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Molnupiravir (MO) is a pyrimidine nucleoside anti‐SARS‐CoV‐2 drug. MO treatment could cause mild liver injury. However, the underlying mechanism of MO‐induced liver injury and the metabolic pathway of MO in vivo are unclear. In this study, metabolomics analysis and molecular biology methods were used to explore these issues. Through metabolomics analysis, it was found that the homeostasis of pyrimidine, purine, lysophosphatidylcholine (LPC), and amino acids in mice was destroyed after MO treatment. A total of 80 changed metabolites were detected. Among these changed metabolites, 4‐ethylphenyl sulfate, dihydrouracil, and LPC 20:0 was related to the elevation of alkaline phosphatase (ALP), interleukin‐6 (IL6), and nuclear factor kappa‐B (NF‐κB). The levels of 4‐ethylphenyl sulfate, dihydrouracil, and LPC 20:0 in plasma were positively correlated with their levels in the liver, suggesting that these metabolites were associated with MO‐induced liver injury. MO treatment could increase NHC and cytidine levels, activate cytidine deaminase (CDA), and increase LPC levels. CDA and LPC could increase the mRNA expression level of toll‐like receptor (TLR). The current study indicated that the elevation of hepatic TLR may be an important reason for MO leading to the liver injury. [ABSTRACT FROM AUTHOR]

The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline. Residents in the area use rototillers for planting and harvesting; however, the depth of the rototillers into the ground is greater than the depth of the pipeline, posing a significant threat to the safe operation of the pipeline. Therefore, it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe operation of pipelines. This article focuses on the Shanxi-Beijing natural gas pipeline, utilizing finite element simulation software to establish a finite element model for the interaction among the machinery, pipeline, and soil, and analyzing the dynamic response of the pipeline. At the same time, a decision tree model is introduced to classify the damage of pipelines under different working conditions, and the boundary value and importance of each influencing factor on pipeline damage are derived. Considering the actual conditions in the hemp yam planting area, targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region. [ABSTRACT FROM AUTHOR]

Object detection in remote sensing images has become a crucial component of computer vision. It has been employed in multiple domains, including military surveillance, maritime rescue, and military operations. However, the high density of small objects in remote sensing images makes it challenging for existing networks to accurately distinguish objects from shallow image features. These factors contribute to many object detection networks that produce missed detections and false alarms, particularly for densely arranged objects and small objects. To address the above problems, this paper proposes a feature enhancement feedforward network (FEFN), based on a lightweight channel feedforward module (LCFM) and a feature enhancement module (FEM). First, the FEFN captures shallow spatial information in images through a lightweight channel feedforward module that can extract the edge information of small objects such as ships. Next, it enhances the feature interaction and representation by utilizing a feature enhancement module that can achieve more accurate detection results for densely arranged objects and small objects. Finally, comparative experiments on two publicly challenging remote sensing datasets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As the number of long-distance oil and gas pipelines crossing rivers increases, so does the risk of river oil spills. Previous research on oil spills in water mainly focuses on the oceans, and there are relatively few studies on oil spills in rivers. This study established two-dimensional hydrodynamic and oil spill models for the Lancang River crossing pipeline basin and verified the model's accuracy. The oil spill transport process under different scenarios was simulated, and the oil spill transport state data set was established. The effects of river flow, wind, and leakage mode on the transport behavior of oil spills were studied. The results show that an increase in flow rate accelerates the migration, diffusion, and longitudinal extension behavior of oil spills; Changes in wind speed have less effect on the transport behavior of oil spills under downwind and headwind conditions. The mode of leakage mainly affects the diffusion and longitudinal extension of the oil spill. The oil spill transport state prediction model was established using machine learning combination algorithms. The three combined machine learning algorithms, PSO-SVR, GA-BPNN, and PSO-BPNN, have the best performance in predicting the oil spill migration distance, oil spill area, and the length of the oil spill contamination zone, respectively, with the coefficient of determination (R2) and the 1-Mean Absolute Percentage of Error (1-MAPE) above 0.971, and the prediction model has excellent accuracy. This study can provide support for the rapid development of emergency response plans for river crossing pipeline oil spill accidents. [ABSTRACT FROM AUTHOR]

Scene recognition is a computer vision task that categorizes scenes from photographs. In this paper, we introuduce the Adaptive Local Recalibration Network (ALR-Net), a novel scene recognition method based on convolutional neural networks (CNNs). In comparison to the object classification task, the scene classification images have a more dispersed distribution of information. To solve this issue, we suggest an attention mechanism for locating the discriminative regions for scene recognition. Along with normal data augmentation, we use the regions to guide two additional data augmentation approaches, namely adaptive cropping and adaptive hiding, in order to capture local information more efficiently and specifically. Attention maps are also used to adaptively recalibrate scene feature maps so that discriminative regions receive more attention than others. In addition, we bring in a scene distribution label for each image, which is used to assist the training of attention maps. Extensive studies on two scene recognition benchmarks verified the proposed model's effectiveness: MIT67 (88.37%) and SUN397 (74.24%). [ABSTRACT FROM AUTHOR]

In order to trace the research history of diseases and pests in rubber tree and explore the potential for their natural control, a bibliometric analysis was conducted based on relevant documents retrieved from the Clarivate Analytics Web of Science (WoS) core collection SCI-E database. VOSviewer software was utilized to analyze the research distribution, scientific collaboration, knowledge structure, and research frontiers. The results show that annual publications on the diseases and pests of rubber tree have increased rapidly after 2005 after a long period of emergence and fluctuation. A total of 624 relevant publications from 51 countries/regions were identified. China was the most productive country with 152 documents, most of which were related to Colletotrichum leaf disease, powdery mildew, and other emerging diseases of rubber tree. France and Brazil produced rich research to tackle South American leaf blight, and have established a close collaborative relationship. Based on the analysis of themes and trend topics, pathogenicity mechanisms of fungal pathogens and plant defense mechanisms are currently hot topics. By further looking into the research, the defense-related genes of rubber tree and antagonistic mechanisms behind candidate biocontrol agents reveal great potential in developing natural control strategies. This study provides a useful reference about the progress and evolution of research into diseases and pests in rubber tree. [ABSTRACT FROM AUTHOR]

The impact of the regularity of the surface structure on the intra‐/inter‐particle behavior of luminescent metal nanoclusters (NCs) remains to be unambiguously clarified. Herein, an in‐depth investigation of the relationship between surface structure and the emission pattern of luminescent gold NCs (GNCs) is conducted using a series of NCs coated with opposite‐charged peptides (glutathione [GSH] and poly‐L‐lysine [PLL]) to unravel mutual interactions between ligands and to determine the predominant role of the arrangement of Au(I)‐ligand motifs in respect to their optical properties. GNCs with different emission patterns of 640 nm‐, 820 nm‐, or dual‐emitting are obtained by tuning the adding ratio of PLL to GSH. Moreover, the 640 nm‐emitting GNCs show aggregation‐induced emission enhancement behavior after the introduction of the Pb2+ ion, which is used to determine the quantity of Pb2+ with a limit of detection as low as 44 nm. [ABSTRACT FROM AUTHOR]

The phase equilibriums of the ternary system KCl-(NH2)2CO-H2O at 303.15 K, 323.15 K, 333.15 K, and 343.15 K were studied using the isothermal dissolution equilibrium method, in which the composition of equilibrium solid phase was determined by Schreinemaker's wet residue method and X-ray diffraction (XRD) method. It was found that the ternary system is a simple cosaturated system, without the formation of neither double salt nor solid solution. Wilson and NRTL models were employed to correlate in the solubility data of the system at experimental temperatures. The maximum values of RAD and RMSD of the Wilson model were 2.18 × 10−2 and 0.83 and those of the NRTL model were 1.69 × 10−2 and 0.40, respectively. The two models were utilized to forecast solubility data at various temperatures, and the obtained outcomes were in line with the literature data. Based on the experimental solubility data at 343.15 K, the cooling crystallization process of the system was monitored online by focused beam reflectance measurement (FBRM) and particle video microscope (PVM). The crystal products were characterized by XRD, scanning electron microscope (SEM), and energy dispersive spectrometry (EDS). The results showed that the precipitation of (NH2)2CO occurred during the crystallization process, and this was followed by KCl. KCl was formed on the surface of (NH2)2CO crystal. The crystal was a simple mixture containing KCl and (NH2)2CO. [ABSTRACT FROM AUTHOR]

Heuristic algorithms are widely used in Mobile Edge Computing(MEC) to improve the performance of mobile devices. However, the time complexity of the heuristic algorithm is high, and it is complex to optimize under constraints. Therefore, we proposed Multi-User Energy Constraint Time Optimization Algorithm(MU-ECTOA) for workflow makespan optimization under energy constraints. MU-ECTOA includes three stages: cluster analysis, evaluation of performance, and workflow offloading. In the first stage, the workflow tasks are classified according to their characteristics; In the second stage, the subtask groups are obtained, and the evaluation results of each subtask group are obtained. In the third stage, the optimal subtask group is selected for offloading and then updated the ready time of edge nodes. Extensive experiments have been conducted, the ACO&GA, Max-Min, Particle Swarm Optimization(PSO), GA-DPSO, and SFLA are taken as the compared methods. The results of MU-ECTOA performs better in aspects in completion time, load balancing, and successful offloading rate compared with other methods. By comparing the results of algorithms, the makespans of the algorithms are close, but the algorithm complexity and load balancing of MU-ECTOA are much better; The time complexity of the MU-ECTOA algorithm is close to the Max-Min’s, but MU-ECTOA performs better in makespan and algorithm reliability. [ABSTRACT FROM AUTHOR]

Deployment of deep convolutional neural networks (CNNs) in single image super-resolution (SISR) for edge computing devices is mainly hampered by the huge computational cost. In this work, we propose a lightweight image super-resolution (SR) network based on a reparameterizable multibranch bottleneck module (RMBM). In the training phase, RMBM efficiently extracts high-frequency information by utilizing multibranch structures, including bottleneck residual block (BRB), inverted bottleneck residual block (IBRB), and expand–squeeze convolution block (ESB). In the inference phase, the multibranch structures can be combined into a single 3 × 3 convolution to reduce the number of parameters without incurring any additional computational cost. Furthermore, a novel peak-structure-edge (PSE) loss is proposed to resolve the problem of oversmoothed reconstructed images while significantly improving image structure similarity. Finally, we optimize and deploy the algorithm on the edge devices equipped with the rockchip neural processor unit (RKNPU) to achieve real-time SR reconstruction. Extensive experiments on natural image datasets and remote sensing image datasets show that our network outperforms advanced lightweight SR networks regarding objective evaluation metrics and subjective vision quality. The reconstruction results demonstrate that the proposed network can achieve higher SR performance with a 98.1 K model size, which can be effectively deployed to edge computing devices. [ABSTRACT FROM AUTHOR]

For a long time, the problems of cold start and sparse data have always been the key problems to be solved by the recommendation system. Researchers usually use auxiliary information to deal with the aforementioned problems, thereby achieving the purpose of enhancing the recommendation effect. For example, the multitask feature learning framework (MKR) uses knowledge graphs as auxiliary information to enhance recommendations. However, the MKR algorithm has the problem of insufficient semantic information representation which affect the recommendation results. Thus, a multitask recommendation algorithm based on DeepFM and graph convolutional network (DeepFM_GCN) is proposed. The graph convolution network is used to deeply mine auxiliary entity information in the knowledge graph to supplement the sparse item semantics information in the recommendation task. Through the method of cross compression unit combined with Deep Neural Network to achieve feature sharing items and entities which to make up for the impact of insufficient feature representation. Then the DeepFM_GCN model utilizes DeepFM to deeply mine the interaction feature of users and items to avoid inaccurate items recommended to users. From the analysis of the experimental results, the DeepFM_GCN model can more fully explore user and item features, accordingly avoiding semantic ambiguity and improving prediction accuracy. [ABSTRACT FROM AUTHOR]

In the study of polymer flooding, researchers usually ignore the genetic stress properties of viscoelastic fluids. In this paper, we investigate the process of viscoelastic fluid flooding the remaining oil in the dead end. This work uses the fractional-order Maxwell in the traditional momentum equation. Furthermore, a semi-analytic solution of the flow control equation for fractional-order viscoelastic fluids is derived, and the oil-repelling process of viscoelastic fluids is simulated by a secondary development of OpenFOAM. The results show that velocity fractional-order derivative α significantly affects polymer solution characteristics, and increasing the elasticity of the fluid can significantly improve the oil repelling efficiency. Compared to the Newtonian fluid flow model, the fractional order derivative a and relaxation time b in the two-parameter instanton equation can accurately characterize the degree of elasticity of the fluid. The smaller the a, the more elastic the fluid is and the higher the oil-repelling efficiency. The larger the b, the less elastic the fluid is and the lower the cancellation efficiency. Moreover, the disturbance of the polymer solution to the dead end is divided into two elastic perturbation areas. The stronger the elasticity of the polymer solution, the higher the peak value of the area in the dead end and the higher the final oil displacement efficiency. [ABSTRACT FROM AUTHOR]

In the current study, we explored the role of Mg2+-doped CaSO4/β-TCP composite biopolymer in regulating macrophage polarization and its relation with enhanced osteogenic differentiation of periodontal ligament stem cells. Furthermore, mechanism underling the regulation of macrophage polarization by CaSO4/β-TCP was evaluated. Mg2+-doped CaSO4/β-TCP composite was characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD). Macrophage polarization was characterized using flow cytometry analysis. Macrophage morphometric analysis was conducted by FITC phalloidin staining. Western blot and qRT-PCR assays were used to assess gene expression levels and miRNAs, respectively. SEM morphology of CaSO4/β-TCP ceramic revealed a particle size of 10–50 μm, and XRD spectrum showed that characteristic peak of samples was consistent with that of CaSO4 and β-TCP. Results from flow cytometry evidenced significant upregulation of M2 macrophage markers after adding ceramic biopolymer, indicating the induction of inactivated M0 macrophage polarization to M2 macrophage. Macrophage morphometric analysis revealed development of lamellar pseudopodia on day 7 in CaSO4/β-TCP group. Furthermore, flow cytometry revealed high positivity rate of 90.34% (CD44) and 89.36% (CD146). qRT-PCR results showed that the level of miR-21-5p was significantly decreased in M2 macrophages. Moreover, western blot analysis revealed upregulated expression levels of RUNX2, osterix (Osx), and osteopontin (OPN), and ELISA exhibited increase in cytokine levels (IL-1β, IL-10, TGF-β1, and BMP-2) in the presence of macrophages, indicating the osteogenic differentiation ability of periodontal ligament stem cells. The study evidenced the regulation of macrophage polarization by Mg2+-doped CaSO4/β-TCP composite ceramic and its mediation through lncRNA PVT1/miR-21-5p/smad2 molecular axis. [ABSTRACT FROM AUTHOR]

This study focused on the development of a method for the rapid detection of acid-neutralising adulterants in raw milk using a milk composition analyser. Qualitative analysis for the discrimination of different acid-neutralising acid adulterants in raw milk and quantification of NaSCN in adulterated raw milk were conducted, combined with chemometrics. The results showed that the milk component analyser combined with principal component analysis (PCA) could judge whether raw milk samples were adulterated but cannot identify the types of adulterated substances. Although partial least squares discrimination analysis (PLS-DA) can distinguish some adulterated raw milk samples, the accuracy rate was only 56.3%; the random forest (RF) model could recognise most adulterated raw milk samples with an accuracy rate of 97.5% and the F1-score was 0.9638. In the prediction model of NaSCN adulteration concentration in raw milk constructed by RF, the coefficient of determination (R2) was 0.9889, and the root means square error (RMSE) was 3.28 × 10−4, suggesting a high prediction performance of the model. The effectiveness of the method for the detection of real samples in practical production was also proved. Based on the above results, it could conclude that the milk component analyser, combined with chemometrics, effectively distinguished acid-neutralising adulterants in raw milk. These findings provide a reference for the rapid detection of adulterants and the quality control of raw milk. [ABSTRACT FROM AUTHOR]

Introduction: Globally, Pseudomonas aeruginosa (PA) is emerging as a predominant nosocomial pathogen that often induces aggressive and even deadly infections. Pseudomonas type III repressor A (PtrA) can be activated specifically by copper ions and interacts with type-III transcriptional activator ExsA. This study aims to provide insight into the PtrA-mediated regulation of the pathogenicity and antibiotics resistance of PA. Methods and Results: The results of transcriptome sequencing analyses and real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) showed that PtrA plays a dual regulatory role in the virulence systems of PA: negatively regulates the type-III secretion system (T3SS) and positively regulates the quorum-sensing system (QS). The ptrA mutant attenuated extracellular virulence related to QS like pyocyanin, elastase, rhamnolipids, proteolytic activity, and biofilm production. According to adhesion and invasion experiments, PtrA can not only contribute to the adhesiveness but also the invasive of PA. Moreover, the PtrA-mediated regulation of PA pathogenicity was determined both in vivo and in vitro through cytotoxicity and Galleria mellonella survival experiments. In addition, apart from virulence, PtrA was found to influence the carbapenems resistance of PA. After deleting ptrA, the minimum inhibitory concentration (MIC) of carbapenems antibiotics was decreased by 2-fold, while a 2– 8 fold increase was noted for the complemented strain. Conclusion: Our findings establish that PtrA exerts a regulatory role in both pathogenicity and carbapenems resistance of PA. This work may shed light on a novel target for the clinical treatment of PA. [ABSTRACT FROM AUTHOR]

Microservice design is a new paradigm of cloud application development. Different from monolithic design, microservice enjoys merits of fine‐grained and loosely coupled services, and it is becoming more and more popular. The application developed with microservice has a good advantage in independent development and flexible deployment, especially for complex distributed systems. However, there is a big gap between the reliability requirements and microservice‐based cloud applications. This article proposes a reliability model of microservice‐based cloud application by using predicate Petri net. First, a microservice reliability requirement is given, some basic concepts of predicate Petri net are defined with syntax and semantics. Second, a microservice reliability strategy is proposed, which uses microservice instances and circuit breaker to improve the reliability of the system. Based on the constructed microservice reliability model, the correctness of predicate Petri net modelling and the effectiveness of the strategies are proven theoretically. Finally, an example is given to illustrate the establishment and analysis process of the model, and several groups of experiments are carried out to verify the effectiveness and feasibility of the method. Experimental results show that the proposed microservice reliability strategy is effective. [ABSTRACT FROM AUTHOR]

In the process of the continuous development of the Internet of Things, cloud computing has been applied in many fields, how to guarantee the quality of service, such as low latency, high bandwidth, high reliability etc., has become a challenging problem. This paper proposes a method to model and optimize reliability for microservice‐based cloud applications using multi‐agent system (MAS), thus maximizing the reliability of cloud computing and dynamically scheduling microservices to minimize the delay within the budget. Firstly, a dynamic microservice scheduling scheme is proposed to provide efficient computing services by using MAS. A hierarchical cloud computing model is formed by predicated Petri net (PrT net) and the properties of constructed model are analysed. Secondly, agents have been utilized to describe the essential characteristics of microservice scheduling process in the cloud applications. The partial critical path (PCP) aims to maximize the reliability of cloud applications under the limitation of budget and meet the user‐defined deadline. Finally, the proposed PCPRO algorithm has been applied to cloud environment, which is suitable for different scientific workflows in the cloud computing environment. The effectiveness of this method is verified by simulation, the experiment results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

NOD-like receptor (NLR)X1 (NLRX1) is a negative regulator of inflammation by inhibiting nuclear factor-κB (NF-κB) signaling and downstream pro-inflammatory factors. However, its post-translational modification and how it participates in regulating the inflammatory responses in macrophages are still unclear. Here, we found that NLRX1 was modified with O-linked N-acetylglucosamine (O-GlcNAc). The interaction and co-localization between NLRX1 and O-GlcNAc transferase (OGT) was validated by co-immunoprecipitation and confocal microscopy analysis, and the nucleotide-binding domain (NBD) region of NLRX1 was required for its interaction with OGT. NLRX1 protein increased significantly after treatment with a high dose of OGT inhibitor OSMI-1. Elevated O-GlcNAcylation level promoted NLRX1 ubiquitination and decreased NLRX1 stability proved by ubiquitination and cycloheximide (CHX) chase experiments, and enhanced the interaction between NLRX1 and inhibitor of nuclear factor kappaB kinase-α (IKK-α), thus reducing the expression of inflammatory cytokine IL-1β in M1 macrophages. Together, our results indicate that the interaction between NLRX1 and O-GlcNAcylation coordinates and modulates the inflammatory process in macrophages. [ABSTRACT FROM AUTHOR]

Deep-learning-based segmentation methods have achieved excellent results. As two main tasks in computer vision, instance segmentation and semantic segmentation are closely related and mutually beneficial. Spatial context information from the semantic features can also improve the accuracy of instance segmentation. Inspired by this, we propose a novel instance segmentation framework named partial atrous cascade R-CNN (PAC), which effectively improves the accuracy of the segmentation boundary. The proposed network innovates in two aspects: (1) A semantic branch with a partial atrous spatial pyramid extraction (PASPE) module is proposed in this paper. The module consists of atrous convolution layers with multi-dilation rates. By expanding the receptive field of the convolutional layer, multi-scale semantic features are greatly enriched. Experiments shows that the new branch obtains more accurate segmentation contours. (2) The proposed mask quality (MQ) module scores the intersection over union (IoU) between the predicted mask and the ground truth mask. Benefiting from the modified mask quality score, the quality of the segmentation results is judged credibly. Our proposed network is trained and tested on the MS COCO dataset. Compared with the benchmark, it brings consistent and noticeable improvements in the case of using the same backbone. [ABSTRACT FROM AUTHOR]

Due to the lack of research on the characteristics of different clusters of Enterobacter cloacae complex (ECC), this study aimed to characterize and explore the differences among species of the ECC. An analysis based on hsp60 showed that Enterobacter hormaechei was predominant in ECC. Interestingly, the antibiotic resistance rates of clusters were different, among which E. hormaechei subsp. steigerwaltii (cluster VIII) and Enterobacter cloacae IX (cluster IX) possessed high resistant rates to ciprofloxacin and levofloxacin, but cluster II (Enterobacter kobei) had low resistant rates. Cluster II exhibited a strong biofilm formation ability. Different motility and protease production ability were shown for distinct clusters. A PCR analysis showed that clusters I, III, VI, VIII, and IX carried more virulence genes, while cluster II had fewer. Clusters I, VIII, and IX with high pathogenicity were evaluated using the Galleria mellonella infection model. Thus, the characteristics of resistance, biofilm-forming ability, mobility, and virulence differed among the clusters. The strains were divided into 12 subgroups based on hsp60. The main clusters of ECC clinical strains were I, II, III, VI, VIII, and IX, among which IX, VIII, and I were predominant with high resistance and pathogenicity, and cluster II (E. kobei) was a special taxon with a strong biofilm formation ability under nutrient deficiency, but was associated with low resistance, virulence, and pathogenicity. Hence, clinical classification methods to identify ECC subgroups are an urgent requirement to guide the treatment of clinical infections. [ABSTRACT FROM AUTHOR]

Once a natural gas pipeline leaks, it will not only cause waste of resources and environmental pollution but also cause huge casualties and property losses in severe cases. Therefore, research on the diffusion mechanism of natural gas leakage and diffusion can help to take timely countermeasures to reduce the impact of accidents. Based on the study of gas diffusion mechanism and Gauss plume model in the process of natural gas leakage, the parameters influencing the gas diffusion are summarized, and the influence of various factors on the gas diffusion is studied by using the Phast software. The results show that: with the increase of pipeline pressure, the diffusion distance of leakage gas in all directions increases significantly; with the increase of wind speed, the diffusion distance from downwind increases significantly, and the vertical and upwind diffusion distance of natural gas decreases rapidly; with the increase of temperature, the diffusion distance of natural gas in all directions decreases slightly; with the increase of atmospheric stability, it can promote the diffusion of natural gas in a certain range. Beyond this range, it will inhibit the diffusion of natural gas. The research results are of great significance to the study of influencing factors of natural gas diffusion. [ABSTRACT FROM AUTHOR]

Microservice architecture is a cloud-native architectural style, which has attracted extensive attention from the scientific research and industry communities to benefit independent development and deployment. However, due to the complexity of cloud-based platforms, the design of fault-tolerant strategies for microservice-oriented cloud applications becomes challenging. In order to improve the quality of service, it is essential to focus on the microservice with more criticality and maximize the reliability of the entire cloud application. This paper studies the modeling and analysis of service reliability in the cloud environment. Firstly, a formal description language is defined to model microservice, user request, and container accurately. Secondly, the reliability analysis is conducted to measure a critical microservice's fluctuation and vibration attributes within a period, and the related properties of the constructed model are analyzed. Thirdly, a fault-tolerant strategy with redundancy operation has been proposed to optimize cloud application reliability. Finally, the effectiveness of the method is verified by experiments. The simulation results show that the algorithm obtains the maximum benefits and has high performance through several experiments. [ABSTRACT FROM AUTHOR]

Just-in-time software defect prediction (JIT-SDP) is a fine-grained software defect prediction technology, which aims to identify the defective code changes in software systems. Effort-aware software defect prediction is a software defect prediction technology that takes into consideration the cost of code inspection, which can find more defective code changes in limited test resources. The traditional effort-aware defect prediction model mainly measures the effort based on the number of lines of code (LOC) and rarely considers additional factors. This paper proposes a novel effort measure method called Multi-Metric Joint Calculation (MMJC). When measuring the effort, MMJC takes into account not only LOC, but also the distribution of modified code across different files (Entropy), the number of developers that changed the files (NDEV) and the developer experience (EXP). In the simulation experiment, MMJC is combined with Linear Regression, Decision Tree, Random Forest, LightGBM, Support Vector Machine and Neural Network, respectively, to build the software defect prediction model. Several comparative experiments are conducted between the models based on MMJC and baseline models. The results show that indicators ACC and P opt of the models based on MMJC are improved by 35.3% and 15.9% on average in the three verification scenarios, respectively, compared with the baseline models. [ABSTRACT FROM AUTHOR]

Purpose: The emergence of colistin resistance among Gram-negative bacteria (GNB) poses a serious public health threat. Therefore, it is necessary to enhance the antibacterial activity of colistin through the combination with other drugs. In this study, we demonstrated the synergistic activity and the possible synergy mechanism of colistin with PFK-158 against colistin-resistant GNB, including non-fermenting bacteria and Enterobacteriaceae. Patients and Methods: Thirty-one colistin-resistant GNB, including Pseudomonas aeruginosa (n = 9), Acinetobacter baumannii (n = 5), Escherichia coli (n = 8) and Klebsiella pneumoniae (n = 9), were collected as the experimental strains and the minimum inhibitory concentrations (MICs) of colistin, other routine antimicrobial agents and PFK-158 against all strains were determined by the broth microdilution method. The synergistic activity of colistin with PFK-158 was assessed by the checkerboard assay and time-kill assay. The biofilm formation assay and scanning electron microscopy were used to demonstrate the biofilm formation effect of colistin with PFK-158 against colistin-resistant GNB. Results: The results of the checkerboard assay showed that when colistin was used in combination with PFK-158, synergistic activity was observed against the 31 colistin-resistant GNB. The time-kill assay presented a significant killing activity of colistin with PFK-158 against the 9 colistin-resistant GNB selected randomly, including Pseudomonas aeruginosa (n = 6), Acinetobacter baumannii (n = 1), Escherichia coli (n = 1), and Klebsiella pneumoniae (n = 1). The biofilm formation assay and scanning electron microscopjihy showed that colistin with PFK-158 can effectively suppress the formation of biofilm and reduce the cell arrangement density of biofilm against most experimental strains. Conclusion: The results of the performed experiments suggest that the combination of colistin and PFK-158 may be a potential new choice as a new antibiofilm group for the treatment of infections caused by the colistin-resistant GNB. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to evaluate the in vitro activity of meropenem-vaborbactam (MVB) against a collection of carbapenem-resistant Escherichia coli (CREC) isolates and to compare the activity with other antibiotics with regard to different separation sites, carbapenem-resistant mechanisms, and sequence types (STs). Methods: A total of 58 CREC strains were used as the experimental strains from the First Affiliated Hospital of Wenzhou Medical University in southeastern China. The minimum inhibitory concentrations of MVB, ceftazidime-avibactam, and tigecycline against all the experimental strains were determined by the microdilution broth method. Results: MVB exhibited higher antimicrobial activity (83% susceptibility) than that of other antibiotics, except for colistin and tigecycline. The susceptibility of CREC strains towards MVB varied with regard to carbapenem-resistant mechanisms and STs, especially in Klebsiella pneumoniae carbapenemase (KPC)-positive isolates and ST8 isolates. Conclusion: MVB exhibited considerably high activity against KPC-producing and ST8 CREC isolates. It has the great potential to be an alternative for the treatment of infections caused by CREC after determining the type of carbapenemase, the susceptibility to MVB and/or STs. [ABSTRACT FROM AUTHOR]

Automatic aircraft engine defect detection is a challenging but important task in industry which can ensure safe air transportation and flight. In this paper, we propose a fast and accurate feature weighting network (FWNet) to solve the problem of defect scale variation and improve detection accuracy. The framework is designed based on recent popular convolutional neural networks and feature pyramid. To further boost the representation power of the network, a new feature weighting module (FWM) was proposed to recalibrate the channel-wise attention and increase the weights of valid features. The model was trained and tested on a self-built dataset, which consisted of 1916 images and contained three defect types: ablation, crack and coating missing. Extensive experimental results verify the effectiveness of the proposed FWM and show that the proposed method can accurately detect engine defects of different scales and different locations. Our method obtains 89.4% mAP and can run at 6FPS, which surpasses other state-of-the-art detection methods and can quickly provide diagnostic basis for aircraft maintenance inspectors in practical applications. [ABSTRACT FROM AUTHOR]

Cloud computing has been increasingly concerned in scientific computing area. More and more enterprises and research institutes have migrated their applications to the clouds. Due to the complexity of cloud computing system in structural and behavioral aspects, how to design the fault tolerant cloud computing system becomes a challenging problem. This paper investigates the modeling and analysis of fault tolerant strategy for deadline constrained task scheduling in cloud computing. First, a formal description language is defined to accurately model the different components of cloud application, and use it to characterize the operational mechanisms and fault behaviors. Second, we propose a fault tolerant strategy, which includes the scheduling mechanism, synchronization mechanism, and exception mechanism, to dynamically compute the execution mode and required virtual machine for tasks, thus ensuring the reliability and real-time requirement of cloud application. An enforcement algorithm is also designed to realize the proposed strategy. Third, the techniques of Petri nets are provided to analyze and validate the correctness of proposed method. Finally, several experiments are done to illustrate that the reliability of cloud application is improved and its deadline is met. [ABSTRACT FROM AUTHOR]

With the exponential increase in the number of IoT devices and the amount of emitted data from these devices, it is expensive and inefficient to offload all tasks to the remote data center. How to optimize the energy consumption of application requests from IoT devices meeting the deadline constraint is also a challenge. Fog computing adjacent to users has the feature of lower service delay but less resource than the remote cloud. Fog does not appear to replace cloud, they are complementary to each other, cooperation between them is worth studying. This paper proposes a general IoT-fog-cloud architecture that fully exploits the advantages of fog and cloud. Then, the energy and time efficient computation offloading and resource allocation is formulated into the energy and time cost minimization problem. We then propose an ETCORA algorithm to solve the problem, improving the energy consumption and completion time of application requests. Finally, extensive simulations are carried out to verify that the proposed method indeed outperforms the other two methods in reducing energy consumption and completion time of requests. [ABSTRACT FROM AUTHOR]

An effective method for addressing the configuration optimization problem (COP) in Software Product Lines (SPLs) is to deploy a multi-objective evolutionary algorithm, for example, the state-of-the-art SATIBEA. In this paper, an improved hybrid algorithm, called SATIBEA-LSSF, is proposed to further improve the algorithm performance of SATIBEA, which is composed of a multi-children generating strategy, an enhanced mutation strategy with local searching and an elite inheritance mechanism. Empirical results on the same case studies demonstrate that our algorithm significantly outperforms the state-of-the-art for four out of five SPLs on a quality Hypervolume indicator and the convergence speed. To verify the effectiveness and robustness of our algorithm, the parameter sensitivity analysis is discussed and three observations are reported in detail. [ABSTRACT FROM AUTHOR]

Just-in-time software defect prediction (JIT-SDP) is an active topic in software defect prediction, which aims to identify defect-inducing changes. Recently, some studies have found that the variability of defect data sets can affect the performance of defect predictors. By using local models, it can help improve the performance of prediction models. However, previous studies have focused on module-level defect prediction. Whether local models are still valid in the context of JIT-SDP is an important issue. To this end, we compare the performance of local and global models through a large-scale empirical study based on six open-source projects with 227417 changes. The experiment considers three evaluation scenarios of cross-validation, cross-project-validation, and timewise-cross-validation. To build local models, the experiment uses the k-medoids to divide the training set into several homogeneous regions. In addition, logistic regression and effort-aware linear regression (EALR) are used to build classification models and effort-aware prediction models, respectively. The empirical results show that local models perform worse than global models in the classification performance. However, local models have significantly better effort-aware prediction performance than global models in the cross-validation and cross-project-validation scenarios. Particularly, when the number of clusters k is set to 2, local models can obtain optimal effort-aware prediction performance. Therefore, local models are promising for effort-aware JIT-SDP. [ABSTRACT FROM AUTHOR]

In order to improve software reliability, software defect prediction is applied to the process of software maintenance to identify potential bugs. Traditional methods of software defect prediction mainly focus on designing static code metrics, which are input into machine learning classifiers to predict defect probabilities of the code. However, the characteristics of these artificial metrics do not contain the syntactic structures and semantic information of programs. Such information is more significant than manual metrics and can provide a more accurate predictive model. In this paper, we propose a framework called defect prediction via attention-based recurrent neural network (DP-ARNN). More specifically, DP-ARNN first parses abstract syntax trees (ASTs) of programs and extracts them as vectors. Then it encodes vectors which are used as inputs of DP-ARNN by dictionary mapping and word embedding. After that, it can automatically learn syntactic and semantic features. Furthermore, it employs the attention mechanism to further generate significant features for accurate defect prediction. To validate our method, we choose seven open-source Java projects in Apache, using F1-measure and area under the curve (AUC) as evaluation criteria. The experimental results show that, in average, DP-ARNN improves the F1-measure by 14% and AUC by 7% compared with the state-of-the-art methods, respectively. [ABSTRACT FROM AUTHOR]

Multi-objective evolutionary algorithm (MOEA) has been widely applied to software product lines (SPLs) for addressing the configuration optimization problems. For example, the state-of-the-art SMTIBEA algorithm extends the constraint expressiveness and supports richer constraints to better address these problems. However, it just works better than the competitor for four out of five SPLs in five objectives and the convergence speed is not significantly increased for largest Linux SPL from 5 to 30 min. To further improve the optimization efficiency, we propose a parallel framework SMTPORT, which combines four corresponding SMTIBEA variants and performs these variants by utilizing parallelization techniques within the limited time budget. For case studies in LVAT repository, we conduct a series of experiments on seven real-world and highly-constrained SPLs. Empirical results demonstrate that our approach significantly outperforms the state-of-the-art for all the seven SPLs in terms of a quality Hypervolume metric and a diversity Pareto Front Size indicator. [ABSTRACT FROM AUTHOR]

Dexamethasone has been illegally used as a growth-promoting agent to obtain an economic benefit from increased muscle development. An immunochromatographic assay was developed for the quantitative detection of dexamethasone using a probe based on upconversion luminescence nanoparticles. Lanthanide-doped upconversion nanoparticles were synthesized in a system comprising water and ethylene glycol. Various parameters, including the immunoreaction time, the structure of the coating hapten, and the type and concentration of surfactant, were optimized. Based on the optimal conditions, the assay exhibits good dynamic linear detection in the range of 0.05-5 ng/mL with an IC50 of 0.58 ng/mL. The detection limits for dexamethasone in tissue are 0.3 μg/kg. The assay worked well using a 30% methanol extraction solution, giving an average recovery of 84.5%. The assay strip can quantitate on-site using a portable upconversion luminescence reader with a cumulative analysis time of only 10 min, and its fluorescence decay after 6 months is < 25%. [ABSTRACT FROM AUTHOR]

The fracture toughness and R-curves of welded joints of API X90 pipeline steels were experimentally studied. Single-edge- notched tension (SENT) specimens and a normalization method were adopted. Joints welded by using three technologies (A, B, and C) were investigated. The results show that the SENT specimen could be used to test the fracture toughness and R-curves of the welded joint; the welded joint of welding method B presented the poorest fracture toughness; and the R-curves and fracture toughness of the heat-affected zone were seriously affected by the weld zone and base metal. A new idea to test the fracture toughness and R-curves of the heat-affected zone was also proposed. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of cloud computing, many distributed data centers have been deployed. This means larger energy consumption requirements from the data center. How to reduce the cost of data center has received significant attention recently. Although there are several efforts in studying energy consumption of the data center, very few have considered modeling and analyzing cost‐aware job scheduling for the cloud data center. To address this emerging problem, we propose a systematic approach that considers both basic elements and their relationships in cloud data center. First, we present a formal language to describe the cloud data center, and a job scheduling net is proposed to formally model the basic elements such as user request, Web portal, data center, and server. Second, we minimize the total cost of the cloud data center by considering the multidimensional resource and local electricity price on the basis of the state space of constructed model. The dynamic job scheduling algorithm and its specific execution steps are proposed based on the alternating direction method of multipliers algorithm. Third, the operational semantics and related theories of Petri nets for establishing the correctness of our proposed method are presented. Finally, a series of simulations are performed to illustrate that the proposed method can guarantee the correct behavior of job scheduling in the cloud data center while meeting the required cost. [ABSTRACT FROM AUTHOR]

Various Trichoderma species possess significance in agricultural systems as biofertilizers or biocontrol agents (BCAs). Besides these beneficial features, certain Trichoderma species can also act as agricultural pests, causing the green mold disease of cultivated mushrooms. This double-faced nature of the genus in agricultural environments points at the importance of proper monitoring tools, which can be used to follow the presence and performance of candidate as well as patented and/or registered biocontrol strains, to assess the possible risks arising from their application, but also to track harmful, unwanted Trichoderma species like the green molds in mushroom growing facilities. The objective of this review is to discuss the molecular tools available for the species- and strain-specific monitoring of Trichoderma, ranging from immunological approaches and fingerprinting tools to exogenous markers, specific primers used in polymerase chain reaction (PCR) as well as “omics” approaches. [ABSTRACT FROM AUTHOR]

Cloud computing has attracted much attention recently in both industry field and academic research area. More and more Internet applications are moving to the cloud environment. However, it is difficult to construct perfectly secure mechanisms facing up with complex and various attacks in cloud computing, the efficient attack-defense strategy is highly demanded. In this paper, a stochastic game model is proposed based on combining stochastic Petri nets with game theory, which is used to describe the attack-defense behaviors in cloud computing. The physical machine, attack-defense behavior, and their attributes are also modeled by stochastic game model thus forming the attack-defense game model of cloud computing. On this basis, the Nash equilibrium of attack-defense process in physical machine is computed to get the optimal defense strategy. The related theories of Petri nets and the reachable states of attack-defense game model are used to formally verify the correctness and effectiveness of the proposed method. The enforcement algorithm is proposed to make cloud computing dynamically evaluate and select the defense strategy to against attack behavior as quickly as possible. Both case study and simulation results show that the proposed method can adapt quickly to the changes in cloud application thus improving the security of cloud computing. [ABSTRACT FROM AUTHOR]

Magnaporthe oryzae, the causal agent of blast disease, is one of the most destructive plant pathogens, causing significant yield losses on staple crops such as rice and wheat. The fungus infects plants with a specialized cell called an appressorium, whose development is tightly regulated by MAPK signaling pathways following the activation of upstream sensors in response to environmental stimuli. Here, we show the expression of the Glycogen synthase kinase 3 (GSK3) MoGSK1 in M. oryzae is regulated by Mps1 MAP kinase, particularly under the stressed conditions. Thus, MoGSK1 is functionally characterized in this study. MoGsk1 is functionally homologues to the Saccharomyces cerevisiae GSK3 homolog MCK1. Gene replacement of MoGSK1 caused significant delay in mycelial growth, complete loss of conidiation and inability to penetrate the host surface by mycelia-formed appressorium-like structures, consequently resulting in loss of pathogenicity. However, the developmental and pathogenic defects of Δmogsk1 are recovered via the heterologous expression of Fusarium graminearum GSK3 homolog gene FGK3, whose coding products also shows the similar cytoplasmic localization as MoGsk1 does in M. oryzae. By contrast, overexpression of MoGSK1 produced deformed appressoria in M. oryzae. In summary, our results suggest that MoGsk1, as a highly conservative signal modulator, dictates growth, conidiation and pathogenicity of M. oryzae. [ABSTRACT FROM AUTHOR]

Leakage of underwater crude oil pipes causes severe pollution to soil and water, and results in great economic loss. To predict the diffusion area of spilled oil before it reaches the water's surface and to reduce the time required for emergency response, numerical simulations were conducted on underwater spilled oil diffusion of bare crude oil pipes using FLUENT software. The influences of water-surface wind speed, leakage hole diameter, water velocity, and initial leakage velocity on oil diffusion were analyzed. The results revealed the following: (1) with wind blowing on the surface of the water, the vertical displacement of spilled oil jet-flow was affected by the combined action of water flow and wind, making it difficult for a high-speed jet-flow to form. A horizontal oil flow mostly moved in the direction of the bottom water, and frontier oil droplets dispersed quickly; (2) during the diffusion of spilled oil in water, the maximum horizontal displacement mostly increased linearly, while the maximum vertical displacement initially increased quickly and then slowed; (3) the greater the initial velocity and leakage hole diameter, the higher the oil jet-flow and the wider the diffusion area; the higher the water flow rate and water-surface wind speed, the smaller the vertical displacement of spilled oil. The existence of water-surface wind had no obvious influence on the horizontal displacement of underwater spilled oil. [ABSTRACT FROM AUTHOR]

Cloud computing has attracted much interest recently from both industry and academia. However, the scale and highly dynamic nature of cloud application imposes significant new challenges to resource management, and efficient resource scheduling schemes are highly demanded. In this paper, we propose a systematic method to address the reliability, running time, and failure processing of resource scheduling in cloud computing. A reflection mechanism is used to abstract the resource scheduling process as a metaobject. Petri nets are used to construct the base layer model, meta layer model, metaobject protocol, and other components, thus forming the resource scheduling model. The adaptive resource scheduling strategy is converted into CTL formulas, and the properties are analyzed. Meanwhile, an enforcement algorithm is proposed, which can guarantee the correct behavior of cloud computing while meeting the required reliability within deadline constraints. The operational semantics and related theories of Petri nets help prove its effectiveness and correctness. We have also performed a series of simulations to evaluate our approach. Results show that it can help reveal the structural and behavioral characteristics of cloud computing and improve the efficiency of resource management. [ABSTRACT FROM PUBLISHER]

Cloud computing has become an important, useful paradigm for building applications with cloud services. However, cloud services exist in heterogeneous environments on the Internet. It is challenging to guarantee the reliability of cloud applications. Although there are efforts studying cloud and grid service reliability, very few have considered the modeling and analysis of the reliability of cloud applications. To address this emerging, important problem, we propose the first systematic approach that considers both cloud application elements and their running environment so as to faithfully model the dynamics of cloud computing. First, we present a formal description language to model the different components of a cloud application, and use it to analyze the static and dynamic factors affecting the reliability of cloud applications. Second, we propose reliability assurance strategies to ensure that cloud applications dynamically meet their required reliability. Third, Computation Tree Logic (CTL) is used to convert the reliability assurance strategy into the CTL formulas. We present operational semantics and related theories of Petri nets for establishing the correctness of our proposed method. Finally, a series of simulations are performed to evaluate the efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

Improving the separation efficiency of the inclined oil/water separator, a new type of gravity separation equipment, is of great importance. In order to obtain a comprehensive understanding of the internal flow field of the separation process of oil and water within this separator, a numerical simulation based on Euler multiphase flow analysis and the realizable k-ε two equation turbulence model was executed using Fluent software. The optimal value ranges of the separator’s various structural parameters used in the numerical simulation were selected through orthogonal array experiments. A field experiment on the separator was conducted with optimized structural parameters in order to validate the reliability of the numerical simulation results. The research results indicated that the horizontal position of the dispenser, the hole number, and the diameter had significant effects on the oil/water separation efficiency, and that the longitudinal position of the dispenser and the position of the weir plate had insignificant effects on the oil/water separation efficiency. The optimal structural parameters obtained through the orthogonal array experiments resulted in an oil/water separation efficiency of up to 95%, which was 4.996% greater than that realized by the original structural parameters. [ABSTRACT FROM AUTHOR]