Search Results (80)

This article examines the concept of ming 命 (mandate/command or fate/destiny) in the Analects, Mencius, and Zhuangzi, exploring its relationship to tian 天 (Heaven). Across these works, ming retains an intrinsic connection to tian—an inviolable cosmic force beyond human control. All three texts exhibit profound reverence and submission to tian, acknowledging the boundary between human control and cosmic inevitability, yet, at the same time, advocating active alignment with tian’s ordained patterns. In the Analects, a central tension emerges between tian’s teleological purpose—centered on preserving human culture and ethical cultivation—and the seemingly arbitrary fluctuations of individual fate, particularly regarding lifespan and personal fulfillment. This tension persists in the Mencius, articulated as a conflict between the political disorder of Mencius’ contemporary era and tian’s normative moral order. The Zhuangzi, by contrast, resolves this tension through advocating for withdrawal from the political life, as well as a radical reinterpretation of tian. Stripping tian off the Confucian moral–cultural imperatives, the text deconstructs dichotomies like life and death, championing inner equanimity via flowing with the cosmic transformation. Full article

This study examines the safety and cost-effectiveness of implementing shoulder rumble strips (SRS) and road lighting on Saudi Arabian freeways, providing insights into their roles in fostering sustainable transport systems. By leveraging the Highway Safety Manual (HSM) framework, this research develops localized Crash Modification Factors (CMFs) for these interventions, ensuring evidence-based and context-specific evaluations. Data were collected for two periods—pre-pandemic (2017–2019) and post-pandemic (2021–2022). For each period, we obtained traffic crash records from the Saudi Highway Patrol database, traffic volume data from the Ministry of Transport and Logistic Services’ automated count stations, and roadway characteristics and pavement-condition metrics from the National Road Safety Center. The findings reveal that SRS reduces fatal and injury run-off-road crashes by 52.7% (CMF = 0.473) with a benefit–cost ratio of 14.12, highlighting their high cost-effectiveness. Road lighting, focused on nighttime crash reduction, decreases such crashes by 24% (CMF = 0.760), with a benefit–cost ratio of 1.25, although the adoption of solar-powered lighting systems offers potential for greater sustainability gains and a higher benefit–cost ratio. These interventions align with global sustainability goals by enhancing road safety, reducing the socio-economic burden of crashes, and promoting the integration of green technologies. This study not only provides actionable insights for achieving KSA Vision 2030’s target of improved road safety but also demonstrates how engineering solutions can be harmonized with sustainability objectives to advance equitable, efficient, and environmentally responsible transportation systems. Full article

Based on the new high-resolution multibeam bathymetry data collected by the “Dongfanghong 3” vessel in 2023 in the Parece Vela Basin (PVB) and previous magnetic anomaly data, we systematically analyze the seafloor topographical changes of abyssal hills and oceanic core complexes (OCCs) in the “Chaotic Terrain” region, and the revised seafloor spreading model is constructed in the PVB. Using detailed analysis of the seafloor topography, we identify typical geomorphological features associated with seafloor spreading, such as regularly aligned abyssal hills and OCCs in the PVB. The direction variations of seafloor spreading in the PVB are closely related to mid-ocean ridge rotation and propagation. The formation of OCCs in the “Chaotic Terrain” can be explained by links to the continuous and persistent activity of detachment faults and dynamic adjustments controlled by variations of deep magma supply in the different segments in the PVB. We use 2D discrete Fourier image analysis of the seafloor topography to calculate the aspect ratio (AR) values of abyssal hills in the western part of the PVB. The AR value variations reveal a distinct imbalance in magma supply across various regions during the basin spreading process. Compared to the “Chaotic Terrain” area, the region with abyssal hills indicates a higher magma supply and greater linearity on seafloor topography. AR values fluctuated between 2.1 and 1.7 of abyssal hills in the western segment, while in the “Chaotic Terrain”, they dropped to 1.3 due to the lower magma supply. After the formation of the OCC-1, AR values increased to 1.9 in the eastern segment, and this shows the increase in magma supply. Based on changes in seafloor topography and variations in magma supply across different segments of the PVB, we propose that the seafloor spreading process in the magnetic anomaly linear strip 9-6A of the PVB mainly underwent four formation stages: ridge rotation, rift propagation, magma-poor supply, and the maturation period of OCCs. Full article

The disadvantages of traditional strip fertilization technology for corn planting in China include low fertilizer utilization rates, unstable operation quality, and environmental pollution. Therefore, in this study, a synchronous hole fertilization device for corn planting based on real-time intelligent control is designed, aiming to reduce fertilizer application and increase efficiency through the precise alignment technology of the seed and fertilizer. This device integrates an electric drive precision seeding unit, a slot wheel hole fertilization unit, and a multi-sensor coordinated closed-loop control system. An STM32 single-chip micro-computer is used to dynamically analyze the seed–fertilizer timing signal, and a double closed-loop control strategy (the position loop priority is higher than the speed loop) is used to correct the spatial phase difference between the seed and fertilizer in real time to ensure the precise control of the longitudinal distance (40~70 mm) and the lateral distance (50~80 mm) of the seed and fertilizer. Through the Box–Behnken response surface method, a field multi-factor test was carried out to analyze the mechanism of influence of the implemented forward speed (A), per-hole target fertilizing amount (B), and plant spacing (fertilizer hole interval) (C) on the seed–fertilizer alignment qualification rate (Y₁) and the coefficient of variation in the hole fertilizing amount (Y₂). The results showed that the order of primary and secondary factors affecting Y₁ was A > C > B, and that the order affecting Y₂ was C > B > A; the comprehensive performance of the device was best with the optimal parameter combination of A = 4.2 km/h, B = 4.4 g, and C = 30 cm, with Y₁ as high as 94.024 ± 0.694% and Y₂ as low as 3.147 ± 0.058%, which is significantly better than the traditional strip application method. The device realizes the precise regulation of 2~6 g/hole by optimizing the structural parameters of the outer groove wheel (arc center distance of 25 mm, cross-sectional area of 201.02 mm², effective filling length of 2.73~8.19 mm), which can meet the differentiated agronomic needs of ordinary corn, silage corn, and popcorn. Field verification shows that the device significantly improves the spatial distribution of the concentration of fertilizer, effectively reduces the amount of fertilizer applied, and improves operational stability and reliability in multiple environments. This provides technical support for the regional application of precision agricultural equipment. Full article

The shear strength of a steel shear wall (SSW) is typically governed by the yield strength of the steel. However, changes in mechanical properties beyond yielding—particularly those related to steel hardening and the effects of gravity loads—are not yet fully understood. These factors are critical for accurately assessing the shear capacity of SSWs during seismic events. In the current study, a method to calculate the shear force–displacement curve of a steel shear wall while considering the compression effect is presented, which incorporates both steel hardening and gravity effects. The analysis derives strains in tensile strips undergoing shear deformation using a strip model. Corresponding stresses are then determined using the stress–strain relationships obtained from tensile tests of the steel. Furthermore, the vertical stress induced by gravity loads is modeled using a three-segment distribution proposed before. For each tensile strip, the tension field stress is calculated by accounting for reductions due to vertical stress and the influence of steel hardening through the von Mises yield criterion. This approach enables the development of a shear force–displacement curve, which is subsequently validated against results from an experimentally verified finite element model. The findings demonstrate that the pushover curves predicted by this method closely align with those obtained from finite element analysis. Notably, the results indicate that the shear strength provided by the CAN/CSA-S16-01 equation may be overestimated by approximately 4%, 9%, and 18% when the vertical compression stresses are 50, 100, and 150 MPa for a wall with a slenderness of 150, respectively. Full article

Industrial image anomaly detection plays a critical role in intelligent manufacturing by automatically identifying defective products through visual inspection. While unsupervised approaches eliminate dependency on annotated anomaly samples, current teacher–student framework-based methods still face two fundamental limitations: insufficient discriminative capability for structural anomalies and suboptimal anomaly feature decoupling efficiency. To address these challenges, we propose a Synthetic-Anomaly Contrastive Distillation (SACD) framework for industrial anomaly detection. SACD comprises two pivotal components: (1) a reverse distillation (RD) paradigm whereby a pre-trained teacher network extracts hierarchically structured representations, subsequently guiding the student network with inverse architectural configuration to establish hierarchical feature alignment; (2) a group of feature calibration (FeaCali) modules designed to refine the student’s outputs by eliminating anomalous feature responses. During training, SACD adopts a dual-branch strategy, where one branch encodes multi-scale features from defect-free images, while a Siamese anomaly branch processes synthetically corrupted counterparts. FeaCali modules are trained to strip out a student’s anomalous patterns in anomaly branches, enhancing the student network’s exclusive modeling of normal patterns. We construct a dual-objective optimization integrating cross-model distillation loss and intra-model contrastive loss to train SACD for feature alignment and discrepancy amplification. At the inference stage, pixel-wise anomaly scores are computed through multi-layer feature discrepancies between the teacher’s representations and the student’s refined outputs. Comprehensive evaluations on the MVTec AD and BTAD benchmark demonstrate that our method is effective and superior to current knowledge distillation-based approaches. Full article

Intrinsic parameter estimation by self-calibration is commonly used in Unmanned aerial vehicle (UAV)-based photogrammetry with Structure from Motion (SfM). However, obtaining stable estimates of these parameters from image-based SfM—which relies solely on images, without auxiliary data such as ground control points (GCPs)—remains challenging. Aerial imagery acquired with the constant-pitch (CP) flight pattern often exhibits non-linear deformations, highly unstable intrinsic parameters, and even alignment failures. We hypothesize that CP flights form a “critical configuration” that renders certain intrinsic parameters indeterminate. Through numerical experiments, we confirm that a CP flight configuration does not provide sufficient constraints to estimate focal length (f) and the principal point coordinate (cy) in image-based SfM. Real-world CP datasets further demonstrate the pronounced instability of these parameters. As a remedy, we show that by introducing intermediate strips into the CP flight plan—what we call a CP-Plus flight—can effectively mitigate the indeterminacy of f and cy in simulations and markedly improve their stability in all tested cases. This approach enables more effective image-only SfM workflows without auxiliary data, simplifies data acquisition, and improves three-dimensional reconstruction accuracy. Full article

Electroplating sludge, a hazardous waste generated from the electroplating industry, contains significant quantities of heavy metals such as Cu, Cr, and Ni. Improper disposal of these metals poses severe environmental and health risks. This study proposes a comprehensive resource recovery process for Cu, Ni, and Cr from electroplating sludge, involving leaching, solvent extraction, stripping, and precipitation. The extraction efficiency of three extractants (P507, LIX984, and M5640) was evaluated, with M5640 demonstrating superior performance in Cu recovery (near 100%) at pH 3.0–4.0. Multi-stage extraction and stripping experiments further optimized metal recovery, achieving high efficiencies for Cu, Cr, and Ni. The recovered metals were precipitated as CuCO₃, CrPO₄, and Ni(OH)₂, with wastewater discharge meeting environmental discharge standards. This study not only enriches the technical approaches for the selective recovery of high-value metals from electroplating sludge with complex components, but also closely aligns with the laws, regulations, and policies of the Chinese government regarding environmental governance. It serves as a driving force for promoting the construction of “waste-free cities” and the establishment of a closed-loop circular economy industrial chain. Full article

Architectural ambiance refers to the mood perceived in a built environment, assessed through human reactions to virtual drawings of prospective spaces. This paper investigates the use of a ready-made artificial intelligence model to automate this task. Based on professional BIM models, videos of virtual tours of typical urban areas were built: a business district, a strip mall, and a residential area. GPT-4V was used to assess the aesthetic quality of the built environment based on keyframes of the videos and characterize these spaces shaped by subjective attributes. The spatial qualities analyzed through subjective human experience include space and scale, enclosure, style, and overall feelings. These factors were assessed with a diverse set of mood attributes, ranging from balance and protection to elegance, simplicity, or nostalgia. Human participants were surveyed with the same questions based on the videos. The answers were compared and analyzed according to these subjective attributes. Our findings indicate that, while GPT-4V demonstrates adequate proficiency in interpreting urban spaces, there are significant differences between the AI and human evaluators. In nine out of twelve cases, the AI’s assessments aligned with the majority of human voters. The business district environment proved more challenging to assess, while the green environment was effectively modeled. Full article

The online dynamic collection of irrigation and plant physiological information is crucial for the precise irrigation management of nutrient solutions and efficient crop cultivation in vegetable soilless substrate cultivation facilities. In this study, an intelligent weighing system was installed in a tomato substrate cultivation greenhouse. The monitored values from the intelligent weighing system’s pressure-type module were used to calculate irrigation start–stop times, frequency, volume, drainage volume, drainage rate, evapotranspiration, evapotranspiration rate, and stomatal conductance. In contrast, the monitored values of the suspension-type weighing module were used to calculate the amount of weight change in the plants, which supported the dynamic and quantitative characterization of substrate cultivation irrigation and crop growth based on an intelligent weighing system. The results showed that the monitoring curves of pressure and flow sensors based on the pressure-type module could accurately identify the irrigation start time and number of irrigations and calculate the irrigation volume, drainage volume, and drainage rate. The calculated irrigation amount was closely aligned with that determined by an integrated-water–fertilizer automatic control system (R² = 0.923; mean absolute error (MAE) = 0.105 mL; root-mean-square error (RMSE) = 0.132 mL). Furthermore, transpiration rate and leaf stomatal conductance were obtained through inversion, and the R², MAE, and RMSE of the extinction coefficient correction model were 0.820, 0.014 mol·m⁻²·s⁻¹, and 0.017 mol·m⁻²·s⁻¹, respectively. Compared to traditional estimation methods, the MAE and RMSE decreased by 12.5% and 15.0%, respectively. The measured values of fruit picking and leaf stripping linearly fitted with the calculated values of the suspended weighing module, and R², MAE, and RMSE were 0.958, 0.145 g, and 0.143 g, respectively. This indicated that data collection based on the suspension-type weighing module could allow for a dynamic analysis of plant weight changes and fruit yield. In summary, the intelligent weighing system could accurately analyze irrigation information and crop growth physiological indicators under the practical application conditions of facility vegetable substrate cultivation, providing technical support for the precise management of nutrient solutions. Full article

Volume of change provides a comprehensive and objective reflection of land surface transformation, meeting the emerging demand for feature change monitoring in the era of big data. However, existing land surface monitoring methods often focus on a single dimension, either horizontal or vertical, making it challenging to achieve quantitative volumetric change monitoring. Accurate volumetric change measurements are indispensable in many fields, such as monitoring open-pit coal mines. Therefore, the main content and conclusions of this paper are as follows: (1) A method for Automatic Control Points Extraction from ICESat-2/ATL08 products was developed, integrating Land cover types and Phenological information (ACPELP), achieving a mean absolute error (MAE) of 1.05 m in the horizontal direction and 1.99 m in the vertical direction for stereo change measurements. This method helps correct image positioning errors, enabling the acquisition of geospatially aligned GaoFen-7 (GF-7) imagery. (2) A function-based classification system for open-pit coal mines was established, enabling precise extraction of stereoscopic change region to support accurate volumetric calculations. (3) A method for calculating the mining and stripping volume of open-pit coal mines based on GF-7 imagery is proposed. The method utilizes photogrammetry to extract elevation features and combines spectral features with elevation data to estimate stripping volumes, achieving an excellent error rate (ER) of 0.26%. The results indicate that our method is cost-effective and highly practical, filling the gap in accurate and comprehensive monitoring of land surface changes. Full article

Multimodal remote sensing image registration aims to achieve effective fusion and analysis of information by accurately aligning image data obtained by different sensors, thereby improving the accuracy and application value of remote sensing data in engineering. However, current advanced registration frameworks are unable to accurately register large-scale rigid distortions, such as rotation or scaling, that occur in multi-source remote sensing images. This paper presents a stable and high-precision end-to-end registration network that incorporates dual-branch feature extraction to address the stringent registration requirements encountered in practical engineering applications. The deep neural network consists of three parts: dual-branch feature extraction, affine parameter regression, and spatial transformation network. In the upper branch of the dual-branch feature extraction module, we designed a combination of multi-scale convolution and Swin Transformer to fully extract features of remote sensing images at different scales and levels to better understand the global structure and context information. In the lower branch, we incorporate strip convolution blocks to capture remote contextual information from various directions in multimodal images. Additionally, we introduce an efficient and lightweight ResNet module to enhance global features. At the same time, we developed a strategy to parallelize various convolution kernels in affine parameter regression networks, aiming to enhance the accuracy of transformation parameters and the robustness of the model. We conducted experiments on panchromatic–multispectral, infrared–optical, and SAR–optical image pairs with large-scale rigid transformations. The experimental results show that our method achieves the best registration effect. Full article

Extracting accurate surface reflectance from multispectral UAV (unmanned aerial vehicle) imagery is a fundamental task in remote sensing. However, most studies have focused on short-endurance UAVs, with limited attention given to long-endurance UAVs due to the challenges posed by dynamically changing incident radiative energy. This study addresses this gap by employing a solar trajectory model (STM) to accurately estimate incident radiative energy, thereby improving reflectance calculation precision. The STM method addresses the following key issues: The experimental results demonstrated that the root mean square error (RMSE) of the STM method in Shanghai was 15.80% compared to the standard reflectance, which is 51% lower than the downwelling light sensor (DLS) method and 37% lower than the traditional method. This indicates that the STM method provides results that are more accurate, aligning closely with standard values. In Tianjin, the RMSE was 24% lower than the DLS method and 65% lower than the traditional method. The STM effectively mitigates inconsistencies in incident radiative energy across different image strips captured by long-endurance UAVs, ensuring uniform reflectance accuracy in digital orthophoto maps (DOMs). The proportion of corrected reflectance errors within the ideal range (±10%) increased by 24% compared to the histogram matching method. Furthermore, the optimal flight duration for long-endurance UAVs launched at noon was extended from 50 min to 150 min. In conclusion, this study demonstrates that applying the STM to correct multispectral imagery obtained from long-endurance UAVs significantly enhances reflectance calculation accuracy for DOMs, offering a practical solution for improving reflectance imagery quality under clear-sky conditions. Full article

By replacing ordinary concrete in the compressed zone with high-ductility materials, it is possible to improve the ductility of reinforced concrete beams. The effects of the properties of the materials in the compressed zone and the height of the zone on the performance of steel-reinforced concrete beams were investigated experimentally and theoretically. The performances of the steel-reinforced concrete beams strengthened with slurry infiltrated fiber concrete (SIFCON) in the compression zone were tested by four-point bending experiments. Based on an accurate validation of the experimental results, a parametric analysis using the finite strip method was conducted. The tested results show that after replacing the ordinary concrete in the compressed zone with SIFCON, the strain distribution of the concrete beam cross-section remains linear along the height, adhering to the plane section assumption. An equation was developed using a strip method, and the prediction results showed an error of less than 11% compared to the experimental data. The theoretical calculations predict that the moment–curvature relationship of the beams enhanced with high-ductility-cement-based materials aligns well with the experimental results. This study reveals that adjusting the height, initial modulus, and compressive strength of compressed zone materials effectively enhances ductility, with minimal impact on load-bearing capacity. Increasing the material strength and height improves the ultimate curvature and maximum bending moment. The elastic modulus of the compressed zone has a greater effect on the ultimate curvature than on the maximum bending moment. With a replacement of the compressed zone height of 60 mm (section height 300 mm), the ultimate curvature increases by 177% when the elastic modulus of the material is increased by 2.5 times. The present study provides a calculation method for the retrofitting and reinforcement of over-reinforced concrete beams. Full article

Urban afforestation is increasingly regarded as a key strategy for fostering biodiversity to restore and enhance the ecosystem services needed to counteract the effects of climate change in built-up areas. In Italy, several experimental afforestation projects have been launched as part of the National Recovery and Resilience Plan (NRRP), focusing on cities or metropolitan areas such as Milan, Rome, Pistoia and Campobasso. These projects follow a multidisciplinary approach, integrating botanists, foresters, urban planners, landscape architects and remote sensing specialists. The goal is to address the challenging complexity of urban forest restoration through reforestation and afforestation actions. Key innovations include the integration of transdisciplinary methodologies (landscape analysis, landscape design, forest and plant ecology) with the application of advanced remote sensing technologies and participatory community engagement frameworks to address ecological and social challenges. Experimental plots have been set up across various urban areas, testing a range of planting schemes to maximise climate change resilience and ensure long-term ecological sustainability. Emphasis has been placed on selecting drought-tolerant and thermophilic species that are better adapted to widespread warming and local urban heat islands. ‘Biodiversity strips’ with perennial flowers for insects, shrubs with berries for birds and nests for wild bees and vertebrates have been set up to enhance biodiversity in new afforestation areas. Advanced monitoring tools, such as Light Detection and Ranging (LiDAR) and multi-sensor drones, have been employed alongside field observations to assess forest growth, species survival, structural complexity and biodiversity enhancement over time. Historical analyses of landscape patterns and ecological connectivity over the past 200 years, along with evaluations of afforestation projects from the last 70 years, have provided critical insights into the successes and challenges of previous interventions, serving as a guide for future efforts. By focusing on ecological connectivity, the integration of afforested areas into the urban matrix, and citizen engagement, the current project aims to align urban forestry efforts with sustainable development goals. This comprehensive project framework addresses environmental restoration and the social and aesthetic impacts on local communities, contributing to the overall resilience and well-being of urban and peri-urban ecosystems. Full article