaluation system for this task. Experiments conducted on the Flare7K++ and NRGD, using five evaluation metrics and comparing six state-of-the-art methods, demonstrate that our method achieves superior performance in both subjective and objective metrics compared to existing advanced methods. Full article

Introduction: Primary hyperparathyroidism (pHPT) is an endocrine disorder characterized by excessive parathyroid hormone production, typically due to adenomas, hyperplasia, or carcinoma. Preoperative imaging plays a critical role in guiding surgical planning, particularly in selecting patients for minimally invasive procedures. While first-line imaging techniques, such as ultrasound and 99mTc-sestamibi scintigraphy, are standard, advanced second-line imaging modalities like 18F-fluorocholine PET/CT (FCH-PET) and four-dimensional computed tomography (4D-CT) have emerged as valuable tools when initial diagnostics are inconclusive. Methods: This article provides an updated review and recommendations of the role of these advanced imaging techniques in localizing parathyroid adenomas. Results: FCH-PET has shown exceptional sensitivity (94% per patient, 96% per lesion) and is particularly useful in detecting small or ectopic adenomas. Despite its higher sensitivity, it can yield false positives, particularly in the presence of thyroid disease. On the other hand, 4D-CT offers detailed anatomical imaging, aiding in the identification of parathyroids in challenging cases, including recurrent disease and ectopic glands. Studies suggest that FCH-PET and 4D-CT exhibit similar diagnostic performance and could be complementary in preoperative planning of most difficult situations. Conclusions: This article also emphasizes a multimodal approach, where initial imaging is followed by advanced techniques only in cases of uncertainty. Although 18F-fluorocholine PET/CT is favored as a second-line option, 4D-CT remains invaluable for its high spatial resolution and ability to guide surgery in complex cases. Despite limitations in evidence, these imaging modalities significantly enhance the accuracy of parathyroid localization, contributing to more targeted and minimally invasive surgery. Full article

The accurate prediction of soil organic matter (SOM) content is essential for promoting sustainable soil management and addressing global climate change. Due to multiple factors such as topography and climate, especially in mountainous areas, SOM spatial prediction faces significant challenges. The main novelty of this study lies in proposing a geographic positional encoding mechanism that embeds geographic location information into the feature representation of a Transformer model. The encoder structure is further modified to enhance spatial awareness, resulting in the development of the Geo-Positional Transformer (GPTransformer). Furthermore, this model is integrated with a 1D-CNN to form a dual-branch neural network called the Geo-Positional Transformer-CNN (GPTransCNN). This study collected 1490 topsoil samples (0–20 cm) from cultivated land in Longshan County to develop a predictive model for mapping the spatial distribution of SOM across the entire cultivated area. Different models were comprehensively evaluated through ten-fold cross-validation, ablation experiments, and uncertainty analysis. The results show that GPTransCNN has the best performance, with an R² improvement of approximately 43% over the Transformer, 19% over the GPTransformer, and 15% over the 1D-CNN. This study demonstrates that by incorporating geographic positional information, GPTransCNN effectively combines the global modeling capabilities of the GPTransformer with the local feature extraction strengths of the 1D-CNN, which can improve the accuracy of SOM mapping in mountainous areas. This approach provides data support for sustainable soil management and decision-making in response to global climate change. Full article

This paper examines the impact of carbon capture and storage (CCS) deployment on national carbon intensity (CI) across 43 countries from 2010 to 2020. Using a dynamic common correlated effects (DCCE) log–log panel, we estimate the elasticity of CI with respect to sectoral CCS facility counts within four income-group panels and the full sample. In the high-income panel, CCS in direct air capture, cement, iron and steel, power and heat, and natural gas processing sectors produces statistically significant CI declines of 0.15%, 0.13%, 0.095%, 0.092%, and 0.087% per 1% increase in facilities, respectively (all p < 0.05). Upper-middle-income countries exhibit strong CI reductions in direct air capture (–0.22%) and cement (–0.21%) but mixed results in other sectors. Lower-middle- and low-income panels show attenuated or positive elasticities—reflecting early-stage CCS adoption and infrastructure barriers. Robustness checks confirm these patterns both before and after the 2015 Paris Agreement and between emerging and developed economy panels. Spatial analysis reveals that the United States and United Kingdom achieved 30–40% CI reductions over the decade, whereas China, India, and Indonesia realized only 10–20% declines (relative to a 2010 baseline), highlighting regional deployment gaps. Drawing on these detailed income-group insights, we propose tailored policy pathways: in high-income settings, expand tax credits and public–private infrastructure partnerships; in upper-middle-income regions, utilize blended finance and technology-transfer programs; and in lower-income contexts, establish pilot CCS hubs with international support and shared storage networks. We further recommend measures to manage CCS’s energy and water penalties, implement rigorous monitoring to mitigate leakage risks, and design risk-sharing contracts to address economic uncertainties. Full article

Accurate measurement uncertainty quantification and its propagation are critical for dimensional compliance in precision manufacturing. This study presents a novel framework that examines the evolution of measurement error along the axial length of CNC-turned components, focusing on spatial and material-specific factors. A systematic experimental comparison was conducted between a manual Digital Vernier Caliper (DVC) and an automated Coordinate Measuring Machine (CMM) using five engineering materials: Aluminum Alloy 6061, Brass C26000, Bronze C51000, Carbon Steel 1020 Annealed, and Stainless Steel 304 Annealed. Dimensional measurements were taken from five consecutive machining zones to capture localized metrological behaviors. The results indicated that the CMM consistently achieved lower expanded uncertainty (as low as 0.00166 mm) and minimal propagated uncertainties (≤0.0038 mm), regardless of material hardness or cutting position. In contrast, the DVC demonstrated significantly higher uncertainty (up to 0.03333 mm) and propagated errors exceeding 0.035 mm, particularly in harder materials and unsupported zones affected by surface degradation and fixture variability. Root-sum-square (RSS) modeling confirmed that manual measurements are more prone to operator-induced error amplification. While the DVC sometimes recorded lower absolute errors, its substantial uncertainty margins hampered measurement reliability. To statistically validate these findings, a two-way ANOVA was performed, confirming that both the measurement system and machining zone significantly impacted uncertainty, as well as their interaction. These results emphasize the importance of material-informed and zone-sensitive metrology, highlighting the advantages of automated systems in sustaining measurement repeatability and dimensional stability in high-precision applications. Full article

This study critically examines the rapid proliferation of megaprojects across the Arab region, with a focus on the Gulf Cooperation Council (GCC) countries, where large-scale developments are strategically deployed to reshape global economic influence and enhance geopolitical positioning. Megaprojects, characterized by their vast scale, substantial financial investment, and long-term impact, remain a subject of intense academic debate. While much of the literature questions their economic viability, citing frequent cost overruns and misalignment with localized urban priorities, megaprojects continue to emerge worldwide. Governments and developers promote megaprojects as catalysts for foreign investment, tourism growth, and enhancing the global stature of host countries and regions. Beyond financial and economic imperatives, megaprojects are fundamentally shaped by socio-spatial, socio-political, and capital accumulation dynamics, each playing a critical role in their justification and implementation. These interconnected forces influence the prioritization of large-scale developments, often reinforcing their persistence as dominant urban and infrastructural strategies despite well-documented uncertainties and risks. The study employs a comparative case study approach to analyze two high-profile megaprojects: Masdar City in Abu Dhabi and The Line in NEOM, Saudi Arabia. By examining their underlying motivations, political, social, and economic dynamics, and projected success factors, the study aims to provide an evidence-based assessment of the forces driving these large-scale developments and their potential for completion and long-term viability. This study contributes to the ongoing discourse on megaproject development by offering a nuanced, evidence-based analysis of the socio-political and economic forces shaping large-scale urban initiatives in the Arab region. By critically evaluating the motivations and viability of Masdar City and The Line, this research provides valuable insights that can inform future scholarly inquiries into the governance, planning, and long-term sustainability of megaprojects. The Study offers a strategic framework for policymakers, urban planners, and investors to make more informed, balanced decisions that align large-scale developments with broader economic and social priorities, mitigating risks associated with cost overruns, feasibility challenges, and socio-spatial disparities. Full article

Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV (unmanned aerial vehicle)-borne near-infrared (NIR) surveys, multi-temporal Sentinel-2 imagery, and historical Google Earth orthophotos to precisely map pipeline locations and establish a surface baseline for future monitoring. Each dataset was processed within a unified least-squares framework to delineate pipeline axes from surface anomalies (vegetation stress, soil discoloration, and proxies) and rigorously quantify positional uncertainty, with findings validated against RTK-GNSS (Real-Time Kinematic—Global Navigation Satellite System) surveys of an excavated trench. The combined approach yielded sub-meter accuracy (±0.3 m) with UAV data, meter-scale precision (≈±1 m) with Google Earth, and precision up to several meters (±13.0 m) with Sentinel-2, significantly improving upon inaccurate legacy maps (up to a 300 m divergence) and successfully guiding excavation to locate a pipeline segment. The methodology demonstrated seasonal variability in detection capabilities, with optimal UAV-based identification occurring during early-vegetation growth phases (NDVI, Normalized Difference Vegetation Index ≈ 0.30–0.45) and post-harvest periods. A Sentinel-2 analysis of 221 cloud-free scenes revealed persistent soil discoloration patterns spanning 15–30 m in width, while Google Earth historical imagery provided crucial bridging data with intermediate spatial and temporal resolution. Ground-truth validation confirmed the pipeline location within 0.4 m of the Google Earth-derived position. This integrated, cost-effective workflow provides a transferable methodology for enhanced pipeline mapping and establishes a vital baseline of surface signatures, enabling more effective future monitoring and proactive maintenance to detect leaks or structural failures. This methodology is particularly valuable for water utility companies, municipal infrastructure managers, consulting engineers specializing in buried utilities, and remote-sensing practitioners working in pipeline detection and monitoring applications. Full article