Search

Abstract Nonlinear spin dynamics is essential for predicting complex behaviors of spin systems and has a wide range of applications in physics. Although spin systems have been extensively studied, many intricate behaviors such as self-organizing or chaotic oscillations still need to be better understood with a thorough analysis of nonlinear dynamics. Here we investigate the coupled dynamics of a collection of spins with multiple Larmor frequencies for modeling an inhomogeneous magnetic field and reveal that the nonlinearity induced by a feedback scheme leads to much richer stable dynamic phases, including quasi-periodic orbits and chaos besides the usual limit cycles. We further discuss the robustness of these dynamical phases against experimental noises and the feasibility of realization in various spin systems. Our findings contribute to future observation of nonlinear dynamical phases and prospective applications in spin masers, time crystals and quasi-crystals, and precision measurement.

Abstract Coal Mining enterprises deploy numerous monitoring devices to ensure safe and efficient production using target detection technologies. However, deploying deep detection models on edge devices poses challenges due to high computational loads, impacting detection speed and accuracy. A mining target detection dataset has been created to address these issues, featuring key targets in coal mining scenes such as miners, safety helmets, and coal gangue. A model is proposed to improve real‐time performance for edge mining detection tasks. Detection performance is enhanced by incorporating a Pixel‐wise Normalization Spatial Attention Module (PN‐SAM) into the MobileNet‐v3 bneck structure and replacing the h‐swish activation function with Mish, providing more prosperous gradient information transfer. The proposed model, YOLO‐v4‐LSAM, shows a 3.2% mAP improvement on the VOC2012 dataset and a 2.4% improvement on the mining target dataset compared to YOLO‐v4‐Tiny, demonstrating its effectiveness in mining environments. These enhancements enable more accurate and efficient detection in resource‐constrained edge environments, contributing to safer and more reliable monitoring in coal mining operations.

Abstract Considering the challenges of low multi‐object detection accuracy and difficulty in identifying small targets caused by challenging environmental conditions including irregular lighting patterns and ambient noise levels in the mining environment with autonomous electric locomotives. A new network model based on SOD−YOLOv5s−4L has been proposed to detect multi‐objects for autonomous electric locomotives in underground coal mines. Improvements have been applied in YOLOv5s to construct the SOD−YOLOv5s−4L model, by introducing the SIoU loss function to address the mismatch between real and predicted bounding box directions, facilitating the model to learn target position information more efficiently. This research introduces a decoupled head to enhance feature fusion and improve the positioning precision of the network model, enabling rapid capture of multi‐scale target features. Furthermore, the detection capability of the model has been increased by introducing the small target detection layer which is developed by increasing the number of detection layers from three to four. The experimental results on multiple object detection dataset show that the proposed model achieves significant improvement in mean average precision (mAP) of almost 98% for various types of targets and an average precision (AP) of nearly 99% for small targets on the other hand it achieves 5.19% (mAP) and 9.79% (AP) compared to the YOLOv5s model. Furthermore, comparative analysis with other models like YOLOv7 and YOLOv8 shows that the proposed model has superior performance in terms of object detection.

Abstract In generic closed quantum systems, the complexity of operators increases under time evolution governed by the Heisenberg equation, reflecting the scrambling of local quantum information. However, when systems interact with an external environment, the system-environment coupling allows operators to escape from the system, inducing a dynamical transition between the scrambling phase and the dissipative phase. This transition is known as the environment-induced information scrambling transition, originally proposed in Majorana fermion systems. In this work, we advance this discovery by investigating the transition in charge-conserved systems with space-time randomness. We construct solvable Brownian Sachdev-Ye-Kitaev models of complex fermions coupled to an environment, enabling the analytical computation of operator growth. We determine the critical dissipation strength, which is proportional to $$n(1-n)$$ n ( 1 - n ) with n being the density of the complex fermions, arising from the suppression in the quantum Lyapunova exponent due to the Pauli blockade in the scattering process. We further analyze the density dependence of maximally scrambled operators at late time. Our results shed light on the intriguing interplay between information scrambling, dissipation, and conservation laws.

Abstract Current brain tumor treatments are limited by the skull and BBB, leading to poor prognosis and short survival for glioma patients. We introduce a novel minimally-invasive brain tumor suppression (MIBTS) device combining personalized intracranial electric field therapy with in-situ chemotherapeutic coating. The core of our MIBTS technique is a wireless-ultrasound-powered, chip-sized, lightweight device with all functional circuits encapsulated in a small but efficient “Swiss-roll” structure, guaranteeing enhanced energy conversion while requiring tiny implantation windows ( ~ 3 × 5 mm), which favors broad consumers acceptance and easy-to-use of the device. Compared with existing technologies, competitive advantages in terms of tumor suppressive efficacy and therapeutic resolution were noticed, with maximum ~80% higher suppression effect than first-line chemotherapy and 50–70% higher than the most advanced tumor treating field technology. In addition, patient-personalized therapy strategies could be tuned from the MIBTS without increasing size or adding circuits on the integrated chip, ensuring the optimal therapeutic effect and avoid tumor resistance. These groundbreaking achievements of MIBTS offer new hope for controlling tumor recurrence and extending patient survival.

In the realm of precision cattle health monitoring, this paper introduces the development and evaluation of a novel wearable continuous health monitoring device designed for cattle. The device integrates a sustainable solar-powered module, real-time signal acquisition and processing, and a storage module within an animal ergonomically designed curved casing for non-invasive cattle health monitoring. The curvature of the casing is tailored to better fit the contours of the cattle’s neck, significantly enhancing signal accuracy, particularly in temperature signal acquisition. The core module is equipped with precision temperature sensors and inertial measurement units, utilizing the Arduino MKR ZERO board for data acquisition and processing. Field tests conducted on a cohort of ten cattle not only validated the accuracy of temperature sensing but also demonstrated the potential of machine learning, particularly the Support Vector Machine algorithm, for precise behavior classification and step counting, with an average accuracy of 97.27%. This study innovatively combines real-time temperature recognition, behavior classification, and step counting organically within a self-powered device. The results underscore the feasibility of this technology in enhancing cattle welfare and farm management efficiency, providing clear direction for future research to further enhance these devices for large-scale applications.

AimsThis study aims to systematically analyze the global trends in glioma methylation research using bibliometric methodologies. We focus on identifying the scholarly trajectory and key research interests, and we utilize these insights to predict future research directions within the epigenetic context of glioma.MethodsWe performed a comprehensive literature search of the Web of Science Core Collection (WoSCC) to identify articles related to glioma methylation published from January 1, 2004, to December 31, 2023. The analysis included full-text publications in the English language and excluded non-research publications. Analysis and visualization were performed using GraphPad Prism, CiteSpace, and VOSviewer software.ResultsThe search identified 3,744 publications within the WoSCC database, including 3,124 original research articles and 620 review articles. The research output gradually increased from 2004 to 2007, followed by a significant increase after 2008, which peaked in 2022. A minor decline in publication output was noted during 2020–2021, potentially linked to the coronavirus disease 2019 pandemic. The United States and China were the leading contributors, collectively accounting for 57.85% of the total research output. The Helmholtz Association of Germany, the German Cancer Research Center (DKFZ), and the Ruprecht Karls University of Heidelberg were the most productive institutions. The Journal of Neuro-Oncology led in terms of publication volume, while Neuro-Oncology had the highest Impact Factor. The analysis of publishing authors revealed Michael Weller as the most prolific contributor. The co-citation network analysis identified David N. Louis's article as the most frequently cited. The keyword analysis revealed “temozolomide,” “expression,” “survival,” and “DNA methylation” as the most prominent keywords, while “heterogeneity,” “overall survival,” and “tumor microenvironment” showed the strongest citation bursts.ConclusionsThe findings of this study illustrate the increasing scholarly interest in glioma methylation, with a notable increase in research output over the past two decades. This study provides a comprehensive overview of the research landscape, highlighting the importance of temozolomide, DNA methylation, and the tumor microenvironment in glioma research. Despite its limitations, this study offers valuable insights into the current research trends and potential future directions, particularly in the realm of immunotherapy and epigenetic editing techniques.

Biological soil crusts (BSCs) play an essential role in soil stabilization and nutrient cycling in arid environments, being a hotspot of microbial activity including soil enzyme production. However, the changes in microbial communities of the BSCs from different age are poorly understood. In over a 60-year dune revegetation chronosequence (2021, 2016, 1973, and 1962) in the southeastern Tengger Desert, China, we compared the development of BSCs and underlying sands using metagenome sequencing and enzyme assays. In both BSCs and the underlying sand substrate, enzyme activity increased along the time series but was always greater for BSCs than the substrate, emphasizing the potential for nutrient cycling activity. A clear trend in community composition and co-occurrence network complexity was observed in the BSCs: the family-level taxonomic network of BSCs became more connected in the older age BSCs, while in the substrate, there was no such trend. Unexpectedly, considering their low relative abundance and diversity, archaea emerged as major keystones in establishing community networks, being key to network stability. In the underlying substrate, by contrast, archaea did not play this key role. During the time series of BSCs, the dominant archaeal groups were Nitrososphaeria, Methanomicrobia, Halobacteria, and an unclassified Thaumarchaeota, mainly linked to functional genes related to carbon and nitrogen, such as CHB2, xylA, amyA, amoB, nxrA, nxrB, and nirB. This suggests that in BSCs, the key role of archaea relates to their role in nitrogen cycling. This perspective adds to the emerging view that archaea play an important role in community structure and function in dryland environments.

Abstract Background Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH. Results We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal . Conclusions Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

Abstract Background Copy number alteration (CNA) is one of the major genomic variations that frequently occur in cancers, and accurate inference of CNAs is essential for unmasking intra-tumor heterogeneity (ITH) and tumor evolutionary history. Single-cell DNA sequencing (scDNA-seq) makes it convenient to profile CNAs at single-cell resolution, and thus aids in better characterization of ITH. Despite that several computational methods have been proposed to decipher single-cell CNAs, their performance is limited in either breakpoint detection or copy number estimation due to the high dimensionality and noisy nature of read counts data. Results By treating breakpoint detection as a process to segment high dimensional read count sequence, we develop a novel method called DeepCNA for cross-cell segmentation of read count sequence and per-cell inference of CNAs. To cope with the difficulty of segmentation, an autoencoder (AE) network is employed in DeepCNA to project the original data into a low-dimensional space, where the breakpoints can be efficiently detected along each latent dimension and further merged to obtain the final breakpoints. Unlike the existing methods that manually calculate certain statistics of read counts to find breakpoints, the AE model makes it convenient to automatically learn the representations. Based on the inferred breakpoints, we employ a mixture model to predict copy numbers of segments for each cell, and leverage expectation–maximization algorithm to efficiently estimate cell ploidy by exploring the most abundant copy number state. Benchmarking results on simulated and real data demonstrate our method is able to accurately infer breakpoints as well as absolute copy numbers and surpasses the existing methods under different test conditions. DeepCNA can be accessed at: https://github.com/zhyu-lab/deepcna . Conclusions Profiling single-cell CNAs based on deep learning is becoming a new paradigm of scDNA-seq data analysis, and DeepCNA is an enhancement to the current arsenal of computational methods for investigating cancer genomics.

Deep neural networks have made remarkable contributions in many fields such as computer vision and natural language processing. However, due to their complex internal structures, for a long time, there is a lack of trust in deep neural networks, particularly in the domains like healthcare, finance, traffic safety, etc. To explain the internal mechanism of deep neural networks, a series of interpretable models, which mainly focus on enhancing the interpretability of deep learning models, has been proposed. This study pursues to data interpretability by analyzing the significance of image features with the goal of mining essential information contained in various visual features. Super-pixel segmentation is firstly applied to divide the image into several coherent visual feature regions, and these regions are subsequently overwritten by certain marks. Next, image occlusion is realized by modifying the pixel values of these feature regions according to the marks. Finally, we conduct comparative analytics of the classification results between the original data and occluded data to scrutinize the importance of key visual features. A collection of experimental studies is conducted and the results consistently demonstrate that the proposed method enables to observe the classifier’s dependence on different features, offering a new perspective on interpretability rooted in data analytics.

Quantifying the straw-nitrogen (N) in soil N pools and exploring soil microbial phylogenetic diversity involved in N mineralization are important for effectively managing crop straw to optimize crop N use under climate change. This study used 15N-labeled rice straw to quantify straw-N mineralization in the rhizosphere and N uptake of rice plants in response to elevated CO2 (700 ppm) and warming (2 °C above ambient). The CO2 and temperature co-elevation resulted in 20 mg kg−1 of straw-N mineralized in the rhizosphere, 50 % greater than the ambient control. A similar trend was found for soil-N mineralization. The CO2 and temperature co-elevation significantly increased the gaseous loss of straw-derived N from the rhizosphere, and straw-N in microbial biomass fraction, soluble organic and mineral N fractions in bulk soil. It also increased microbial biomass N originated from the rhizosphere soil. Irrespective of climatic treatments, the amount of mineralized N from straw and soil was greater in the rhizosphere than bulk soil. The CO2 and temperature co-elevation increased plant N uptake by 32 % with the dominant origin of soil-N rather than straw-N. With straw amendment, elevated CO2 plus warming significantly (p

Genome-wide association studies (GWAS) have identified thousands of variants in the human genome with autoimmune diseases. However, identifying functional regulatory variants associated with autoimmune diseases remains challenging, largely because of insufficient experimental validation data. We adopt the concept of semi-supervised learning by combining labeled and unlabeled data to develop a deep learning-based algorithm framework, sscNOVA, to predict functional regulatory variants in autoimmune diseases and analyze the functional characteristics of these regulatory variants. Compared to traditional supervised learning methods, our approach leverages more variants’ data to explore the relationship between functional regulatory variants and autoimmune diseases. Based on the experimentally curated testing dataset and evaluation metrics, we find that sscNOVA outperforms other state-of-the-art methods. Furthermore, we illustrate that sscNOVA can help to improve the prioritization of functional regulatory variants from lead single-nucleotide polymorphisms and the proxy variants in autoimmune GWAS data.

Abstract This paper proposes a new method for real-time terrain recognition-based navigation for mobile robots. Mobile robots performing tasks in unstructured environments need to adapt their trajectories in real-time to achieve safe and efficient navigation in complex terrains. However, current methods largely depend on visual and IMU (inertial measurement units) that demand high computational resources for real-time applications. In this paper, a real-time terrain identification-based navigation method is proposed using an on-board tapered whisker-based reservoir computing system. The nonlinear dynamic response of the tapered whisker was investigated in various analytical and Finite Element Analysis frameworks to demonstrate its reservoir computing capabilities. Numerical simulations and experiments were cross-checked with each other to verify that whisker sensors can separate different frequency signals directly in the time domain and demonstrate the computational superiority of the proposed system, and that different whisker axis locations and motion velocities provide variable dynamical response information. Terrain surface-following experiments demonstrated that our system could accurately identify changes in the terrain in real-time and adjust its trajectory to stay on specific terrain.

A decentralized marking fault diagnosis method is proposed to solve the problem that increasing the number of unobservable transitions may result in the inability to diagnose faulty markings, in a class of decentralized systems modeled by labeled Petri nets. Assuming that each local site knows the structure of the labeled Petri net, and the subnet induced by unobservable transitions is acyclic. The decentralized architecture consists of a set of local sites communicating with a coordinator that determines whether the faults have occurred in the system, which are modeled by markings. First, each local site constructs the corresponding dual verifier and calculates the local marking fault diagnosis state according to the local observations. Then, it exchanges the corresponding information with the coordinator according to the two proposed diagnosis protocols. Finally, the coordinator calculates the global diagnosis state according to the received information. In addition, the marking diagnosability under both protocols is analyzed. A sufficient and necessary condition for marking fault diagnosis in the decentralized architecture under the second protocol is proved.

This study synthesizes Ni-doped perovskite-structured LaFeO3 composite materials via a one-step hydrothermal method, characterizes the morphology and structure of the materials, and tests their gas sensing performance. The test results show that compared to pure LaFeO3 material, the gas sensing performance of Ni-doped LaFeO3 material is improved in all aspects. Specifically, LFO-Ni2% exhibits a response as high as 102 towards 100 ppm of triethylamine at 190 °C, along with better selectivity and stability. Furthermore, the gas sensing mechanism is investigated. On one hand, doping with an appropriate proportion of Ni can lead to the formation of more-complete and smaller-sized microsphere structures with pores. This is beneficial for the adsorption of oxygen from the air onto the material surface, as well as for the diffusion of the target gas to the surface of the material, thereby enhancing gas sensitivity performance. On the other hand, the doped Ni enters the interior of the LaFeO3 crystal, replacing some of the cations in LaFeO3, increasing the concentration of charge carriers in the material, and reducing the material’s resistance. The sample can adsorb more oxygen, promoting the reaction between adsorbed oxygen and the target gas, and thereby improving the gas sensitivity performance of the sample.

Abstract As it is known, existing mixed near‐field and far‐field non‐circular source localisation rely on the uniform linear array (ULA). Compared with the ULA, the sparse array can obtain more consecutive lags and a larger physical array aperture. So a novel symmetric flipped nested array (SFNA) is proposed for mixed near‐field and far‐field non‐circular source localisation. At the same time, according to the special geometry of the array, the non‐circular phase information can be removed and the existing algorithm can be used to solve the problem of sparse array estimating the mixed near‐field and far‐field non‐circular signals. First, by exchanging the positions of the subarrays in the traditional nested array and then flipping them, a new SFNA is formed; second, the authors present the maximum consecutive lags and composition method of SFNA. For example, 4MN +4M −3 consecutive lags can be obtained by using a 2M + 2N −1 array element; third, in the estimation of mixed near‐field and far‐field non‐circular sources, a special cumulant is used to eliminate the range of the mixed near‐field and far‐field non‐circular sources and a one‐dimensional (1‐D) spectral search is used to obtain all the directions of arrival, and the range can be obtained by defining the range spectrum; finally, numerical simulation results demonstrate the superiority of the proposed array.

Abstract Network function virtualization (NFV) decouples network functions from hardware devices. However, it introduces security challenges due to its reliance on software, which facilitates attacks. This security problem has a significant negative impact on the interests of users. Existing deployment methods are not suitable for SFC requests with a security demand, causing the use of substrate resources unreasonable and lower acceptance ratio. Moreover, a strict delay requirement is another challenge for NFV. To make the use of the substrate resources more reasonable and reduce the transmission delay, this paper proposes a security-constraint and function-mutex-constraint consolidation (SFMC) method for virtual network function (VNF) to reduce resource consumption and transmission delay. In addition, a security-aware service function chain (SASFC) deployment method for load balance and delay optimization is presented, which deploys service function chains according to the consolidated results of the SFMC method. The SASFC method first obtains a candidate server node set using resource, hosting capacity, security and node load constraints. It then obtains candidate paths according to the metric of the minimum transmission delay and link load constraint using the Viterbi algorithm. Finally, the path with the highest VNF security level match degree among the candidate paths is adopted to deploy virtual links, and the corresponding server nodes are employed to deploy VNFs. As a result, the SASFC method makes the use of substrate resources more reasonable. It improves the acceptance ratio and long-term average revenue to cost ratio, reduces transmission delay, and achieves load balancing. Experiment results show that when the number of VNFs is five, the acceptance ratio and long-term average revenue to cost ratio of the SASFC method are close to 0.75 and 0.88, which are higher than those of the compared methods. Its transmission delay and proportion of bottleneck nodes are 7.71 and 0.024, which are lower than those of the compared methods. The simulations demonstrate the effectiveness of the SASFC method.

Crop removal of cations and anions is one of the main factors causing soil acidification. Mollisols, also known as black soils, which are inherently neutral soils, have been acidified due to irrational land use. However, the magnitude of acidification of black soils in relation to crop removal under crop rotation and field management remains unclear. In this study, we collect soil samples from 9- and 10-year long-term trials with seven treatments of soybean and corn monoculture, and corn–soybean rotation under different fertilization or straw return in the Mollisol region of Northeast China. Total removal of cations and anions is 371–508 and 92–125 kg ha−1 in corn but 166–250 and 56–82 kg ha−1 in soybean, which leads to the production of 7.06–10.2 kmol ha−1 H+ for corn and 4.43–5.77 kmol ha−1 H+ for soybean. Among the four cations (K+, Ca2+, Na+, and Mg2+) analyzed in different tissues of crops, K+ contributes more to the total H+ production, where K+ in corn stem contributes more than 55% H+, while K+ in soybean seed contributes more than 64% H+. The sum of Ca2+ and Mg2+ removal for soybean is 37.6–66.9 kg ha−1, 1.26–2.07 times (p < 0.05) greater than for corn. Net H+ produced by corn and soybean removal may potentially lead to a soil acidification of 0.053–0.074 and 0.032–0.045 year−1 units pH, respectively. The greater the corn or soybean biomass, the higher the net H+ production. Continuous soybean and combination of chemical fertilizer for corn but no chemical fertilizer for soybean reduce the soil pH buffering capacity (pHBC), while other treatments increase the soil pHBC, but there is no effect for continuous corn. Compared to normal corn–soybean rotation, soil pH in 9-year continuous corn declines from 5.76 to 5.63, while 10-year continuous soybean declines from 5.68 to 5.43 (p < 0.05). However, soil pH with chemical fertilizer for corn and dairy manure for soybean increases by 0.60 and 0.89 units (p < 0.05). Changes in pH might not be fully related to crop ion removal. Manure application with crop rotation is an effective approach to alleviate soil acidification.

Soil amelioration in coastal saline areas plays an important role in alleviating land resource shortages, improving regional ecological environments, ensuring food security, and promoting economic development. Plastic mulching (M) and the combination of freezing saline water irrigation and plastic mulching (WIM) are successful amelioration practices that dramatically reduce the salinity of surface soil and facilitate plant growth in coastal saline soil. However, the bacterial responses that are closely related to these amelioration practices in coastal saline soil remain poorly understood. In this study, bacterial richness and diversity, community composition, and potential ecological functions in the rhizosphere and bulk soils of cotton in M and WIM treatments, along with a control treatment, were investigated using high-throughput sequencing in a coastal saline field. The results showed that both the M and WIM treatments increased bacterial richness and alpha diversity, which were in general significantly higher in bulk soil than in rhizosphere soil. Non-metric multidimensional scaling and the Bray–Curtis dissimilarity analysis revealed that the bacterial community in rhizosphere soil was assembled far from those in the control and bulk soils and behaved more specifically in rhizosphere soil than in bulk soil. The relative abundances of most of the dominant phyla showed opposite trends of variation in bulk and rhizosphere soils compared to those in control soil in both M and WIM treatments; in particular, the specific bacterial groups of Proteobacteria and Actinobacteria decreased in bulk soil but significantly increased in rhizosphere soil. Functional groups of chemoheterotrophy, aerobic chemoheterotrophy, and nitrate reduction were predominant in rhizosphere rather than bulk soil, according to the Functional Annotation of Prokaryotic Taxa. These findings improve the understanding of the mechanism of bacterial responses to amelioration practices M and WIM in coastal saline soils and provide valuable information for the development of amelioration techniques based on agricultural practices and soil microbiome to enhance plants’ adaptability to saline soil in the future.

Abstract The beneficial effect of crop residue amendment on soil organic carbon (SOC) stock and stability depends on the functional response of soil microbial communities. Here we synchronized microbial metagenomic analysis, nuclear magnetic resonance and plant-15N labeling technologies to gain understanding of how microbial metabolic processes affect SOC accumulation in responses to differences in N supply from residues. Residue amendment brought increases in the assemblage of genes involved in C-degradation profiles from labile to recalcitrant C compounds as well as N mineralization. The N mineralization genes were correlated with the C and N accumulation in the particulate and mineral-associated C pools, and plant-derived aliphatic forms of SOC. Thus, the combined C and N metabolic potential of the microbial community transforms residue into persistent organic compounds, thereby increasing C and N sequestration in stable SOC pools. This study emphasizes potential microbially mediated mechanisms by which residue N affects C sequestration in soils.

Abstract In recent years, the XACML (eXtensible Access Control Markup Language) is widely used in a variety of research fields, especially in access control. However, when policy sets defined by the XACML become large and complex, the policy evaluation time increases significantly. In order to improve policy evaluation performance, we propose an optimization algorithm based on the DPCA (Density Peak Cluster Algorithm) to improve the clustering effect on large-scale complex policy sets. Combined with this algorithm, an efficient policy evaluation engine, named DPEngine, is proposed to speed up policy matching and reduce the policy evaluation time. We compare the policy evaluation time of DPEngine with the Sun PDP, HPEngine, XEngine and SBA-XACML. The experiment results show that (1) when the number of requests reaches 10,000, the DPEngine evaluation time on a large-scale policy set with 100,000 rules is approximately 2.23%, 3.47%, 3.67% and 4.06% of that of the Sun PDP, HPEngine, XEngine and SBA-XACML, respectively and (2) as the number of requests increases, the DPEngine evaluation time grows linearly. Compared with other policy evaluation engines, the DPEngine has the advantages of efficiency and stability.

In this article, an improved particle swarm optimization (IPSO) algorithm based on similarity and random mutation is raised. The diversity of particles in the population is decided by the size of the aggregation. When the aggregation degree of particles in the population surpass a certain threshold, the concept of similarity is used to measure the similarity between particles and global extremum, and the particles with higher similarity are discretized by mutation strategy. By increasing the particle swarm’s diversity, the population’s local and global search ability tend to balance. The weight and threshold of the back propagation (BP) neural networks are optimized by the IPSO algorithm. Then, the model of the improved particle swarm optimization back propagation neural network (IPSO-BP) is applied to the aero-optical imaging deviation prediction. The results show that the prediction accuracy of the IPSO-BP model is superior to the PSO-BP model, the extreme learning machine (ELM) model, and the least square support vector machine (LSSVM) model, and its convergence speed is faster than that of the PSO-BP neural network model. Finally, the application of deep learning in aero-optical imaging deviation prediction is analyzed compared with the IPSO-BP neural network model.

In recent years, with the development of deep learning, image color rendering method has become a research hotspot once again. To overcome the detail problems of color overstepping and boundary blurring in the robust image color rendering method, as well as the problems of unstable training based on generative adversarial networks, we propose an color rendering method using Gabor filter based improved pix2pix for robust image. Firstly, the multi-direction/multi-scale selection characteristic of Gabor filter is used to preprocess the image to be rendered, which can retain the detailed features of the image while preprocessing to avoid the loss of features. Moreover, among the Gabor texture feature maps with 6 scales and 4 directions, the texture map with the scale of 7 and the direction of 0° has the comparable rendering performance. Finally, by improving the loss function of pix2pix model and adding the penalty term, not only the training can be stabilized, but also the ideal color image can be obtained. To reflect image color rendering quality of different models more objectively, PSNR and SSIM indexes are adopted to evaluate the rendered images. The experimental results of the proposed method show that the robust image rendered by this method has better visual performance and reduces the influence of light and noise on the image to a certain extent.

The study proposes a way of developing granular models based on optimized subsets of data with different sampling sizes, in which three generally used models, namely Support Vector Machine, K-Nearest Neighbor, and Long Short-Term Memory, are designed and transformed into granular version for achieving a good performance with sufficient functionality. First, a collection of subsets are determined using different sampling methods, which are subsequently applied to play as an essential prerequisite of the proposed models. Then, the principle of justifiable granularity is utilized to the design of interval information granules based on the subsets of data. The design process is associated with a well-defined optimization problem realized by achieving a sound compromise between two conflicting criteria: coverage and specificity. To evaluate the performance of the granular models, two aspects are considered: (i) sampling methods used in determining suitable subsets of data; (ii) different models applied to be transformed into granular models. A series of experimental studies are conducted to verify the feasibility of the proposed granular models.

This paper investigated the verification of detectability for discrete event systems based on a class of partially observed unbounded Petri nets. In an unbounded net system, all transitions and partial places are assumed to be unobservable. The system administrator can only observe a few observable places, i.e., the number of tokens at these places can be observed, allowing for the estimation of current and subsequent states. The concepts of quasi-observable transitions, truly unobservable transitions, and partial markings are used to construct a basis coverability graph. According to this graph, four sufficient and necessary conditions of detectability are proposed. Correspondingly, a specific example is proposed to prove that the detectability can be verified in the unbounded net system. Furthermore, based on the conclusion of detectability, the system’s ability to detect critical states was explored by using the basis coverability graph, called C-detectability. Two real-world examples are proposed to show that the detectability of discrete event systems has not only pioneered new research methods, but also demonstrated that the real conditions faced by this method are more general, and it has overcome the limitations of relying only on the ideal conditions of bounded systems for verification.

Abstract Background Rosai–Dorfman disease (RDD) is a rare, benign, idiopathic non-Langerhans cell histiocytosis. Cases of RDD in the CNS are extremely rare but lethal. RDD is thought to represent a reactive process. Recent studies proposed a subset of RDD cases that had a clonal nature. However, its clone origin is poorly understood. Case presentation We present a rare case of RDD in the CNS with two isolated lesions. These two lesions were removed successively after two operations. No seizure nor recurrence appears to date (2 years follow-up). Morphological and immunohistochemical profiles of these two lesions support the diagnosis of RDD. Based on the whole-exome sequencing (WES) data, we found the larger lesion has a higher tumor mutational burden (TMB) and more driver gene mutations than the smaller lesion. We also found seven common truncal mutations in these two lesions, raising the possibility that they might stem from the same ancestor clone. Conclusions Overall, this is the first report about clonal evolution of RDD in the CNS with two isolated lesions. Our findings contribute to the pathology of RDD, and support the notion that a subset of cases with RDD is a clonal histiocytic disorder driven by genetic alterations.

Root-knot nematodes (RKNs; Meloidogyne spp.), one of the most economically important plant-parasitic nematodes (PPNs), cause severe yield and quality losses in agriculture annually. The application of biological control agents is an environmentally safe and effective approach to control RKNs. Here, we report the genomic characteristics of a Bacillus velezensis strain YS-AT-DS1 (Bv-DS1) isolated from the tidal soil, revealing that it has a 4.73 Mb circular chromosome with an average GC-content of 46.43%, 3,977 genes, 86 tRNAs, and 27 rRNAs, and contains secondary metabolite clusters for producing antimicrobial compounds. In vitro assays indicated that Bv-DS1 has not only antagonistic activities against fungal pathogens, but also shows nematicidal activity, with a mortality rate of 71.62% mortality rates in second-stage juvenile (J2s) Meloidogyne incognita. We then focused on the biocontrol efficiency of Bv-DS1 against M. incognita in pot assays. Preinoculation with Bv-DS1 enhanced tomato growth, and significantly reduced the infection rate of J2s, and the number of galls and egg masses on tomato roots. The underlying mechanism in Bv-DS1-induced resistance to M. incognita was further investigated through split-root experiments, and analysing the expression of the genes related to jasmonic acid (JA), salicylic acid (SA), and the tonoplast intrinsic protein (TIP). The results indicated that Bv-DS1 could not activate host systemic-induced resistance (ISR) in the split-root system of tomatoes. Additionally, the expression of JA- (LOX D and MC) and SA- (PAL2 and PR) responsive genes did not change in Bv-DS1-pretreated plants at 3 and 14 days after nematode inoculation. The presented data showed that JA-and SA-dependent pathways were not required for the biocontrol action of the Bv-DS1 against RKN. The TIP genes, responsible for transport of water and small substrates in plants, have previously been shown to negatively regulate the parasitism of PPNs. Surprisingly, Bv-DS1 compromised the downregulation of TIP1.1 and TIP1.3 by M. incognita. Together, our data suggest that Bv-DS1 exhibits a dual effect on plant growth promotion and protection against RKN, possibly related to the regulation of water and solute transport via TIPs. Thus, the Bv-DS1 strain could be used as a biocontrol agent for RKN control in sustainable agriculture.

Continuous cropping of soybean leads to soil environment deterioration and soil-borne disease exacerbation, which in turn limits the sustainability of agricultural production. Chitin amendments are considered promising methods for alleviating soybean continuous cropping obstacles; however, the underlying mechanisms of soil sickness reduction remain unclear. In this study, soil amendments with pure and crude chitin at different addition dosages were employed to treat diseased soil induced by continuous cropping of soybean for five years. Chitin amendments, especially crude chitin, remarkably increased soil pH, available phosphorus (AP), potassium (AK) and nitrate nitrogen ( NO3−-N) contents, and improved soybean plant growth and soil microbial activities (FDA). Additionally, chitin application significantly enriched the relative abundances of the potential biocontrol bacteria Sphingomonas, Streptomyces, and Bacillus and the fungi Mortierella, Purpureocillium, and Metarhizium while depleted those of the potential plant pathogens Fusarium, Cylindrocarpon and Paraphoma. Moreover, chitin amendments induced looser pathogenic subnetwork structures and less pathogenic cooperation with other connected microbial taxa in the rhizosphere soils. The structural equation model (SEM) revealed that pure and crude chitin amendments promoted soybean plant growth by indirectly regulating soil pH-mediated soil microbial activities and potentially beneficial microbes, respectively. Therefore, the reduction strategies for continuous cropping obstacles by adding pure and crude chitin were distinct; pure chitin amendments showed general disease suppression, while crude chitin exhibited specific disease suppression. Overall, chitin amendments could suppress potential plant pathogens and improve soil health, thereby promoting soybean growth, which provides new prospects for cultivation practices to control soybean continuous cropping obstacles.

Crop photosynthetic capacity in response to climate change likely constrains crop productivity and adaptability to changing environments, which requests the investigation on the dynamics of photosynthetic parameters over growth season among varieties, especially in cold-temperate regions. Three Japonica rice cultivars i.e., Shoubaimao (SH), Hejiang 19 (HJ); Longjing 31, (LJ). were planted under the control, e[CO2] (700 μmol mol-1), warming (2°C above the air temperature) and the co-elevation of [CO2] and temperature in open-top chambers (OTC). The objective of this study is to examine the rice photosynthetic parameters, water use efficiency (WUE) and yield formation in responses to the co-elevation of [CO2] and temperature which is the main predicted features of future climate. e[CO2] significantly increased An of SH, HJ and LJ by 37%, 39% and 23% in comparison to 34%, 34% and 27% under elevated [CO2] plus warming, respectively. However, An had a weak response to warming for three cultivars. [CO2] and temperature co-elevation significantly decreased the stomatal conductance, resulting in a significant increase of the WUE. e[CO2] significantly increased Vc, max, Jmax and Jmax/Vc, max. e[CO2] significantly increased grain yield and grain number of all cultivars. The positive effect of co-elevation of [CO2] and temperature on grain yield was less than e[CO2]. Warming is likely to partially offset the increased photosynthetic rate caused by e[CO2]. The [CO2] and temperature co-elevation may be favorable to rice crop with increasing the photosynthetic ability of rice crop and improving water use efficiency. The present study provided evidence that the rice genotypic difference in photosynthetic potential under [CO2] and temperature co-elevation. Therefore, it is crucial to explore a broader range of phenotypes and cultivars to be applied to climate change response research, advancing the knowledge that climate change impacts rice crop under the cold-temperate climate region.

To explore the crossover linkage of the bacterial infections resulting from the viral infection, within the host body, a computational framework is developed. It analyzes the additional pathogenic effect of Streptococcus pneumonia, one of the bacteria that can trigger the super-infection mechanism in the COVID-19 syndrome and the physiological effects of innate immunity for the control or eradication of this bacterial infection. The computational framework, in a novel manner, takes into account the action of pro-inflammatory and anti-inflammatory cytokines in response to the function of macrophages. A hypothetical model is created and is transformed to a system of non-dimensional mathematical equations. The dynamics of three main parameters (macrophages sensitivity κ, sensitivity to cytokines η and bacterial sensitivity ϵ), analyzes a “threshold value” termed as the basic reproduction number R0 which is based on a sub-model of the inflammatory state. Piece-wise differentiation approach is used and dynamical analysis for the inflammatory response of macrophages is studied in detail. The results shows that the inflamatory response, with high probability in bacterial super-infection, is concomitant with the COVID-19 infection. The mechanism of action of the anti-inflammatory cytokines is discussed during this research and it is observed that these cytokines do not prevent inflammation chronic, but only reduce its level while increasing the activation threshold of macrophages. The results of the model quantifies the probable deficit of the biological mechanisms linked with the anti-inflammatory cytokines. The numerical results shows that for such mechanisms, a minimal action of the pathogens is strongly amplified, resulting in the “chronicity” of the inflammatory process.

Background: As reflected in the WHO classification of glioma since 2020, genomic information has been an important criterion in addition to histology for glioma classification. There is a significant intergrade difference as well as intragrade difference of survival probability among glioma patients. Except the molecular criteria used in the WHO classification, few studies have explored other genomic factors that may be underlying these survival differences, especially in Chinese populations. Here, we used integrative genomic approaches to characterize a Chinese glioma cohort to search for potential prognostic biomarkers.Methods: We recruited 46 Chinese patients with primary malignant glioma. All the patients were analyzed with whole-exome sequencing (WES) and 27 of them were analyzed with RNA-seq. We compared the molecular features between patients in different WHO grades. We classified the glioblastoma (GBM) patients into two groups (good vs poor survival) using six-month progression-free survival (PFS6) status and compared the genomic profiles between the two groups.Results: We found grade II and grade III patients cluster together (LGG) and they are different from GBM in unsupervised clustering analysis with RNA-seq data. Gene set enrichment analysis (GSEA) comparing GBM and the LGG group suggested that GBM had upregulation of multiple pathways related to genome integrity and immune cell infiltration. Further comparison of somatic mutations between the two groups revealed TOPAZ1 as a novel mutation associated with GBM and prevalence of CNV in multiple genes in GBM. Comparison between PFS6 good and poor GBM patients revealed six genes (TRIML2, ROCK1, PKD1, OBSCN, HECTD4, and ADCY7) were significantly mutated and two genes (NTRK1 and B2M) had more CNV alterations in the poor prognosis group.Conclusion: Taken together, our molecular data revealed that GBM patient showed distinct characteristics related to individual gene, chromosome integrity, and infiltrating immune cells compared to LGG (grade II/III) patients. We also identified few novel genes with SNV or CNV, which might be the potential markers for clinical outcome of GBM.

In the field of image enhancement, using deep learning methods to enhance low-light images is currently mainstream. However, the methods often have complex network structures of a large number of parameters, and their training often uses paired data-sets, which are difficult to obtain in actual practice. To solve these problems, this paper proposes a simple generative adversarial network and Retinex model, dubbed RetinexGAN, that is completely trained using unpaired data-sets. It contains a decomposition network and two discriminator networks. To reduce the parameters of the network, only two convolution layers are used in the decomposition network. We show more challenging testing data where some parts of the image are underexposed and others are normal light. Both quantitative and visual results show that RetinexGAN is largely superior to state-of-the-art methods.

Network Function Virtualization (NFV) decouples network functions from hardware, improves the flexibility of resource allocation, and enhances network scalability. Any failures of software and hardware in an NFV environment will result in the interruption of Service Function Chains (SFCs). As one of the key technologies of 5G, NFV has more stringent delay and reliability requirements for services. This paper takes software and hardware failures into account, and proposes a reliability-aware service function chain backup protection (RABP) method to meet SFCs' high reliability and low latency demands. First, we formulate a mixed-integer linear programming model to maximize the revenue-to-cost ratio. Then, we propose a heuristic algorithm for suboptimal solutions. SFC deployment is divided into two stages: primary virtual network function (VNF) deployment and backup VNF deployment. The reliability enhancement and delay optimization deployment method is presented to deploy primary VNFs. A breadth-first search method is used to consolidate primary VNFs as much as possible to improve reliability of SFCs and reduce transmission delay. When deploying backup VNFs, a resource-efficient backup selection method is presented to reduce backup resource consumption while meeting reliability demands. Finally, experiment results show that the proposed RABP method not only has the best acceptance ratio and long-term average revenue to cost ratio, but also reduces backup resource consumption and transmission delay.

To improve the light truck unidirectional self-increasing force brake of the brake performance， the finite element model of unidirectional self-increasing force brake is established， by using Abaqus software， the braking process simulation analysis of brake is carried out， a unidirectional self-increasing force brake of the axial and circumferential of the distribution of temperature field and gravitational field are got， and adopting the method of Manson-Coffin equation to predict the fatigue life of brake drum. The results show that during the whole braking process， the temperature and stress of the brake drum vary in zigzag shape， and the fatigue life of the brake drum is greatly affected by the temperature. In the process of braking， the change law of temperature field of brake drum is mainly affected by heat flux， convection heat transfer and heat conduction. The stress field of brake drum is mainly affected by the initial position and the contact between the inner surface of brake drum and the surface of friction sheet.

Abstract Background A number of simulators have been developed for emulating next-generation sequencing data by incorporating known errors such as base substitutions and indels. However, their practicality may be degraded by functional and runtime limitations. Particularly, the positional and genomic contextual information is not effectively utilized for reliably characterizing base substitution patterns, as well as the positional and contextual difference of Phred quality scores is not fully investigated. Thus, a more effective and efficient bioinformatics tool is sorely required. Results Here, we introduce a novel tool, SimuSCoP, to reliably emulate complex DNA sequencing data. The base substitution patterns and the statistical behavior of quality scores in Illumina sequencing data are fully explored and integrated into the simulation model for reliably emulating datasets for different applications. In addition, an integrated and easy-to-use pipeline is employed in SimuSCoP to facilitate end-to-end simulation of complex samples, and high runtime efficiency is achieved by implementing the tool to run in multithreading with low memory consumption. These features enable SimuSCoP to gets substantial improvements in reliability, functionality, practicality and runtime efficiency. The tool is comprehensively evaluated in multiple aspects including consistency of profiles, simulation of genomic variations and complex tumor samples, and the results demonstrate the advantages of SimuSCoP over existing tools. Conclusions SimuSCoP, a new bioinformatics tool is developed to learn informative profiles from real sequencing data and reliably mimic complex data by introducing various genomic variations. We believe that the presented work will catalyse new development of downstream bioinformatics methods for analyzing sequencing data.

Abstract Background Ischemic stroke poses a severe risk to human health worldwide, and currently, clinical therapies for the disease are limited. Delta opioid receptor (DOR)-mediated neuroprotective effects against ischemia have attracted increasing attention in recent years. Our previous studies revealed that DOR activation by [d-Ala2, d-Leu5] enkephalin (DADLE), a selective DOR agonist, can promote hippocampal neuronal survival on day 3 after ischemia. However, the specific molecular and cellular mechanisms underlying the DOR-induced improvements in ischemic neuronal survival remain unclear. Results We first detected the cytoprotective effects of DADLE in an oxygen–glucose deprivation/reperfusion (OGD/R) model and observed increased viability of OGD/R SH-SY5Y neuronal cells. We also evaluated changes in the DOR level following ischemia/reperfusion (I/R) injury and DADLE treatment and found that DADLE increased DOR levels after ischemia in vivo and vitro. The effects of DOR activation on postischemic autophagy were then investigated, and the results of the animal experiment showed that DOR activation by DADLE enhanced autophagy after ischemia, as indicated by elevated LC3 II/I levels and reduced P62 levels. Furthermore, the DOR-mediated protective effects on ischemic CA1 neurons were abolished by the autophagy inhibitor 3-methyladenine (3-MA). Moreover, the results of the cell experiments revealed that DOR activation not only augmented autophagy after OGD/R injury but also alleviated autophagic flux dysfunction. The molecular pathway underlying DOR-mediated autophagy under ischemic conditions was subsequently studied, and the in vivo and vitro data showed that DOR activation elevated autophagy postischemia by triggering the AMPK/mTOR/ULK1 signaling pathway, while the addition of the AMPK inhibitor compound C eliminated the protective effects of DOR against I/R injury. Conclusion DADLE-evoked DOR activation enhanced neuronal autophagy through activating the AMPK/mTOR/ULK1 signaling pathway to improve neuronal survival and exert neuroprotective effects against ischemia.

The construction of information granules is a significant and interesting topic of Granular Computing (GrC) in which information granules play a vital role in representing and describing data, and it has become one of the most effective frameworks for solving complex problems. In this study, we are interested in the collaborative impacts of several different characteristics on constructing information granules, and a novel synergistic mechanism of the principle of justifiable granularity is utilized in developing information granules. The synergistic mechanism is finalized with a two-phase process—to start with, the principle of justifiable granularity and Fuzzy C-Means Clustering method are combined to develop a collection of information granules. First, the available experimental data is transformed (normalized) into fuzzy sets following the standard Fuzzy C-Means Clustering method. Then, information granules are developed based on the elements located in different clusters with the use of the principle of justifiable granularity. In the sequel, the positions of information granules are updated by considering the collaborative impacts of the other information granules with the parameters of specifying the level of influence. Experimental studies are conducted to illustrate the nature and feasibility of the proposed framework based on the synthetic data as well as a series of publicly available datasets coming from KEEL machine learning repositories.

The pandemic caused by the SARS-CoV2 virus has prompted research into new therapeutic solutions that can be used to treat the CoVid-19 syndrome. As part of this research, immunotherapy, first developed against cancer, is offering new therapeutic horizons also against viral infections. CAR technology, with the production of CAR-T cells (adoptive immunotherapy), has shown applicability in the field of HIV viral infections through second generation CAR-T cells implemented with the “CD4CAR” system with a viral fusion inhibitor. In addition, to avoid the immunoescape of the virus, bi- or trispecific CAR receptors have been developed. Our research group hypothesizes the use of this immunotherapy system against SARS-CoV2, admitting the appropriate adjustments concerning the target-epitope and a possible remodeling of the nuclease related to the action of this virus. For a more in-depth analysis of this hypothesis, a mathematical model has been developed which, starting from the fractional derivative Caputo, creates a system of equations that describes the interactions between CAR-T cells, memory cells, and cells infected with SARS-CoV2. Through an analysis of the existence and non-negativity of the solutions, the hypothesis is stabilized; then is further demonstrated through the use of the piece-wise derivative and the consequent application of the formula of Newton polynomial interpolation.

Single-cell DNA sequencing (scDNA-seq) enables high-resolution profiling of genetic diversity among single cells and is especially useful for deciphering the intra-tumor heterogeneity and evolutionary history of tumor. Specific technical issues such as allele dropout, false-positive errors, and doublets make scDNA-seq data incomplete and error-prone, giving rise to a severe challenge of accurately inferring clonal architecture of tumor. To effectively address these issues, we introduce a new computational method called SCClone for reasoning subclones from single nucleotide variation (SNV) data of single cells. Specifically, SCClone leverages a probability mixture model for binary data to cluster single cells into distinct subclones. To accurately decipher underlying clonal composition, a novel model selection scheme based on inter-cluster variance is employed to find the optimal number of subclones. Extensive evaluations on various simulated datasets suggest SCClone has strong robustness against different technical noises in scDNA-seq data and achieves better performance than the state-of-the-art methods in reasoning clonal composition. Further evaluations of SCClone on three real scDNA-seq datasets show that it can effectively find the underlying subclones from severely disturbed data. The SCClone software is freely available at https://github.com/qasimyu/scclone.

The degradation of black soil is a serious problem with the decrease in soil organic matter (SOM) content in northeast China, and animal manure as a reservoir of antibiotic resistance genes (ARGs) is commonly amended into soil to sustain or increase the SOM content. However, the potential effect of SOM content on soil resistome remains unclear. Here, a soil microcosm experiment was established to explore the temporal succession of antibiotic resistance genes (ARGs) and bacterial communities in three black soils with distinct difference in SOM contents following application of poultry manure using high-throughput qPCR (HT-qPCR) and MiSeq sequencing. A total of 151 ARGs and 8 mobile genetic elements (MGEs) were detected across all samples. Relative abundance of ARGs negatively correlated with SOM content. Manure-derived ARGs had much higher diversity and absolute abundance in the low SOM soils. The ARG composition and bacterial community structure were significantly different in three soils. A random forest model showed that SOM content was a better predictor of ARG pattern than bacterial diversity and abundance. Structural equation modeling indicated that the negative effects of SOM content on ARG patterns was accomplished by the shift of bacterial communities such as the bacterial diversity and abundance. Our study demonstrated that SOM content could play an important role in the dissemination of ARGs originated from animal manures, these findings provide a possible strategy for the suppression of the spread of ARGs in black soils by increasing SOM content.

The feedback control system with network-connected components is vulnerable to cyberattacks. We study a problem of attack detection in supervisory control of discrete-event systems. The scenario of a system subjected to actuator enablement attacks is considered in this article. We also consider that some unsafe places that should be protected from an attacker exist in the system, and some controllable events that are disabled by a supervisor might be re-enabled by an attacker. This article proposes a defense strategy to detect actuator enablement attacks and disable all controllable events after detecting an attack. We design algorithmic procedures to determine whether the system can be protected against damage caused by actuator enablement attacks, where the damage is predefined as a set of “unsafe” places. In this way, the system property is called “AE-safe controllability”. The safe controllability can be verified by using a basis diagnoser or a basis verifier. Finally, we explain the approach with a cargo system example.

Many multi-agent systems (MASs) can be regarded as hybrid systems that contain continuous variables and discrete events exhibiting both continuous and discrete behavior. An MAS can accomplish complex tasks through communication, coordination, and cooperation among different agents. The complex, adaptive and dynamic characteristics of MASs can affect their stability that is critical for MAS performance. In order to analyze the stability of MASs, we propose a stability analysis method based on invariant sets and Lyapunov’s stability theory. As a typical MAS, an unmanned ground vehicle formation is used to evaluate the proposed method. We design discrete modes and control polices for the MAS composed of unmanned ground vehicles to guarantee that the agents can cooperate with each other to reliably achieve a final assignment. Meanwhile, the stability analysis is given according to the definition of MAS stability. Simulation results illustrate the feasibility and effectiveness of the proposed method.

Automated manufacturing systems (AMSs) are prone to be in deadlock states when resource allocation is unreasonable. Reasonably allocating system resources to achieve deadlock control is a primary task of the design of an AMS. This paper proposes a multi-step look-ahead deadlock prediction method to obtain an optimal deadlock avoidance policy for a class Petri nets, without calculating a complete reachability graph. As the reachability graph of a large-scale Petri net model is usually large, the analysis and calculation process to obtain the optimal deadlock avoidance policy is complicated. We first simplify a Petri net model by using the existing Petri net reduction methods or removing non-shared resources to simplify the model structure. Then we calculate the dead zone markings of the simplified model through a reverse generation method, and finally develop a multi-step look-ahead deadlock prediction method to obtain an optimal deadlock avoidance policy for a class of AMSs.

At present, the existing abnormal event detection models based on deep learning mainly focus on data represented by a vectorial form, which pay little attention to the impact of the internal structure characteristics of feature vector. In addition, a single classifier is difficult to ensure the accuracy of classification. In order to address the above issues, we propose an abnormal event detection hybrid modulation method via feature expectation subgraph calibrating classification in video surveillance scenes in this paper. Our main contribution is to calibrate the classification of a single classifier by constructing feature expectation subgraphs. First, we employ convolutional neural network and long short-term memory models to extract the spatiotemporal features of video frame, and then construct the feature expectation subgraph for each key frame of every video, which could be used to capture the internal sequential and topological relational characteristics of structured feature vector. Second, we project expectation subgraphs on the sparse vector to combine with a support vector classifier to calibrate the results of a linear support vector classifier. Finally, the experiments on a common dataset named UCSDped1 and a coal mining video dataset in comparison with some existing works demonstrate that the performance of the proposed method is better than several the state-of-the-art approaches.

The analysis of influence spread in monoplex (single-layer) networks where all interactions are treated equally has been widely studied since the early 2000s. In complex networks, e.g., social networks, multi-agent networks, etc., the elements of networks interact in many ways, which results in multiplex (multiple-layer) networks. In this paper, we analyze the multiplexity-aware influence spread in two extended linear threshold models with two different protocols that explain how an agent updates its state combining signals from all layers. We then generalize the cohesiveness structural property to multiplex networks that explains why multiplexity facilitates or impedes a propagation. Based on this property, we present the optimal solutions by solving integer linear programming problems for influence minimization in multiplex linear threshold models.

Cyber-physical systems have been highly integrated into many contemporary infrastructures. As this integration deepens, the importance of protecting these systems from unauthorized access and data corruption increases. Nowadays, cyber-physical systems are not well protected against network attacks. One solution is to improve the security of a system by encrypting the transmitted data. In this paper, we consider the encryption of supervisors of discrete event systems modeled with deterministic finite-state automata. We propose an encryption framework of supervisory control systems based on the matrix notation of automata. The purpose of using matrix notation is to make it suitable for homomorphic encryption schemes over integers, which are emerging in the cryptography area. We calculate the entropy of the matrix notation and find that as the size of a system increases, it gets smaller and approaches zero. Owing to the low entropy of the matrix notation, we propose an algorithm to enhance its entropy. By applying the entropy-enhancing process, the distribution characteristics of entries in matrices or vectors can be hidden to avoid a brute force attack. Correspondingly, we propose an entropy restoration algorithm to ensure that the control action can be transmitted correctly.

Accurate protein contact map prediction is essential for de novo protein structure prediction. Over the past few years, deep learning has brought a significant breakthrough in protein contact map prediction and optimized deep learning architectures are highly desired for performance improvement. As an emerging deep learning architecture, the generative adversarial network (GAN) has shown the powerful capability of learning intrinsic patterns, which inspires us to comprehensively exploit GAN for predicting accurate protein contact maps. In this study, we present GANcon, a novel GAN-based deep learning architecture for protein contact map prediction, which to the best of our knowledge is the first GAN-based approach in this field. Instead of using a single neural network, GANcon is composed of two competitive networks that are evolving through adversarial learning. The generator network employs a dedicated encoder-decoder architecture that can efficiently capture the underlying contact information from versatile protein features to generate contact maps, while the discriminator network learns the differences between generated contact maps and real ones and promotes the generator network to produce more accurate contact maps. Moreover, to deal with the imbalance problem and take into account the symmetry of contact maps, we also propose a novel symmetrical focal loss, which can further enhance the effectiveness of adversarial learning for better performance. The experimental results on several datasets demonstrate that GANcon outperforms many state-of-the-art methods, indicating the effectiveness of our method for predicting protein contact maps. GANcon is freely available at https://github.com/melissaya/GANcon.