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With the popularity of GPS-equipped smart devices, spatial crowdsourcing (SC) techniques have attracted growing attention in both academia and industry. A fundamental problem in SC is assigning location-based tasks to workers under spatial-temporal constraints. In many real-life applications, workers choose tasks on the basis of their preferred trajectories. However, by existing trajectory-aware task assignment approaches, tasks assigned to a worker may be far apart from each other, resulting in a higher detour cost as the worker needs to deviate from the original trajectory more often than necessary. Motivated by the above observations, we investigate a trajectory-aware task coalition assignment (TCA) problem and prove it to be NP-hard. The goal is to maximize the number of assigned tasks by assigning task coalitions to workers based on their preferred trajectories. For tackling the TCA problem, we develop a batch-based three-stage framework consisting of task grouping, planning, and assignment. First, we design greedy and spanning grouping approaches to generate task coalitions. Second, to gain candidate task coalitions for each worker efficiently, we design task-based and trajectory-based pruning strategies to reduce the search space. Furthermore, a 2-approximate algorithm, termed MST-Euler, is proposed to obtain a route among each worker and task coalition with a minimal detour cost. Third, the MST-Euler Greedy (MEG) algorithm is presented to compute an assignment that results in the maximal number of tasks assigned and a parallel strategy is introduced to boost its efficiency. Extensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed algorithms.

The meteorological environment has a profound impact on global health (e.g., air quality), science and technology (e.g., rocket launches), and economic development (e.g., poverty reduction) etc. Meteorological prediction presents numerous challenges to both academia and industry due to its multifaceted nature which encompasses real-time observations and complex modeling. Recent research adopt graph convolutional recurrent network and establish coordinate information to obtain local spatial-temporal pattern. However, the model only utilizes the local spatial-temporal information and fail to fully consider the dynamic meteorological situation. To address the above limitations, we propose a Dynamic Spatial-Temporal Graph Neural Network (DSTGNN) to learn global-local meteorological features. Specifically, we divide the global spatial-temporal information along the timeline to obtain local spatial-temporal information. For the global aspect, we design a random throwedge module during the neighborhood propagation process in graph neural network (GNN) to extract the features and adapt to the dynamic situation. We also establish convolution operation module to learn the features. Next, we perform information fusion on the two modules to capture sufficient features. In addition, we employ graph ordinary differential equation (ODE) network and utilize the coordinate information to obtain the long-term features and coordinate relationships. In the local aspect, we first construct a GNN to conduct graph embedding. Then, we integrate another GNN into a gated recurrent unit (GRU) and also use the coordinate information to explore the features and coordinate relationships. Finally, we combine the global and local features via a global-local features learning layer for meteorological prediction. Experimental results on the four real-world meteorological datasets show that DSTGNN outperforms the baseline models.

Community Search (CS) aims to enable online and personalized discovery of communities. Recently, attention to the CS problem in directed graphs (di-graph) needs to be improved despite the extensive study conducted on undirected graphs. Nevertheless, the existing studies are plagued by several shortcomings, e.g., Achieving high-performance CS while ensuring the retrieved community is cohesive is challenging. This paper uses the D-truss model to address the limitations of investigating the CS problem in large di-graphs. We aim to implement millisecond-level D-truss CS in di-graphs by building a summarized graph index. To capture the interconnectedness of edges within D-truss communities, we propose an innovative equivalence relation known as D-truss-equivalence, which allows us to divide the edges in a di-graph into a sequence of super nodes (s-nodes). These s-nodes form the D-truss-equivalence-based index, DEBI, an index structure that preserves the truss properties and ensures efficient space utilization. Using DEBI, CS can be performed without time-consuming access to the original graph. The experiments indicate that our method can achieve millisecond-level D-truss community query while ensuring high community quality. In addition, dynamic maintenance of indexes can also be achieved at a lower cost.

Photocatalytic CO2reduction technology has engaged significant attention due to its high efficiency, high selectivity, and environmental friendliness. However, its application is severely restrained by issues such as low separation efficiency of photogenerated carriers and a limited light absorption range. This work proposes an innovative MgCr2O4/MgIn2S4magnesium-based spinel/spinel heterostructure photocatalyst to improve the photocatalytic CO2reduction efficiency through the synergistic contributions of S-scheme heterojunction and photothermal effect. On the one hand, the unique S-scheme charge transfer mechanism enables the effective separation of photogenerated carriers. On the other hand, the photothermal effect allows an accelerated charge migration by increasing the reaction center temperature. Moreover, the abundant oxygen vacancies serve as electron traps and CO2adsorption sites, unifying reaction and adsorption sites and substantially improving catalytic efficiency. Under UV–vis and UV–vis-NIR illumination, the average CO yields of the MgCr2O4/MgIn2S4composite are 8.03 and 15.62 μmol g–1h–1, respectively, greatly higher than those of pure MgCr2O4and MgIn2S4samples. Furthermore, the fabricated photocatalyst demonstrates excellent performance and structure stability. Therefore, this work may offer a new strategy for designing efficient and stable photocatalysts.

Fuzzy-rough sets (FRS) provide an outstanding theoretical tool for feature selection (FS). Whilst promising, the FRS model is sensitive to noisy information and ineffectively applicable to the data with large class density difference, with existing FRS-based FS methods only tackling one of these challenges. Therefore, to overcome both of these issues, this article presents a robust FS algorithm using linear reconstruction measure for the first time. First, a pseudo FRS model is proposed, where the distribution-aware linear reconstruction relation serving as the fuzzy similarity relation is constructed by considering the insight of meaningful information (i.e., distribution information of samples and density information of classes) to enhance the robustness and the pseudofuzzy rough approximations are further redefined based on $k$-Nearest Neighbor ($k$NN) granules determined by the linear reconstruction coefficients to empower the antinoise ability. Then, the pseudo FRS model is employed to guide the robust FS algorithm from the perspective of redundant filter, strongly relevant priority, and discriminative selection to determine the final feature subset. The experimental results on 31 datasets and practical applications (i.e., cancer diagnosis and face recognition) demonstrate that the reduct gained by the proposed approach generally outperforms those attained by alternative implementations of FRS-based FS and state-of-the-art FS techniques.

The tremendous expansion of edge data traffic poses great challenges to network bandwidth and service responsiveness for mobile computing. Edge caching has emerged as a promising method to alleviate these issues by storing a portion of data at the network edge. However, existing caching approaches suffer from either poor caching efficiency with low content-hit ratio or unintelligence of caching policies lacking self-adjustability. In this article, we propose ICE, a novel Intelligent Edge Caching scheme using a deep reinforcement learning (DRL) method to capture specific valuable information from the requested data. With the benefit of our proposed popularity model based on Newton's law of cooling, ICE fully takes into account the popularity of the contents to be cached and leverages the formulated Markov decision model to decide whether or not the contents should be cached. Moreover, to further improve the caching efficiency, we propose a novel distributed multi-node caching framework, named DCCC, assisted by a multi-tiered caching hierarchy. Comprehensive experiments show that the single-node ICE scheme greatly improves the cache hit rate and contents exchanging time in comparison with both DRL-based and legacy approaches, and our distributed multi-node caching scheme DCCC further significantly improves the overall utilization of caching space.

Identifying compound-protein interactions (CPIs) is critical in drug discovery, as accurate prediction of CPIs can remarkably reduce the time and cost of new drug development. The rapid growth of existing biological knowledge has opened up possibilities for leveraging known biological knowledge to predict unknown CPIs. However, existing CPI prediction models still fall short of meeting the needs of practical drug discovery applications. A novel parallel graph convolutional network model for CPI prediction (ParaCPI) is proposed in this study. This model constructs feature representation of compounds using a unique approach to predict unknown CPIs from known CPI data more effectively. Experiments are conducted on five public datasets, and the results are compared with current state-of-the-art (SOTA) models under three different experimental settings to evaluate the model's performance. In the three cold-start settings, ParaCPI achieves an average performance gain of 26.75%, 23.84%, and 14.68% in terms of area under the curve compared with the other SOTA models. In addition, the results of the experiments in the case study show ParaCPI's superior ability to predict unknown CPIs based on known data, with higher accuracy and stronger generalization compared with the SOTA models. Researchers can leverage ParaCPI to accelerate the drug discovery process.

Cisplatin is widely used in tumor chemotherapy, but nephrotoxicity is an unavoidable side effect of cisplatin. Several studies have demonstrated that mesenchymal stromal cells (MSCs) ameliorate cisplatin-induced kidney injury, but the underlying mechanisms are unknown. In this study, the cisplatin-induced kidney injury mouse model was established by subjecting a single intraperitoneal injection with cisplatin. One hour before cisplatin injection, the mice received human bone marrow MSCs (hBM-MSCs) with or without siRNA-transfection, recombinant human tumor necrosis factor-α-stimulated gene/protein 6 (rhTSG-6), or PBS through the tail vein. In addition, cisplatin-stimulated HK-2 cells were treated with hBM-MSCs or rhTSG-6. Human BM-MSCs treatment remarkably ameliorated cisplatin-induced acute and chronic kidney injury, as evidenced by significant reductions in serum creatinine (Scr), blood urea nitrogen, tubular injury, collagen deposition, α-smooth muscle actin accumulation, as well as inflammatory responses, and by remarkable increased anti-inflammatory factor expression and Treg cells infiltration in renal tissues. Furthermore, we found that only a few hBM-MSCs engrafted into damaged kidney and that the level of human TSG-6 in the serum of mice increased significantly following hBM-MSCs administration. Moreover, hBM-MSCs significantly increased the viability of damaged HK-2 cells and decreased the levels of inflammatory cytokines in the culture supernatant. However, the knockdown of the TSG-6 gene in hBM-MSCs significantly attenuated their beneficial effects in vivo and in vitro. On the contrary, treated with rhTSG-6 achieved similar beneficial effects of hBM-MSCs. Our results indicate that systemic administration of hBM-MSCs alleviates cisplatin-induced acute and chronic kidney injury in part by paracrine TSG-6 secretion.Graphical Abstract

Cache miss analysis has become one of the most important things to improve the execution performance of a program. Generally, the approaches for analyzing cache misses can be categorized into dynamic analysis and static analysis. The former collects sampling statistics during program execution but is limited to specialized hardware support and incurs expensive execution overhead. The latter avoids the limitations but faces two challenges: inaccurate execution path prediction and inefficient analysis resulted by the explosion of the program state graph. To overcome these challenges, we propose CBANA, an LLVM- and process address space-based lightweight, efficient, and flexible cache behavior analysis framework. CBANA significantly improves the prediction accuracy of the execution path with awareness of inputs. To improve analysis efficiency and utilize the program preprocessing, CBANA refactors loop structures to reduce search space and dynamically splices intermediate results to reduce unnecessary or redundant computations. CBANA also supports configurable hardware parameter settings, and decouples the module of cache replacement policy from other modules. Thus, its flexibility is established. We evaluate CBANA by using the popular open benchmark PolyBench, graph workloads, and our synthetic workloads with good and poor data locality. Compared with the popular dynamic cache analysis tools Perf and Valgrind, the cache miss gap is less than 3.79% and 2.74% respectively with over ten thousand data accesses for the synthetic workloads, and the time reduction is up to 92.38% and 97.51% for the multiple-path workloads. Compared with the popular static cache analysis tool Heptane, CBANA achieves a time reduction of 97.71% while ensuring accuracy at the same time.

With the development of foundation engineering, finding new types of foundations with higher bearing capacity and stability has become an important research direction. In this paper, a new type of combined foundation consisting of diaphragm walls and pile groups is proposed, taking advantage of the benefits of both diaphragm walls and pile foundations. Finite element software is used to establish combined foundation models with diaphragm walls ranging from 1 m to 4 m in height, and model tests are conducted to validate the reliability of the finite element results. Subsequently, the settlement, axial force, and distribution of the horizontal axial force of the combined foundations under vertical loading are investigated for four different height combinations. Compared to pile foundations, combined foundations can significantly improve the ultimate bearing capacity of the foundation. The axial forces in both the piles and diaphragm walls decrease abruptly at the connection between the two structures. The diaphragm walls can bear larger vertical loads, and a tensile force is generated in the 1 m high diaphragm wall between the pile groups, while compressive forces are generated at higher heights. Additionally, the horizontal bearing capacity of the combined foundation is examined by analyzing its horizontal displacement, bending moment of the piles and diaphragm walls, and horizontal axial force of the diaphragm walls. The horizontal bearing capacity of the combined foundation is 26% higher than that of the pile foundation. The 1 m high diaphragm wall provides constraint to the pile top, reducing the horizontal displacement. As the height of the diaphragm wall increases, the constraint effect weakens, and the horizontal bearing capacity at a height of 4 m mainly relies on the lower part of the pile body.

This paper investigates the implementation of Federated Learning (FL) in an over-the-air computation system with volatile clients, where each client operates under a limited energy budget and may unexpectedly drop out during local training sessions. The dropout of clients not only wastes energy but also diminishes their participation frequency, necessitating careful client selection by the server in each communication round. However, the diversity of training tasks and the random nature of client dropout present challenges such as the absence of an explicit objective function and the unavailability of client performance metrics. To address these challenges, we first analyze the convergence of the over-the-air federated learning system with volatile clients to identify the key factor influencing the model's convergence speed. Building upon this analysis, we propose an approximation of the objective function as the optimization goal for client selection. To mitigate energy waste, we introduce a dynamic client selection strategy termed DCSE, based on Exp3 with multiple plays and energy constraints, aiming to reconcile the dilemma of unknown local training states and limited resource constraints. Theoretical analysis demonstrates that our proposed solution maintains a constant bound on the difference from the optimal solution, affirming its theoretical feasibility. Furthermore, experimental results validate the effectiveness of the proposed strategy in enhancing FL by accelerating convergence speed, improving test accuracy, and reducing wasted energy.

Sparse tensor contraction (SpTC) is an important operator in tensor networks, which tends to generate a large amount of sparse high-dimensional data, placing higher demands on the computational performance and storage bandwidth of the processor. Using GPUs with powerful arithmetic characteristics is a reliable choice for accelerating SpTC, however, the high dimensionality and sparsity of tensor makes GPU-accelerated SpTC operators suffer from the difficulties of low computational intensity and high memory consumption. The recent introduction of Tensor Core Units (TCUs) on GPUs brings even more powerful arithmetic, which exacerbates the memory wall problem. To cope with the challenges, this paper proposes a new BCB format that linearizes the indices of multidimensional blocks to reduce block index accesses and uses a bitmap to store the distribution of non-zero elements in a block to reduce the storage overhead. A parallel blocking algorithm of BCB-SpTC is designed to divide the binary linear indices into free and contracted indexes to improve the pairing overhead of computational tasks. Then based on the characteristic computation method of TCUs, the proprietary filling method of TCUs is designed to overcome the inefficiency of parallel computation of sparse data on TCUs. Finally, experimental results on the A100 dataset show that BCB-SpTC improves the acceleration ratio by $1.1\times$1.1× to $21.3\times$21.3× over the existing SpTC GPU method.

China is confronted with a severe air pollution challenge, wherein thermal power generation plays a significant role. In recent years, substantial efforts have been made in ultra-low emission retrofitting of coal-fired power plants, however, quantitative study regarding its subsequent impact on air quality is limited. In this study, we estimated the emission reduction of thermal power plants from the perspective of online monitoring system during 2014 ∼ 2016, and investigated the accompanying impacts on air quality in typical regions of China by using a regional chemical model WRF-Chem. The results indicate that the ultra-low emission retrofitting of thermal power plants, which was initiated in 2014, has achieved significant progress, with nearly 80% of planned tasks completed by 2016. As a result, emissions of SO2, NOxand PM2.5from thermal power plants notably decreased by 67.5%∼72.8% nationwide between 2014 and 2016. WRF-Chem simulations demonstrate that the ultra-low emission retrofitting effectively reduces air pollutant concentrations. Specifically, the monthly mean concentrations of SO2, NO2and PM2.5in typical regions have decreased by 0.6 ∼ 1.7, 2.2 ∼ 3.7 and 2.6 ∼ 5.0 µg m− 3, respectively, representing an improvement of 3.1%∼10.4%, particularly notable in winter. Regional variations in installed thermal power capacity and completion of the ultra-low emission retrofitting have led to differential improvements in air quality, with the Yangtze River Delta region exhibiting the most significant reduction in air pollution concentrations, surpassing the Beijing-Tianjin-Hebei and Pearl River Delta regions by up to 2.2 µg m− 3. This study serves as a valuable reference for the ultra-low emission retrofitting of thermal power industry and provides essential data support for future air quality management strategies.

Unsupervised multi-view bipartite graph clustering (MVBGC) is a fast-growing research, due to promising scalability in large-scale tasks. Although many variants are proposed by various strategies, a common design is to construct the bipartite graph directly from the input data, i.e., only consider the unidirectional “encoding” process. However, “encoding-decoding” mechanism is a popular design for deep learning, the most representative one is auto-encoder (AE). Enlightened by this, this paper rethinks existing MVBGC paradigms and transfers the “encoding-decoding” design into graph machine learning, and proposes a novel framework termed auto-encoding multi-view bipartite graph clustering (BGAE), which integrates encoding, bipartite graph construction, and decoding modules in a self-supervised learning manner. The encoding module extracts a latent joint representation from the input data, the bipartite graph construction module learns a bipartite graph with connectivity constraint in latent semantic space, and the decoding module recreates the input data via the bipartite graph. Therefore, our novel BGAE combines representation learning, bipartite graph learning, reconstruction learning, and label inference into a unified framework. All the modules are seamlessly integrated and mutually reinforcing for clustering-friendly purposes. Extensive experiments verify the superiority of our novel design and the significance of “decoding” process. To the best of our knowledge, this is the first attempt to explore “encoding-decoding” design in traditional MVBGC.

In multiagent reinforcement learning, policy evaluation is a central problem. To solve this problem, decentralized temporal-difference (TD) learning is one of the most popular methods, which has been investigated in recent years. However, existing decentralized variants of TD learning often suffer from slow convergence due to the sensitive selection of learning rates. Inspired by the great success of adaptive gradient methods in the training of deep neural networks, this article proposes a decentralized adaptive TD $(\lambda )$ learning algorithm for general $\lambda $ with linear function approximation, referred to as D-AMSTD $(\boldsymbol {\lambda })$ , which can mitigate the selective sensitivity of learning rates. Furthermore, we establish the finite-time performance bounds of D-AMSTD $(\boldsymbol {\lambda })$ under the Markovian observation model. The theoretical results show that D-AMSTD $(\boldsymbol {\lambda })$ can linearly converge to an arbitrarily small size of neighborhood of the optimal weight. Finally, we verify the efficacy of D-AMSTD $(\boldsymbol {\lambda })$ through a variety of experiments. The results show that D-AMSTD $(\boldsymbol {\lambda })$ outperforms existing decentralized TD learning methods.

Detecting genes that affect specific traits (such as human diseases and crop yields) is important for treating complex diseases and improving crop quality. A genome-wide association study (GWAS) provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms. Many GWAS summary statistics data related to various complex traits have been gathered recently. Studies have shown that GWAS risk loci and expression quantitative trait loci (eQTLs) often have a lot of overlaps, which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS. In this review, we review three types of gene-trait association detection methods of integrating GWAS summary statistics and eQTLs data, namely colocalization methods, transcriptome-wide association study-oriented approaches, and Mendelian randomization-related methods. At the theoretical level, we discussed the differences, relationships, advantages, and disadvantages of various algorithms in the three kinds of gene-trait association detection methods. To further discuss the performance of various methods, we summarize the significant gene sets that influence high-density lipoprotein, low-density lipoprotein, total cholesterol, and triglyceride reported in 16 studies. We discuss the performance of various algorithms using the datasets of the four lipid traits. The advantages and limitations of various algorithms are analyzed based on experimental results, and we suggest directions for follow-up studies on detecting gene-trait associations.

Meloxicam (MLX) is a novel nonsteroidal anti-inflammatory drug, but on the other hand, it has become one of the common microcontaminants in surface waters and sewage. Herein, we report the preparation of a ternary-metal Zn(II)–Cd(II)–Eu(III) nanocluster 1for the response of MLX through the enhancement of lanthanide luminescence. The luminescence sensing behavior of 1is expressed by the equation I615nm= 3060 × [MLX] + 46,604, which can be used in the quantitative analysis of MLX concentrations in meloxicam dispersible tablets. Filter paper strips bearing 1can be used to qualitatively detect MLX by a color change to red under a UV lamp. The luminescence response time is no more than five s, and the detection limit is as low as 2.31 × 10–2nM.

A nine-metal Zn(II)–Eu(III) nanoring 1with a diameter of about 2.3 nm was constructed by the use of a long-chain Schiff base ligand. It shows a luminescence response to neopterin (Neo) through the enhancement of lanthanide emission with high selectivity and sensitivity, which can be used to quantitatively analyze the concentrations of Neo in fetal calf serum and urine. The luminescence sensing of 1to Neo is temperature-dependent, and it displays more obvious response behavior at lower temperatures. Filter paper strips bearing 1can be used to qualitatively detect Neo by the color change from chartreuse to red under a UV lamp. The limit of detection is as low as 3.77 × 10–2nM.

As 6G communication technology advances, there is a growing trend of incorporating blockchain technology, which has already demonstrated its effectiveness in multiple areas. Merging blockchain technology with 6G communication opens up novel prospects for consumer electronics, facilitating the creation of secure, private, and decentralized networks, along with pioneering applications and services. We explore the synergistic incorporation of blockchain with 6G communication networks to enable secure and decentralized connectivity tailored for consumer electronics. To this end, a multi-party, dependable framework comprising intelligent edge servers, blockchain consensus, and resource-constrained electronic devices is proposed. Analytical models characterize the system’s unique cost and incentive tradeoffs, accounting for factors like energy, latency, credibility, and capacity. We analyze symmetric and asymmetric information scenarios, providing insights into optimal resource allocation strategies in different knowledge conditions within the network. Extensive simulations validate gains over benchmarks across mobile augmented reality gaming and distributed machine learning workloads, achieving over 90% offloading efficiency within 50ms latency targets as infrastructure scales up to 100 edge servers and 2000 devices. These results establish the feasibility of blended edge intelligence, cryptography, and wireless advancements in realizing next-generation consumer solutions spanning metaverse, ambient computing, and industrial IoT while preserving user control.

Measurable quantitative information is a fundamental set of information related to the quantity of objects, which widely exists in various texts including medical records, national standards and technical reports. In recent years, some researchers have recognized the importance of measurable quantitative information in helping to analyze healthcare and have proposed pipeline-based approaches, but there are still many challenging issues with measurable information extraction. This paper proposes a span-based joint model (SCLert) for measurable quantitative information extraction, which incorporates Chinese generic features and lexicon information for improving the effectiveness of measurable quantitative information recognition. Compared to pipeline extraction methods that require two independent models, our joint extraction model combines both measurable quantitative information recognition and association within one model. The span-based representation enhances the boundary information of entity and enables more accurate extraction of measurable quantitative information. We evaluated the model on a Standard Quantitative Information dataset with 3106 Chinese text sentences and a Clinical Quantitative Information dataset with 1359 Chinese electronic medical records. Experiment results show that our joint model outperforms baseline models by an improvement of 3.67% on recognition and 6.61% on association in F1-score on the standard datasets.

Face forgery detection has become a new research hotspot. Though existing detection works have achieved impressive performance, they are difficult to achieve a proper trade-off between detection accuracy and model complexity. To solve this problem, we design some low-complexity modules and construct a lightweight dynamic fusion network (LDFnet) to achieve high accuracy and lightweight face forgery detection. Firstly, we regard significant local visual artifacts as a correct semantic feature needed for detection. A spatial group-wise enhance (SGE) module is introduced as a supervision to suppress possible noise and capture local artifacts. Secondly, we design a manipulation trace extraction block (TraceBlock), which can replace vanilla convolution to achieve global inference, thus capturing the texture information in the global scope. Based on TraceBlock, we construct a global texture representation (GTR) network to extract global manipulation features hierarchically. Finally, we design a dynamic fusion mechanism (DFM) to fully fuse local and global clues, and dynamically generate a more discriminating feature representation. Extensive experimental results show that the proposed LDFnet is significantly superior to the previous detection works on some popular face forgery datasets, such as FF++, DFDC, CelebDF and HFF. In particular, LDFnet only uses 963k model parameters and 801M FLOPs, which is far lower than the calculation cost of face forgery detection based on large model, and achieves better detection results.

This article discusses the invariant subspaces that are restricted to be external direct sums. Some existence conditions are presented that facilitate finding such invariant subspaces. This problem is related to the decomposition of coupled differential-difference equations, leading to the possibility of lowering the dimensions of coupled differential-difference equations. As has been well documented, lowering the dimension of coupled differential-difference equations can drastically reduce the computational time needed in stability analysis when a complete quadratic Lyapunov–Krasovskii functional is used. Most known ad hoc methods of reducing the order are special cases of this formulation.

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proven to be of vital significance in applications since the unreliable distant–distance similarity estimation would degrade clustering performance. Although existing localized MKC algorithms exhibit improved performance compared with globally designed competitors, most of them widely adopt the KNN mechanism to localize kernel matrix by accounting for $\tau $ -nearest neighbors. However, such a coarse manner follows an unreasonable strategy that the ranking importance of different neighbors is equal, which is impractical in applications. To alleviate such problems, this article proposes a novel local sample-weighted MKC (LSWMKC) model. We first construct a consensus discriminative affinity graph in kernel space, revealing the latent local structures. Furthermore, an optimal neighborhood kernel for the learned affinity graph is output with naturally sparse property and clear block diagonal structure. Moreover, LSWMKC implicitly optimizes adaptive weights on different neighbors with corresponding samples. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algorithms. The source code of LSWMKC can be publicly accessed from https://github.com/liliangnudt/LSWMKC.

This paper explores the design of an architecture that replaces Disk with Persistent Memory (PM) to achieve the highest I/O throughput in Log-Structured Merge Tree (LSM-Tree) based key-value stores (KVS). Most existing LSM-Tree based KVSs use PM as an intermediate or smoothing layer, which fails to fully exploit PM's unique advantages to maximize I/O throughput. However, due to PM's distinct characteristics, such as byte addressability and short erasure time, simply replacing existing storage with PM does not yield optimal I/O performance. Furthermore, LSM-Tree based KVSs often face slow read performance. To tackle these challenges, this paper presents HPDK, a hybrid PM-DRAM KVS that combines level compression for LSM-Trees in PM with a B${}^{+}$+-tree based in-memory search index in DRAM, resulting in high write and read throughput. HPDK also employs a key-value separation design and a live-item rate-based dynamic merge method to reduce the volume of PM writes. We implement and evaluate HPDK using a real PM drive, and our extensive experiments show that HPDK provides 1.25-11.8 and 1.47-36.4 times higher read and write throughput, respectively, compared to other state-of-the-art LSM-Tree based approaches.

The updated residential-level fine-grained digital map is essential for last-mile delivery. However, many of those low-level roads are not recorded in maps due to the high mapping costs. With the digitization of the logistics industry, couriers’ trajectories become a promising data source to complete missing roads in maps. Existing trajectory-based map updating work rely on heavy parameter tuning to overcome the positioning error issue due to their unsupervised nature, and are not able to handle issues of unreliable road indicators and skewed data distributions. To tackle those challenges, in this article, we propose a framework DelvMap to complete missing roads in maps based on couriers’ trajectories and satellite images. DelvMap first leverages a dual signal fusion network (DSFNet) to extract an inferred map from both data modalities, which fully exploits the positive and negative signals in the satellite images to fuse with roads indicated from trajectories, then employs a map completion algorithm to complete the existing map with the inferred map, the connection strategy of which is adaptive to the number of traversing trajectories. Experiments show DelvMap outperforms baselines by at least 11.0% in TOPO F1 on the real-world dataset. Finally, we demonstrate a multimodal map updating system based on DelvMap.

In this paper, we employ general results on the value distributions of perfect nonlinear functions from $\mathbb {F}_{p^{m}}$ to $\mathbb {F}_{p}$ to give a unified approach to determining the weight distributions of two classes of linear codes over $\mathbb {F}_{p}$ constructed from perfect nonlinear functions, where $p$ is an odd prime and $m$ is an odd number. When $m$ is even, we give some mild additional conditions for similar conclusions to hold.

Sensing data acquired with crowdsensing are generally stored at central cloud servers, since massive data are involved and sensing devices do not have enough space to store them. Although each sensing device only has limited storage capacity, the total size of storage across thousands of devices can be considerable. In view of this, this article addresses decentralized storage problem in mobile crowdsensing system, providing an alternative to cloud-based data storage. By investigating a virtual sensor model, the movement of a participant in the target sensing area is formulated as a random sampling over the data field related to this area. With a particular encoding algorithm, the data field is compressed into only one measurement along with a random sampling process. Each participant stores its own measurements as if various compressed snapshots of the data field are separately stored by different participants. We further investigate a recovery algorithm, reconstructing the original data field by carefully decoding enough measurements. Extensive experiments validate the proposed storage scheme under various crowdsensing scenarios, and our scheme achieves excellent performance in terms of recruitment overhead, decoding time, and decoding accuracy.

The prediction of molecular properties remains a challenging task in the field of drug design and development. Recently, there has been a growing interest in the analysis of biological images. Molecular images, as a novel representation, have proven to be competitive, yet they lack explicit information and detailed semantic richness. Conversely, semantic information in SMILES sequences is explicit but lacks spatial structural details. Therefore, in this study, we focus on and explore the relationship between these two types of representations, proposing a novel multimodal architecture named ISMol. ISMol relies on a cross-attention mechanism to extract information representations of molecules from both images and SMILES strings, thereby predicting molecular properties. Evaluation results on 14 small molecule ADMET datasets indicate that ISMol outperforms machine learning (ML) and deep learning (DL) models based on single-modal representations. In addition, we analyze our method through a large number of experiments to test the superiority, interpretability and generalizability of the method. In summary, ISMol offers a powerful deep learning toolbox for drug discovery in a variety of molecular properties.

In recent years, cyber-physical systems (CPS) have been widely deployed in industrial manufacturing fields and our daily living domains. End–end–edge collaboration, coupling mobile edge computing and device-to-device communication, is a promising computation paradigm to meet the stringent real-time demands of large-scale CPS applications. However, energy and reliability concerns should be carefully addressed in end–end–edge collaboration-empowered large-scale CPS due to the limited energy supply and inherent openness characteristic of end devices. In this article, we explore the reliability-driven energy optimization of end–end–edge collaborated large-scale CPS applications. We develop a reliability-driven end–end–edge collaboration approach to deal with the energy minimization problem. Our approach first designs a clustering method to quantify differentiated energy demands by analyzing the energy dissipation composition of heterogeneous applications. Afterward, our approach leverages incremental control and swarm intelligence-based techniques to obtain energy-efficient reliability-guaranteed task offloading solutions for differentiated application clusters. Experimental results reveal that our approach achieves 51.48% energy savings compared with peer algorithms.

Magnetic flux leakage (MFL) testing has been widely used to evaluate pipe integrity due to its high detection efficiency. However, motion-induced eddy currents (MIECs) occur in a steel pipe because of the relative movement between pipelines and magnets, resulting in diminished pipe magnetization and a distorted signal shape. Current approaches treat MIECs as a new excitation source or utilize compensation algorithms to eliminate the effect of MIECs, but they require collaborative environments and Big Data training. In this article, a physical compensation method that uses the polarization angle of magnets is proposed to address the issue. First, the generation, propagation, and distribution of MIECs in the pipe are analyzed to elucidate the action mechanism of MIECs on MFL signals. Second, a controllable magnetization method is utilized to achieve continuous saturation magnetization of the pipe. Third, an obliquely incident magnetic field is injected into the pipe to reduce the asymmetric of MIECs. Finally, an experimental pipe is applied to intuitively illustrate the velocity compensation effect. Results evidenced the capability to accurately detect defects in the range of 0–3 m/s. The ease of the proposed method has a bright future for high-speed MFL evaluation in the service of the pipe.

With the unprecedented scalability issues rising in vehicular edge computing (VEC), we argue in this paper that the scalability, along with the remarkable growth of demands for offloading, should be integrated into the modelling for effective offloading decision-making strategies requested by a large number of vehicles. A two-stage game-theory model can depict offloading decision-making strategies by considering both the revenue of network operators and the cost of VEC users. However, heuristic processes of solving such models show significant limitations in terms of high computational complexity and energy consumption due to the changing VEC environment. Therefore, our objective in this study is to solve the game-theory model efficiently and achieve scalable offloading for the changing VEC environment. We first develop a two-stage game-theory model for the offloading decision-making strategy for VEC, by which an operator’s revenue, energy consumption and latency are considered. Then a neural network (NN) model is designed to learn the predicted behaviours of the established game-theory model for offloading decisions in a more efficient manner. After that, a feature-based transfer learning algorithm is proposed for scalable offloading optimization under unseen VEC environments. Experimental results show that the proposed NN can significantly improve the efficiency of solving the game theory model, and the developed transfer learning approach can effectively achieve the scalability of offloading decisions in a changing VEC environment. The results demonstrate that the accuracy of the proposed transfer learning approach is 37% higher than that of several state-of-the-art algorithms, and the runtime halves.

There has been a growing penetration of renewable energy sources (RES) into power grids in recent years. As extreme weather events and cyber-attacks frequently occur, grid resilience has been an important issue. Vehicle-to-Vehicle (V2V) energy trading has great potential to improve the stability and reliability of resilient power grids integrated with high-penetrated RES. V2V energy trading is a distributed peer-to-peer (P2P) application. As a decentralized distributed ledger technology, blockchain is an ideal platform for V2V energy trading. The consensus mechanism of blockchain determines whether the V2V energy trading blockchain (ETB) can improve grid resilience. However, most studies in the ETB currently utilize conventional consensus mechanisms. Due to their substantial computational requirements and communication overhead, these consensus algorithms are not well-suited for real-time service applications like energy trading. We propose a novel BAC-SDS consensus specifically for V2V ETB, thus enabling resilient grids to maximise the use of renewable energy. We propose an Electric Vehicle (EV) leader election based on cryptography and adopt the sharding technique to enhance the system's scalability. Furthermore, our approach ensures the secure transfer of energy and value, contingent upon the condition that all EVs exhibit reasonable behavior and retain the proofs they possess. We implement the V2V ETB on Hyperledger Fabric. The experiments demonstrate (1) that V2V ETB significantly enhances the resilience of the power grid compared to traditional centralized trading models and (2) the consensus mechanism proposed in this article is better suited for V2V energy trading than existing mechanisms, exhibiting superior performance in terms of security, throughput, and scalability, thus further enhancing the resilience of the power grid.

With the proliferation of the Internet of Things (IoT), mobile edge computing (MEC) has great potential to achieve low latency, high reliability, and low energy consumption. However, in collaborative MEC environments, user movement and task migration may cause task transmission and processing delays, resulting in elevated task response times. Therefore, system performance and user experience need to be ensured by rational task offloading and resource management. At the same time, the protection of user data privacy is becoming increasingly important as a challenge to be overcome. To address the problems of intense resource competition and privacy leakage in MEC, the federated learning for the TD3-based task offloading (FedTO) algorithm is proposed. The algorithm has a dual objective of energy consumption and task response time while protecting user privacy. It employs a cryptographic local model update and aggregation mechanism and uses deep reinforcement learning (DRL) to obtain an efficient task offloading decision. Based on the mobile trajectories of real devices, and the pre-deployment of base station locations, experimental results show that the FedTO algorithm ensures task data security. It also effectively reduces the total energy consumption and average task response time of the system, which further improves the system utility.

Recent strides in nanotechnology have given rise to nanozymes, nanomaterials designed to emulate enzymatic functions. Despite their promise, challenges such as batch-to-batch variability and limited atomic utilization persist. This study introduces Pt(Glu)2, a platinum glutamic acid complex, as a versatile small-molecule peroxidase mimic. Synthesized through a straightforward method, Pt(Glu)2exhibits robust catalytic activity and stability. Steady-state kinetics reveal a lower Km value compared to that of natural enzymes, signifying strong substrate affinity. Pt(Glu)2was explored for controllable chemical modification and integration into cascade reactions with natural enzymes, surpassing other nanomaterials. Its facile synthesis and seamless integration enhance cascade reactions beyond the capabilities of nanozymes. In biosensing applications, Pt(Glu)2enabled simultaneous detection of cholesterol and alkaline phosphatase in human serum with high selectivity and sensitivity. These findings illustrate the potential of small molecule mimetics in catalysis and biosensing, paving the way for their broader applications.

Experienced developers often leverage well-tuned libraries and allocate their routines for computing tasks to enhance performance when building modern scientific and engineering applications. However, such well-tuned libraries are meticulously customized for specific target architectures or environments. Additionally, the performance of their routines is significantly impacted by the actual input data of computing tasks, which often remains uncertain until runtime. Accordingly, statically allocating these library routines may hinder the adaptability of applications and compromise performance, particularly in the context of heterogeneous systems. To address this issue, we propose the Compiler-Assisted Adaptive Library Routines Allocation (COALA) framework for heterogeneous systems. COALA is a fully automated mechanism that employs compiler assistance for dynamic allocation of the most suitable routine to each computing task on heterogeneous systems. It allows the deployment of varying allocation policies tailored to specific optimization targets. During the application compilation process, COALA reconstructs computing tasks and inserts a probe for each of these tasks. Probes serve the purpose of conveying vital information about the requirements of each task, including its computing objective, data size, and computing flops, to a user-level allocation component at runtime. Subsequently, the allocation component utilizes the probe information along with the allocation policy to assign the most optimal library routine for executing the computing tasks. In our prototype, we further introduce and deploy a performance-oriented allocation policy founded on a machine learning-based performance evaluation method for library routines. Experimental verification and evaluation on two heterogeneous systems reveal that COALA can significantly improve application performance, with gains of up to 4.3x for numerical simulation software and 4.2x for machine learning applications, and enhance system utilization by up to 27.8%.