Explore the vast range of titles available.

In the context of building the Grand Canal National Cultural Park, the heritage conservation of the Grand Canal has entered a new stage since its inscription as a World Heritage site. Scientifically evaluating the value of various cultural landscape projects along the Grand Canal is the fundamental basis for coordinating the conservation of the Grand Canal’s World Heritage sites, land use control, and urban development construction. Based on the research on the evolution of cultural landscape value paradigm, the multi-dimensional value evaluation system of the Grand Canal cultural landscape is constructed. Based on the coupling coordination degree theory, the synergistic adjustment mechanism of the conservative value and developmental value system of the canal cultural landscape is explored. The value evaluation of the 10 contemporary cultural landscape projects implemented since the application of the Grand Canal in Hangzhou is carried out, and the coupling coordination of their conservation and utilization is analyzed. The cultural landscape conservation and utilization mode of the Grand Canal in Hangzhou is discussed, which provides a reference for the cultural landscape construction along the “city-river interdependence” Grand Canal.

A 70 MPa Type IV hydrogen composite pressure vessel (CPV) was instrumented with embedded Fiber Bragg Grating (FBG) sensors to realize in situ strain monitoring during hydraulic fatigue cycles. FBG arrays were co-wound with carbon fibers during the filament winding process, forming an integrated multi-point sensing network within the composite layers. Hydraulic fatigue tests were conducted under pressure cycling between 2 MPa and 87.5 MPa, reaching 48,000 cycles. The embedded FBG sensors were able to stably record cyclic strain evolution with peak amplitudes of approximately 6000 με in the hoop layer and 3500 με in the helical layer under hydraulic cycling. The hoop layers exhibited gradually decreasing strain amplitudes from the inner to outer regions, while the helical layer maintained stable signal performance. Analysis of fiber survival times indicated that the FBGs embedded in helical layers remained functional throughout the entire test, confirming the long-term monitoring capability under high-pressure oil environments. This study demonstrates a practical embedded-sensing approach compatible with the filament-winding process, providing experimental support for fatigue-life evaluation and in-service safety monitoring of high-pressure hydrogen storage vessels.

Various urban environmental and social challenges have emerged during the rapid urban development. Urban agriculture has emerged as one of the practical solutions to address these urban issues and climate change. This study aims to establish a decision model for urban agriculture regeneration that can be applied to improve the implementation of related projects. The study first reviews existing research on Urban Agriculture within the Greyfield Regeneration Environments (UAGR) and outlines the processes involved, including project initiation, construction, and operation management. It identifies 25 factors influencing UAGR and employs the Fuzzy Delphi method (FDM) to prioritize them based on expert judgments. Subsequently, the interpretative structural model (ISM) analysis method is applied to analyze the interrelationships among the 11 most important factors. Matrix operations and MATLAB programming are utilized to establish the influence relationship model based on expert questionnaires to determine the influence between each pair of factors. This results in a hierarchically structured decision model for UAGR. Finally, the decision-making model is applied to analyze the case study in Shanghai and Hangzhou. As urban agricultural activities are proliferating in rapid urbanization, the establishment of a decision-making model for UAGR can offer practical guidance to practitioners, facilitating the development of urban agriculture and mitigating climate change.

The Yunyao Aerospace Constellation Program is the core project being developed by Yunyao Aerospace Technology Co., Ltd., Tianjin, China. It aims to provide scientific data for weather forecasting, as well as research on the ionosphere and neutral atmosphere. It is expected to launch 90 high time resolution weather satellites. Currently, the Yunyao space constellation provides nearly 16,000 BDS, GPS, GLONASS, and Galileo multi-system occultation profile products on a daily basis. This study initially calculates the precise orbits of Yunyao LEO satellites independently using each GNSS constellation, allowing the derivation of the neutral atmospheric refractive index profile. The precision of the orbit product was evaluated by comparing carrier-phase residuals (ranging from 1.48 cm to 1.68 cm) and overlapping orbits. Specifically, for GPS-based POD, the average 3D overlap accuracy was 4.93 cm, while for BDS-based POD, the average 3D overlap accuracy was 5.18 cm. Simultaneously, the global distribution, the local time distribution, and penetration depth of the constellation were statistically analyzed. BDS demonstrates superior performance with 21,093 daily occultation profiles, significantly exceeding GPS and GLONASS by 15.9% and 121%, respectively. Its detection capability is evidenced by 79.75% of profiles penetrating below a 2 km altitude, outperforming both GPS (78.79%) and GLONASS (71.75%) during the 7-day analysis period (DOY 169–175, 2023). The refractive index profile product was also compared with the ECWMF ERA5 product. At 35 km, the standard deviation of atmospheric refractivity for BDS remains below 1%, while for GPS and GLONASS it is found at around 1.5%. BDS also outperforms GPS and GLONASS in terms of the standard deviation in the atmospheric refractive index. These results indicate that Yunyao satellites can provide high-quality occultation product services, like for weather forecasting. With the successful establishment of the global BDS-3 network, the space signal accuracy has been significantly enhanced, with BDS-3 achieving a Signal-in-Space Ranging Error (SISRE) of 0.4 m, outperforming GPS (0.6 m) and GLONASS (1.7 m). This enables superior full-link occultation products for BDS.

After the decline of water transportation along the Grand Canal, the integration of urban development and the preservation of cultural heritage along the canal has become imperative. This paper takes the titled landscape as its research perspective and investigates the cultural significance of the canal through its historical, spatial, artistic, and spiritual dimensions, identifying the “Ten Canal Scenes” (TCS) that encapsulate both the canal’s heritage and the unique characteristics of Hangzhou, with the aim of establishing notable urban cultural landmarks. Archival analysis, average nearest neighbor (ANN) analysis, nuclear density analysis, and clustering of resource sites are first used to identify cultural landscape features. Evaluation and decision-making techniques are then used to comprehensively assess and categorize the conservation and utilization value for the TCS based on the value evaluation framework. Finally, it proposes strategies for enhancing the comprehensive values of titled landscapes and addressing socio-economic and cultural dimensions. These efforts seek to reconcile the preservation of the canal’s cultural heritage with the ongoing regeneration and development of the city and propose references for a localized canal development model based on titled landscapes.

We used a novel shape-restricted Cox model to determine the desirable ER expression cutoff to predict breast cancer prognoses. Our model treats ER as a continuous variable using a flexible monotone-shaped Cox regression to assess its association with survival outcomes holistically. The study included 3055 patients with stage II/III HER2-negative breast cancer. The primary outcomes were time to recurrence or death (TTR) and overall survival (OS). The shape-restricted Cox model identified 10% ER as the preferred cutoff to predict TTR. The finding was confirmed by the log-rank test and standard Cox model that patients with ER ≥ 10% had TTR benefit over ER < 10% (log-rank p < 0.001). No OS or TTR benefit of adjuvant endocrine therapy was observed in patients with 1% ≤ ER < 10% (HR 0.877, 95% CI 0.481–1.600, p = 0.668 for TTR and HR 0.698, 95% CI 0.337–1.446, p = 0.333 for OS). Using the shape-restricted Cox model, this study suggests a potential preferred threshold of 10% for predicting TTR. The findings could assist physicians in effectively weighing the benefits and risks of adjuvant endocrine therapy for patients with ER < 10% disease, particularly in cases involving severe adverse events.

Background: In safety-critical environments, human error is a leading cause of accidents, with the loss of situation awareness (SA) being a key contributing factor. Accurate SA assessment is essential for minimizing such risks and ensuring operational safety. Traditional SA measurement methods have limitations in dynamic real-world settings, while physiological signals, particularly EEG, offer a non-invasive, real-time alternative for continuous SA monitoring. However, the reliability of SA measurement based on physiological signals depends on the accuracy of SA labeling. Objective: This study aims to design an effective SA measurement paradigm specific to high-speed train driving, investigate more accurate physiological signal-based SA labeling methods, and explore the relationships between SA levels and key physiological metrics based on the developed framework. Methods: This study recruited 19 male high-speed train driver trainees and developed an SA measurement paradigm specific to high-speed train driving. A method combining subjective SA ratings and task performance was introduced to generate accurate SA labels. Results: The results of statistical analysis confirmed the effectiveness of this paradigm in inducing SA level changes, revealing significant relationships between SA levels and key physiological metrics, including eye movement patterns, ECG features (e.g., heart rate variability), and EEG power spectral density across theta, alpha, and beta bands. Conclusions: This study supports the use of multimodal physiological signals for SA assessment and provides a theoretical foundation for future applications of SA monitoring in railway operations, contributing to enhanced operational safety.

The frequent occurrence of disasters has brought significant challenges to increasingly complex urban systems. Resilient city planning and construction has emerged as a new paradigm for dealing with the growing risks. Infrastructure systems like transportation, lifelines, flood control, and drainage are essential to the operation of a city during disasters. It is necessary to measure how risks affect these systems’ resilience at different spatial scales. This paper develops an infrastructure risk and resilience evaluation index system in city and urban areas based on resilience characteristics. Then, a comprehensive infrastructure resilience evaluation is established based on the risk–resilience coupling mechanism. The overall characteristics of comprehensive infrastructure resilience are then identified. The resilience transmission level and the causes of resilience effects are analyzed based on the principle of resilience scale. Additionally, infrastructure resilience enhancement strategies under different risk scenarios are proposed. In the empirical study of Zhengzhou City, comprehensive infrastructure resilience shows significant clustering in the city area. It is high in the central city and low in the periphery. Specifically, it is relatively high in the southern and northwestern parts of the airport economy zone (AEZ) and low in the center. The leading driving factors in urban areas are risk factors like flood and drought, hazardous materials, infectious diseases, and epidemics, while resilience factors include transportation networks, sponge city construction, municipal pipe networks, and fire protection. This study proposes a “risk-resilience” coupling framework to evaluate and analyze multi-hazard risks and the multi-system resilience of urban infrastructure across multi-level spatial scales. It provides an empirical resilience evaluation framework and enhancement strategies, complementing existing individual dimensional risk or resilience studies. The findings could offer visualized spatial results to support the decision-making in Zhengzhou’s resilient city planning outline and infrastructure special planning and provide references for resilience assessment and planning in similar cities.

Soil salinization has become a critical constraint on agricultural productivity and eco-logical sustainability in arid regions. The accurate mapping of its spatial distribution is essential for sustainable land management. Although many studies have used satellite remote sensing combined with machine learning or convolutional neural networks (CNN) for soil salinity monitoring, most CNN approaches rely on single-scale convolution kernels. This limits their ability to simultaneously capture fine local detail and broader spatial patterns. In this study, we developed a multi-scale deep learning framework to enhance salinity prediction accuracy. We target the root-zone soil salinity in the Wei-Ku Oasis. Sentinel-2 multispectral imagery and Sentinel-1 radar backscatter data, together with topographic, climatic, soil texture, and groundwater covariates, were integrated into a unified dataset. We implemented the workflow using the Google Earth Engine (GEE; earthengine-api 0.1.419) and Python (version 3.8.18) platforms, applying the Sequential Forward Selection (SFS) algorithm to identify the optimal feature subset for each model. A multi-branch convolutional neural network (MB-CNN) with parallel 1 × 1 and 3 × 3 convolutional branches was constructed and compared against random forest (RF), 1 × 1-CNN, and 3 × 3-CNN models. On the validation set, MB-CNN achieved the best performance (R2 = 0.752, MAE = 0.789, RMSE = 1.051 dS∙m−1, nRMSE = 0.104), showing stronger accuracy, lower error, and better stability than the other models. The soil salinity inversion map based on MB-CNN revealed distinct spatial patterns consistent with known hydrogeological and topographic controls. This study innovatively introduces a multi-scale convolutional kernel parallel architecture to construct the multi-branch CNN model. This approach captures environmental characteristics of soil salinity across multiple spatial scales, effectively enhancing the accuracy and stability of soil salinity inversion. It provides new insights for remote sensing modeling of soil properties.

Against the backdrop of rapid urbanization, with the expansion of administrative boundaries, some former villages have been transformed from administrative to urban in the sense that they have become special “village-to-city areas”; in this context, the housing pattern, which was previously dominated by self-built houses, is facing many challenges. In particular, the frequent occurrence of safety accidents in self-built houses in the village conversion areas in recent years constitutes an important component of urban spatial vulnerability. However, the ensuing “one-size-fits-all” ban on self-built housing has also raised concerns among scholars. In order to better guide the planning and construction of self-built houses, official safety inspections, planning guidance, and institutional constraints are essential. However, the safety inspection of self-built houses across China is difficult. On the one hand, it is challenging to obtain data on individual buildings (e.g., age, use, building structure ratio, foundation, structural condition, illegal demolition and alteration, and illegal use), and the methods of obtaining such data rely mainly on the basic checking of the safety grids under the responsibility of grassroots safety officers. However, the current organizational system of safety officers is not perfect, and the relevant evaluation training also has limitations. On the other hand, due to the city’s finances, development stage, and other reasons, the agricultural-to-residential areas in the cities of poverty-stricken counties are not likely to be renewed as rapidly as the cities of developed regions but instead may face long-term renewal timelines. Therefore, for the agricultural resettlement areas in the cities of poverty-stricken counties, it is necessary to screen the current problems, systematically study the mechanism and strategy of their renewal based on the management framework of the whole process, and carry out the organic renewal of self-built houses, so as to gradually realize a safe and resilient development mode. This paper establishes a framework for the renewal of self-built houses oriented to security resilience based on the theory of fortress land under the urban form theory of Conzen. Taking Lianhua County as a case study, we analyze the problems and issues related to self-built housing areas through an investigation of the current situation. Then, in response to the existing problems, based on the comprehensive investigation of the safety of self-built houses, we clarify the planning objectives and value orientation and suggest (i) the adoption of hierarchical and classified planning and construction control for the self-built housing areas of villages converted to residences in accordance with the local conditions; (ii) the enhancement of government supervision in the use of self-built houses and the establishment of laws and regulations; and (iii) renewal planning in an orderly manner to enhance the safety resilience of the self-built housing areas. Based on the renewal study of self-built houses in Lianhua County, a systematic exploration of the planning, construction, and governance strategies of self-built houses in China is carried out, which can provide a reference for the decision making of relevant departments.