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As the water source for the middle route of the South-to-North Water Transfer Project, the Han River in China plays a role of the world’s largest inter-basin water transfer project. However, this human-interfered area has suffered from over-standard pollution emission and water blooms in recent years, which necessitates urgent awareness at both national and provincial scales. To perform a comprehensive analysis of the water quality condition of this study area, we apply both the water quality index (WQI) and minimal WQI (WQI min ) methods to investigate the spatiotemporal variation characteristics of water quality. The results show that 8 parameters consisting of permanganate index (PI), chemical oxygen demand (COD), total phosphorus (TP), fluoride (F-), arsenic (As), plumbum (Pb), copper (Cu), and zinc (Zn) have significant discrepancy in spatial scales, and the study basin also has a seasonal variation pattern with the lowest WQI values in summer and autumn. Moreover, compared to the traditional WQI, the WQI min model, with the assistance of stepwise linear regression analysis, could exhibit more accurate explanation with the coefficient of determination (R 2 ) and percentage error (PE) values being 0.895 and 5.515%, respectively. The proposed framework is of great importance to improve the spatiotemporal recognition of water quality patterns and further helps develop efficient water management strategies at a reduced cost.

Developing active-layer systems with both high performance and mechanical robustness is a crucial step towards achieving future commercialization of flexible and stretchable organic solar cells (OSCs). Herein, we design and synthesize a series of acceptors BTA-C6, BTA-E3, BTA-E6, and BTA-E9, featuring the side chains of hexyl, and 3, 6, and 9 carbon-chain with ethyl ester end groups respectively. Benefiting from suitable phase separation and vertical phase distribution, the PM6:BTA-E3-based OSCs processed by o -xylene exhibit lower energy loss and improved charge transport characteristic and achieve a power conversion efficiency of 19.92% (certified 19.57%), which stands as the highest recorded value in binary OSCs processed by green solvents. Moreover, due to the additional hydrogen-bonding provided by ethyl ester side chain, the PM6:BTA-E3-based active-layer systems achieve enhanced stretchability and thermal stability. Our work reveals the significance of dynamic hydrogen-bonding in improving the photovoltaic performance, mechanical robustness, and morphological stability of OSCs. Developing high-performance and mechanically robust active-layer systems is crucial to commercializing flexible organic solar cells. Here, authors design small molecule acceptors with ethyl ester side chains and achieve certified efficiency of over 19% for mechanically robust devices.

Marine activities occupy an important position in human society. The accurate classification of ships is an effective monitoring method. However, traditional image classification has the problem of low classification accuracy, and the corresponding ship dataset also has the problem of long-tail distribution. Aimed at solving these problems, this paper proposes a fine-grained classification method of optical remote sensing ship images based on deep convolution neural network. We use three-level images to extract three-level features for classification. The first-level image is the original image as an auxiliary. The specific position of the ship in the original image is located by the gradient-weighted class activation mapping. The target-level image as the second-level image is obtained by threshold processing the class activation map. The third-level image is the midship position image extracted from the target image. Then we add self-calibrated convolutions to the feature extraction network to enrich the output features. Finally, the class imbalance is solved by reweighting the class-balanced loss function. Experimental results show that we can achieve accuracies of 92.81%, 93.54% and 93.97%, respectively, after applying the proposed method on different datasets. Compared with other classification methods, this method has a higher accuracy in optical aerospace remote sensing ship classification.

The stability of rock pillar is crucial for ensuring the construction safety of twin tunnels with small clearance, especially when transitioning from the traditional left–right tunnel layouts to the up-down configurations due to complex and variable site constraints. However, there are limited researches on the evaluation and comparative study of the stability of these two types of rock pillars in twin tunnels. This paper introduces the yield approach index (YAI) as a measure to assess the stability of rock pillar in twin tunnels with small clearance, and various influencing factors including side pressure coefficient (SPC), stress release rate (SRR), and the thickness of rock pillar (characterised by the ratio of rock pillar thickness to tunnel diameter, RPT/TD) are considered in the analysis. The study compares and analyzes the stability differences of the rock pillar in different situations. It is observed that the two sides of up-down tunnels pose a higher risk while the rock pillar in the left–right configuration being the most vulnerable. The stability of the rock pillar between the up-down tunnels is significantly higher than that of the left–right tunnels under similar conditions. Moreover, the up-down tunnels exhibit greater sensitivity to SPC, whereas the left–right tunnels are more sensitive to SRR. Additionally, the study reveals that increasing the RPT/TD can effectively improve the stability of the rock pillar within a specific range (1/4 to 2/3). The research method and obtained results of this paper can provide some important references for the stability evaluation and design of twin tunnels with small clearance.

It is widely recognized that benthic sediment plumes generated by deep-sea mining may pose significant potential risks to ecosystems, yet their dispersion behavior remains difficult to predict with accuracy. In this study, we combined laboratory experiments with three-dimensional numerical simulations using the Environmental Fluid Dynamics Code (EFDC) to investigate the dispersion of sediment plumes composed of particles of different sizes. Laboratory experiments were conducted with deep-sea clay samples from the western Pacific under varying conditions for plume dispersion. Experimental data were used to capture horizontal diffusion and vertical entrainment through a Gaussian plume model, and the results served for parameter calibration in large-scale plume simulations. The results show that ambient current velocity and discharge height are the primary factors regulating plume dispersion distance, particularly for fine particles, while discharge rate and sediment concentration mainly control plume duration and the extent of dispersion in the horizontal direction. Although the duration of a single-source release is short, continuous mining activities may sustain broad dispersion and result in thicker sediment deposits, thereby intensifying ecological risks. This study provides the first comprehensive numerical assessment of deep-sea mining plumes across a range of particle sizes with clay from the western Pacific. The findings establish a mechanistic framework for predicting plume behavior under different operational scenarios and contribute to defining threshold values for discharge-induced plumes based on scientific evidence. By integrating experimental, theoretical, and numerical approaches, this work offers quantitative thresholds that can inform environmentally responsible strategies for deep-sea resource exploitation.

With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure generation process is initially employed to transform RF fingerprint features from time-domain waveforms to two-dimensional figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A universal software radio peripheral receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95% identification accuracy in a real environment.

Solitary waves are unique in nonlinear systems, but their formation and propagation in the nonlinear fluid-structure interactions have yet to be further explored. As a typical nonlinear system, the buckling of solid thin films is fundamentally related to the film-substrate interface that is further vulnerable to environments, especially when fluids exist. In this work, we report an anomalous, solitary-wave-like blister (SWLB) mode of MoS 2 thin films in a humid environment. Unlike the most common telephone-cord and web buckling deformation, the SWLB propagates forward like solitary waves that usually appear in fluids and exhibits three-dimensional expansions of the profiles during propagation. In situ mechanical, optical, and topology measurements verify the existence of an interfacial water nanolayer, which facilitates a delamination of films at the front side of the SWLB and a readhesion at the tail side owing to the water nanolayer-induced fluid-structure interaction. Furthermore, the expansion morphologies and process of the SWLB are predicted by our theoretical model based on the energy change of buckle propagation. Our work not only demonstrates the emerging SWLB mode in a solid material but also sheds light on the significance of interfacial water nanolayers to structural deformation and functional applications of thin films. ‘Solitary waves are unique in nonlinear systems. Here, the authors report on an anomalous, solitary wave-like blister (SWLB) of MoS2 thin films, which propagates forward like solitary waves appearing in fluids. The SWLB results from fluid structure interaction due to an interfacial water nanolayer.’

Background The appropriateness of hypertension thresholds for triggering action to prevent cardiovascular and renal complications among non-White populations in the UK is subject to question. Our objective was to establish ethnicity-specific systolic blood pressure (SBP) cutoffs for ethnic minority populations and assess the efficacy of these ethnicity-specific cutoffs in predicting adverse outcomes. Methods We analyzed data from UK Biobank, which included 444,418 participants from White, South Asian, Black Caribbean, and Black African populations with no history of cardiorenal complications. We fitted Poisson regression models with continuous SBP and ethnic groups, using Whites as the referent category, for the composite outcome of atherosclerotic cardiovascular disease, heart failure, and chronic kidney disease. We determined ethnicity-specific thresholds equivalent to the risks observed in Whites at SBP levels of 120, 130, and 140 mm Hg. We adjusted models for clinical characteristics, sociodemographic factors, and behavioral factors. The performance of ethnicity-specific thresholds for predicting adverse outcomes and associated population-attributable fraction (PAF) was assessed in ethnic minority groups. Results After a median follow-up of 12.5 years (interquartile range, 11.7–13.2), 32,662 (7.4%) participants had incident composite outcomes. At any given SBP, the predicted incidence rate of the composite outcome was the highest for South Asians, followed by White, Black Caribbean, and Black African. For an equivalent risk of outcomes observed in the White population at an SBP level of 140 mm Hg, the SBP threshold was lower for South Asians (123 mm Hg) and higher for Black Caribbean (156 mm Hg) and Black African (165 mm Hg). Furthermore, hypertension defined by ethnicity-specific thresholds was a stronger predictor and resulted in a larger PAF for composite outcomes in South Asians (21.5% [95% CI, 2.4,36.9] vs. 11.3% [95% CI, 2.6,19.1]) and Black Africans (7.1% [95% CI, 0.2,14.0] vs. 5.7 [95% CI, -16.2,23.5]) compared to hypertension defined by guideline-recommended thresholds. Conclusions Guideline-recommended blood pressure thresholds may overestimate risks for the Black population and underestimate risks for South Asians. Using ethnicity-specific SBP thresholds may improve risk estimation and optimize hypertension management toward the goal of eliminating ethnic disparities in cardiorenal complications.

Due to the limited space in urban area, subway tunnels have to be designed and constructed with marginal clearance between each other. In the construction of subway tunnels, the installation and dismantling of temporary lining prone to affect the existing subway and new subway. In this paper, based on the Nijiaqiao Station Project in Chengdu Metro, stress state and deformation of the existing subway and the new subway, induced by dismantling of temporary lining, are compared and analyzed. It is found that different installation sequence of lining and dismantling temporary lining will affect the deformation of the subway, especially in the floor. The deformation and stress conditions of the structure are the least affected when all secondary linings are constructed in the first place and then all temporary linings are dismantled; and condition II is the worst and leads to excessive deformation and stress. Based on the analysis of the construction efficiency and influence of dismantling temporary lining, it is suggested that the combination form of divisional lining and dismantling temporary lining (i.e., condition III) can be adopted for the construction of closely-undercrossing subway. Finally, it is found that the field monitoring results are basically consistent with the simulation results, which verifies the validity of the numerical simulation. The results obtained in this paper can provide some vital references for the design and construction of similar projects (i.e., closely-undercrossing cases) in the future.

Developing materials with ultrahigh thermal conductivity is crucial for thermal management and energy conversion. The recent development of generative models and machine learning (ML) holds great promise for predicting new functional materials. However, these data-driven methods are not tailored to identifying energetically stable structures and accurately predicting their thermal properties, as they lack physical constraints and information about the complexity of atomic many-body interactions. Here, we show how combining deep generative models of crystal structures with quantum-accurate, fast ML interatomic potentials can accelerate the prediction of materials with ultrahigh lattice thermal conductivity while ensuring energy optimality. We exploit structural symmetry and similarity metrics derived from atomic coordination environments to enable fast exploration of the structural space produced by the generative model. Additionally, we propose an active-learning-based protocol for the on-the-fly training of ML potentials to achieve high-fidelity predictions of stability and lattice thermal conductivity in prospective materials. Applying this method to carbon materials, we screen 100,000 candidates and identify 34 carbon polymorphs, approximately a quarter of which had not been previously predicted, to have lattice thermal conductivity above 800 W m −1 K −1 , reaching up to 2,400 W m −1 K −1 aside from diamond. These findings provide a viable pathway toward the ML-assisted prediction of periodic materials with exceptional thermal properties.