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Wearable sweat biosensors have gained attention due to their potential for non-invasive health monitoring. However, practical applications still face challenges in integrating multiplex sensors primarily due to sweat accumulation, complex microfluidic preparation processes, and degradability of substrate materials. In this study, a multiplex sweat sensor based on leather fluidic transport is proposed. The pre-treated leather exhibits favorable hydrophilicity, air permeability, and biocompatibility. Furthermore, high sensitivity and stability multiplex sensor electrodes for pH, chloride, potassium, and sodium ions have been integrated into the leather-based fluid transport to fabricate an integrated wearable sweat device. By enabling sweat analysis compatible with routine and daily activities, the integrated wearable sweat device achieves continuous and long-term monitoring of body physiology. [ABSTRACT FROM AUTHOR]

Global warming caused by massive carbon dioxide emissions can lead to a chain of ecological disasters. As one of the main sources of carbon emissions, transportation is of great significance, and the evaluation of its connections with carbon emissions is necessary to achieve "carbon neutrality". Taking Beijing as an example, this study evaluated traffic efficiency (TE) by utilizing principal component analysis and fuzzy comprehensive evaluation. Using the Tapio decoupling model and coupling coordination degree model, the corresponding relationship between urban low carbon level (LCL) and TE was explored. The results showed the following: (1) The total carbon emission (CE) level exhibited fluctuating variation from increasing to decreasing. The carbon emission intensity (CEI) continued to slow down, and the rapid growth of population density played a key role in low-carbon development. (2) The traffic operations continually showed a positive trend in development. TE increased from a step-like to a slow shape, until it declined in 2020 due to the pandemic. (3) TE and LCL both developed from low coordination to an extreme level of coordination. Per capita carbon emission (CEP) and TE presented an inverted U-shaped curve; meanwhile, with increases in TE, the decline in CEI slowed. In addition, the weak decoupling of TE changed to become strong, due to CE and CEP, and maintained a strong decoupling state from CEI. (4) There is a necessity for the rational planning of land use for transportation infrastructure, the encouragement of a combination of public and private transportation, and the strengthening of the maintenance of the relative infrastructure and the management of traffic behaviors to attain a win–win situation. The results provide a reference for optimizing the traffic structure to achieve "carbon neutrality". [ABSTRACT FROM AUTHOR]

As a new type of backfill material, Self-compacting solidified soil (SCSS) takes the abandoned slurry of cast-in-place piles after dewatering and reduction as the main raw material, which brings a problem of coordinating the working performance with the mechanical property under the condition of high mobility. In this paper, hydroxypropyl methyl cellulose (HPMC) and metakaolin were introduced as additives to solve this problem. First, the workability and mechanical properties of SCSS were regulated and optimized by means of the water seepage rate test, the flowability test, and the unconfined compressive strength test. Second, this study also used X-ray diffraction (XRD) and scanning electron microscopy (SEM) to investigate the effects of HPMC and metakaolin on the physical phase and microstructure of SCSS. In this way, the results showed that there was a significant impact on the flowability of SCSS, that is, when the dosage reached 0.3%, the water seepage rate of SCSS was reduced to less than 1%, and the compressive strength at 7 days reached its peak. At the same time, HPMC weakened the strength growth of SCSS in the age period of 7 days to 14 days. However, the addition of metakaolin promoted its compressive strength. XRD analysis showed that the additives had no significant effects on the physical phases. And, from the SEM results, it can be seen that although the water-retaining effect of HPMC makes hydration of cement more exhaustive, more ettringite (AFt) can be observed in the microstructure. In addition, it can be observed that the addition of metakaolin can generate more hydrated calcium silicate (C-S-H) due to the strong surface energy possessed by metakaolin. As a result of the above factors, SCSS filled the voids between particles and improved the interface structure between particles, thus enhanced the compressive strength. [ABSTRACT FROM AUTHOR]

Motivation Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation. Results We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands. To accomplish this, a novel attentive graph reconstruction neural network and ligand optimization scheme are proposed. Attentive graph reconstruction neural network reconstructs original ligands and optimizes them through adversarial representations derived from their bioactivity prediction process. Experimental results on nine drug targets reveal that out of the 667 molecules generated through OLB-AC optimization on datasets comprising 974 low-activity, noninhibitor, or highly toxic ligands, 49 are recognized as known highly active, inhibitor, or nontoxic ligands beyond the datasets' scope. The 27 out of 49 matched molecular pairs generated by OLB-AC reveal novel transformations not present in their training sets. The adversarial representations employed for ligand optimization originate from the gradients of bioactivity predictions. Therefore, we also assess OLB-AC's prediction accuracy across 33 different bioactivity datasets. Results show that OLB-AC achieves the best Pearson correlation coefficient (r 2) on 27/33 datasets, with an average improvement of 7.2%–22.9% against the state-of-the-art bioactivity prediction methods. Availability and implementation The code and dataset developed in this work are available at github.com/Yueming-Yin/OLB-AC. [ABSTRACT FROM AUTHOR]

Very high-resolution remote sensing images hold promising applications in ground observation tasks, paving the way for highly competitive solutions using image processing techniques for land cover classification. To address the challenges faced by convolutional neural network (CNNs) in exploring contextual information in remote sensing image land cover classification and the limitations of vision transformer (ViT) series in effectively capturing local details and spatial information, we propose a local feature acquisition and global context understanding network (LFAGCU). Specifically, we design a multidimensional and multichannel convolutional module to construct a local feature extractor aimed at capturing local information and spatial relationships within images. Simultaneously, we introduce a global feature learning module that utilizes multiple sets of multi-head attention mechanisms for modeling global semantic information, abstracting the overall feature representation of remote sensing images. Validation, comparative analyses, and ablation experiments conducted on three different scales of publicly available datasets demonstrate the effectiveness and generalization capability of the LFAGCU method. Results show its effectiveness in locating category attribute information related to remote sensing areas and its exceptional generalization capability. Code is available at https://github.com/lzp-lkd/LFAGCU. [ABSTRACT FROM AUTHOR]

A Prussian blue analogue was synthesized using biomass leather waste as a precursor by doping with Co2+ ions. This material, demonstrates good performance in both the oxygen reduction reaction and oxygen evolution reaction, and exhibits excellent charge–discharge performance and stability in zinc–air batteries. [ABSTRACT FROM AUTHOR]

Climate and landscape structure are widely recognized as the primary drivers of soil erosion; however, the spatiotemporal variability of their effects remains insufficiently understood, limiting our comprehension of the dynamic processes of soil erosion. To address this gap, this study analyzed soil erosion trends on the Loess Plateau from 2000 to 2018. extreme Gradient Boosting was used to identify key climatic and landscape structural factors, while a geographically and temporally weighted regression model was applied to assess the spatiotemporal variability of these influences. The results indicate a decreasing trend in soil erosion from 2000 to 2008, followed by a sharp increase from 2008 to 2018. Grassland edge density emerged as the most important factor, followed closely by grassland percentage and annual precipitation. Temporally, the positive effect of annual precipitation has been intensifying since 2010, contributing to increased erosion, while landscape structural factors progressively enhanced their hydrological regulatory roles, reflecting dynamic interactions with climate. Spatially, the direction of climatic influences remained generally stable, consistently promoting erosion, although by 2018, the effects of average annual temperature and annual sunshine duration reversed to suppress erosion in specific areas. In contrast, landscape structural influences exhibited greater spatial variability, often fluctuating or reversing depending on topography, human activity, and land use. This variability applied specifically and differentially to each metric of fragmentation and diversity, highlighting the critical importance of trade-offs in landscape management. The findings emphasize the complexity and dynamics of soil erosion in response to climate and landscape structure, suggesting implications for the development of spatially targeted soil erosion control strategies that accommodate the phases of temporal variation., (Copyright © 2024. Published by Elsevier B.V.)

Aims: A new model of computational fluid dynamics (CFD)-based algorithm for coronary CT angiography (CCTA)-derived fractional flow reserve (FFR) (CT-FFR) analysis by expanding the coronary tree to smaller-diameter lumen (0.8 mm) using Newton-Krylov-Schwarz (NKS) method to solve the three-dimensional time-dependent incompressible Navier-Stokes equations has been developed; however, the diagnostic performance of this new method has not been sufficiently investigated. The aim of this study was to determine the diagnostic performance of a novel CT-FFR technique by expanding the coronary tree in the CFD domain., Methods and Results: Six centres enrolled 338 symptomatic patients with suspected or known coronary artery disease (CAD) who prospectively underwent CCTA and FFR. Stenosis assessment in CCTA and CT-FFR analysis were performed in independent core laboratories. Haemodynamically significant stenosis was defined by a CT-FFR and FFR ≤ 0.80, and anatomically obstructive CAD was defined as a CCTA with stenosis ≥ 50%. Diagnostic performance of CT-FFR was evaluated against invasive FFR using receiver operating characteristic (ROC) curve analysis. The correlation between CT-FFR and invasive FFR was analysed using the Spearman correlation coefficient and Bland-Altman analysis. Intra-observer and inter-observer agreements were evaluated utilizing the intraclass correlation coefficient (ICC). In this study, 338 patients with 422 targeted vessels were investigated, revealing haemodynamically significant stenosis in 31.1% (105/338) of patients and anatomically obstructive stenosis in 54.1% of patients. On a per-vessel basis, the area under the ROC curve for CT-FFR was 0.94 vs. 0.76 for CCTA ( P < 0.001). Per-vessel accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 89.8%, 89.3%, 90.0%, 79.0%, and 99.2%, respectively, for CT-FFR and were 68.4%, 82.8%, 62.3%, 48.1%, and 89.6%, respectively, for CCTA stenosis. CT-FFR and FFR were well correlated ( r = 0.775, P < 0.001) with a Bland-Altman bias of 0.0011, and limits of agreement from -0.1509 to 0.1531 ( P = 0.770). The ICCs with CT-FFR for intro- and inter-observer agreements were 0.919 (95% CI: 0.866-0.952) and 0.909 (95% CI: 0.851-0.945), respectively. The average computation time for CT-FFR analysis was maintained at 11.7 min., Conclusion: This novel CT-FFR model with the inclusion of smaller lumen provides high diagnostic accuracy in detecting haemodynamically significant CAD. Furthermore, the integration of the NKS method ensures that the computation time remains within an acceptable range for potential clinical applications in the future., Competing Interests: Conflict of interest: M.Z. has received research grants from National Natural Science Foundation of China and Shanghai Municipal Key Clinical Specialty, and serves as a consultant for United Imaging. J.G. has received research grants from National Key Research and Development Program of China and Shanghai Clinical Research Center for Interventional Medicine and China Cardiovascular Health Alliance-Access Research Fund. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

AutoDock Vina and its derivatives have established themselves as a prevailing pipeline for virtual screening in contemporary drug discovery. Our Vina-GPU method leverages the parallel computing power of GPUs to accelerate AutoDock Vina, and Vina-GPU 2.0 further enhances the speed of AutoDock Vina and its derivatives. Given the prevalence of large virtual screens in modern drug discovery, the improvement of speed and accuracy in virtual screening has become a longstanding challenge. In this study, we propose Vina-GPU 2.1, aimed at enhancing the docking speed and precision of AutoDock Vina and its derivatives through the integration of novel algorithms to facilitate improved docking and virtual screening outcomes. Building upon the foundations laid by Vina-GPU 2.0, we introduce a novel algorithm, namely Reduced Iteration and Low Complexity BFGS (RILC-BFGS), designed to expedite the most time-consuming operation. Additionally, we implement grid cache optimization to further enhance the docking speed. Furthermore, we employ optimal strategies to individually optimize the structures of ligands, receptors, and binding pockets, thereby enhancing the docking precision. To assess the performance of Vina-GPU 2.1, we conduct extensive virtual screening experiments on three prominent targets, utilizing two fundamental compound libraries and seven docking tools. Our results demonstrate that Vina-GPU 2.1 achieves an average 4.97-fold acceleration in docking speed and an average 342% improvement in EF1% compared to Vina-GPU 2.0. The source code and tools for Vina-GPU 2.1 are freely available accompanied by comprehensive instructions and illustrative examples.

Ultra-High Toughness Cementitious Composite (UHTCC) represents a composite material meticulously engineered on the foundation of micromechanical principles. The multi-crack cracking and strain-hardening characteristics of UHTCC enable it to be applied to orthotropic steel decks to control the crack width. Different from most studies which only focus on hybrid fiber or fatigue characteristics, this paper studies the influence of hybrid fiber content on static mechanical properties, flexural toughness, and flexural fatigue characteristics of UHTCC under different stress levels. The compressive and flexural strength, bending toughness, and fatigue damage of UHTCC under different fiber ratios were compared, and the fatigue properties of hybrid fiber UHTCC were verified. The results reveal that hybrid fiber exerts a more pronounced effect on toughness, augmenting the maximum folding ratio by 23.7%. Single-doped steel fiber UHTCC exhibits a characteristic strain-softening phenomenon attributable to inadequate fiber content, whereas the bending toughness index of hybrid fiber UHTCC surpasses that of SF1.5P0 by 18.6%. Under low-stress conditions, UHTCC demonstrates a nearly threefold increase in bending fatigue life with a mere 1% steel fiber content, while the influence of polyvinyl alcohol (PVA) fiber on fatigue life is more significant: with an increase of only 1/5 volume content, the fatigue life increased by 29.8%, reaching a maximum increase of 43.2% at 1/4 volume content. Furthermore, the fatigue damage accumulation curve of UHTCC follows a three-stage inverted S-shaped trajectory. The inclusion of PVA fiber facilitates early initiation of stable cracking during the fatigue failure process, thereby advancing the entire strain stability development stage and mitigating external load forces through the proliferation of micro-cracks. Consequently, compared to SF1P0, the ε 0 of SF1P5 experiences a significant increase, reaching 143.43%.

Lane detection under extreme conditions presents a highly challenging task that requires capturing each crucial pixel to predict the complex topology of lane lines and differentiate the various lane types. Existing methods predominantly rely on deep feature extraction networks with substantial parameters or the fusion of multiple prediction modules, resulting in large model sizes, embedding difficulties, and slow detection speeds. This article proposes a Proportional Feature Pyramid Network (P-FPN) through fusing the weights into the FPN for lane detection. For obtaining a more accurately detecting result, the cross refinement block is introduced in the P-FPN network. The cross refinement block takes the feature maps and anchors as inputs and gradually refines the anchors from high to low level feature maps. In our method, the high-level features are explored to predict lanes coarsely while local-detailed features are leveraged to improve localization accuracy. Extensive experiments on two widely used lane detection datasets, The Chinese Urban Scene Benchmark for Lane Detection (CULane) and the TuSimple Lane Detection Challenge (TuSimple) datasets, demonstrate that the proposed method achieves competitive results compared with several state-of-the-art approaches., Competing Interests: The authors declare there are no competing interests., (©2024 Hui et al.)