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The steel–aluminum hybrid body closure panels can achieve a more balanced and lightweight performance. However, the differences in the physical properties of metal sheets and the complex changes in the properties of the adhesive material result in cumulative deviations in the composite-forming process. This paper proposes a deformation pre-compensation modeling method for the autobody closure panels hemming system oriented towards the process chain, in response to the problem that single-process optimization cannot obtain global optimal results. Taking the car door scaled model as an example, based on surface reconstruction and node compensation, the curing deformation amount is fed back in advance to the gluing and hemming processes. The deformation deviation is corrected through geometric parameter pre-compensation to achieve overall process shape control and optimization. Research shows that this method can significantly reduce the surface differences and gaps of hemming structures with dissimilar materials, and a single iteration can reduce the assembly surface difference by more than 90%. This provides a reference for improving the manufacturing quality of steel–aluminum hybrid body closure panels. [ABSTRACT FROM AUTHOR]

Rapid urbanization has altered the natural surface properties and spatial patterns, increasing the risk of urban waterlogging. Assessing the probability of urban waterlogging risk is crucial for preventing and mitigating the environmental risks associated with urban waterlogging. This study aims to evaluate the impact of different urban spatial morphologies on the probability of urban waterlogging risk. The proposed assessment framework was demonstrated in Guangzhou, a high-density city in China. Firstly, a spatial weight naive Bayes model was employed to map the probability of waterlogging risk in Guangzhou. Secondly, the World Urban Database and Access Portal Tools (WUDAPT)-based method was used to create a local climate zone (LCZ) map of Guangzhou. Then, the range of waterlogging risk and the proportion of risk levels were analyzed across different LCZs. Finally, the Theil index was used to measure the disparity in waterlogging risk exposure among urban residents. The results indicate that 16.29% of the area in Guangzhou is at risk of waterlogging. Specifically, 13.06% of the area in LCZ 2 is classified as high risk, followed by LCZ 1, LCZ 8, and LCZ 10, with area proportions of 11.42%, 8.37%, and 6.26%, respectively. Liwan District has the highest flood exposure level at 0.975, followed by Haizhu, Yuexiu, and Baiyun. The overall disparity in waterlogging exposure in Guangzhou is 0.30, with the difference between administrative districts (0.13) being smaller than the difference within the administrative districts (0.17). These findings provide valuable insights for future flood risk mitigation and help in adopting effective risk reduction strategies at urban planning level. [ABSTRACT FROM AUTHOR]

Counting the number of trees and obtaining information on tree crowns have always played important roles in the efficient and high-precision monitoring of forest resources. However, determining how to obtain the above information at a low cost and with high accuracy has always been a topic of great concern. Using deep learning methods to segment individual tree crowns in mixed broadleaf forests is a cost-effective approach to forest resource assessment. Existing crown segmentation algorithms primarily focus on discrete trees, with limited research on mixed broadleaf forests. The lack of datasets has resulted in poor segmentation performance, and occlusions in broadleaf forest images hinder accurate segmentation. To address these challenges, this study proposes a supervised segmentation method, SegcaNet, which can efficiently extract tree crowns from UAV images under natural light conditions. A dataset for dense mixed broadleaf forest crown segmentation is produced, containing 18,000 single-tree crown images and 1200 mixed broadleaf forest images. SegcaNet achieves superior segmentation results by incorporating a convolutional attention mechanism and a memory module. The experimental results indicate that SegcaNet's m I o U values surpass those of traditional algorithms. Compared with FCN, Deeplabv3, and MemoryNetV2, SegcaNet's m I o U is increased by 4.8%, 4.33%, and 2.13%, respectively. Additionally, it reduces instances of incorrect segmentation and over-segmentation. [ABSTRACT FROM AUTHOR]

Polyhalogenated carbazoles (PHCZs) are a class of nitrogen-containing heterocyclic compounds that are widely distributed throughout the marine environment and sediment. These compounds share structural and toxicity similarities with dioxins. However, our understanding of the toxicological effects of PHCZs on marine organisms and their underlying molecular mechanisms remains limited. In this study, we employed the marine model organism Mugilogobius chulae as the experimental subject and selected 2,7-dibromocarbazole (2,7-DBCZ), a compound known for its high toxicity and detection frequency, to conduct both an acute toxicity test and transcriptome analysis on M. chulae embryos. Our findings revealed that the 96 h median lethal concentration (LC50) of 2,7-DBCZ for M. chulae embryos was 174 μg/L, with a median effective concentration (EC50) resulting in pericardial edema deformity of 88.82 μg/L. Transcriptome analysis revealed significant impacts on various systems in M. chulae embryos following exposure to 2,7-DBCZ, including the sensory, cardiovascular, immune, and endocrine systems. Furthermore, this compound perturbed signaling pathways such as phototransduction, protein folding and processing, amino acid metabolism, lipid transport, and exogenous compound metabolism. Notably, transcript abundance of the CYP1A gene associated with the activation of the AhR signaling pathway, similar to dioxin-like compounds, was 18.18 times higher than that in the control group. This observation suggests that M. chulae embryos mount a stress response when exposed to PHCZs. In summary, this study contributes to our understanding of the toxicological implications of PHCZ in marine fish and offers a theoretical foundation for risk assessment and regulatory frameworks for PHCZs in the marine environment. [ABSTRACT FROM AUTHOR]

Few-shot learning (FSL) is a challenging problem. Transfer learning methods offer a straightforward and effective solution to FSL by leveraging pre-trained models and generalizing them to new tasks. However, pre-trained models often lack the ability to highlight and emphasize salient features, a gap that attention mechanisms can fill. Unfortunately, existing attention mechanisms encounter issues such as high complexity and incomplete attention information. To address these issues, we propose a dimensionally enhanced attention (DEA) module for FSL. This DEA module introduces minimal additional computational overhead while fully attending to both channel and spatial information. Specifically, the feature map is first decomposed into 1D tensors of varying dimensions using strip pooling. Next, a multi-dimensional collaborative learning strategy is introduced, enabling cross-dimensional information interactions through 1D convolutions with adaptive kernel sizes. Finally, the feature representation is enhanced by calculating attention weights for each dimension using a sigmoid function and weighting the original input accordingly. This approach ensures comprehensive attention to different dimensions of information, effectively characterizing data in various directions. Additionally, we have found that knowledge distillation significantly improves FSL performance. To this end, we implement a logit standardization self-distillation method tailored for FSL. This method addresses the issue of exact logit matching, which arises from the shared temperature in the self-distillation process, by employing logit standardization. We present experimental results on several benchmark datasets where the proposed method yields significant performance improvements. [ABSTRACT FROM AUTHOR]

Masson pine (Pinus massoniana Lamb.) is a tree species that is widely distributed throughout southern China and holds significant economic and ecological value. The main objective of our study was to assess the effects of thinning on aboveground biomass increments and tree diversity in both the overstory and understory. Additionally, the underlying factors and mechanisms responsible for driving changes in biomass increment were analyzed. Four different thinning treatments (control, light thinning, moderate thinning, and heavy thinning) were implemented in 214 plots (~1800 tree ha−1) in three Masson pine forests in Hunan Province, China. A robustly designed experiment was used with over six years of repeated measurements. The differences in biomass increment and tree diversity among the different treatments were compared using repeated measures ANOVAs. The Mantel test was used to determine environmental metrics correlated with biomass increments across tree strata. Structural equation modeling was utilized to explore the multivariate relationships among site environment, tree diversity, and post-treatment biomass increment. The results indicated that thinning overall increased biomass increment, the Shannon index, and the Gini index, while decreasing the Dominance index over time. Moderate thinning (25%–35% of trees removed) was found to promote overstory biomass increment to 9.72 Mg·ha−1·a−1 and understory biomass increment to 1.43 Mg·ha−1·a−1 six years post-thinning, which is significantly higher than that of other treatments. Environmental metrics such as light intensity, soil organic matter, and other soil physiochemical properties were positively correlated with biomass increments, and their effects on the overstory and understory differed. Structural equation modeling revealed that thinning treatments, environmental metrics, tree diversity, and their interactions could be the main drivers for biomass increments across tree strata. Specifically, thinning treatments, light intensity, and tree size diversity (Gini index) had significant effects on overstory biomass increment, while understory species richness (Shannon index) and soil organic matter affected understory biomass increment. In conclusion, moderate thinning is an effective silvicultural treatment for stimulating biomass increments of both the overstory and understory in Masson pine forests in southern China if a middle period (e.g., six years) is considered. Some factors, such as species richness, tree size diversity, and environmental metrics (e.g., light and soil), are suggested for consideration to improve the efficiency of thinning. [ABSTRACT FROM AUTHOR]

Urbanization and climate change have led to rising urban temperatures, increasing heat-related health risks. Assessing urban heat risk is crucial for understanding and mitigating these risks. Many studies often overlook the impact of block types on heat risk, which limits the development of mitigation strategies during urban planning. This study aims to investigate the influence of various spatial factors on the heat risk at the block scale. Firstly, a GIS approach was used to generate a Local Climate Zones (LCZ) map, which represents different block types. Secondly, a heat risk assessment model was developed using hazard, exposure, and vulnerability indicators. Thirdly, the risk model was demonstrated in Guangzhou, a high-density city in China, to investigate the distribution of heat risk among different block types. An XGBoost model was used to analyze the impact of various urban spatial factors on heat risk. Results revealed significant variations in heat risk susceptibility among different block types. Specifically, 33.9% of LCZ 1–4 areas were classified as being at a high-risk level, while only 23.8% of LCZ 6–9 areas fell into this level. In addition, the pervious surface fraction (PSF) had the strongest influence on heat risk level, followed by the height of roughness elements (HRE), building surface fraction (BSF), and sky view factor (SVF). SVF and PSF had a negative impact on heat risk, while HRE and BSF had a positive effect. The heat risk assessment model provides valuable insights into the spatial characteristics of heat risk influenced by different urban morphologies. This study will assist in formulating reasonable risk mitigation measures at the planning level in the future. [ABSTRACT FROM AUTHOR]

In this study, a multi-stage planning framework was constructed by using SWMM simulation modeling and NSGA-II and applied to optimize the layout of integrated grey–green infrastructure (IGGI) under land use change and climate change scenarios. The land use change scenarios were determined based on the master plan of the study area, with imperviousness of 50.7% and 62.0% for stage 1 and stage 2, respectively. Rainfall trends for stage 1 and stage 2 were determined using Earth-E3 from the CMIP6 model. The rainfall in stage 2 increased by 14.9% from stage 1. Based on these two change scenarios, the spatial configuration of IGGI layouts with different degrees of centralization of the layout (DCL) under the two phases was optimized, with the lowest life cycle cost (LCC) as the optimization objective. The results showed that the layout with DCL = 0 had better performance in terms of LCC. The LCC of the layout with DCL = 0 was only 66.9% of that of the layout with DCL = 90.9%. In terms of Tech-R, stage 2 had better performance than stage 1. Furthermore, the average technological resilience (Tech-R) index of stage 2 was 0.8–3.4% higher than that of stage 1. Based on the LCC and Tech-R indices of all of the layouts, TOPSIS was used to compare the performance of the layouts under the two stages, and it was determined that the layout with DCL = 0 had the best economic and performance benefits. The results of this study will be useful in exploring the spatial configuration of urban drainage systems under land use change and climate change for sustainable stormwater management. [ABSTRACT FROM AUTHOR]

Electric power operation, as one of the key fields in the world, faces particularly prominent safety issues. Ensuring the safety of operators has become the most fundamental requirement in power operation. However, there are some safety hazards in power construction. These hazards are mainly due to weak safety awareness among staff and the failure to standardize the wearing of safety helmets. In order to effectively address this situation, technical means such as video surveillance technology and computer vision technology can be utilized to monitor whether staff are wearing helmets and provide timely feedback. Such measures will greatly enhance the safety level of power operation. This paper proposes an improved lightweight helmet detection algorithm named YOLO-M3C. The algorithm first replaces the YOLOv5s backbone network with MobileNetV3, successfully reducing the model size from 13.7 MB to 10.2 MB, thereby increasing the model's detection speed from 42.0 frames per second to 55.6 frames per second. Then, the CA attention mechanism is introduced into the backbone network to enhance the feature extraction capability of the model. Finally, in order to further improve the detection recall rate and accuracy of the model, a knowledge distillation of the model was carried out. The experimental results show that, compared with the original YOLOv5s algorithm, the average accuracy of the improved YOLO-M3C algorithm is improved by 0.123, and the recall rate is the same. These results verify that the algorithm YOLO-M3C has excellent performance in target detection and recognition, which can improve accuracy and confidence, while reducing false detection and missing detection, and effectively meet the needs of helmet-wearing detection. [ABSTRACT FROM AUTHOR]

Skyrise greenery, including green roofs and vertical gardens, has emerged as an indispensable tool for sustainable urban planning with multiple ecological and economic benefits. A bibliometric analysis was used to provide a systematic review of the functions associated with skyrise greenery in urban landscapes. Key research tools, including the "Bibliometrix" R package and "CiteSpace" 6.2 R4, highlight the depth and breadth of the literature covering skyrise greenery. In 2000–2022, a total of 1474 original journal articles were retrieved. Over this period, there was an exponential increase in the number of publications, reflecting both enhanced knowledge and increasing concerns regarding climate change, the urban heat island, and urbanization. Of the total, ~58% of the articles originated from China, followed by the USA, Italy, Australia, and Canada. The research themes, such as urban heat islands, carbon sequestration, hydrology, and air quality, have been identified as the frontier in this fields. Furthermore, researchers from developed countries contributed the most publications to this domain, while developing countries, such as China, play an increasing role in the design and performance evaluation of vertical greenery. Key benefits identified in vertical green systems (e.g., green roofs and walls) include thermal regulation, sustainable water management, air-quality improvement, noise reduction, and biodiversity enhancement. In addition, several potential future research prospectives are highlighted. This review provides a comprehensive insight into exploring the pivotal role of skyrise greenery in shaping sustainable, resilient urban futures, coupled with sustainable urban planning. [ABSTRACT FROM AUTHOR]

Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the dual-view analysis of a single breast, thereby neglecting the potentially valuable information between bilateral mammograms. In this paper, we propose a Four-View Correlation and Contrastive Joint Learning Network (FV-Net) for the classification of bilateral mammogram images. Specifically, FV-Net focuses on extracting and matching features across the four views of bilateral mammograms while maximizing both their similarities and dissimilarities. Through the Cross-Mammogram Dual-Pathway Attention Module, feature matching between bilateral mammogram views is achieved, capturing the consistency and complementary features across mammograms and effectively reducing feature misalignment. In the reconstituted feature maps derived from bilateral mammograms, the Bilateral-Mammogram Contrastive Joint Learning module performs associative contrastive learning on positive and negative sample pairs within each local region. This aims to maximize the correlation between similar local features and enhance the differentiation between dissimilar features across the bilateral mammogram representations. Our experimental results on a test set comprising 20% of the combined Mini-DDSM and Vindr-mamo datasets, as well as on the INbreast dataset, show that our model exhibits superior performance in breast cancer classification compared to competing methods. [ABSTRACT FROM AUTHOR]

The dimensional accuracy and microstructure affect the service performance of parts fabricated by wire arc additive manufacturing (WAAM). Regulating the geometry and microstructure of such parts presents a challenge. The coupling method of an artificial neural network and finite element (FE) is proposed in this research for this purpose. Back-propagating neural networks (BPNN) based on optimization algorithms were established to predict the bead width (BW) and height (BH) of the deposited layers. Then, the bead geometry was modeled based on the predicted dimension, and 3D FE heat transfer simulation was performed to investigate the evolution of temperature and microstructure. The results showed that the errors in BW and BH were less than 6%, and the beetle antenna search BPNN model had the highest prediction accuracy compared to the other models. The simulated melt pool error was less than 5% with the experimental results. The decrease in the ratio of the temperature gradient and solidification rate induced the transition of solidified grains from cellular crystals to columnar dendrites and then to equiaxed dendrites. Accelerating the cooling rate increased the primary dendrite arm spacing and δ-ferrite content. These results indicate that the coupling model provides a pathway for regulating the dimensions and microstructures of manufactured parts. [ABSTRACT FROM AUTHOR]

Nature-based solutions (NbSs) are considered to form an innovative stormwater management approach that has living resolutions grounded in natural processes and structures. NbSs offer many other environmental benefits over traditional grey infrastructure, including reduced air pollution and climate change mitigation. This review predominantly centers on the hydrological aspect of NbSs and furnishes a condensed summary of the collective understanding about NbSs as an alternatives for stormwater management. In this study, which employed the CIMO (Context, Intervention, Mechanism, Outcome) framework, a corpus of 187 NbS-related publications (2000–2023) extracted from the Web of Science database were used, and we expounded upon the origins, objectives, and significance of NbSs in urban runoff and climate change, and the operational mechanisms of NbSs (including green roofs, permeable pavements, bioretention systems, and constructed wetlands), which are widely used in urban stormwater management, were also discussed. Additionally, the efficacy of NbSs in improving stormwater quality and quantity is discussed in depth in this study. In particular, the critical role of NbSs in reducing nutrients such as TSS, TN, TP, and COD and heavy metal pollutants such as Fe, Cu, Pb, and Zn is emphasized. Finally, the main barriers encountered in the promotion and application of NbSs in different countries and regions, including financial, technological and physical, regulatory, and public awareness, are listed, and future directions for improving and strategizing NbS implementation are proposed. This review gathered knowledge from diverse sources to provide an overview of NbSs, enhancing the comprehension of their mechanisms and applications. It underscores specific areas requiring future research attention. [ABSTRACT FROM AUTHOR]

The notable absence of juvenile Pseudolarix amabilis trees in forest understories suggests their vulnerability to ecological niche competition, leading to limited survival prospects. This study examines the key factors limiting the growth of P. amabilis seedlings by investigating the effects of five ecological factors: light intensity, rainfall, groundwater level, soil type, and type of fertilization, on the growth of one-year-old P. amabilis seedlings. Our results demonstrate that increasing the light intensity promotes plant growth by augmenting the leaf count, leaf biomass, plant height, stem biomass, root biomass, and total biomass. Further analysis reveals that increased light intensity influences biomass allocation, reducing the specific leaf area and leaf–stem biomass ratio, and favoring root and stem growth over leaf investment. Rainfall, groundwater level, fertilization type, and rhizosphere soil type primarily influence root growth by impacting the soil's physicochemical properties. Specifically, rising groundwater levels lower the soil temperature and increase the soil moisture, total potassium content, and soil pH, leading to reductions in root biomass, plant height, net height increment, leaf number, and total biomass. When groundwater levels reach 21 cm and 28 cm, submerging the surface soil layer, root biomass decreases by 1.6 g/plant (−51.6%) and 2.3 g/plant (−74.2%), respectively. Further analysis reveals a gradual decrease in the root–shoot ratio above the 14 cm groundwater level, while the specific leaf area and leaf–stem biomass ratio remains unaffected, indicating stronger belowground root stress compared to aboveground stem and leaf components. The results highlight light intensity as the key ecological factor determining the growth of P. amabilis seedlings. These findings underscore the importance of considering light intensity in the management of natural stands, the cultivation of artificial forests, and the nursery cultivation of endangered P. amabilis. [ABSTRACT FROM AUTHOR]

Extensive research has shown that nanolayered structures are capable of suppressing the shear banding in metallic glass in nanoindentation experiments. However, the specific mode and mechanism of the shear banding underneath the indenter remains unknown. Also, the quantification of shear banding-induced strain localization is still a challenge. Herein, the size-dependent shear banding behavior of a CuTiZrNb high-entropy alloy-based nanolayered glass with individual layer thicknesses (h) ranging from 5 to 80 nm was systematically investigated by nanoindentation tests. It was found that the hardness of the designed structure was almost size-independent. Yet, a clear transition in the deformation modes from the cutting-like shear bands to the kinking-like ones was discovered as h decreased to 10 nm. Moreover, multiple secondary shear bands also appeared, in addition to the primary ones, in the sample with h = 10 nm. The transition leads to an obvious strain delocalization, as clearly illustrated by the proposed theoretical model, which is based on the assumption of a pure shear stress state to quantify the shear banding-induced strain localization. The strain delocalization results from the higher density of amorphous/amorphous interfaces that exhibit the change in morphology with a refined layered glass structure. [ABSTRACT FROM AUTHOR]

Concurrent meteorological extremes (CMEs) represent a class of pernicious climatic events characterized by the coexistence of two extreme weather phenomena. Specifically, the juxtaposition of Urban Extreme Rainfall (UER) and Urban Extreme Heat (UEH) can precipitate disproportionately deleterious impacts on both ecological systems and human well-being. In this investigation, we embarked on a meticulous risk appraisal of CMEs within China's Greater Bay Area (GBA), harnessing the predictive capabilities of three shared socioeconomic pathways (SSPs) namely, SSP1-2.6, SSP3-7.0, and SSP5-8.5, in conjunction with the EC-Earth3-Veg-LR model from the CMIP6 suite. The findings evidence a pronounced augmentation in CME occurrences, most notably under the SSP1-2.6 trajectory. Intriguingly, the SSP5-8.5 pathway, typified by elevated levels of greenhouse gas effluents, prognosticated the most intense CMEs, albeit with a temperate surge upon occurrence. Additionally, an ascendant trend in the ratio of CMEs to the aggregate of UER and UEH portends an escalating susceptibility to these combined events in ensuing decades. A sensitivity analysis accentuated the pivotal interplay between UER and UEH as a catalyst for the proliferation of CMEs, modulated by alterations in their respective marginal distributions. Such revelations accentuate the imperative of assimilating intricate interdependencies among climatic anomalies into evaluative paradigms for devising efficacious climate change countermeasures. The risk assessment paradigm proffered herein furnishes a formidable instrument for gauging the calamitous potential of CMEs in a dynamically shifting climate, thereby refining the precision of prospective risk estimations. [ABSTRACT FROM AUTHOR]

Simple Summary: The effect of macrophages on tumor cells in the tumor microenvironment of hepatocellular carcinoma (HCC) has attracted more attention. In this study, expression levels of MMP9 and MICA were significantly elevated in macrophages and tumor cells, respectively. The IFN-γ pathway was activated in MICA+ tumor cells and MMP9+ macrophages. The interaction between MICA+ tumor cells and MMP9+ macrophages was mediated through the PROS1-AXL pathway. MMP9 was mainly expressed in M2-like macrophages. IRF1 induced the upregulation of PROS1 and MICA in HCC cells. MICA+ tumor cells stimulated the secretion of MMP9 from macrophages through the PROS1-AXL axis, thereby facilitating the proteolytic shedding of MICA into soluble MICA. This study provided valuable insights and supported the therapeutic application of AXL inhibition in HCC. Background: tumor-associated macrophages (TAMs) constitute a significant proportion of non-cancerous cells within the intricate tumor microenvironment (TME) of hepatocellular carcinoma (HCC). Understanding the communication between macrophages and tumor cells, as well as investigating potential signaling pathways, holds promise for enhancing therapeutic responses in HCC. Methods: single-cell RNA-sequencing data and bulk RNA-sequencing data were derived from open source databases Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Through this analysis, we elucidated the interactions between MICA+ tumor cells and MMP9+ macrophages, primarily mediated via the PROS1-AXL axis in advanced HCC. Subsequently, we employed a range of experimental techniques including lentivirus infection, recombinant protein stimulation, and AXL inhibition experiments to validate these interactions and unravel the underlying mechanisms. Results: we presented a single-cell atlas of advanced HCC, highlighting the expression patterns of MICA and MMP9 in tumor cells and macrophages, respectively. Activation of the interferon gamma (IFN-γ) signaling pathway was observed in MICA+ tumor cells and MMP9+ macrophages. We identified the existence of an interaction between MICA+ tumor cells and MMP9+ macrophages mediated via the PROS1-AXL axis. Additionally, we found MMP9+ macrophages had a positive correlation with M2-like macrophages. Subsequently, experiments validated that DNA damage not only induced MICA expression in tumor cells via IRF1, but also upregulated PROS1 levels in HCC cells, stimulating macrophages to secrete MMP9. Consequently, MMP9 led to the proteolysis of MICA. Conclusion: MICA+ HCC cells secreted PROS1, which upregulated MMP9 expression in macrophages through AXL receptors. The increased MMP9 activity resulted in the proteolytic shedding of MICA, leading to the release of soluble MICA (sMICA) and the subsequent facilitation of tumor immune escape. [ABSTRACT FROM AUTHOR]

There are many problems in the development of urban space in China. Among them, urban tunnels generally pass through many sections with very complicated geological conditions, and the construction will encounter great difficulties, so the mechanical behavior of shield segments in different complex strata is worth discussing. In this paper, the axial force, bending moment and pore water pressure of shield tunnel segments in the soft and hard uneven stratum, clay stratum and fully weathered granite stratum of overlying buildings are studied by establishing a rectangular element mechanical model based on the field test method. The analysis shows that the mechanical properties of shield tunnels in different strata are quite different, but their mechanical properties change stages are the same. The earth pressure on the left and right sides of the test ring is asymmetric in the soft and hard uneven stratum, and the vault pressure is much greater than the vault bottom pressure. The distribution of earth pressure in each position of the segment ring in clay stratum is relatively balanced, and the earth pressure on both sides is relatively small; in the fully weathered granite layer of the overlying building, the segment ring of the test ring is subjected to greater additional stress, and the internal force of the segment is much greater than that without the overlying building. Exploring the similarities and differences of segment stress in these three complex strata can provide an important basis for the design and construction of shield segments in complex strata. [ABSTRACT FROM AUTHOR]

To evaluate the secretory function of adrenal incidentaloma, this study explored the usefulness of a contrast-enhanced computed tomography (CECT)-based radiomics model for distinguishing aldosterone-producing adenoma (APA) from non-functioning adrenal adenoma (NAA). Overall, 68 APA and 60 NAA patients were randomly assigned (8:2 ratio) to either a training or a test cohort. In the training cohort, univariate and least absolute shrinkage and selection operator regression analyses were conducted to select the significant features. A logistic regression machine learning (ML) model was then constructed based on the radiomics score and clinical features. Model effectiveness was evaluated according to the receiver operating characteristic, accuracy, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis. In the test cohort, the area under the curve (AUC) of the Radscore model was 0.869 [95% confidence interval (CI), 0.734–1.000], and the accuracy, sensitivity, specificity, and F1 score were 0.731, 1.000, 0.583, and 0.900, respectively. The Clinic–Radscore model had an AUC of 0.994 [95% CI, 0.978–1.000], and the accuracy, sensitivity, specificity, and F1 score values were 0.962, 0.929, 1.000, and 0.931, respectively. In conclusion, the CECT-based radiomics and clinical radiomics ML model exhibited good diagnostic efficacy in differentiating APAs from NAAs; this non-invasive, cost-effective, and efficient method is important for the management of adrenal incidentaloma. [ABSTRACT FROM AUTHOR]

Net primary production (NPP) serves as a crucial indicator of the ecosystem's capacity to capture atmospheric CO2. Gaining insights into the dynamics of NPP and its driving mechanisms is pivotal for optimizing ecosystem carbon sink resource management. Since the implementation of the Grain-for-Green Program (GFGP) in 1999, the Yellow River Basin (YRB) has been one of the most significant areas for ecological restoration in China. However, our knowledge regarding the interannual variability (IAV) of NPP and the underlying driving forces in this region remains incomplete. In this study, we utilized a light use efficiency model to assess the spatiotemporal dynamics, IAV, and driving factors of NPP in the YRB during the period from 1999 to 2018. Our findings revealed that the average annual NPP in the YRB approximated 189.81 Tg C. Over the study duration, NPP significantly increased in 79.63% of the basin with an overall increasing rate of 6.76 g C m−2 yr−1. The most prominent increase was observed in the key GFGP implementation area, predominantly in the semi-humid region. Notably, the middle altitude region (1–1.5 km), semi-humid region, and grassland emerged as the primary contributors to the basin's total vegetation carbon sequestration. However, it is worth emphasizing that there was substantial IAV in the temporal trends of NPP, with the semi-humid region being the most influential contributor (62.66%) to the overall NPP IAV in the YRB. Further analysis of the driving mechanisms unveiled precipitation as the primary driver of NPP IAV in the YRB with a contribution of 62.9%, followed by temperature (23.07%) and radiation (14.03%). Overall, this study deepened our understanding of the IAV and driving mechanisms of NPP in the YRB under ecological restoration, and provided scientific support for optimizing the management of regional carbon sequestration resources. [ABSTRACT FROM AUTHOR]

Biogas can be regarded as a dispatchable renewable source when changing into the demand-oriented operation mode (DO), thus could be used for complementing with solar and wind power in distributed energy system (DES) as a substitute for chemical energy storage. However, if the DO is implemented in regional DES, uncertainties are emerged caused by the complex interest interaction between the seller and the buyer groups formed by the biogas plant and the DES's dispatching center, thus making the development trend of DO unknown. In this context, this study explored the diffusion law of DO in regional DES by establishing a mathematical model based on an evolutionary game between the two major stakeholders, during which the evolutionarily stable strategy (ESS) was deduced for understanding their strategy selections, and then the dynamic diffusion trend was simulated by the system dynamics via a case example. Finally, the sensitivity analysis of parameters is carried out and the optimal policy instruments are proposed according to the main influencing factors. The study revealed that when the DES can realize monetized returns from socio-environmental benefits, the adoption of DO becomes more feasible. Importantly, the revenue generated from electricity sales, by the dispatching center when they do not utilize biogas, emerged as the most critical parameter influencing the ultimate outcomes. The limitations of this research and modeling are discussed to lay a foundation for further improvement. [ABSTRACT FROM AUTHOR]

Low-Impact Development (LID) represents a cogent strategy designed to conserve or reestablish antecedent hydrological states through an array of innovative mechanisms and methodologies. Since the dawn of the millennium, LID-centric research has demonstrated a persistent upward trajectory, mainly focusing on its capacity to mitigate climate change repercussions, particularly runoff and peak flows. However, a standardized rubric and toolkit for LID evaluation remain elusive. While numerous studies have documented the hydrological and water quality benefits of LID, the impacts of climate change on its effectiveness remain uncertain due to varying spatial and temporal climate patterns. This comprehensive review examined 1355 peer-reviewed articles in English, comprising both research articles and reviews, indexed in the Web of Science up until 2022. Findings from the bibliometric analysis revealed significant contributions and emergent trends in the field. Notably, there is an increasing emphasis on performance evaluation and efficiency of LID systems, and on understanding their impact on hydrology and water quality. However, this review identified the lack of a standardized LID evaluation framework and the uncertainty in LID effectiveness due to varying climate patterns. Furthermore, this study highlighted the urgent need for optimization of current hydrological models, advancement of LID optimization, modeling, monitoring, and performance, and stakeholder awareness about LID functionality. This review also underscored the potential future research trajectories, including the need to quantify LID's effectiveness in urban flooding and water quality management and refining LID simulation models. Cumulatively, this review consolidates contemporaneous and prospective research breakthroughs in urban LID, serving as an indispensable compendium for academics and practitioners in the discipline. [ABSTRACT FROM AUTHOR]

This bibliometric review elucidates the emerging intersection of Internet of Things (IoT) technologies and Green Stormwater Infrastructure (GSI), demonstrating the potential to reshape urban stormwater management. The study analyzes a steadily increasing corpus of literature since 2013, pointing out considerable international collaboration. Prominent contributions originate from the United States, Canada, Italy, China, and Australia, underscoring the global acknowledgement of the potential of IoT-enhanced GSI. Diverse GSI applications such as green roofs, smart rain barrels, bioretention systems, and stormwater detention ponds have demonstrated enhanced efficiency and real-time control with IoT integration. However, existing literature reveals several challenges, notably the requirement of advanced monitoring, the development of predictive optimization strategies, and extensive scalability. Comprehensive cost–benefit analyses are also critical for the widespread acceptance of IoT-integrated GSI. Current research addresses these challenges by exploring innovative strategies such as microbial-fuel-cell-powered soil moisture sensors and large-scale RTC bioretention systems. Emphasis is also on the need for security measures against potential digital threats. Future research needs to focus on real-time data-based monitoring plans, model validation, continuous optimization, and supportive policy frameworks. As the world confronts urban development, climate change, and aging infrastructure, IoT and GSI synergism presents a promising solution for effective stormwater management and enhancement of cultural ecosystem services. Continued exploration in this promising domain is crucial to pave the way for smarter, greener urban environments. [ABSTRACT FROM AUTHOR]

The technology of submerged carbon powder injection with CO2 and O2 mixed gas (SCPI-COMG) is a new type of powder injection technology. It can increase the molten bath carbon content and improve the molten steel quality by injecting carbon powder directly into the molten steel with CO2 and O2 mixed gas. To optimize the process parameters of this novel technology, the mechanism of this technology and the effect of SCPI-COMG on EAF steelmaking were investigated in this study. Based on an induction furnace experiment, the effects of molten bath carburization and fluid flow on the scrap melting were analyzed. A mathematical model of the axis of gas jets in liquid steel was built to analyze the impact behaviors of gas jets in liquid steel. Based on the results of this theoretical model, for a gas jet in liquid steel, with α ≥ 20°, the horizontal inject distance decreases with α increasing and with 0° ≤ α ≤ 20°, the horizontal inject distance increases with α increasing. Finally, based on the newly built materials and energy balance model of EAF steelmaking with SCPI-COMG, the influences of the gas-solid parameters on the EAF steelmaking technical indexes were also analyzed.is very useful for optimizing the process parameters of EAF steelmaking with SCPI-COMG. The results of this study are very useful to optimize the process parameters of EAF steelmaking with SCPI-COMG of Gas Jet in Liquid Steel. [ABSTRACT FROM AUTHOR]

Ecological fire prevention forest belts can effectively alleviate the spread of forest fires and reduce the harm caused by forest fires. Exploring the distribution and changes in suitable growth areas for fire-resistant forest species under the effects of climate change can provide effective references for the introduction of ecological fire prevention and tree species preservation in the region. This study is based on the distribution data of six typical ecological fire prevention forest species in the subtropical regions of China. The maximum entropy model (MaxEnt), optimized by the ENMeval data package, was used to analyze the potential relationship between the ecological environment variables and fire prevention forest species. The potential distribution of certain tree species in the historical period and in future periods is simulated. In addition, the area changes, migration trends, and stable areas of tree species under climate change are also discussed. The research results indicated the following: (1) The AUC values of the optimized model are all higher than 0.9, indicating the optimal prediction results. (2) The climate variables that have the greatest impact on the suitable habitat of Schima superba were the annual mean temperature, precipitation of the driest month, and mean diurnal range. Quercus glauca was mainly influenced by the minimum temperature of the coldest month and the precipitation of the warmest quarter. Castanopsis eyrei was mainly influenced by the precipitation of the driest month and the annual precipitation. The distribution of suitable growth areas for Symplocos sumuntia is mainly influenced by the precipitation of the driest month. The distribution of Camellia oleifera was influenced by the minimum temperature of the coldest month. The potential habitat distribution of Photinia serratifolia was greatly influenced by annual precipitation. (3) Until 2090, the expansion degree of the suitable growth area will be Symplocos sumuntia (51.05%) > Schima superba (19.41%) > Camellia oleifera (10.14%) > Quercus glauca (6.80%) > Castanopsis eyrei (2.34%) > Photinia serratifolia (−6.97%). (4) The centroid of Schima superba will migrate northward. Quercus glauca will migrate northeast. The suitable areas for the migration of Symplocos sumuntia and Castanopsis eyrei will move in a northwest direction, with repeated changes in alum migration, as well as with the largest migration span for Castanopsis eyrei. In addition, Camellia oleifera will move southwest. The centroid of Photinia serratifolia will migrate to the southeast. (5) The six fire-resistant tree species in this study were noted to have excellent stability in Guizhou, Hunan, Jiangxi, Fujian, Guangdong, and Guangxi. This conclusion can provide an effective reference for the introduction of ecological fire prevention tree species and the protection of tree species under climate change in subtropical forest-fire-prone areas in China. [ABSTRACT FROM AUTHOR]

As the digitization of the supply side continues to advance, the takeaway industry has made a significant contribution to economic growth. However, the rapid expansion of the scale has also brought many social problems, merchants provide low-quality goods out of the psychology of opportunity, and the uneven quality of goods and asymmetric information not only bring great regulatory problems for the takeaway platform, but also make it difficult for consumers to identify the platform, merchants, and consumers as takeaway platform stakeholders, it is difficult to integrate resources to achieve value co-creation. Therefore, in order to realize the value co-creation among the stakeholders of the takeaway platform, a three-party evolutionary game model was constructed to analyze and simulate the strategic choices of stakeholders under different situations through simulation experiments and to explore the sensitive influence of each factor. The results of the study show the following: shaping a scientific reward and punishment system and setting reasonable rewards and punishments within a limited threshold; platforms, consumers using word-of-mouth effects to amplify the loss of network externalities that merchants have to bear when they provide low-quality services, and improving the consumer feedback mechanism to reduce the cost of feedback are all effective measures to promote the active participation of takeaway platform stakeholders in value co-creation and promote the sustainable and healthy development of the takeaway industry. [ABSTRACT FROM AUTHOR]

Tetracycline (TC) and arsenic contaminants are two main pollutants in aquaculture and livestock husbandry, and they have drawn worldwide attention. To address this issue, a novel N-doped carbon@magnesium silicate (CMS) was fabricated via a facile and low-cost hydrothermal route, adopting glucose and ammonia as C and N sources, respectively. The synergetic combination of carbon and magnesium silicate makes CMS possess a high surface area of 201 m2/g and abundant functional groups. Due to the abundant C- and N-containing functional groups and Mg-containing adsorptive sites, the maximum adsorption capacity values of CMS towards As(V) and TC are 498.75 mg/g and 1228.5 mg/g, respectively. The type of adsorption of As(V) and TC onto CMS is monolayer adsorption. An adsorption kinetic study revealed that the mass transfer and intraparticle process dominates the sorption rate of As(V) and TC adsorption onto CMS, respectively. Various functional groups synthetically participate in the adsorption process through complexion, π–π EDA interactions, and hydrogen bonds. This work provides a one-step, low-cost route to fabricate a N-doped carbonaceous adsorbent with a high surface area and abundant functional groups, which has great potential in the application of practical sewage treatment. [ABSTRACT FROM AUTHOR]

In this paper, novel humidity sensors based on montmorillonite, kaolinite, and composite films coated on micro-cantilevers were prepared to measure the relative humidity (RH) values by the deflection of a micro-cantilever (MC) at room temperature. The humidity-sensing properties, such as response and recovery, sensitivity, repeatability, humidity hysteresis, and long-term stability, were investigated in the range of working humidity (10–80% RH). The humidity response in the close humidity range of 10% RH to 80% RH revealed a linear increase in water absorption of montmorillonite, kaolinite, and montmorillonite/kaolinite mixed dispersant (1:1) as a function of RH with linear correlation factors between the humidity change and deflection estimated to be 0.994, 0.991, and 0.946, respectively. Montmorillonite's sensitivity was better than kaolinite's, with the mixed-clay mineral film's response falling somewhere in between. This research provides a feasible and effective approach to constructing high-performance MC humidity sensors that can be operated at room temperature based on clay minerals. [ABSTRACT FROM AUTHOR]

Climate change represents a paramount challenge for humanity in the 21st century. Green infrastructure (GI), due to its myriad environmental and societal benefits, has emerged as an essential natural life support system and a pivotal strategy to combat climate change-induced risks. Consequently, GI has garnered considerable global interest. As of now, comprehensive and systematic environmental impact assessments of GI are underway worldwide. Nonetheless, there remains a conspicuous scarcity of life-cycle approaches to delineate the evolutionary trajectory of this domain. Employing three bibliometric software tools—the R language "Bibliometrix" package (version 4.0.1), CiteSpace (version 6.2.R2 Basic), and "VOSviewer" (version 1.6.18)—this study scrutinizes the progression of the GI paradigm until 2022. An exhaustive review of 1124 documents published on the Web of Science between 1995 and 2022 facilitates an overarching evaluation of GI, encompassing environmental, economic, and social facets from a life-cycle standpoint. The analysis results reveal that (1) the majority of current studies accentuate the economic and environmental efficacy of GI throughout its life cycle, with the social performance receiving comparatively less focus, potentially due to the difficulties in formulating a social life-cycle-assessment database; (2) contemporary research predominantly concentrates on the life-cycle carbon footprint of GI, warranting further exploration into its water and carbon footprints; and (3) multi-objective optimization emerges as a promising avenue for future GI investigations. This review thus furnishes a comprehensive understanding of the performance of GI from a life-cycle perspective. [ABSTRACT FROM AUTHOR]

Therapeutic cancer vaccines are considered as one of the most cost-effective ways to eliminate cancer cells. Although many efforts have been invested into improving their therapeutic effect, transient maturation and activations of dendritic cells (DCs) cause weak responses and hamper the subsequent T cell responses. Here, we report on an alum-stabilized Pickering emulsion (APE) that can load a high number of antigens and continue to release them for extensive maturation and activations of antigen-presenting cells (APCs). After two vaccinations, APE/OVA induced both IFN-γ-secreting T cells (Th1) and IL-4-secreting T cells (Th2), generating effector CD8+ T cells against tumor growth. Additionally, although they boosted the cellular immune responses in the spleen, we found that multiple administrations of cancer vaccines (three or four times in 3-day intervals) may increase the immunosuppression with more PD-1+ CD8+ and LAG-3+ CD8+ T cells within the tumor environment, leading to the diminished overall anti-tumor efficacy. Combining this with anti-PD-1 antibodies evidently hindered the suppressive effect of multiple vaccine administrations, leading to the amplified tumor regression in B16-OVA-bearing mice. [ABSTRACT FROM AUTHOR]

In order to improve the low efficiency and malfunction caused by the FWRV (four-way reversing valve) when the working mode of the heat pump air conditioning system changes in refrigeration and heating, it is of great significance to study the structure of the FWRV. In this paper, the control equation of the sliding block movement process is constructed to simulate the reversing process of the FWRV in fluid mechanics, and a detailed study is carried out from the sliding block speed, sliding block acceleration, sliding block force, refrigerant leakage, and other aspects. Some studies indicate that structural improvement in the slider could greatly enhance the working performance of the FWRV. Increasing the height of the slider not only improves the pressure relief ability of the FWRV and prevents the slider from being damaged by high-pressure gas impact but also reduces the leakage of refrigerant during the directional process; when reducing the length of the slider, although it improves the pressure relief capacity of the FWRV, it also increases the amount of refrigerant leakage. The research results provide a theoretical basis and direction for designers to improve the capability of the FWRV. [ABSTRACT FROM AUTHOR]

Glyoxal and methylglyoxal are important volatile organic compounds in the atmosphere. The gas–particle partitioning of these carbonyl compounds makes significant contributions to O3 formation. In this study, both the gas- and particle-phase glyoxal and methylglyoxal concentrations at the foot and top of Mount Hua were determined simultaneously. The results showed that the gaseous-phase glyoxal and methylglyoxal concentrations at the top were higher than those at the foot of the mountain. However, the concentrations for the particle phase showed the opposite trend. The average theoretical values of the gas–particle partitioning coefficients of the glyoxal and methylglyoxal concentrations (4.57 × 10−10 and 9.63 × 10−10 m3 μg−1, respectively) were lower than the observed values (3.79 × 10−3 and 6.79 × 10−3 m3 μg−1, respectively). The effective Henry's law constants (eff.KH) of the glyoxal and methylglyoxal were in the order of 108 to 109 mol/kgH2O/atm, and they were lower at the foot than they were at the top. The particle/gas ratios (P/G ratios) of the glyoxal and methylglyoxal were 0.039 and 0.055, respectively, indicating more glyoxal and methylglyoxal existed in the gas phase. The factors influencing the partitioning coefficients of the glyoxal and methylglyoxal were positively correlated with the relative humidity (RH) and negatively correlated with the PM2.5 value. Moreover, the partitioning coefficient of the glyoxal and methylglyoxal was more significant at the top than at the foot of Mount Hua. [ABSTRACT FROM AUTHOR]

TC17 titanium alloy is widely used in the aerospace industry, but its combustion behavior and microstructure after combustion are rarely investigated. Herein, the ignition critical oxygen pressure, combustion velocity, and microstructure after the combustion of TC17 titanium alloy were investigated by promoted ignition combustion tests under an oxygen-enriched environment. The results indicated that there were three stages, ignition, splash, and flame propagation, for the combustion process of the TC17 alloy. As compared to TC11 titanium alloy, the TC17 titanium alloy exhibited a similar ignition critical oxygen pressure with the same size, but an obviously faster burning rate, which followed a power law relationship with the oxygen pressure. The segregation of Cr, Mo, and Al was observed in the interdendritic phase of the melting zone and the interface between the melting zone and the heat-affected zone. The segregation of Cr at the liquid/solid interface can be responsible for accelerating the burning kinetic of the TC17 alloy by decreasing the interfacial temperature. [ABSTRACT FROM AUTHOR]

This article introduces a novel framework for diagnosing faults in rolling bearings. The framework combines digital twin data, transfer learning theory, and an enhanced ConvNext deep learning network model. Its purpose is to address the challenges posed by the limited actual fault data density and inadequate result accuracy in existing research on the detection of rolling bearing faults in rotating mechanical equipment. To begin with, the operational rolling bearing is represented in the digital realm through the utilization of a digital twin model. The simulation data produced by this twin model replace traditional experimental data, effectively creating a substantial volume of well-balanced simulated datasets. Next, improvements are made to the ConvNext network by incorporating an unparameterized attention module called the Similarity Attention Module (SimAM) and an efficient channel attention feature referred to as the Efficient Channel Attention Network (ECA). These enhancements serve to augment the network's capability for extracting features. Subsequently, the enhanced network model is trained using the source domain dataset. Simultaneously, the trained model is transferred to the target domain bearing using transfer learning techniques. This transfer learning process enables the accurate fault diagnosis of the main bearing to be achieved. Finally, the proposed method's feasibility is validated, and a comparative analysis is conducted in comparison with similar approaches. The comparative study demonstrates that the proposed method effectively addresses the issue of low mechanical equipment fault data density, leading to improved accuracy in fault detection and classification, along with a certain level of robustness. [ABSTRACT FROM AUTHOR]

With the development of the digital economy, the algorithms and big data technologies of e-commerce platforms have gradually turned into double-edged swords. While realising personalised recommendations, they also provide information technology support for the use of algorithmic prices to discriminate and extract residual value from consumers. Consumers frequently use Black Cat and third-party media to complain, resulting in a significant negative impact. Therefore, in order to regulate algorithmic price discrimination, using e-commerce platforms, local governments and consumers act as game subjects, taking an evolutionary game approach. We analyse the impact of different situations and factors on the system's evolutionary stability strategy and conduct its verification via simulation experiments. This study shows that several measures, such as increasing cooperation with the media; establishing clear regulatory rules to reduce the extent of algorithmic price discrimination and the grey revenue of e-commerce platforms; establishing a long-term mechanism for consumer feedback; improving rewards and punishments to increase the probability of successful regulation and penalties by local governments; sharing information to reduce the cost of consumer regulation; and setting reasonable bonus thresholds based on government revenue and consumer regulation costs, can effectively regulate algorithmic price discrimination and promote the sustainable development of e-commerce platforms. [ABSTRACT FROM AUTHOR]

Bio-swales have gained significant attention as an effective means of stormwater management in urban areas, reducing the burden on conventional rainwater management systems. Despite this increasing interest, a comprehensive assessment of the status of bio-swale research is lacking. In response, this article employs two powerful information-visualizing software tools, the "Bibliometrix" R package and "CiteSpace", to conduct a quantitative investigation of 323 English language sources published in the Web of Science prior to 2022. The objective is to provide a comprehensive examination of bio-swale research from multiple perspectives, shedding light on current advancements and future research trends. The findings reveal (1) a persistent annual increase in bio-swale-related publications and (2) the predominant focus on regulating services, such as hydrology, water quality, and biodiversity, with hot topics within these areas, including the influencing factors, climate, modeling, soil contaminants, and biodiversity at both macro and micro levels. Furthermore, our study concludes that future research necessitates interdisciplinary and interterritorial collaboration, a broader focus that encompasses the social, economic, ecological, and engineering aspects of bio-swales, and the adoption of diverse research methodologies. Given the currently limited research on biodiversity in bio-swales, this area holds the potential to become a future research hotspot. By harnessing the insights and findings of our study, researchers can gain a more profound understanding of the current state of bio-swale research and devise effective strategies to further propel this critical area of study. [ABSTRACT FROM AUTHOR]

Unsmooth reversing is one of the most common faults in the four-way reversing valve of the air conditioning system, and the airflow in the valve chamber and the shape of the slider of the reversing valve are the main factors in unsmooth reversing. In order to study the influence of the airflow in the chamber and the slider shape on the reversing process, the fluid flow in the valve chamber of a four-way reversing valve is obtained using computational fluid dynamics (CFD) simulations, and the influence of the slider structure on slider thrust and resistance of the four-way reversing valve is analyzed. Four-way reversing valves with no-cutting, straight-cutting and arc-cutting sliders are employed to evaluate the reversing characteristics. A pressure measuring device for a four-way reversing valve chamber is designed, which is performed to test the chamber pressure of three different types of sliders of four-way reversing valves. The maximum error of experimental tests and simulations is within 5% of engineering tolerance, so as to verify the reliability of numerical simulation. The resistance-to-thrust ratio of the slider is raised to evaluate the performance of the reversing, which can guide the design of the valve and slider. The results indicate that the chamber pressure and resistance-thrust ratio with the cutting slider are reduced, which is beneficial to the reversing of the four-way reversing valve. Compared with the straight-cutting structure, the arc-cutting slider four-way reversing valve has a more stable reversing process and better comprehensive reversing performance. Results demonstrate that both the straight-cutting structure and arc-cutting structure of the slider can improve the process performance but the arc-cutting slider is better. [ABSTRACT FROM AUTHOR]

Urban flooding disasters have become increasingly frequent in rural-urban fringes due to rapid urbanization, posing a serious threat to the aquatic environment, life security, and social economy. To address this issue, this study proposes a flood disaster risk assessment framework that integrates a Weighted Naive Bayesian (WNB) classifier and a Complex Network Model (CNM). The WNB is employed to predict risk distribution according to the risk factors and flooding events data, while the CNM is used to analyze the composition and correlation of the risk attributes according to its network topology. The rural-urban fringe in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is used as a case study. The results indicate that approximately half of the rural-urban fringe is at medium flooding risk, while 25.7% of the investigated areas are at high flooding risk. Through driving-factor analysis, the rural-urban fringe of GBA is divided into 12 clusters driven by multiple factors and 3 clusters driven by a single factor. Two types of cluster influenced by multiple factors were identified: one caused by artificial factors such as road density, fractional vegetation cover, and impervious surface percentage, and the other driven by topographic factors, such as elevation, slope, and distance to waterways. Single factor clusters were mainly based on slope and road density. The proposed flood disaster risk assessment framework integrating WNB and CNM provides a valuable tool to identify high-risk areas and driving factors, facilitating better decision-making and planning for disaster prevention and mitigation in rural-urban fringes. [ABSTRACT FROM AUTHOR]

The deep learning method for natural-image object detection tasks has made tremendous progress in recent decades. However, due to multiscale targets, complex backgrounds, and high-scale small targets, methods from the field of natural images frequently fail to produce satisfactory results when applied to aerial images. To address these problems, we proposed the DET-YOLO enhancement based on YOLOv4. Initially, we employed a vision transformer to acquire highly effective global information extraction capabilities. In the transformer, we proposed deformable embedding instead of linear embedding and a full convolution feedforward network (FCFN) instead of a feedforward network in order to reduce the feature loss caused by cutting in the embedding process and improve the spatial feature extraction capability. Second, for improved multiscale feature fusion in the neck, we employed a depth direction separable deformable pyramid module (DSDP) rather than a feature pyramid network. Experiments on the DOTA, RSOD, and UCAS-AOD datasets demonstrated that our method's average accuracy (mAP) values reached 0.728, 0.952, and 0.945, respectively, which were comparable to the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Polypropylene (PP)/sepiolite nanocomposites were prepared using the melt blending technique. The effects of nano-sepiolite content on the mechanical property, thermal property, crystallinity, morphology and rheological property of PP/sepiolite nanocomposites were investigated. The organic modified sepiolites (OSep) were dispersed evenly in PP matrix after surface treatment. The addition of OSep improved the storage modulus and thermal stability, showing a strong interaction between OSep and PP matrix. With the increase of OSep content, the fluidity of PP/OSep composites first increased due to the lubrication of surface modifiers and then decreased due to the interaction between OSep and PP. The size of the toughening agent elastomer first increased and then decreased, and the impact notched strength of PP/Osep composites first decreased and then increased. The loading of OSep also reduced the crystallinity and shrinkage rate of PP. PP/OSep nanocomposites have potential applications in high-performance automotive lightweight materials. [ABSTRACT FROM AUTHOR]

Alluvial fans are an important land resource with agricultural potential in Qinghai–Tibet Plateau. The spatiotemporal variation in land use/cover is an important indicator to understand the value of alluvial fans and protect and make scientific use of such fans. In this study, the spatiotemporal characteristics of land use/cover are determined by analysing the land use/cover changes of alluvial fans in the Lhasa River Basin (LRB) at different times, counties/districts, altitudes, and gradients. Results show that the area of cultivated land and the artificial land provided by alluvial fans for LRB has continuously increased. In 2000, 2010, and 2020, 17.72%, 21.84%, and 24.17% of cultivated land and 7.89%, 7.51%, and 25.24% of artificial land in LRB were provided by alluvial fans, respectively. At all altitudes and slopes, cultivated land and artificial land are increasing but the increasing part is basically due to the massive loss of grassland. The spatiotemporal changes in all land use/cover types of alluvial fans were dominated by human activities, although they were also influenced by natural factors to some degree. [ABSTRACT FROM AUTHOR]

Baoji is a medium-sized city in the Guanzhong Plain of northwest China. The compositions of three important organic groups, namely n-alkanes, polycyclic aromatic hydrocarbons (PAHs), and saccharides in atmospheric aerosol with different aerodynamic diameters in power were determined. Both seasonal and daily trends of the target organic chemical groups were demonstrated. The concentration levels of total quantified n-alkanes and saccharides in total suspended particles (TSP) in winter were 541 ± 39 and 651 ± 74 ng·m−3, respectively, much higher than those of the other three seasons. A high total quantified PAHs concentration level of 59.6 ± 6.4 ng·m−3 was also seen in wintertime. n-Alkanes showed a bimodal percent distribution in spring, autumn, and winter. Two peaks were found with the particle sizes of 0.7 μm < Dp < 2.1 μm and 3.3 μm < Dp < 4.7 μm, respectively. In summer, a unimodal was seen with a peak of 4.7 μm < Dp < 5.8 μm. Dehydrated saccharides and PAHs present a unimodal size distribution peaking at the aerodynamic diameters of 0.7 µm < Dp < 2.1 µm. In contrast to glucose and fructose, they mainly exist in the coarse mode particles and have the highest concentrations at aerodynamic diameters of 4.7 µm < Dp < 9.0 µm. The geometric mean diameters (GMD) of n-alkanes and saccharides of the fine particles in winter were higher than in the other seasons. Compared with the data in 2008, the fossil fuel-derived n-alkanes and PAHs in winter decreased by nearly an order of magnitude in 2017. Both the carbon preference index (CPI) of n-alkanes and the diagnostic ratios of PAHs suggest that coal combustion and vehicle exhaust were the major pollution sources of the organic groups in the two decades. It should be noted that the contribution of traffic emissions greatly increased from 2008 to 2017, consistently with a large raise of registered vehicles in Baoji city. The overall results confirm that the control measures conducted by the local government in the recent decade mitigated the air pollution in this city. [ABSTRACT FROM AUTHOR]

Iron is a trace element necessary for cell growth, development, and cellular homeostasis, but insufficient or excessive level of iron is toxic. Intracellularly, sufficient amounts of iron are required for mitochondria (the center of iron utilization) to maintain their normal physiologic function. Iron deficiency impairs mitochondrial metabolism and respiratory activity, while mitochondrial iron overload promotes ROS production during mitochondrial electron transport, thus promoting potential disease development. This review provides an overview of iron homeostasis, mitochondrial iron metabolism, and how mitochondrial iron imbalances-induced mitochondrial dysfunction contribute to diseases. [ABSTRACT FROM AUTHOR]

Sponsorship is very important for the sustainable development of sports, and the motivation of sponsors will have an impact on consumers' willingness to buy the products of sports sponsors. This paper takes China's CBA sports sponsors, TCL Corporation and Li Ning Corporation, as examples to conduct an empirical stud. Structural equation model, regression analysis, and correlation analysis are used in our study. The results show that altruistic sponsorship motivation has a positive impact on consumer attitude and purchase intention. Sponsorship egoistic motivations have a reverse impact on consumer attitudes. However, the reverse impact on purchase intention is relatively low. Consumer attitude plays a mediating role in the process of sponsorship motivation influencing purchase intention. Based on SOR (Stimulus- Organism-Response) theory, this paper proposes a model to study the impact of sponsorship motivation on customers' purchase intention and comes to the conclusion of the intermediary effect of consumer attitude in the impact of sponsorship motivation on customers' purchase intention. Our study provides a reference for the study of the impact of sports sponsorship motivation on consumer purchase intention. [ABSTRACT FROM AUTHOR]

With the advantages of lower capacitance, faster operation and smaller size, the amorphous silicon antifuse is very promising for high-speed and high-density FPGA. However, the leakage current of the conventional amorphous silicon antifuse in the off-state is high, which decreases its performance and reliability. Although the leakage current of the SiNX or SiOX antifuse is small, the proper thickness of the dielectric layer is not easily controlled by PECVD processes. In addition, the highly undesirable switch-off behavior is common to almost all metal-to-metal antifuse structures. Focusing on the study of amorphous silicon multilayer dielectric structures, a novel antifuse with the structure of Al/α-Si:H,N/α-Si:H/Al was proposed, which was manufactured by nitrogen plasma treatment for an α-Si:H film surface. Through surface plasma treatment, the hydrogen content of the dielectric layer is stable, the film surface is smoother, the leakage current is reduced, the switch-off behavior is eliminated, the programming voltage is more concentrated and the on-state resistance distribution is more compact. The results demonstrated that surface plasma treatment with proper time for the dielectric layer could significantly improve the performance and reliability of the Al/α-Si:H,N/α-Si:H/Al antifuse. Furthermore, the fabrication process of the α-Si:H,N/α-Si:H structure has excellent compatibility, controllability and simplicity. [ABSTRACT FROM AUTHOR]

Postprandial Hyperglycemia (PPHG) persistently threatens patients' health. Therefore, accurate diabetes prediction is crucial for effective blood glucose management. Most current methods primarily focus on analyzing univariate blood glucose data using traditional neural networks, neglecting the importance of spatiotemporal modeling of multivariate data at the node and subgraph levels. This study aimed to evaluate the accuracy of using deep learning (DL) techniques to predict diabetes based on multivariable blood glucose data, aiming to improve resource allocation and decision-making in healthcare. We introduce a Nonlinear Aggregated Graph Neural Network (NLAGNN) that utilizes continuous multivariate historical blood glucose data from multiple patients to predict blood glucose levels over time, addressing the challenge of accurately extracting strong and weak correlation features. We preliminarily propose a Nonlinear Fourier Graph Neural Operator (NFGO) for nonlinear node representation, which effectively reduces meaningless noise. Additionally, a dynamic partitioning of graphs is introduced, which divides the a hypergraph into distinct subgraphs, enabling the further processing of strongly correlated features at the node and subgraph levels, ultimately obtaining the final prediction through layer aggregation. Extensive experiments on three datasets show that our proposed method achieves competitive results compared to existing advanced methods. [ABSTRACT FROM AUTHOR]