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The physical layer secret key generation exploiting wireless channel reciprocity has demonstrated its viability and effectiveness in various wireless scenarios, such as the Internet of Things (IoT) network, mobile communication network, and industrial control system. Most of the existing studies rely on the quantization technique to convert channel measurements into secret bits for confidential communications. However, non-simultaneous packet exchanges in time-division duplex systems and noise effects usually induce inconsistent quantization results and mismatched secret bits. Although recent research has spent significant effort mitigating such non-reciprocity, it is still far from practical error-free key generation. Unlike previous quantization-based approaches, we take a different viewpoint to match the randomly manipulated (i.e., permuted or edited) channel measurements between a pair of users by minimizing their discrepancy holistically. Specifically, two novel secret key generation algorithms based on bipartite graph matching (BM-SKG) and edited sequence alignment (SA-SKG) are developed. BM-SKG allows two users to generate the same secret key based on the permutation order of channel measurements, while SA-SKG aims to align the edited channel measurements between a pair of users for secret key agreement. In both algorithms, one user can preset the secret key and embed encrypted messages in the exchanged data packets, which reduces communication overheads in key generation. Extensive experimental results show that both BM-SKG and SA-SKG algorithms achieve error-free key agreement on channel measurements at a low cost under various scenarios.

A graph neural network (GNN) is a powerful architecture for semi-supervised learning (SSL). However, the data-driven mode of GNNs raises some challenging problems. In particular, these models suffer from the limitations of incomplete attribute learning, insufficient structure capture, and the inability to distinguish between node attribute and graph structure, especially on label-scarce or attribute-missing data. In this article, we propose a novel framework, called graph coneighbor neural network (GCoNN), for node classification. It is composed of two modules: GCoNN $_{\Gamma }$ and GCoNN $_{\mathop {\Gamma }\limits ^{\circ }}$ . GCoNN $_{\Gamma }$ is trained to establish the fundamental prototype for attribute learning on labeled data, while GCoNN $_{ {\mathop {\Gamma }\limits ^{\circ }}}$ learns neighbor dependence on transductive data through pseudolabels generated by GCoNN $_{\Gamma }$ . Next, GCoNN $_{\Gamma }$ is retrained to improve integration of node attribute and neighbor structure through feedback from GCoNN $_{ {\mathop {\Gamma }\limits ^{\circ }}}$ . GCoNN tends to convergence iteratively using such an approach. From a theoretical perspective, we analyze this iteration process from a generalized expectation–maximization (GEM) framework perspective which optimizes an evidence lower bound (ELBO) by amortized variational inference. Empirical evidence demonstrates that the state-of-the-art performance of the proposed approach outperforms other methods. We also apply GCoNN to brain functional networks, the results of which reveal response features across the brain which are physiologically plausible with respect to known language and visual functions.

Kidneys are intricate three-dimensional structures in the body, yet the spatial and molecular principles of kidney health and disease remain inadequately understood. We generated high-quality datasets for 81 samples, including single-cell, single-nuclear, spot-level (Visium) and single-cell resolution (CosMx) spatial-RNA expression and single-nuclear open chromatin, capturing cells from healthy, diabetic and hypertensive diseased human kidneys. Combining these data, we identify cell types and map them to their locations within the tissue. Unbiased deconvolution of the spatial data identifies the following four distinct microenvironments: glomerular, immune, tubule and fibrotic. We describe the complex organization of microenvironments in health and disease and find that the fibrotic microenvironment is able to molecularly classify human kidneys and offers an improved prognosis compared to traditional histopathology. We provide a comprehensive spatially resolved molecular roadmap of the human kidney and the fibrotic process, demonstrating the clinical utility of spatial transcriptomics.

Here, a nonhydrostatic alternative scheme (NAS) is proposed for the grey zone where the nonhydrostatic impact on the atmosphere is evident but not large enough to justify the necessity to include an implicit nonhydrostatic solver in an atmospheric dynamical core. The NAS is designed to replace this solver, which can be incorporated into any hydrostatic models so that existing well-developed hydrostatic models can effectively serve for a longer time. Recent advances in machine learning (ML) provide a potential tool for capturing the main complicated nonlinear-nonhydrostatic relationship. In this study, an ML approach called a neural network (NN) was adopted to select leading input features and develop the NAS. The NNs were trained and evaluated with 12-day simulation results of dry baroclinic-wave tests by the Weather Research and Forecasting (WRF) model. The forward time difference of the nonhydrostatic tendency was used as the target variable, and the five selected features were the nonhydrostatic tendency at the last time step, and four hydrostatic variables at the current step including geopotential height, pressure in two different forms, and potential temperature, respectively. Finally, a practical NAS was developed with these features and trained layer by layer at a 20-km horizontal resolution, which can accurately reproduce the temporal variation and vertical distribution of the nonhydrostatic tendency. Corrected by the NN-based NAS, the improved hydrostatic solver at different horizontal resolutions can run stably for at least one month and effectively reduce most of the nonhydrostatic errors in terms of system bias, anomaly root-mean-square error, and the error of the wave spatial pattern, which proves the feasibility and superiority of this scheme.

Blockchain-enabled Internet of Vehicles (BloVs) provides a reliable collaboration environment for traffic entities and promotes road safety and traffic efficiency through real-time sharings between vehicles and infrastructure. However, a bottleneck is likely to emerge as the Internet of Vehicles scales up. In this paper, we propose a novel Graphical Consensus-based Sharding (GCS) framework, which is underpinned by four important strategies: 1) A graphical consensus is adopted as its intra-shard consensus, where the consensus group is set up according to its maximal connected subgraph, and the leader is elected by its reliability weight. The consensus group is refreshed intermittently by alternating the leader role. 2) Within GCS, the intra-shard data are stored in the local chain, while a block-based directed acyclic graph, rather than a chained structure, is employed as the main chain. The local chain is used to respond to requests within each shard, and the main chain supports the cross-shard sharings. GCS will parallelly optimize the throughput of the blockchain-enabled Internet of Vehicles. 3) GCS further introduces the shard backup and the node scheduling to handle shard failure and overheating by using new backup strategies and a temporal-spatial graph convolutional network prediction model, respectively. 4) An off-chain transmission algorithm is presented for secure sharing between the infrastructure and the vehicles. Simulation results show that the number of Transactions Per Second is 1.69 times higher than that of the non-sharding blockchain, and the pending time is dramatically reduced compared to the mainstreaming sharding approach, which is 1.02 s.

The two-factor authentication (2FA) has drawn increasingly attention as the mobile devices become more prevalent. For example, the user's possession of the enrolled phone could be used by the 2FA system as the second proof to protect his/her online accounts. Existing 2FA solutions mainly require some form of user-device interaction, which may severely affect user experience and creates extra burdens to users. In this article, we propose a secure 2FA system utilizing the proximity of a user's enrolled phone and the login device as the second proof without requiring the user's interactions. The basic idea of our 2FA system is to derive location signatures based on acoustic beep signals emitted alternately by both devices and sensing the echoes with microphones, and compare the extracted signatures for proximity detection. Moreover, to further enhance the security of our system, we also design a device authentication scheme which derives the acoustic fingerprint between the login device and enrolled phone to verify the identity of two devices. Given the received beep signal, our system designs a period selection scheme to identify two sound segments accurately: the chirp period is the sound segment propagating directly from the speaker to the microphone whereas the echo period is the sound segment reflected back by surrounding objects. To achieve an accurate proximity detection, we develop a new energy loss compensation extraction scheme by utilizing the extracted chirp periods to estimate the intrinsic differences of energy loss between microphones of the enrolled phone and the login device. Our proximity detection component then conducts the similarity comparison between the identified two echo periods after the energy loss compensation to effectively determine whether the enrolled phone and the login device are in proximity for 2FA. Moreover, to provide higher security, our device fingerprint-assisted proximity detection further utilizes the overall energy loss between the login device and enrolled phone as their hardware fingerprint to authenticate the identity of two devices. Our experimental results show that our system is accurate in providing 2FA and robust to both man-in-the-middle (MiM) and co-located attacks across different scenarios and device models.

ABSTRACT The cracking behaviour of cement-based materials subjected to freeze-thaw cycles significantly affects the engineering properties of the concrete materials and structures. The expansion of the crack width depends on [...]

ABSTRACTThe post-fire mechanical performance of aluminum alloys is a key parameter for residual structural behavior evaluation, repair and reinforcement design of aluminum alloy structures. In this paper, the post-fire residual [...]

ABSTRACTThe hotel industry is vulnerable to various external crises, such as the recent COVID-19 pandemic. Social media is one of the primary platforms for hotel crisis communication. Accordingly, this study adopted the perspective of message appeal and tried to develop effective hotel crisis communication messages. An online experiment was conducted where 260 Chinese customers were included. The results showed that emotional-appeal messages are more effective in attracting customers for luxury hotels, while functional-appeal messages are more suitable for economic hotels. The results also showed that perceived safety mediated the relationship between the message appeal and booking intentions and that this mediating relationship is moderated by the hotel type. This study further discussed theoretical and practical implications.

In ground-penetrating radar (GPR) data, clutter and noise are commonly observed in B-scan images, which can seriously affect the interpretability of the GPR data. In this article, we propose wavelet-GAN, a deep-learning network that integrates generative adversarial network (GAN) and discrete wavelet transform (DWT). Wavelet-GAN could decompose the GPR image into multiple frequency subimages and remove clutter. Additionally, we solve the problem of error handling when there are no features in the dataset through micro datasets, dataset fine-tuning, high-speed training, and multiple feature generalization. Our method decomposes GPR image by DWT, then convolutional neural network (CNN) and GAN are, respectively, used to reconstruct low-frequency and high-frequency target signal information. Finally, the information of different frequency bands is combined into a new GPR image by inverse DWT (IDWT). Wavelet-GAN uses a small-scale dataset for training, which enables it to make rapid adjustments to process new target types, even if we only have one typical target data. We compare our method with traditional methods and other deep-learning based methods and demonstrate that our wavelet-GAN performs better in real data processing. Finally, we apply this method as a data-preprocessing tool for machine learning inversion and tested its feasibility.

The indoor location awareness has drawn increasing attention as the mobile apps are used extensively in our daily lives. Existing indoor localization solutions either require a pre-installed infrastructure or can only achieve room-level accuracy, which could not provide a function-location service for mobile devices. In this work, we propose a new active sensing system that enables smartphones to identify some pre-defined indoor locations robustly without requiring any additional sensors or pre-installed infrastructure. The main idea behind our system is to utilize the acoustic signatures, which are derived from the mobile device by emitting a beep signal and selecting its echoes created by dominant reflectors, as the robust fingerprint for location identification. Given the microphone samplings, our system designs a correlation based technique to accurately detect the beginning points of echoes from the received beep signal. To achieve a robust location identification, we develop a new echo selection scheme to select echoes created by dominant reflectors by exploiting the relationships between propagation delays of different orders of echoes. To deal with the variable number of selected echoes, our location identification component then derives histograms from selected echoes and uses the one-against-all SVM classifiers to determine the current location. Our experimental results show that our proposed system is accurate and robust for location identification under various real-world scenarios.

Wood–plastic composites are prepared by melting and mixing wood flour (WF) and thermoplastics, which have green and biodegradable advantages and are of great significance in the development of the era of global resource scarcity. In this work, recycled polypropylene and WF were used as raw materials and commercially available maleic anhydride–grafted polypropylene (MAH-g-PP) was used as a compatibilizer. The extrusion injection-molding method was used and investigated by varying the amounts of WF and compatibilizer added. The composites were analyzed for micromorphology, melt mass flow rate, mechanical properties and thermal stability by scanning electron microscopy, using a melt flow rate tester, using an electronic universal testing machine and thermogravimetric analysis, respectively. According to the results, WF shows a positive effect in terms of the storage and loss moduli of the composites, but it shows a negative effect in terms of thermal stability and mechanical properties. As an effective compatibilizer, MAH-g-PP plays an important role in improving the mechanical properties of the composites, particularly at an additional level of 4 wt%, which maximizes the overall performance of the composites.

Aiming at the high-performance requirements of Internet of Things (IoT) scenarios, a cognitive radio-inspired nonorthogonal multiple access (CR-NOMA) scheme in short packet communication is investigated. Primary and secondary users share the same nonorthogonal communication resource block to achieve high spectral efficiency. A priority selection combination method is designed and employed by the primary user considering the nonnegligible decoding error rate in short packet communication. We focus on the transmission performance of the secondary users while ensuring the reliable communication of the primary user as a priority. To characterize the performance of the CR-NOMA network, approximate closed-form expressions of the average block error rate (BLER) for primary and secondary users are derived and analyzed. The results show that the communication reliability and throughput of the primary user with the CR-NOMA scheme are enhanced compared with the no-CR scheme. Meanwhile, the secondary users can also achieve reliable communication on the premise of improving the performance of the primary user through spectrum sharing. A performance tradeoff between primary and secondary users can be achieved through power allocation. Furthermore, there is a tradeoff between BLER and throughput, and the maximum throughput can be achieved by choosing an optimal blocklength.

Objectives: To investigate acute ischemic stroke (AIS) patients with systemic lupus erythematosus (SLE) clinical and imaging features, and to explore the impact of SLE on the short-term prognosis of AIS patients. Methods: A nested case–control study was conducted in The First Affiliated Hospital of Zhengzhou University between October 1, 2019, and May 31, 2021. The case group consisted of 28 AIS patients diagnosed with SLE, and 112 AIS patients without SLE were selected by incidence density sampling as a control group. Results: Among 140 patients, the mean age was (48.4 ± 15.6) years, and 130 (92.9%) were females. Higher levels in low-density lipoprotein cholesterol (LDL-C) (2.5 mmol/L vs. 2.1 mmol/L; P= 0.049), D-dimer (DDi) (0.7 mg/L vs. 0.3 mg/L; P= 0.02), and C-reactive protein (CRP) (13.2 mg/L vs. 6.3 mg/L; P= 0.002) were observed in the case group. On imaging, the case group was more prevalent in simultaneous involvement of the anterior and posterior circulation (42.9% vs. 17.0%; P= 0.004), multiple infarcts (46.4% vs. 20.5%; P= 0.008) and stroke of other undetermined etiologies (SUE) (28.6% vs. 6.3%; P= 0.002) than the control group. SLE (OR 5.94, 95%CI [1.04–34.39]; P= 0.045) was an independent risk factor for a poor short-term prognosis of AIS patients. Conclusions: Higher levels of LDL-C, CRP, and DDi, multiple infarcts and simultaneous involvement of the anterior, and posterior circulation were more prevalent in the AIS patients with SLE. Further, SLE was also found as an independent risk factor for AIS patients’ poor short-term prognosis.

One main challenge for simplifying node-link diagrams of large-scale social networks lies in that simplified graphs generally contain dense subgroups or cohesive subgraphs. Graph triangles quantify the solid and stable relationships that maintain cohesive subgraphs. Understanding the mechanism of triangles within cohesive subgraphs contributes to illuminating patterns of connections within social networks. However, prior works can hardly handle and visualize triangles in cohesive subgraphs. In this paper, we propose a triangle-based graph simplification approach that can filter and visualize cohesive subgraphs by leveraging a triangle-connectivity called k-truss and a force-directed algorithm. We design and implement TriGraph, a web-based visual interface that provides detailed information for exploring and analyzing social networks. Quantitative comparisons with existing methods, two case studies on real-world datasets, and feedback from domain experts demonstrate the effectiveness of TriGraph.

The growth rate of pulmonary nodules is a critical clue to the cancerous diagnosis. It is essential to monitor their dynamic progressions during pulmonary nodule management. To facilitate the prosperity of research on nodule growth prediction, we organized and published a temporal dataset called NLSTt with consecutive computed tomography (CT) scans. Based on the self-built dataset, we develop a visual learner to predict the growth for the following CT scan qualitatively and further propose a model to predict the growth rate of pulmonary nodules quantitatively, so that better diagnosis can be achieved with the help of our predicted results. To this end, in this work, we propose a parameterized Gempertz-guided morphological autoencoder (GM-AE) to generate any future-time-span high-quality visual appearances of pulmonary nodules from the baseline CT scan. Specifically, we parameterize a popular mathematical model for tumor growth kinetics, Gompertz, to predict future masses and volumes of pulmonary nodules. Then, we exploit the expected growth rate on the mass and volume to guide decoders generating future shape and texture of pulmonary nodules. We introduce two branches in an autoencoder to encourage shape-aware and textural-aware representation learning and integrate the generated shape into the textural-aware branch to simulate the future morphology of pulmonary nodules. We conduct extensive experiments on the self-built NLSTt dataset to demonstrate the superiority of our GM-AE to its competitive counterparts. Experiment results also reveal the learnable Gompertz function enjoys promising descriptive power in accounting for inter-subject variability of the growth rate for pulmonary nodules. Besides, we evaluate our GM-AE model on an in-house dataset to validate its generalizability and practicality. We make its code publicly available along with the published NLSTt dataset.

•We describe a strain of S. Typhimurium resistant to 28 antibiotics.•The strain carried 54 AMR genes, and blaOKP-B-10was found in Salmonellafirst.•The strain carried two novel plasmids.•pSH11G993-1 carried 14 AMR genes, including blaCMY-2, mphA, and ermB.•pSH11G993-2 endowed the strain virulence and resistance.

Short-packet communication is an important feature of the Internet of Things (IoT). For allowing massive IoT devices to connect to a network through short packets, this paper designs a cognitive radio-inspired non-orthogonal multiple access (NOMA) scheme, where the IoT users will not necessarily occupy an exclusive spectrum. To further improve the connections in an IoT network, we propose a general NOMA scheme that serves multiple IoT users. Since the IoT devices are generally homogeneous, a user fairness-oriented power allocation scheme is designed for the proposed NOMA scheme. Besides, for comparison purposes, the benchmark orthogonal multiple access (OMA) scheme and its corresponding user fairness transmission scheme are also proposed. The closed-form expressions of average block error rates for secondary users are derived over Nakagami-$m$ channels in both NOMA and benchmark OMA scheme. Simulation and analysis results show that a faraway primary network will benefit the spectrum sharing. In addition, shorter packets and more different channel conditions improve the NOMA gains over OMA. Besides, power allocation factors are found to be capable of achieving user fairness and the fairness transmission is achieved by the proposed scheme. Interestingly, when the channel differences are bigger enough, paring an extra NOMA user nearly does not affect the previous system performance.

ABSTRACT Cable stress relaxation causes tension loss at high stress in high-strength structural cables, thereby influencing the stiffness and strength of cable-supported or tension structures. Previous studies have mainly focused [...]

ABSTRACT With excellent corrosion resistance and appearance, stainless steels have been widely used in building structures. Although fire accidents in steel buildings have happened occasionally in recent years, only few [...]

The traditional structural design process of rural buildings requires a lot of trial calculations and iterations of modelling work. However, rural buildings are subject to construction costs and cannot be professionally designed and checked by designers like urban buildings. Their safety and economy are difficult to meet the requirements. Therefore, an intelligent design concept of rural light steel frame structure was proposed, including intelligent modeling and intelligent optimization. Based on the automatic layer recognition method and optical character recognition technology, the BIM intelligent modeling method of rural light steel frame structure corresponding to the standard atlas of rural buildings was proposed, including layer recognition, axis text data extraction, primary selection and automatic arrangement of components. The intelligent modeling results basically met the requirements of practical application. Based on the proposed two-stage genetic algorithm, an intelligent optimization method of rural light steel frame structure was given. The optimization speed was fast and the optimization effect was good. The proposed intelligent design method was verified by practical case. The results showed that the proposed intelligent design concept of rural light steel frame structure was feasible. Compared with the traditional manual design method, the design period could be shortened by more than 80%, and the structural design indexes were comparable.

As the core component of the More Electric Aircraft (MEA) hydraulic system, the partial energy Electric Pump System (EPS) determines the performance of the hydraulic servo device. In this paper, a three-phase Fault-Tolerant Permanent Magnet Motor (FTPMM) for the partial energy EPS is proposed. The three-phase FTPMM drive system for the partial energy EPS can replace the traditional engine pump source hydraulic system, which can save costs, reduce weight and volume, and improve efficiency. Moreover, it can solve the problem of high reliability application of the motor drive system in aeroengine. The slot-pole combinations and centrifugal height are designed and analyzed for the proposed three-phase FTPMM, and a prototype model was produced based on this. Finally, the basic electromagnetic performance and fault-tolerant performance of three-phase FTPMM are verified through simulation and experiment. The results show that the proposed three-phase FTPMM has good fault isolation and suppression capabilities under winding fault.

Austenitic S30408 stainless steel exhibits good low-temperature resistance and good welding performance. This steel is often used in liquefied natural gas stainless steel storage tanks. During the construction process, the tank wall is primarily connected by butt weld joints. Because welded joints are easily affected by temperature, low-temperature weld cracking can reduce the safety of structures. To study the cryogenic mechanical properties of austenitic S30408 stainless steel welded joints at low temperatures, the low-temperature mechanical properties of austenitic S30408 stainless steel base metal and welded joint components were studied by tensile tests from − 60 to 20 °C and scanning electron microscopy analysis of fractures at various temperatures. The results show that when the temperature decreases, the stress–strain curve of base metal components changes from a power function type to an inverted "s" type; in addition, secondary hardening occurs. The yield strength and tensile strength of the welded joint and base metal increased with decreasing temperature, and the elongation and reduction of area decreased. The plastic deformation capacity of the welded joint was significantly lower than that of the base metal, and there were obvious inclusions in the microstructure.

To address the high-performance requirements of Internet of Things (IoT) application scenarios, we design a non-orthogonal multiple access (NOMA) assisted cognitive short packet communication system in this paper, in which the primary user is a mobile node and the secondary network shares the spectrum of primary network for transmission subject to transmit power constraints. To characterize the performance of the proposed system, the average block error rate (BLER) and throughput performance of the secondary users are derived and analyzed, and the theoretical results are verified using Monte Carlo simulations. To facilitate the performance improvement of network communication, the analysis and insights on the node's mobility and imperfect channel estimation are provided. The results show that both node's mobility and imperfect channel estimation result in the degradation of system performance. In addition, our examination shows that the increase in distance between primary and secondary networks indeed reduces interference and improves performance. Interestingly, we further find that shorter blocklength enhances the throughput performance for a long-distance scenario between primary transmitter and secondary users. In contrast, the opposite result is obtained for the short-distance scenario, which means that short packet communication is prominent in our proposed system.

One-step and two-step pathways are proposed to synthesize cytokinin in plants. The one-step pathway is mediated by LONELY GUY (LOG) proteins. However, the enzyme for the two-step pathway remains to be identified. Here, we show that quantitative trait locus GY3may boost grain yield by more than 20% through manipulating a two-step pathway. Locus GY3encodes a LOG protein that acts as a 5′-ribonucleotide phosphohydrolase by excessively consuming the cytokinin precursors, which contrasts with the activity of canonical LOG members as phosphoribohydrolases in a one-step pathway. The residue S41 of GY3 is crucial for the dephosphorylation of iPRMP to produce iPR. A solo-LTRinsertion within the promoter of GY3suppressed its expression and resulted in a higher content of active cytokinins in young panicles. Introgression of GY302428increased grain yield per plot by 7.4% to 16.3% in all investigated indicabackgrounds, which demonstrates the great value of GY302428in indicarice production.

ABSTRACT Low-yield point steels began to be widely used for energy dissipation in seismic design. Traditional aseismic designs often utilize section-cutting measurements to induce plasticity to be happened at designed [...]

The dominant frequency modes of pre-summer extreme precipitation events (EPEs) over South China (SC) between 1998 and 2018 were investigated. The 67 identified EPEs were all characterized by the 3–8-d (synoptic) frequency band. However, multiscale combined modes of the synoptic and three low-frequency bands [10–20-d (quasi-biweekly, QBW); 15–40-d (quasi-monthly, QM); and 20–60-d (intraseasonal)] accounted for the majority (63%) of the EPEs, and the precipitation intensity on the peak wet day was larger than that of the single synoptic mode. It was found that EPEs form within strong southwesterly anomalous flows characterized by either lower-level cyclonic circulation over SC or a deep trough over eastern China. Bandpass-filtered disturbances revealed the direct precipitating systems and their life cycles. Synoptic-scale disturbances are dominated by mid-high latitude troughs, and the cyclonic anomalies originate from downstream of the Tibetan Plateau (TP). Given the warm and moist climate state, synoptic-scale northeasterly flows can even induce EPEs. At the QBW and QM scales, the disturbances originate from the tropical Pacific, downstream of the TP, or mid-high latitudes (QBW only). Each is characterized by cyclonic-anticyclonic wave trains and intense southwesterly flows between them within a region of large horizontal pressure gradient. The intraseasonal disturbances are confined to tropical regions and influence SC by marginal southwesterly flows. It is concluded that low-frequency disturbances provide favorable background conditions for EPEs over SC and synoptic-scale disturbances ultimately induce EPEs on the peak wet days. Both should be simultaneously considered for EPE predictions over SC.

ABSTRACT Studies on bending properties of cables have long history but those researches are mainly around electrical conductors with very limit experimental discussions on structural cables. One approach on dynamic [...]

Concrete-filled steel plate composite walls are increasingly used in high-rise buildings; their mechanical properties at room temperature have produced fruitful results. However, research on their mechanical properties in fire and at high temperatures is scarce. Moreover, published studies on the fire resistance of concrete-filled steel plate composite walls have not considered the impact of fire protection measures. Based on previous fire resistance research, a fire resistance test was designed in this study for four walls under the combined action of axial compression and a single-sided fire under the ISO-834 standard heating curve. Apart from that, the existing fire protections have the disadvantages of too thick, easy falling off and cracking or difficult to install. In order to eliminate the disadvantages of existing fire prevention measures, new composite fireproof structure using a rock wool board and a thin fire-retardant coating is proposed. The effects of no fireproof structures, autoclaved lightweight concrete plate fireproof structures, thick-coated fire-retardant structures, and new composite fireproof structures on the fire resistance of concrete-filled steel plate composite walls were studied experimentally. The results show that the fire resistance of the four test specimens meets the 3 h fire resistance limit of the primary load-bearing component in the specification. Based on the experimental results, reasonable fire protection methods are suggested for concrete-filled steel plate composite walls in actual projects, and an experimental basis is provided for further numerical simulation.

During the tensioning process of the cable-supported structure, the sliding friction between cable wires and joint will have a great influence on the mechanical properties of the structure. However, due to the complex structure of the cable and the combined action of bending deformation and large pre-tension at the cable-strut joint, it is difficult to accurately determine the coefficient of sliding friction (CSF) between cable wires and joint, and thus the overall mechanical properties of the structure cannot be accurately evaluated. Based on the twisting characteristics of the cable, this paper establishes a friction-slip test model and a refined numerical model to carry out detailed study of the mechanical behavior of contact between cable wires and joint during the tensioning process. Firstly, combined with the calculation results of contact mechanics, it is finds that the stress distribution at straight and curved sections of the cable are non-uniform along its axis and each cross section, and the non-uniformity coefficient of the internal force distribution of the cable is further quantified. At the same time, the local bending of the cable will intensify the uneven stress distribution in the circumferential direction of the cable. The non-uniformity of stress reveals the contact working mechanism of the cable-joint pair. Then, the change of the prestress loss between cable wires and joint with the tensioning process is analyzed, and the parametric analysis is conducted to reveal the effect of the cable included angle, tensile force, and lay length on the CSF. The results find that the prestress loss increases approximately linearly with the increase of tension force and decreases with the increase of the cable included angle, but the CSF is less affected by the lay length during the tensioning process. Finally, a method for valuing the CSF of the cable-supported structure is developed.

ABSTRACT Although numerous FE models have been proposed to analyze the mechanical behavior of simple strands, they seldom examine the contact condition and lateral loading behavior that may influence local [...]

Predicting the future trajectories of pedestrians is of increasing importance for many applications such as autonomous driving and social robots. Nevertheless, current trajectory prediction models suffer from limitations such as lack of diversity in candidate trajectories, poor accuracy, and instability. In this paper, we propose a novel Sequence Entropy Energy-based Model named SEEM, which consists of a generator network and an energy network. Within SEEM we optimize the sequence entropy by taking advantage of the local variational inference of $f$f-divergence estimation to maximize the mutual information across the generator in order to cover all modes of the trajectory distribution, thereby ensuring SEEM achieves full diversity in candidate trajectory generation. Then, we introduce a probability distribution clipping mechanism to draw samples towards regions of high probability in the trajectory latent space, while our energy network determines which trajectory is most representative of the ground truth. This dual approach is our so-called all-then-one strategy. Finally, a zero-centered potential energy regularization is proposed to ensure stability and convergence of the training process. Through experiments on both synthetic and public benchmark datasets, SEEM is shown to substantially outperform the current state-of-the-art approaches in terms of diversity, accuracy and stability of pedestrian trajectory prediction.

Purpose: The propagation of elastic waves in porous media has always been an important research topic in the fields of geotechnical engineering, ocean engineering, and geophysics, but the energy problem of propagation at different media interfaces is relatively less involved. Methods: This article adopts theoretical derivation and parameter analysis methods to study the energy characteristics of reflection and transmission of SV waves at the interface of layered unsaturated soil. Firstly, a reflection and transmission model of planar SV waves at the interface of layered unsaturated soil was established. Using the Helmholtz vector decomposition principle, a theoretical expression for the amplitude ratio of SV waves at the interface was derived. Secondly, combined with the reflection amplitude ratio, various wave energy coefficients are further solved. Finally, the relationship between energy reflection/transmission coefficient and incident angle, soil saturation, and porosity was analyzed and discussed. Results: The results show that the amplitude and energy reflection/transmission coefficients are not only affected by the incident angle but varied significantly with the change of saturation and porosity. The energy carried by the incident SV wave mainly occupied by the reflected SV wave. Conclusions: This conclusion has certain guiding significance for the theoretical research of soil dynamics and related engineering exploration.

Retrons are a class of multigene antiphage defense systems typically consisting of a retron reverse transcriptase, a non-coding RNA, and a cognate effector. Although triggers for several retron systems have been discovered recently, the complete mechanism by which these systems detect invading phages and mediate defense remains unclear. Here, we focus on the retron Ec86 defense system, elucidating its modes of activation and mechanisms of action. We identified a phage-encoded DNA cytosine methyltransferase (Dcm) as a trigger of the Ec86 system and demonstrated that Ec86 is activated upon multicopy single-stranded DNA (msDNA) methylation. We further elucidated the structure of a tripartite retron Ec86-effector filament assembly that is primed for activation by Dcm and capable of hydrolyzing nicotinamide adenine dinucleotide (NAD+). These findings provide insights into the retron Ec86 defense mechanism and underscore an emerging theme of antiphage defense through supramolecular complex assemblies.

Aromatics are the preponderant component of fluid catalytic cracking (FCC) slurry oil, with notable differences in molecular structure between the light and heavy aromatics. The effect of these differences on the formation and optically anisotropic texture of mesophase remains elusive, impeding the preparation of high-quality mesophase pitch. This paper aims to regulate the ratio of light to heavy aromatics in the FCC slurry oil for optimizing the optically anisotropic texture of mesophase pitch. The results show that the heavy aromatics possess higher aromaticity, longer alkane side chains, larger molecular size and more reactive structures (methylene bridges, n-alkane structures) compared with the light aromatics. As an “accelerator”, heavy aromatics can form mesophase quickly. Light aromatics not only served as a “diluent,” but also assumed the function of a “lubricant”, which facilitated the orderly arrangement of aromatic molecular lamellar. The system exhibits moderate reactivity that inhibits excessive condensation and modifies the structural orientation of pyrolysis products with the 2/5 ratio of light to heavy aromatics. Under these conditions, a high-quality mesophase pitch (d002 = 3.485 Å, Lc = 19.832 Å) with wide-area optically anisotropic texture, suitable softening point (262 °C) and short polymerization time (t = 5.5 h) was obtained. This work provides theoretical support for the preparation of high-quality mesophase pitch by regulating the components of slurry oil.

Despite the dominated anode materials of graphite for lithium-ion batteries (LIBs), the poor rate capability and inferior cyclability restain their applications under extreme conditions due to their deficient interfacial chemistry. Herein, an ultrathin but Li2CO3-rich solid electrolyte interphase (SEI) is manipulated by the decomposition of ethylene carbonate (EC) on graphite anode with nitrogenous amorphous carbon coating (denoted as GMIB-2P). Specifically, Li2CO3can enable a decreased charge transfer resistance and faster Li+diffusion capability. In addition, we demonstrate the catalytic effect of pyrrolic-N on graphite surface with prominent wettability induces the rapid decomposition of EC with low reaction energy barrier through density function theory (DFT) calculation. Consequently, GMIB-2P delivers an outstanding capacity retention ratio of 90.82% after 300 cycles at 1 C and an improved initial Coulombic efficiency (ICE, 90.77% vs. 85.35%). More importantly, GMIB-2P possesses durable-cycling capability even with lean electrolytes of 5 μL mg-1and as well as 85.68% capacity retention for over 300 cycles at 60 ℃. Our work highlights the key role of EC in tailoring interfacial chemistry from structure regulations and provides new guidelines to design more reliable anodes for LIBs under extreme conditions.

With the rapid development of renewable energy, large amounts of power need to be transmitted to load centers, and series-capacitor compensation (SCC) plays an important role in renewable power transmission. However, it has been pointed out that SCC interacts with inverters and threatens system stability. This paper investigates the influence of SCC on inverter control, and proposes strategies for enhancing system stability based on the instability mechanism. First, the impacts of SCC on inverter current control and synchronization control are analyzed. A current control model is established by a system transfer function, and a synchronization control model focusing on transient stability is established based on the traditional synchronous reference frame phase-locked loop (SRF-PLL). Bode and nonlinear analysis methods are utilized in the stability analysis of both current control and synchronization control. It is found that SCC has little effect on inverter current control. However, it seriously affects synchronization control. SCC reduces the stability range of synchronization control, and causes system instability when there is a large frequency disturbance. In order to improve system stability, two approaches have been proposed. These approaches are optimizing a PI controller, and designing a band-pass filter (BPF) inside the PLL. Finally, simulations and experiments are presented to verify the correctness of theories.

ABSTRACTSolar energy is crucial to the transformation of the energy system, and it is important to promote ‘green’ energy technologies to achieve the 2015 UN Paris Agreement targets. This study takes a holistic view of consumers’ energy-related behaviour by identifying the factors driving the adoption of rooftop solar photovoltaic and by distinguishing the electricity conservation practices of solar and non-solar households. We reveal a nuanced understanding of the rebound effect from a behavioural perspective, which may help solar energy policy formulation and efficient decarbonization pathways in Australia and elsewhere. A face-to-face survey of 325 households in a regional city, principal component analysis and binary probit analysis were used to investigate the intention to install rooftop solar photovoltaic panels. The results show that home ownership, unwillingness to sacrifice personal comforts to save electricity, being pro-energy efficiency, and income are positively related to rooftop solar installation. In addition, different segments, such as the ‘energy-efficiency advocate’, the ‘digital tool lovers’ and the ‘behaviour change advocates’ were identified. Finally, recommendations for policy and practice are made to promote rooftop solar and more careful use of electricity in the residential sector.

Inspired by natural photosynthesis, photocatalytic NADH regeneration has drawn increasing interest in the recent decade as it provides a perfect approach for NAD+reduction into NADH, which can be further consumed by oxidordeuctase for enzymatic redox reactions. However, two issues still remain unsolved in this procedure. First, the photocatalytic efficiency in NAD+hydrogenation requires further improvement. Second, the rhodium electron mediator [Cp*Rh(bpy)H2O]2+(M), which is always required for selective 1,4-NADH regeneration, is difficult to recover because of its good solubility in aqueous solution. Given the high price of M, it is highly wasteful and inefficient if it only spends once. Here, we report a Cp*Rh(bpy)Cl implanted conjugated microporous polymer DTS/Rh@CMPs which can be employed as a highly effective visible light photocatalysts for in situ NADH regeneration without using additional M. In addition, the insertion of Rh complex into a polymer skeleton, as demonstrated in UV–vis, fluorescence, photocurrent and electrochemical impedance, dramatically improves the light absorption capacity and the electron separation and transfer efficiency. Compared with that of DTS@CMP-1 with M, an enhanced reaction yield of 33% was determined in DTS/Rh@CMP-1 suggesting that intramolecular electron transfer has a better activity than that of intermolecular electron transfer in photocatalytic NAD+reduction. Moreover, as the Rh complex is rooted firmly in a polymer framework, negligible Rh loss and conversion decrease in NADH regeneration are observed. When the DTS/Rh@CMP-1 was coupled with yeast alcohol dehydrogenase (YADH, from Saccharomyces cerevisiae), 1.36 mM of methanol was accumulated, implying an excellent biocompatibility of DTS/Rh@CMP-1 and a high feasibility of photobiocatalysis for formaldehyde hydrogenation.

Carbon-based material-supported precious metal catalysts have important applications in fuel-cell cathode electrocatalysis due to the excellent conductivity and abundance of active sites. Here, a nitrogen-doped graphene-supported bimetallic palladium–copper composite catalyst (Pd3Cu1/N-rGO) was prepared by heat-treating the aniline monomer and graphene oxide polymer, followed by supporting palladium–copper metal particles. The change in the structure and surface state of the carbon matrix can effectively control the particle size and dispersion of alloy particles and improve the catalytic performance of the catalyst to reduce the amount of palladium. The alloy nanoparticles of the prepared Pd3Cu1/10N-rGO-800 composite catalyst material are about 1.75 nm in size and the Pd content is less than 10%, which demonstrates an excellent oxygen reduction reaction (ORR) catalytic performance with a mass-normalized current density of 2.35 times that of commercial Pt/C (20 wt % Pt), mainly a four-electron transfer process, and higher catalytic stability than commercial platinum carbon (Pt/C). Most importantly, the prepared catalyst showed better methanol tolerance and much higher stability, and Pd3Cu1/10N-rGO-800 is used as a cathode catalyst material in a zinc–air battery; it presents a higher peak power density (164.39 mW/cm2) and faster discharge performance. The N-doped graphene facilitated the dispersion of the Pd–Cu nanoparticles, effectively reduced the size of alloy particles, maximized the exposure of active sites on the surface, and improved the electrocatalytic activity of oxygen reduction. Pd–Cu nanoparticles supported on N-doped graphene will potentially offer an alternative approach to utilize non-platinum metal catalysts in electrocatalyst applications of oxygen reduction.

More than 800 million people suffer from kidney disease, yet the mechanism of kidney dysfunction is poorly understood. In the present study, we define the genetic association with kidney function in 1.5 million individuals and identify 878 (126 new) loci. We map the genotype effect on the methylome in 443 kidneys, transcriptome in 686 samples and single-cell open chromatin in 57,229 kidney cells. Heritability analysis reveals that methylation variation explains a larger fraction of heritability than gene expression. We present a multi-stage prioritization strategy and prioritize target genes for 87% of kidney function loci. We highlight key roles of proximal tubules and metabolism in kidney function regulation. Furthermore, the causal role of SLC47A1in kidney disease is defined in mice with genetic loss of Slc47a1and in human individuals carrying loss-of-function variants. Our findings emphasize the key role of bulk and single-cell epigenomic information in translating genome-wide association studies into identifying causal genes, cellular origins and mechanisms of complex traits.

The progress of electric vehicles is highly inhibited by the limited energy density and growth of dendrite Li in current power batteries. Breakthroughs and improvements in electrolyte chemistry are highlighted to directly address the above issues, namely, the development of electrolytes with a high lithium-ion transference number (tLi+), enabling one to effectively restrict the concentration polarization during repetitious cycling. Herein, we propose a novel ether-based copolymer-based gel polymer electrolyte (ECP-based GPE) by in situ copolymerization as an intriguing strategy to achieve a high tLi+of ∼0.64. Molecular dynamics simulations and finite element method analyses illustrate the enhanced Li+diffusion process (DLi+, ∼1.76 × 10–10m2s–1) in ECP-based GPE with a homogeneous electric potential accommodated around the lithium metal anode. Therefore, such a high-tLi+-based electrolyte renders a high reversibility of dendrite-free lithium plating/stripping at a high areal capacity (5 mA cm–2/5 mA h cm–2) in an Li||Li symmetric cell and facilitates superior cycling performances (over 1000 cycles) at a high rate (5 C) with a capacity retention of ∼91.1% in Li||LiFePO4batteries, promoting the practical application of solid-state lithium metal batteries.

To explore the mechanical behavior of circular steel tube components strengthened with welded casings, two groups of six specimens are designed, and two working conditions of zero-load reinforcement and load reinforcement are defined. Typical failure modes, ultimate bearing capacity, deflection curves, and strain data of measuring points are obtained. The FE modeling method considering the thermal effect of the welding process is proposed and verified. A total of 446 finite element models are established to explore the effects of the initial defects, the slenderness ratio, the boundary conditions, and the area ratio of the welded casing to the circular steel tube on the ultimate bearing capacity of the strengthened components. In addition, the ultimate bearing capacity equations for zero-load strengthened components is proposed using regression fitting and considering the adverse effects of the residual stresses due to the rolling and welding processes. Moreover, equations for calculating the deformation and the ultimate bearing capacity of the load-strengthened components considering the effect of the welding heat are also derived. Finally, the experimental, numerical, and theoretical analyses results were consistent in high calculation validity and efficiency.