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Channel estimation (CE) plays a key role in reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) communication systems, while it poses a challenging task due to the passive nature of RIS and the cascaded channel structures. In this paper, a partially decoupled atomic norm minimization (PDANM) framework is proposed for CE of RIS-aided MIMO systems, which exploits the three-dimensional angular sparsity of the channel. In particular, PDANM partially decouples the differential angles at the RIS from other angles at the base station and user equipment, reducing the computational complexity compared with existing methods. A reweighted PDANM (RPDANM) algorithm is proposed to further improve CE accuracy, which iteratively refines CE through a specifically designed reweighting strategy. Building upon RPDANM, we propose an iterative approach named RPDANM with adaptive phase control (RPDANM-APC), which adaptively adjusts the RIS phases based on previously estimated channel parameters to facilitate CE, achieving superior CE accuracy while reducing training overhead. Numerical simulations demonstrate the superiority of our proposed approaches in terms of running time, CE accuracy, and training overhead. In particular, the RPDANM-APC approach can achieve higher CE accuracy than existing methods within less than 30 percent training overhead while reducing the running time by tens of times.

This paper proposes an active aerial reconfigurable intelligent surface (ARIS) assisted secure communication framework by integrating sensing and positioning against a mobile eavesdropper. In the proposed scheme, the base station (BS) beamforms the private information to the legitimate user and jams the eavesdropper with artificial noise (AN), while reconfiguring the phases and amplitudes of the passive signal by the active ARIS for promoting secure communications. To acquire the channel state information of the time-vary wiretap channel, the BS tracks the position of the eavesdropper by exploiting the reflected AN. Based on the tracked position of the eavesdropper in the previous time slot, we propose a secure communication scheme that aims to maximize the secrecy rate in the current time slot. This scheme is assisted by the ARIS through jointly optimizing the passive beamforming of the privacy information and AN, the reflection matrix of the ARIS, and the position of the ARIS. In the case of this non-convex quandary with highly coupled variables, we opt to disassemble it into three constituent subproblems and design an alternating optimization framework, where the optimal power beamforming at the BS is derived using a successive convex approximation method and semi-positive definite relaxation technique, the reconfigurable coefficient of the ARIS is optimized using the majorization-minimization algorithm, and the optimal position of the ARIS using the three-dimensional network is obtained by the deep deterministic policy gradient algorithm. Simulation results demonstrate the superior performance of the proposed scheme in the context of the secrecy rate when compared with benchmark schemes. By adopting the active beamforming and positioning technique, the secrecy rate can be increased by 38.3% and 10.8%, respectively.

Aircraft wake turbulence is an inherent outcome of aircraft flight, presenting a substantial challenge to air traffic control, aviation safety and operational efficiency. Building upon data obtained from coherent Doppler Lidar detection, and combining Dynamic Bayesian Networks(DBN) with Genetic Algorithm-optimized Backpropagation Neural Networks(GA-BPNN), this paper proposes a model for the inversion of wake vortex parameters. During the wake vortex flow field simulation analysis, the wind and turbulent environment were initially superimposed onto the simulated wake velocity field. Subsequently, Lidar-detected echoes of the velocity field are simulated to obtain a data set similar to the actual situation for model training. In the case study validation, real measured data underwent preprocessing and were then input into the established model. This allowed us to construct the wake vortex characteristic parameter inversion model. The final results demonstrated that our model achieved parameter inversion with only minor errors. In a practical example, our model in this paper significantly reduced the mean square error of the inverted velocity field when compared to the traditional algorithm. This study holds significant promise for real-time monitoring of wake vortices at airports, and is proved a crucial step in developing wake vortex interval standards.

The CexCo1−xP designed using the Ce-ZIF-67 precursor optimizes hydrogen binding through electronic modulation, achieving high efficiency for HER and overall water splitting.

Since specific hardware characteristics and low-level programming model are adapted to both NVIDIA GPU and new generation Sunway architecture, automatically translating mature CUDA kernels to Sunway ATHREAD kernels are realistic but challenging work. To address this issue, swCUDA, an auto parallel code translation framework is proposed. To that end, we create scale affine translation to transform CUDA thread hierarchy to Sunway index, directive based memory hierarchy and data redirection optimization to assign optimal memory usage and data stride strategy, directive based grouping-calculation-asynchronous-reduction (GCAR) algorithm to provide general solution for random access issue. swCUDAutilizes code generator ANTLR as compiler frontend to parse CUDA kernel and integrate novel algorithms in the node of abstracted syntax tree (AST) depending on directives. Automatically translation is performed on the entire Polybench suite and NBody simulation benchmark. We get an average 40x speedup compared with baseline on the Sunway architecture, average speedup of 15x compared to x86 CPU and average 27 percentage higher than NVIDIA GPU. Further, swCUDAis implemented to translate major kernels of the real world application Gromacs. The translated version achieves up to 17x speedup.

To improve drug utilization, reduce the drug administration frequency, increase the release time, and reduce the drug side effects in the human body, we prepared (KHA/CMC-Fe3+)@CS hydrogel spheres using green and natural potassium humate (KHA), carboxymethyl cellulose (CMC), and chitosan (CS) as raw materials and Fe3+as a crosslinking agent, and loaded them with riboflavin for drug sustained-release study using the drop ball method. The tests with FTIR, SEM, TG, and X-ray diffractometer showed that the coordination among KHA, CMC, and Fe3+formed a three-dimensional network structure, where CS was encapsulated on the surface of the hydrogel spheres via noncovalent bonding, resulting in good thermal stability. The stability, drug loading, swelling, and in vitro release of the (KHA/CMC-Fe3+)@CS hydrogel spheres were investigated. The results showed that the hydrogel spheres were significantly pH-sensitive, with 11.16 g/g higher swelling in an alkaline environment (pH = 7.4) than that in an acidic environment (pH = 1.2). The swelling and drug release process of the hydrogel spheres were analyzed using mathematical models, concluding that the hydrogel swelling follows Schott second-order swelling kinetics, and the drug release mechanism was Fickian delivery mode.

As the application of molecular dynamics (MD) simulations continues to evolve, the demand for accelerating large-scale simulation systems and handling of enormous simulation tasks is steadily increasing. We propose a parallel acceleration method for large-scale MD simulations based on Sunway heterogeneous many-core processors. This method integrates task scheduling, simulation calculations, and data storage, effectively tackling issues related to large-scale simulations and numerous simulation tasks. The task scheduling strategy flexibly handles tasks on various scales and enables parallel execution of multiple tasks. During the simulation calculations, we ported GROMACS to the Sunway architecture and accelerated the calculation of short-range forces through a heterogeneous processor. Our method achieves approximately 10-fold acceleration and 90% scalability when executing a single simulation task. When handling numerous simulation tasks, our method achieves parallel execution of all of the tasks with 90% scalability. By employing our method, we carried out 50 ns simulations on over 3000 distinct conotoxin structures individually within just 5 h. Additionally, we evaluated more than 200 protein–ligand complexes, and the simulation efficiency significantly exceeded that of midsized to small GPU clusters.

We present MobileSky, the first automatic method for real-time high-quality sky replacement for mobile AR applications. The primary challenge of this task is how to extract sky regions in camera feed both quickly and accurately. While the problem of sky replacement is not new, previous methods mainly concern extraction quality rather than efficiency, limiting their application to our task. We aim to provide higher quality, both spatially and temporally consistent sky mask maps for all camera frames in real time. To this end, we develop a novel framework that combines a new deep semantic network called FSNet with novel post-processing refinement steps. By leveraging IMU data, we also propose new sky-aware constraints such as temporal consistency, position consistency, and color consistency to help refine the weakly classified part of the segmentation output. Experiments show that our method achieves an average of around 30 FPS on off-the-shelf smartphones and outperforms the state-of-the-art sky replacement methods in terms of execution speed and quality. In the meantime, our mask maps appear to be visually more stable across frames. Our fast sky replacement method enables several applications, such as AR advertising, art making, generating fantasy celestial objects, visually learning about weather phenomena, and advanced video-based visual effects. To facilitate future research, we also create a new video dataset containing annotated sky regions with IMU data.

Unsupervised deep hashing has demonstrated significant advancements with the development of contrastive learning. However, most of previous methods have been hindered by insufficient similarity mining using global-only image representations. This has led to interference from background or non-interest objects during similarity reconstruction and contrastive learning. To address this limitation, we propose a novel unsupervised deep hashing framework named Fine-grained Similarity-preserving Contrastive learning Hashing (FSCH), which explores fine-grained semantic similarity among different images and their augmented views more comprehensively. It mainly comprises two modules: the global-local fine-grained similarity consistency preservation module and the local fine-grained similarity contrast preservation module. Specifically, we reconstruct local pairwise similarity structures by matching fine-grained patches, in conjunction with global similarity structures based on global hash codes cosine similarity, to generate hash codes with the ability to preserve global-local similarity consistency. Moreover, the preservation of local fine-grained similarity among augmented views is accomplished through the common regional features mutual representation between patches, then we enhance the discriminability of hash codes by mitigating the potential features difference during contrastive learning. Experimental results on four benchmark datasets demonstrate that our FSCH achieves an excellent retrieval performance compared to state-of-the-art unsupervised hashing methods.

This paper proposes an integrated sensing, navigation, and communication (ISNC) framework for safeguarding unmanned aerial vehicle (UAV)-enabled wireless networks against a mobile eavesdropping UAV (E-UAV). To cope with the mobility of the E-UAV, the proposed framework advocates the dual use of artificial noise transmitted by the information UAV (I-UAV) for simultaneous jamming and sensing to facilitate navigation and secure communication. In particular, the I-UAV communicates with legitimate downlink ground users, while avoiding potential information leakage by emitting jamming signals, and estimates the state of the E-UAV with an extended Kalman filter based on the backscattered jamming signals. Exploiting the estimated state of the E-UAV in the previous time slot, the I-UAV determines its flight planning strategy, predicts the wiretap channel, and designs its communication resource allocation policy for the next time slot. To circumvent the severe coupling between these three tasks, a divide-and-conquer approach is adopted. The online navigation design has the objective to minimize the distance between the I-UAV and a pre-defined destination point considering kinematic and geometric constraints. Subsequently, given the predicted wiretap channel, the robust resource allocation design is formulated as an optimization problem to achieve the optimal trade-off between sensing and communication in the next time slot, while taking into account the wiretap channel prediction error and the quality-of-service (QoS) requirements of secure communication. To account for the E-UAV state sensing uncertainty and the resulting wiretap channel prediction error, we employ a fully-connected neural network to model the complicated mapping between the state estimation error variance and an upper bound on the channel prediction error, which facilitates the development of a low-complexity suboptimal user scheduling and precoder design algorithm. Simulation results demonstrate the superior performance of the proposed design compared with baseline schemes and validate the benefits of integrating sensing and navigation into secure UAV communication systems. We reveal that the dual use of artificial noise can improve both sensing and jamming and that navigation is more important for improving the trade-off between sensing and communications than communication resource allocation.

Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevertheless, experimental determination of PPI can be both time-consuming and expensive, motivating the exploration of data-driven deep learning technologies as a viable, efficient, and accurate alternative. Nonetheless, most current deep learning-based methods regarded a pair of proteins to be predicted for possible interaction as two separate entities when extracting PPI features, thus neglecting the knowledge sharing among the collaborative protein and the target protein. Aiming at the above issue, a collaborative learning framework CollaPPI was proposed in this study, where two kinds of collaboration, i.e., protein-level collaboration and task-level collaboration, were incorporated to achieve not only the knowledge-sharing between a pair of proteins, but also the complementation of such shared knowledge between biological domains closely related to PPI (i.e., protein function, and subcellular location). Evaluation results demonstrated that CollaPPI obtained superior performance compared to state-of-the-art methods on two PPI benchmarks. Besides, evaluation results of CollaPPI on the additional PPI type prediction task further proved its excellent generalization ability.

This paper addresses the problem of direction-of-arrival (DOA) estimation for constant modulus (CM) source signals using a uniform or sparse linear array. Existing methods typically exploit either the Vandermonde structure of the steering matrix or the CM structure of source signals only. In this paper, we propose a ${s}$ tructured matrix ${r}$ ecovery ${t}$ echnique (SMART) for CM DOA estimation via fully exploiting the two structures. In particular, we reformulate the highly nonconvex CM DOA estimation problems in the noiseless and noisy cases as equivalent rank-constrained Hankel-Toeplitz matrix recovery problems, in which the Vandermonde structure is captured by a series of Hankel-Toeplitz block matrices, of which the number equals the number of snapshots, and the CM structure is guaranteed by letting the block matrices share a same Toeplitz submatrix. The alternating direction method of multipliers (ADMM) is applied to solve the resulting rank-constrained problems and the DOAs are uniquely retrieved from the numerical solution. Extensive simulations are carried out to corroborate our analysis and confirm that the proposed SMART outperforms state-of-the-art algorithms in terms of the maximum number of locatable sources and statistical efficiency.

Typhoon poses a significant threat to casualties and economic damage in coastal areas. Accurate forecasting of typhoon intensity (TI) is crucial for effective disaster management and mitigation strategies. Despite making certain progress in integrating this task with deep learning techniques, it still faces limitations due to insufficient consideration of the intramodal characteristics and the intermodal discrepancy. In this article, we propose a novel multimodal adversarial fusion (MAF) approach for TI forecasting, which contains feature encoding and multimodal fusion modules. Within the feature encoding module, we adopt a novel block attention mechanism to efficiently reveal the relationships of observational factors in multiple latent spaces, thereby obtaining more comprehensive representations feature of the observational data. Moreover, we introduce a complex convolutional operation to encode the components of wind speed within the reanalysis data, which can maintain their characteristics through the real and imaginary parts of complex numbers during the encoding procedure. Finally, we design the adversarial fusion scheme to effectively narrow the modality gap and facilitate a more comprehensive fusion for TI forecasting. Comprehensive experiments are conducted on CMA Best-Track (CMA-BST)/ERA-Interim data and extensive results verify the effectiveness of our method for TI forecasting.

With the commercial deployment of low earth orbit (LEO) satellites, the future integrated 6G-satellite system represents an excellent solution for ubiquitous connectivity and high-throughput data service to massive users. Due to the heterogeneity of users’ traffic profiles, uneven traffic distribution among beams or users often occurs in LEO satellite systems. Conventional satellite payloads with fixed beam radiation patterns may result in large gaps between requested and allocated capacity. The advances of flexible satellite payloads with dynamic beamforming capabilities enable spot beams to adjust their coverage and adaptively schedule users, thus offering spatial-temporal domain flexibility. Motivated by this, as an early attempt, we investigate how adaptive beam patterns with flexible user scheduling schemes can help alleviate mismatches of requested-transmitted data in uneven-traffic and full-frequency reuse LEO systems. We formulate an optimization problem to jointly determine beam patterns, power allocation, user-LEO association, and user-slot scheduling. The problem is identified as mixed-integer nonconvex programming. We propose an efficient iterative algorithm to solve the problem by first determining beam patterns and user associations at the frame scale, followed by optimizing power allocation and user scheduling at the timeslot scale. The four-decision components are iteratively updated to improve the overall performance. Numerical results demonstrate the benefits brought by adaptive beam patterns and their effectiveness in reducing the mismatch effect in uneven-traffic LEO systems.

Accurate perceptual knowledge of atmospheric characteristics in the propagation path is of great significance for the design of communication systems. However, the atmosphere above the ocean is inhomogeneous, which brings challenges to accurate prediction of propagation loss. Moreover, the atmospheric refractive index distribution model calculated from atmospheric data requires at least two stations with near-sea meteorological data. In real maritime over-the-horizon communication or detection, only one station transmits and receives meteorological data, and the received meteorological data and propagation loss have large temporal noise. To address these issues, first, a denoising model based on the 1-D convolution autoencoder (1-DCAE) is constructed to filter out the temporal noise of input environmental meteorological data and propagation loss. Second, to accurately predict the influence of environmental factors on the prediction of propagation loss, a deep-learning framework called short- and long-term parallel double-flow TrellisNets (SL-TrellisNets) is proposed to predict the loss. Finally, the extensive experiments demonstrated that the root mean square and mean absolute errors (MAEs) of the proposed 1-DCAE are dramatically reduced. Our proposed SL-TrellisNets outperforms the other state-of-the-art techniques in terms of propagation loss prediction. In addition, we analyzed the impact of the environmental factors on the accuracy of over-the-horizon propagation loss (OHPL) prediction.

With the preferable efficiency in storage and computation, hashing has shown potential application in large-scale multimedia retrieval. Compared with traditional hashing algorithms using hand-crafted characteristics, deep hashing inherits the representational capacity of deep neural networks to jointly learn semantic features and hash functions, encoding raw data into compact binary codes with significant discrimination. Generally, most of the current multi-wise hashing methods view the similarity margins between image pairs as constant values in training process. When the distance between sample pairs exceeds the fixed margin, the hashing network would not learn anything. Besides, available hashing methods commonly introduce the random sampling strategy to build training batches and ignore the sample distribution, which is harmful to parameter optimization. In this paper, we propose a novel Deep Adaptive Quadruplet Hashing with probability sampling (DAQH) for discriminative binary code learning. Specifically, with exploring the distribution relationship of raw samples, a non-uniform probability sampling strategy is proposed to build more informative and representative training batches, while maintaining the diversity of training samples. By introducing the prior similarity of sample pairs to calculate corresponding margins, an adaptive margin quadruplet loss is designed to dynamically preserve the underlying semantic relationships with its neighbors. To tune the attributes of binary codes, by combining quadruple regularization and orthogonality optimization, binary code constraint is developed to make the learned embedding with significant discrimination. Extensive experimental results on various benchmark datasets demonstrate our proposed DAQH framework achieves state-of-the-art visual similarity search performance.

The divergent reductive cross-coupling with an ambident electrophile is rare. Previously, we demonstrated a nickel-catalyzed reductive 2-pyridination of aryl iodides with difluoromethyl 2-pyridyl sulfone (2-PySO2CF2H) viaselective C(sp2)–S bond cleavage of the sulfone by using a phosphine ligand. In this communication, we report a novel nickel-catalyzed reductive coupling of aryl iodides and 2-PySO2CF2H reagent, which constitutes a new method for aromatic difluoromethylation. The use of a tridentate terpyridine ligand is pivotal for the selective C(sp3)–S bond cleavage of the sulfone. This method employs readily available nickel catalyst and 2-PySO2CF2H as the difluoromethylation reagent, providing a facile access to difluoromethylarenes under mild reaction conditions without pre-generation of arylmetal reagents.

ABSTRACTBackgroundProtein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.Areas coveredIn this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.Expert opinionComputational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Myriad of machine learning (ML) algorithms refine model parameters iteratively. Existing synchronous data-parallel frameworks can accelerate training with convergence guarantees. However, the pre-assigned workload-based synchronous design still poses great challenges, since fast workers must wait for slow, straggling ones, especially in a heterogeneous computing cluster. Asynchronous alternatives can bypass this performance bottleneck, but at expense of potentially losing convergence guarantees. This article proposes a new time-based flexible synchronous parallel framework (FSP). It provides strict convergence analysis by consistently updating parameters, as well as significant cost reduction by completely unleashing the power of fast workers. It identifies the optimal synchronization frequency, by online balancing costs of parameters’ update and benefits brought by their freshness. Besides the basic goal of keeping all workers fully CPU-utilized, FSP also aims to keep data spread over the cluster fully utilized, so that they can contribute to convergence with equal opportunities. These proposals are all implemented in a prototype system Flegel, with additional engineering optimizations for further performance enhancement and programming facilitation. Experiments demonstrate that Flegel significantly outperforms recent studies.

ABSTRACTThe present study aims to reveal the deterioration law and service life of concrete durability in the saline soil area of western China. For this purpose, the indoor accelerated tests with multiple damage factors including compound salt erosion, dry-wet cycle, freeze-thaw damage, and solar radiation were designed. The deterioration mechanisms of concrete durability were analysed from both macroscopic and microscopic aspects. The Birnbaum-Saunders (B-S) model was selected for modeling, and parameters were estimated by various methods such as moment estimation method (MEM), inverse estimation method (IMEM), quantile estimation Method (QEM), regression estimation method (REM), and maximum likelihood method (MIM). The results showed that under the action of multiple damage factors, the surface of the concrete gradually becomes rough and pitted from the initial smooth and flat surface. As the concrete’s interior gel is not dense, rod-shaped, and needle-shaped corrosion products were formed. For the two-parameter model, the reliability curve, density curve, and failure rate curve obtained by the QEM differ the most from the other 5 types of parameter estimation methods. When the MEM is used for parameter estimation, the difference between the two-parameter model and the three-parameter model under the same reliability level is relatively small.

In the 6G era, Space-Air-Ground Integrated Network (SAGIN) are anticipated to deliver global coverage, necessitating support for a diverse array of emerging applications in high-mobility, hostile environments. Under such conditions, conventional orthogonal frequency division multiplexing (OFDM) modulation, widely employed in cellular and Wi-Fi communication systems, experiences performance degradation due to significant Doppler shifts. To overcome this obstacle, a novel two-dimensional (2D) modulation approach, namely orthogonal time frequency space (OTFS), has emerged as a key enabler for future high-mobility use cases. Distinctively, OTFS modulates information within the delay-Doppler (DD) domain, as opposed to the time-frequency (TF) domain utilized by OFDM. This offers advantages such as Doppler and delay resilience, reduced signaling latency, a lower peak-to-average ratio (PAPR), and a reduced-complexity implementation. Recent studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications (ISAC). In this article, we present an in-depth review of OTFS technology in the context of the 6G era, encompassing fundamentals, recent advancements, and future directions. Our objective is to provide a helpful resource for researchers engaged in the field of OTFS.

In recent years, semantic segmentation technology plays an important role in land resource management tasks. However, many classic semantic segmentation methods often fail to obtain satisfactory results for remote sensing images with a large amount of interference information. To improve this situation, we propose semantic category balance-aware involved anti-interference network (SCBANet). SCBANet has an encoder–decoder structure similar to DeeplabV3+. On this basis, we propose clustering-guided semantic decoupling module (CGSDM), consistency-based anti-interference feature extraction module (CAFEM), relevance-based anti-interference feature extraction module (RAFEM), and optional decoder module based on semantic category balance (ODMSCB) to improve the accuracy of semantic segmentation. CGSDM aims to obtain the information of different semantic categories through $K$ - means clustering algorithm. CAFEM performs an average operation on the feature vectors in each semantic category to obtain semantic consistency information. RAFEM deeply excavates the information contained in each semantic category through the modeling method with self-attention mechanism as the core, making the relationship between pixels within each semantic category to be better understood by the model. ODMSCB classifies the feature map according to the balance of different semantic categories, so that different decoders can be applied to feature maps with different semantic category balance. These four parts complement each other, greatly improving the model’s anti-interference ability while also enhancing the ability to handle category imbalance issue. We compared our method with several of the most advanced deep learning methods on the Vaihingen and Potsdam datasets. The final results demonstrate the superiority of our method.

With the rapid growth of virtual drug data- bases, the need for efficient molecular docking tools for large-scale screening is also growing. We have developed Vina@QNLM 2.0, a novel molecular docking system that leverages the logical processing units and computational processing arrays of heterogeneous multicore architecture processors. Compared to Vina@QNLM, the new version optimizes the docking speed without sacrificing accuracy. This greatly improves the scoring capability for large molecules (molecular weight > 500). Simultaneously, the new system provides enhanced support for applications such as reverse target finding through an improved parallel strategy. Vina@QNLM 2.0 achieves a speedup 20 times higher than that, using logical processing units only during a single docking process. Additionally, we successfully scaled the reverse target finding a task to 122,401 kernel groups with a robust scalability of 80.01%. In practice, we completed a reverse target-seeking for nine glycan molecules with 10,094 proteins within 1 hour.

Predicting drug-target affinity (DTA) is a crucial step in the process of drug discovery. Efficient and accurate prediction of DTA would greatly reduce the time and economic cost of new drug development, which has encouraged the emergence of a large number of deep learning-based DTA prediction methods. In terms of the representation of target proteins, current methods can be classified into 1D sequence- and 2D-protein graph-based methods. However, both two approaches focused only on the inherent properties of the target protein, but neglected the broad prior knowledge regarding protein interactions that have been clearly elucidated in past decades. Aiming at the above issue, this work presents an end-to-end DTA prediction method named MSF-DTA (Multi-Source Feature Fusion-based Drug-Target Affinity). The contributions can be summarized as follows. First, MSF-DTA adopts a novel “neighboring feature”-based protein representation. Instead of utilizing only the inherent features of a target protein, MSF-DTA gathers additional information for the target protein from its biologically related “neighboring” proteins in PPI (i.e., protein-protein interaction) and SSN (i.e., sequence similarity) networks to get prior knowledge. Second, the representation was learned using an advanced graph pre-training framework, VGAE, which could not only gather node features but also learn topological connections, therefore contributing to a richer protein representation and benefiting the downstream DTA prediction task. This study provides new perspective for the DTA prediction task, and evaluation results demonstrated that MSF-DTA obtained superior performances compared to current state-of-the-art methods.

The first two parts of this tutorial on orthogonal time frequency space (OTFS) modulation have discussed the fundamentals of delay-Doppler (DD) domain communications as well as some advanced technologies for transceiver design. In this letter, we will present an OTFS-based integrated sensing and communications (ISAC) system, which is regarded as an enabling technology in next generation wireless communications. In particular, we illustrate the sensing as well as the communication models for OTFS-ISAC systems. Next, we show that benefiting from time-invariant DD channels, the sensing parameters can be used for inferring the communication channels, leading to an efficient transmission scheme. As both functionalities are realized in the same DD domain, we briefly discuss several promising benefits of OTFS-based ISAC systems, which have not been completely unveiled yet. Finally, a range of potential applications of OTFS for the future wireless networks will be highlighted.

The fundamental concepts and challenges of orthogonal time frequency space (OTFS) modulation have been reviewed in Part I of this three-part tutorial. In this second part, we provide an overview of the state-of-the-art transceiver designs for OTFS systems, with a particular focus on the cyclic prefix (CP) design, window design, pulse shaping, channel estimation, and signal detection. Furthermore, we analyze the performance of OTFS modulation, including the diversity gain and the achievable rate. Specifically, comparative simulations are presented to evaluate the error performance of different OTFS detection schemes, and the advantages of coded OTFS systems over coded orthogonal frequency-division multiplexing (OFDM) systems are investigated.

Changes in oceanic variables, such as sea surface temperature (SST) and chlorophyll-a (Chl-a), have important implications for marine ecosystems and global climate change. The deep learning (DL) methods relying on convolutional neural networks can be employed to extract the spatial correlation for the prediction of oceanic variables. However, these methods are inflexible in the cases where some regions, e.g., land and islands, are invalid for the prediction of oceanic variables. By contrast, the graph convolutional network (GCN) is capable of capturing the large-scale spatial dependency existing in the irregular data. Owing to this, in this article, we propose a GCN-based method for the prediction of oceanic variables, including SST and Chl-a, with high accuracy, which is referred to as OVPGCN. The proposed OVPGCN consists of three modules aiming to fully extract the spatial correlation and temporal dependency via modeling the multicharacteristics of the spatio-temporal dynamic evolution. In particular, three modules are implemented to extract the stationary and nonstationary variations in the recent spatio-temporal sequences, the spatial differences between different sites, and the periodic features in historical data, respectively. The well-designed OVPGCN is applied to the monthly SST and Chl-a prediction in the Bohai Sea and the Northern South China Sea (NSCS). The performance demonstrates that the proposed OVPGCN is highly effective and enables to achieve much higher prediction accuracy than the state-of-the-art methods.

This letter is the first part of a three-part tutorial on orthogonal time frequency space (OTFS) modulation, which is a promising candidate waveform for future wireless networks. This letter introduces and compares two popular implementations of OTFS modulation, namely the symplectic finite Fourier transform (SFFT)- and discrete Zak transform (DZT)-based architectures. Based on these transceiver architectures, fundamental concepts of OTFS modulation, including the delay-Doppler (DD) domain, DD domain information multiplexing, and its potential benefits, are discussed. Finally, the challenges ahead for OTFS modulation are highlighted.

Low-order features based on convolution kernel are easy to be distorted when encountering dramatic view angle transformation and atmospheric scattering in remote sensing (RS) images. To address this concern, this article first proposes to operate semantic segmentation of RS images based on the high-order information, which can represent the relative relationship of low-order features and is robust and stable when suffering feature distortion. Besides, semantic decouples have recently been well researched and have achieved significant improvement in image understanding. Thus, in this article, a high-order semantic decoupling network (HSDN) is proposed to disentangle features by semantics based on high-order features. Specifically, HSDN first represents each pixel by calculating the pixel-level affinity as a high-order feature and then clusters these pixels into different semantics. Afterward, an attention-like mask generation module is designed for both intra-semantic and inter-semantic groups, leading to three kinds of masks, including the semantic decoupling mask (SDM), which utilizes each high-order cluster centroid as a mask to compact features intracluster and expand different interclusters, so as to improve semantic disentangle performance to a better extent; semantic enhancement mask (SEM), which records pixel-level relative correlation within a class to sufficiently exploit high-order features and could enhance feature robustness; and boundary supplementary mask (BSM), which aims to process borderline pixels to reduce cluster errors. Finally, by applying masks on pixels both within classes and on borderlines, semantic decoupled features are generated and concatenated to realize segmentation. The quantitative and qualitative experiments are conducted on two large-scale fine-resolution RS image datasets to demonstrate the significant performance of adopting high-order representation. Besides, we also implement numerous experiments to validate the effectiveness of the proposed semantic decouple framework in dealing with complicated and distortion-prone RS image segmentation tasks.

The selective introduction of perfluoro-tert-butyl group (PFtB, the bulkier analogue of CF3group) into arenes has long been sought after but remains a formidable task. We herein report the first general synthetic protocol to realize aromatic perfluoro-tert-butylation. The key to the success is the identification of PFtB phenyl sulfone as a new source of PFtB anion, which reacts with arynes in a highly regioselective manner to afford perfluoro-tert-butylated arenes in high yields. The application of the method is demonstrated by the preparation of sensitive 19F-labeled NMR probes with an extraordinary resolving ability.

In this paper, an intelligent reflecting surface (IRS) is introduced to assist an unmanned aerial vehicle (UAV) communication system based on non-orthogonal multiple access (NOMA) for serving multiple ground users. We aim to minimize the average total system energy consumption by jointly designing the resource allocation strategy, the three dimensional (3D) trajectory of the UAV, as well as the phase control at the IRS. The design is formulated as a non-convex optimization problem taking into account the maximum tolerable outage probability constraint and the individual minimum data rate requirement. To circumvent the intractability of the design problem due to the altitude-dependent Rician fading in UAV-to-user links, we adopt the deep neural network (DNN) approach to accurately approximate the corresponding effective channel gains, which facilitates the development of a low-complexity suboptimal iterative algorithm via dividing the formulated problem into two subproblems and address them alternatingly. Numerical results demonstrate that the proposed algorithm can converge to an effective solution within a small number of iterations and illustrate some interesting insights: (1) IRS enables a highly flexible UAV’s 3D trajectory design via recycling the dissipated radio signal for improving the achievable system data rate and reducing the flight power consumption of the UAV; (2) IRS provides a rich array gain through passive beamforming in the reflection link, which can substantially reduce the required communication power for guaranteeing the required quality-of-service (QoS); (3) Optimizing the altitude of UAV’s trajectory can effectively exploit the outage-guaranteed effective channel gain to save the total required communication power enabling power-efficient UAV communications; (4) NOMA communications offer higher degrees of freedom (DoF) than that of the conventional orthogonal multiple access (OMA) scheme to minimize the average power consumption via optimizing the UAV’s trajectory.

As a commonly used additive in breeding industry, the complex pollutants and metabolites formed by antibiotics and heavy metals in aquaculture wastewater have the characteristics of strong persistence, refractory degradation and easy accumulation, and exist in the environment and human body for a long time, which caused seriously ecological, environmental and health problems. Their effective removal studies are very essential. In this paper, a CQDs-doped bifunctional molecular imprinted BiOCl/Bi3NbO7heterojunction photocatalyst (CQDs-MIP-BNO) was fabricated and the micromorphology along with photoelectric characteristics were explored by SEM, XRD, FTIR, XPS, UV-Vis and PL analysis. The photocatalytic performance experiments implied that 10 % doping of CQDs showed the highest selectivity and photocatalytic activity for the 94 % removal rate of Ceftriaxone Sodium (CTRX) and 80 % of Cr (VI) within 45 min. On this basis, a photocatalytic reactor loaded with the as-synthesized bifunctional catalyst through a sponge mesh covered with CS/PVA film was set up and the actual operation potential was further explored. The results showed that the enhanced removal performance of this bifunctional photocatalyst for CTRX and Cr(VI) made it feasible to efficiently remove one or more low-concentration, high-toxicity antibiotics and heavy-metal pollutants from the water column to validate the utility of combining molecular imprinting and photocatalytic technologies for the treatment of aquaculture wastewater.

In this paper, an iterative framework iterUAis designed for publishing frequency distribution with reduced relative error on multidimensional data under LDP. In each iteration step, the optimized user allocation strategy OUAS is invoked to reduce the relative error in the derived results. In the furture, we are going to investigate the problem of the relative error optimization for mean estimation under LDP.

In the present work, 15 400 yr old geochemical records of a core from the subalpine Daping swamp are presented with the aim to examine the relationship between the chemical weathering and the climatic changes in the region of the western Nanling Mountains, China. The climate of the study region was deeply controlled by the East Asian summer monsoon. The results indicate that, in the past 15 400 yrs, the values of chemical index of alteration (CIA) ranged from 73.9% to 88.2% (mean: 85.3%), suggested a medium and high intensity of chemical weathering. The local exogenous clastic materials, which were derived from the weathered residues, played a key role in contributing towards the sediments. Since the climate-induced chemical weathering exerted strong influences on the geochemical features of weathered residues, the geochemical characteristics of the sediments were deeply impacted by climatic conditions. Wetter and warmer conditions would favor increased chemical weathering, resulting in more leaching of soluble and mobile elements (e.g., Ba and Sr) and leaving the resistant and immobile elements (e.g., Al and Ti) enriched in the weathered residues. These materials were then eroded and transported into the lake, and led to the sediments characterized by the characteristic of having depleted soluble elements. In contrast, dry and cold conditions would result in an opposite trend. In this sense, the geochemical records can serve as proxies to indicate changes of chemical weathering intensity, which were closely related to the evolution of summer monsoon.

ABSTRACTHepatitis B virus (HBV) particles are thought to be secreted from hepatocytes through multivesicular bodies (MVBs); however, the cellular trafficking mechanisms prior to this process remain elusive. It has been reported that CCDC88A/GIV expression, which is involved in multiple aspects of vesicular trafficking, changes dynamically at different phases of chronic HBV infection. In this study, we focused on the role of CCDC88A/GIV in HBV replication. In the liver tissues of chronically HBV-infected patients, HBV infection significantly enhanced CCDC88A/GIV expression, and increased endoplasmic reticulum (ER) stress and autophagosome formation without changing endosome formation. Additionally, colocalization of SHBsAg with early endosomes (~30.2%) far exceeded that with autophagosomes (~3.2%). In hepatoma cells, CCDC88A/GIV and its downstream proteins, DNM2 (dynamin 2; a CCDC88A/GIV effector), CLTC and RAB5A significantly enhanced HBV replication and endosome formation but inhibited autophagosome formation. Blocking endocytosis disrupted HBsAg trafficking to endosomes and caused its accumulation in the ER lumen, which triggered ER stress to initiate the unfolded protein response (UPR). Therefore, HBsAg trafficking into autophagosomes was increased, and the lysosomal activity and maturation, which was inhibited by HBV infection, were restored. Meanwhile, core particles were prevented from entering MVBs. CCDC88A/GIV and its other effector, GNAI3, decreased autophagic flux by enhancing the insulin-induced AKT-MTOR pathway, thereby inhibiting HBV antigens autophagic degradation. In conclusion, CCDC88A/GIV enhanced HBV replication by increasing endosomal trafficking and reducing autophagic degradation of HBV antigens, suggesting that CCDC88A/GIV-mediated endosomal trafficking plays an important role in HBV replication and progeny secretion.Abbreviations: ACTB: actin beta; AO: acridine orange; ATF6: activating transcription factor 6; CCDC88A/GIV: coiled-coil domain containing 88A; CLTC: clathrin heavy chain; CQ: chloroquine; DAPI: 4ʹ,6-diamidino-2-phenylindole; DNM2: dynamin 2; ER: endoplasmic reticulum; ERN1: endoplasmic reticulum to nucleus signaling 1; EIF2A: eukaryotic translation initiation factor 2A; FBS: fetal bovine serum; GNAI3: G protein subunit alpha i3; HBV: hepatitis B virus; HBV RIs: HBV replication intermediates; HBcAg: HBV core protein; HBsAg: HBV surface antigen; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; MVBs: multivesicular bodies; MTOR: mechanistic target of rapamycin kinase; PDI: protein disulfide isomerase; PHH: primary human hepatocyte; pSM2: a HBV replication-competent plasmid; HSPA5/BIP: heat shock protein family A (Hsp70) member 5; SQSTM1/p62: sequestosome 1; siRNA: small interfering RNA; SEM: standard error of the mean; UPR: unfolded protein response

Wavelet transform has been identified as an effective denoising method for ECG signals with its advantage of multi-resolution analysis. However, it should be noted that important morphological features, such as peak of the QRS complex, should be retained after denoising for further medical practice. In this paper, an effective morphological-stabled denoising method for ECG signals is proposed though optimal selection of wavelet basis function, designing a new threshold method, optimising decomposition levels and thresholding scheme. When validated in the MIT-BIH Arrhythmia Database, the denoising method achieved mean square error and signal-to-noise value of 0.0146 and 68.6925 respectively, while successfully retained the QRS complex amplitude close to its full amplitude. Also, a total of 23 simulations were carried out to compare our proposed method with other methods. The experimental results indicate that the proposed denoising method can outperform other state-of-the-art wavelet-based methods while remaining stable in morphology.

With the continuous growth of air traffic flow, the hazard of aircraft encountering wake vortex at high altitude is increasing. To identify the hazard area of wake vortices and increase the capacity of upper airspace, in present work, the dissipation characteristics of the wake vortex at high altitude are analyzed. Considering the changes of flight attitude and trajectory after the aircraft enters the wake, a multi-parametric calculation method is used to study the severities of wake encounters. To determine the minimum acceptable safety level of wake encounters, the upset parameters of different aircraft-type combinations in lower airspace are calculated when the leading aircrafts are 43 typical models. Based on the multi-parametric calculation method, the safety separation of wake at high altitude is studied. Compared with those in medium and low altitudes, wake vortices in high altitudes have larger initial strengths and dissipation rates. When the leading aircraft is a super-heavy or heavy-sized aircraft, it is necessary to increase the safety separation of 1.1–2.1 km. Since the findings here are broadly applicable to assess the severity of high-altitude wake encounter, the approach provided can significantly enhance the safety of air traffic control in upper airspace.

Background: Transition metal sulfide, especially Nickel cobalt sulfide (NiCo2S4), has been widely studied as electrode material based on its excellent electrochemical properties. It is found that increasing the conductivity and stability of the electrode material can greatly improve the performance of the supercapacitor. Methods: Three-dimensional (3D) NiCo2S4 and NiCo2S4/rGO composite material are synthesized via two-step hydrothermal method for high-performance supercapacitor electrode material. Besides, the electrochemical properties of NiCo2S4/rGO have been analyzed by many characterization methods. Results: The experiment shows that pure NiCo2S4 samples exhibit a cubic spinel structure, and their morphologies are 3D flower-like structures. The ultra-pasteurized NiCo2S4 nanospheres have been successfully inserted into the surface of graphene through a hydrothermal method. A large specific surface of NiCo2S4/rGO has been observed from morphology and structure analysis. The specific electric capacity of the NiCo2S4/rGO electrode has reached 1002.9 F/g, when the current density is 1 A/g. Conclusion: It has been found that the capacitance retention rate of NiCo2S4/rGO composite electrode material has increased from 59.6% to 88.5% compared to NiCo2S4 after 2000 cycles at 5 A/g. Moreover, experiments denote that NiCo2S4/rGO electrode material has a larger ion diffusion rate and lesser solution resistance from the Electrochemical Independence Spectrum (EIS).

The recently developed orthogonal time frequency space (OTFS) modulation has shown its capability of coping with the fast time-varying channels in high-mobility environments. In particular, OTFS modulation gives rise to the sparse representation of the delay-Doppler (DD) domain channel model. Hence, one can an enjoy accurate channel estimation by adopting only one pilot symbol. However, conventional OTFS channel estimation schemes require the deployment of guard space to avoid data-pilot interference, which inevitably sacrifices the spectral efficiency. In this letter, we develop a data-aided channel estimation algorithm for a superimposed pilot and data transmission scheme, which can improve the spectral efficiency. To accurately estimate the channel and detect the data symbols, we coarsely estimate the channel based on the pilot symbol, followed by an iterative process which detects the data symbols and refines the channel estimates. Simulation results show that the bit error rate (BER) performance based on the proposed method can approach the baseline scheme with perfect channel estimation.

g-C3N4@CdS nanocomposites with different mass ratios were successfully fabricated by the precipitation method. Different ratios were investigated for their influence on the microstructure, photoelectrochemical, and photocatalytic properties of the as-prepared samples. The experimental results show that g-C3N4@CdS nanocomposites are hexagonal structures with good crystallization, and g-C3N4wraps one-dimensional CdS nanorods. g-C3N4@CdS nanocomposites exhibit excellent visible-light absorption. The PL spectra, transient photocurrent response, and EIS measurements of g-C3N4@CdS composites indicate that heterojunctions accelerate the separation and migration of photo-generated electron-hole pairs and inhibit the recombination of photo-generated carriers. Under simulated sunlight irradiation, the g-C3N4@CdS photocatalyst accelerates the redox reaction and improves photocatalytic efficiency to degrade MO, compared to pure photocatalysts. g-C3N4@ CdS photocatalysts exhibit good degradation efficiency and excellent recyclability. In addition, •O2−and •OH play a key role in the photocatalytic degradation process.

Alzheimer’s disease (AD) is a common neurodegenerative disease with high morbidity among elderly people. A genetic attribution has been extensively proved. Here, we propose to further prioritize genes that harbor single nucleotide variation (SNV) or structural variation (SV) for AD and explore the underlying potential mechanisms through exploiting their expression and methylation spectra. A high-confidence AD-associated candidate gene list was obtained from the ClinVar and Human Gene Mutation Database (HGMD). Genome-wide methylation and expression profiles of AD and normal subjects were downloaded from the Gene Expression Omnibus (GEO). Through comprehensive comparison of expression and methylation levels between AD and normal samples, as well as different stages of AD samples, SORL1 was identified as the most plausible gene for AD incidence and progression. Gene Set Enrichment Analysis (GSEA) revealed significant activation of the ABC (ATP binding cassette) transporter with the aberrant up-regulation of SORL1 within AD samples. This study unfolds the expression and methylation spectra of previously probed genes with SNV or SV in AD for the first time, and reports an aberrant activation of the ABC transporter pathway that might contribute to AD progression. This should shed some light on AD diagnosis and precision treatment.