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The deep learning techniques have propelled significant advancements in intelligent fault diagnosis. However, the limited labeled data due to resource-intensive labeling processes pose the challenges for actual applications. This study proposes an attention-centric model for few-shot fault diagnosis in rotating machinery. The model is informed by few-shot learning (FSL) and integrates internal and external attention (EA) mechanisms, which are leveraged to enhance the feature extraction capability. Performance evaluations under the five-way one-shot setting achieve remarkable results. The accuracy reaches 97.147% for the scenario from artificial damage to real damage, and 95.613% for the scenario of different operational conditions. The critical role of the integrated attention modules is further validated through the ablation study. Comparative analysis with state-of-the-art techniques demonstrates the superior performance of the proposed model. In short, this work provides an alternative method for fault diagnosis under the few-shot limitation.

Composite insulators have been widely used in power transmission lines due to their excellent insulation performance. However, it is impossible to completely avoid flashovers in power transmission lines, so it is crucial to ensure the safety and reliability of these lines. To address this issue, the datasets are collected based on the composite insulator surface images, and three convolutional neural network (CNN) models, VGG, GoogleNet, and ResNet, are established. However, the small number of images in the dataset has made it difficult to achieve accurate classification, so we also establish three generative adversarial network (GAN) models—original GAN, deep convolutional GAN (DCGAN), and Wasserstein GAN (WGAN)—to fit the existing image data and generate virtual images of the insulator surface to expand the dataset. Using transfer learning, we obtained pretrained models and trained them on the expanded dataset. By comparing the test results of different models, we found the best classification model with an accuracy of 97.5%. This shows that the CNN models can effectively classify the surface images of composite insulators, especially after using the virtual images generated by GAN to expand the dataset. The results of this study suggest that these models have potential for engineering applications.

The causality between different variables can reveal the flows of material, energy, and information in the process system. It is beneficial to reflect the relationship between quality variables and process variables. In this study, a concurrent quality and process monitoring method is proposed with intrinsic causality analytics. The proposed method explores the causality between different variables using transfer entropy. Then, the directly related variables and their corresponding time lags are combined to extract convolutional features, which are used to generate feature matrices for process and quality variables. In this way, the quality related information is extracted from the process variables which are directly related to the quality variables. After that, monitoring models are established for each pair of feature matrices, and the monitoring results are integrated to provide a final monitoring result. Since the process disturbances usually smear to directly related variables, the fault signature can be amplified to improve the detection sensitivity when the directly related variables are combined. Finally, the operation status of the process system is identified through the designed monitoring policy, which combines the decisions of different statistics. It is noted that the proposed strategy can be readily generalized to many other existing quality related monitoring methods. Experiments on a real industrial condenser show that the proposed method can distinguish the quality related faults from the process related faults in the condenser. Besides, it has better detection sensitivity than some commonly used quality related monitoring methods.

2-Butenenitrile (2-Bu) is a recently discovered crucial interstellar molecule. Herein, an abnormal NH band was observed in the infrared spectrum of the 2-Bu dimer cation, suggestive of a proton transfer reaction within the cluster. Through a comprehensive theoretical analysis of the IR spectrum of (2-Bu)2+, we discovered not only the formation of a new C–N bond through the attachment of one 2-Bu to another but also the occurrence of a proton transfer reaction in the cluster. This proton was identified as originating from the methyl group of the attaching 2-Bu in the cluster based on the analysis of IR spectra of (2-Bu)+and [2-Bu-acrylonitrile (AN)]+. Furthermore, the detailed reaction process of this ion–molecule reaction is examined with theoretical calculation. This finding contributes significantly to our deeper understanding of ion–molecule reactions in the gas phase and the formation of nitrogen-containing prebiotic molecules in the interstellar medium.

AbstractThis study aimed to evaluate the effect of hot air drying on drying kinetics, quality and flavor changes of Fructus aurantii. The results showed that Midilli model (R2= 0.9995, RMSE= 6.16 × 10−9, χ2= 7.62 × 10−5) was the best model for hot air drying of Fructus aurantii, and the effective moisture diffusion coefficient (Deff) and activation energy (Ea) of hot air drying process was 2.63 × 10−5- 6.61 × 10−4m2/s and 42.36 KJ/moL, respectively. The HS-GC-MS and E-nose techniques were used to evaluate the changes in volatile components and aromatic characteristics. The results of HS-GC-MS showed that linalool content was 6.69%, 5.32% and 5.10% at 50°C, 60°C and 70°C respectively, showing a decreasing trend. Flavor radar fingerprint map showed that sensors S13, S12 and S2 have obviously higher response values, S11 , S5, S4 and S9 have higher response values, but other sensors have no obvious response values. PCA analysis showed that the first principal component (PC1) and the second principal component (PC2) accounted for 57.38% and 22.59% respectively, and the cumulative variance contribution rate was 87.1% (> 85%). The results of flavor radar fingerprint and PCA showed that the drying process obviously changed the aroma characteristics. The overall results showed that hot air drying at 50-60°C could effectively improve the final quality of dried Fructus Aurantiiby maintaining flavor characteristics.

Distributed parallel MPI applications are the dominant workload in many high-performance computing systems. While optimizing MPI application execution is a well-studied field, little work has considered optimizing the initial MPI application launching phase, which incurs extensive cross-machine communications and synchronization. The overhead of MPI application launching can be expensive, accounting for more than million core hours per 10K nodes annually on the production Tianhe-2A supercomputer, which will increase as the number of parallel machines used grows. Therefore, it is critical to optimize the MPI application launching process. This paper presents a novel approach to optimizing the MPI application launch. Our approach adopts a location-aware address generation rule to eliminate the need for address exchange and a topology-aware global communication scheme to optimize cross-machine synchronization. We then design a new application launch procedure to support the proposed optimizations to further reduce the pressure of the shared I/O system. Our techniques have been deployed to production in the Tianhe-2A supercomputer and the Next Generation Tianhe Supercomputer. Experimental results show that our approach scales well and outperforms alternative schemes, reducing the MPI application launching time by over 29% with 320K MPI processes.

Degradation analysis is essential in system health management and remaining useful life prediction. Since the observed degradation data are inevitably contaminated by measurement error, degradation state estimation is hence important for a more accurate evaluation of the health status. There are two challenges for estimating the degradation state. The first is the uncertainty associated with the estimated parameters for the model, and the other is the measurement outlier. Current models usually assume Gaussian measurement errors and they are sensitive to the measurement outlier. To deal with these two challenges, we develop a framework for degradation state estimation under the context of the distributionally robust optimization, which is robust to the parameter uncertainty. We further incorporate the Huber loss into this framework to make it robust to the measurement outlier. A procedure for estimation of the model parameters as well as setting the parameters of the ambiguity set is provided. The effectiveness of the model is validated using numerical and real case studies.

Degradation has become the dominant failure mode for highly reliable engineering systems. Cracking, a fatigue phenomenon composed of sequential phases of crack initiation and propagation, is a major concern for critical aircraft structures. Traditional fracture mechanics analysis cannot fully meet the requirements for assessing reliability indicators from a reliability analysis perspective. Alternatively, the empirical Lifetime Delayed Degradation Process (LDDP) provides an explanatory framework for sequential hard&soft failure mode. This study further generalizes the LDDP framework by introducing the Bayesian method as a Bayes-LDDP model, which incorporates a weakly informative prior derived from historical data of similar systems for both non-destructive and destructive inspections. Additionally, we compare our proposed method to the LDDP approach using specific inspection datasets. Two practical applications are conducted to demonstrate the effectiveness of the Bayes-LDDP model for reliability monitoring and remaining useful life (RUL) prediction in critical aircraft structures using field data. The crack inspection datasets of a transport aircraft and an aircraft core automated maintenance system (CAMS) are utilized for non-destructive and destructive inspections, respectively. The Markov Chain Monte Carlo (MCMC) sampling algorithm is adopted for the Bayes-LDDP, improving the computational efficiency of model parameters estimation compared to the stochastic expectation maximum (SEM) algorithm. Furthermore, the Bayes-LDDP model enables precise inference including the mean time to failure (MTTF) of cracks for destructive inspections and the RUL for non-destructive inspections under the selected optimal model. This extended novel framework provides a clear depiction of the lifetime delayed degradation process from a Bayesian perspective.

Capacity degradation of lithium-ion batteries largely determines the cost, performance and environmental impact of various products such as renewable energy production systems, portable electronics, and electric vehicles. Degradation assessment is thus of great importance for prognostic and health management of batteries, which ensures a stable production and operation. There are typically two leading sources contributing to the degradation of a lithium-ion battery, namely, cycling aging during charge/discharge cycles and calendar aging during idle states. However, most existing studies on degradation assessment either only consider a single source or ignore the coupling of these two sources, which makes the evaluation inefficient. A common observation in practice is that a battery tends to degrade faster when it experiences charge/discharge cycles continuously for a longer time. To capture this feature, this paper introduces the concept of the cumulative uninterrupted cycling duration (CUCD), which is defined as the consecutive operation duration since a battery ends the idle state. The CUCD can also help to model the coupling between the calendar and cycling aging. This paper then proposes to model the drift rate of cycling aging as a function of the CUCD and the functional form is determined with the monotonic spline. We estimate the model parameters by maximizing the likelihood function. Hypothesis testings toward the significance of the coupling between two aging sources and the monotonicity of the drift rate function under cycling aging are also provided. The effectiveness and the superiority of the model are validated using numerical and real case studies. Note to Practitioners—This study aims to characterize the degradation of lithium-ion battery simultaneously considering calendar and cycling agings and the coupling effect. Unlike previous research that only supplied experimental results to qualitatively illustrate the existence of the coupling effect between these two aging sources, this study provides procedures to quantitatively test the significance of such an effect. In addition, previous research ignored the coupling effect in the degradation modeling as it is difficult to capture, this study propose the concept of CUCD to characterize it. The CUCD is also introduced as a stress factor for cycling aging, which is based on the fact that a battery will degrade faster when it experiences charge/discharge cycle continuously for a longer time. Hypothesis testing is also provided to illustrate this monotonicity. To deal with another challenge that the expert knowledge of the functional form between cycling aging and CUCD is unavailable except for the monotonicity, a nonparametric method with the shape-restricted spline is adapted. The estimation procedures for the model parameters are also presented.

Unsupervised fault detection and diagnosis methods generally have the following shortcomings in their projection vectors: 1) they may not be specially designed to differentiate between normal and abnormal samples; 2) they may remain unchanged for different abnormal conditions; and 3) the key variables for the process anomalies may have not been effectively selected. In this study, a fault detection and diagnosis scheme [sparse distribution dissimilarity analytics (SDDA)] is proposed with a lasso penalty and distribution dissimilarity to solve these issues. The proposed method is formulated through a nonconvex optimization problem with a lasso penalty, which aims to maximize the distribution dissimilarity between different data sets. Then, the nonconvex optimization problem is recast to an iterative convex optimization problem using the minorization–maximization algorithm. After that, the constraint conditions are removed using the Karush–Kuhn–Tucker conditions for further simplification. Finally, the unconstraint optimization problem is solved through the proposed feasible gradient direction method. Based on the obtained sparse projection vectors, a fault detection model with both static deviation and dynamic fluctuation is developed. Since the statistics are designed using distribution dissimilarity, some abnormal conditions with small fault magnitudes can also be accurately detected. Besides, a reconstruction-based contribution (RBC) method is proposed for the statistics, and its diagnosability has been strictly demonstrated in theory. The detection and diagnosis performance of the proposed SDDA method is validated using a simulated process and a real industrial process. Experimental results illustrate the superiority of the proposed method to some commonly used methods.

Numerous maintenance strategies have been proposed in the literature related to reliability. This paper focuses on the utilization of reliability importance measures to optimize maintenance strategies. We analyze maintenance strategies based on importance measures and identify areas lacking sufficient research. The paper presents principles and formulas for advanced importance measures within the context of optimizing maintenance strategies. Additionally, it classifies methods of maintenance strategy optimization according to importance measures and outlines the roles of these measures in various maintenance strategies. Finally, it discusses potential challenges that optimization of maintenance strategies based on importance measures may encounter with future technologies.

The presence of antibiotic residues in food products due to the extensive use of antibiotics in animal farming poses significant risks to human health and contributes to antimicrobial resistance. Conventional methods for antibiotic residue detection, such as LC-MS/MS and immunoassays, have limitations in terms of cost, analysis time, and complexity. Graphene-based electrochemical sensors have emerged as promising alternatives for rapid, sensitive, and cost-effective detection of antibiotic residues in food. This review provides a comprehensive overview of recent advancements in the development and application of graphene-based electrochemical sensors for antibiotic residue monitoring. We critically examine the strategies for graphene synthesis, modification, and integration with recognition elements, as well as the fabrication and performance characteristics of these sensors. The unique advantages of graphene, such as high surface area and electrical conductivity, are highlighted, along with the challenges and limitations in practical implementation. Furthermore, we discuss future perspectives and research directions, emphasizing the need for multi-residue detection, sensor miniaturization, and validation in real-world settings. The role of advanced nanomaterial composites, novel recognition elements, and data analysis techniques in improving sensor performance is also explored. By providing a critical assessment of the current state of knowledge and identifying key research gaps and opportunities, this review aims to serve as a valuable resource for researchers and stakeholders interested in developing graphene-based electrochemical sensors for antibiotic residue detection in food safety applications, ultimately contributing to the protection of consumer health and the fight against antimicrobial resistance.

Based on the switching periodic event-triggered scheme (ETS), the control problem is investigated for networked control systems (NCSs) and its application to unmanned aerial vehicles (UAVs). Inspired by the switched system framework, a new switching ETS is proposed to reduce data transmission among the components of the NCS. The feature of the switching ETS is that the triggered conditions are scheduled by switching signals. The switching controller is model-dependent such that the studied NCS is modelled as a switching system with two time-delay subsystems. Applying the switching system technique, the criteria under the designed control scheme are established for stability and the $H_{\infty }$ performance analysis. Finally, an UAV system and a networked invert pendulum are utilized to show the availability of the proposed switching ETS.

Efficient naming of inspection photos of transmission line towers is vital in the maintenance of power grid equipment. Current inspection photo naming methods are mainly manual, which is neither rapid nor effective. Research on inspection photo naming is limited due to a shortage of inspection image datasets and low image resolution. Hence, we gathered inspection photos of real tangent towers using drones and created an inspection photo dataset TTower-345 for automatic naming model training purposes. We proposed an automatic naming model, IELC (improved EfficientNet network and LBP classification model), based on this dataset. IELC comprises a dual-branch structure that integrates a jointly improved EfficientNet model and an local binary patterns (LBP) classification model. Experimental results indicated that the proposed dataset contains more diverse inspection image features, which in turn helped the model learn more features. In our experiments, our proposed automatic naming method achieved a classification accuracy of over 95% and demonstrated reliability by exhibiting good generalisability in practical scenarios.

Reliability evaluation of complex systems like bridge systems or network model is of great interest but challenging as such reliability models cannot be recursively decomposed into combinations of series and parallel systems. The task becomes even more challenging if imprecise parameters are involved. To address such challenges, this article proposes a survival signature-based reliability framework for an imprecise multistate system (IMSS). For probability estimation, the survival signature of IMSS is first defined according to the combination of states of composing elements, and corresponding multistate survival functions are obtained. A simulation method is then developed to calculate the p-box when imprecision is involved. To further address the uncertainty, an approximate Bayesian computation method with a novel Jensen–Shannon divergence-based kernel is developed to perform a stochastic model updating. This method allows a better reliability evaluation result with less uncertainty by calibrating imprecise parameters in the presence of mixed uncertainty. A typical bridge system, which cannot be decomposed as simpler ones, is first cited as a numerical case, followed by a real application example for validation and benefit illustration.

Accurate and automatic detection of pelvic lymph nodes in computed tomography (CT) scans is critical for diagnosing lymph node metastasis in colorectal cancer, which in turn plays a crucial role in its staging, treatment planning, surgical guidance, and postoperative follow-up of colorectal cancer. However, achieving high detection sensitivity and specificity poses a challenge due to the small and variable sizes of these nodes, as well as the presence of numerous similar signals within the complex pelvic CT image. To tackle these issues, we propose a 3D feature-aware online-tuning network (FAOT-Net) that introduces a novel 1.5-stage structure to seamlessly integrate detection and refinement via our online candidate tuning process and takes advantage of multi-level information through the tailored feature flow. Furthermore, we redesign the anchor fitting and anchor matching strategies to further improve detection performance in a nearly hyperparameter-free manner. Our framework achieves the FROC score of 52.8 and the sensitivity of 91.7% with 16 false positives per scan on the PLNDataset. Code will be available at: github.com/SCUsomebody/FAOT-Net/.

Acid/base catalysis plays a pivotal role in the synthesis of versatile chemicals, in which acidic/basic molecules are widely employed as highly effective catalysts. However, challenges associated with the recyclability of these homogeneous catalysts and the complexity of purification procedures impose limitations on their practical applications. Heterogeneous solid catalysts are emerging as viable alternatives to traditional acid/base catalysts. Among these, ceria (CeO2), with the coexistence of surface acidic and basic sites, has demonstrated significant potential for acid/base catalysis. Herein, the present review provides a comprehensive overview of fundamental acid/base reactions catalyzed by CeO2, including the dehydration and hydrolysis processes, the transformation of CO2to dimethyl carbonate (DMC), the hydrogenation of unsaturated groups, etc.In particular, the reaction mechanism is discussed in conjunction with an analysis of the acidic/basic sites on CeO2. Additionally, various strategies to modulate the acid/base properties are outlined on CeO2-based catalysts, with which the enhanced catalytic activity and selectivity are realized.

Cloud systems, which are typical cyber–physical systems, consist of physical nodes and virtualized facilities that collaborate to fulfill cloud computing services. The advent of visualization technology engenders resource sharing and service parallelism in cloud services, introducing novel challenges to system modeling. In this study, we construct a systematic model that concurrently evaluates system reliability, performance, and power consumption (PC) while delineating cloud service disruptions arising from random hardware and software failures. Initially, we depict system states using a birth–death process that accommodates resource sharing and service parallelism. Given the relatively concise service duration and regular failure distributions, we employ transient-state transition probabilities instead of steady-state analysis. The birth–death process effectively links system reliability, performance, and PC through service durations governed by service assignment decisions and failure/repair distributions. Subsequently, we devise a multistage sample path randomization method to estimate system metrics and other factors related to service availability. The findings highlight that the trade-off between performance and PC, under the umbrella of reliability guarantees, hinges on the equilibrium between service duration and unit power. To further delve into the subject, we formulate optimization models for service assignment and juxtapose optimal decisions under varying availability scenarios, workload levels, and service attributes. Numerical results indicate that service parallelism can improve performance and conserve energy when the workload remains moderate. However, as the workload escalates, the repercussions of resource sharing-induced performance loss become more pronounced due to resource capacity limitations. In cases where system availability is constrained, resource sharing should be approached cautiously to ensure adherence to deadline requirements. This study theoretically analyzes the interrelations among system reliability, performance, and PC, offering valuable insights for making informed decisions in cloud service assignments.

Maintenance logs of industrial equipment record descriptive and unstructured operation and maintenance (O&M) information, which is the basis of reliability, availability, and maintainability investigations. However, the construction of failure knowledge graphs as a basis for understanding the failure and maintenance properties of systems is challenging due to the requirement of annotation efforts and domain knowledge. This article proposes a novel semi-supervised method for failure knowledge graph construction. Initially, a semantic module is proposed to extract hidden contextual information from maintenance records and identify corresponding failure modes. The semantic module is trained by unlabeled maintenance records with the assistance of the hard pseudo-label acquisition and the proposed self-training algorithm. Subsequently, a taxonomy induction module is presented to extract failure items and their relationships to construct failure knowledge graphs that provide decision support. The feasibility and superiority of the proposed method are validated by maintenance logs from real wind farms. Overall, the proposed method provides an effective tool for semantic information digitalization of well-cumulated industrial O&M data.

Diabetes is a leading cause of morbidity, mortality and cost of illness1,2. Health behaviours, particularly those related to nutrition and physical activity, play a key role in the development of type 2 diabetes mellitus3. Whereas behaviour change programmes (also known as lifestyle interventions or similar) have been found efficacious in controlled clinical trials4,5, there remains controversy about whether targeting health behaviours at the individual level is an effective preventive strategy for type 2 diabetes mellitus6and doubt among clinicians that lifestyle advice and counselling provided in the routine health system can achieve improvements in health7–9. Here we show that being referred to the largest behaviour change programme for prediabetes globally (the English Diabetes Prevention Programme) is effective in improving key cardiovascular risk factors, including glycated haemoglobin (HbA1c), excess body weight and serum lipid levels. We do so by using a regression discontinuity design10, which uses the eligibility threshold in HbA1c for referral to the behaviour change programme, in electronic health data from about one-fifth of all primary care practices in England. We confirm our main finding, the improvement of HbA1c, using two other quasi-experimental approaches: difference-in-differences analysis exploiting the phased roll-out of the programme and instrumental variable estimation exploiting regional variation in programme coverage. This analysis provides causal, rather than associational, evidence that lifestyle advice and counselling implemented at scale in a national health system can achieve important health improvements.

A main challenge in reliability analysis of repairable systems is to model the heterogeneity in their failure behavior, which can be reflected by the corresponding recurrent failure-time data. To capture the system heterogeneity for data analysis, a system-specific random effect is typically introduced in most existing statistical models. In practice, the random effect of repairable systems tends to be time varying; for example, each repair action could change system's physical properties. Prior studies, however, generally do not take account of this time-varying nature and few of them circumvent the risk of model misspecification on the parametric distribution of frailty. In this article, we propose a semiparametric model that uses multivariate Gaussian convolution processes (MGCPs) to meet the above challenges. First, we use the trend RP to model the baseline intensity function of each repairable system. Based on the baseline intensity function, we then introduce MGCPs to simultaneously factor in heterogeneity and infer commonalities across multiple systems. A Bayesian framework is used for parameter estimation and time-to-failure prediction. Simulation studies show the advantages of our model in terms of robustness and estimation accuracy. A group of oil and gas well systems are used to illustrate the application of the proposed model.

This article proposes a distance-based distributionally robust optimization method to determine the optimal preventive maintenance schedule in hydrothermal power systems, with the aim of minimizing the operation cost in the coming year under the uncertainty of natural inflows. The ambiguity set consisting of a family of distributions close to the reference probability distribution, is used to model the uncertainty, where the reference distribution is generated according to predictive information and historical seasonal prediction error, so it is helpful to implicitly reflect the seasonality of natural inflows. The conservatism of the solution can be adjusted by regulating the scale of the ambiguity set. The column and constraint generation algorithm is employed to decompose the proposed min–max–min problem into a master problem and subproblem, where the maintenance decision is determined in the master problem and the operation decisions are made to identify the worst-case probability distribution in the subproblem. The stochastic dual dynamic programming algorithm is applied to further decompose the multistage subproblem into several smaller stage problems. Case studies were conducted on a modified IEEE 30-bus system and real hydrothermal power systems in China to demonstrate the effectiveness of the proposed method.

The emergence of electric vehicles promotes the development of Internet of Vehicle (IoV). However, there are a series of security problems to be solved in the IoV. Firstly, how to allow vehicle users to find the nearest non-queuing charging pile without detours is a challenge. Secondly, applications in the IoV are delay-sensitive, while high communication delays caused by security mechanisms would bring serious consequences to vehicles. Thirdly, the verifiability and fairness between charging and payment are difficult to be guaranteed. Aiming at the efficiency and security problems of the charging service in the IoV, this paper proposes a blockchain-based intelligent and fair IoV charging service system. According to the multi-factor constraints between vehicles and charging piles, a multi-factor IoV branch and bound algorithm is proposed to intelligently recommend charging piles for vehicles and maximize the overall energy saving. We propose the cross-area consensus protocol to achieve low latency in vehicle communication. In addition, we ensure the fairness between charging and payment through a payment channel protocol based on verifiable encrypted signatures. Finally, we implement the proposed scheme, and we put the project prototype on an open source platform is available at: https://github.com/chenruonan/blockchain-based-intelligent-and-fair-IoV-charging-service-protocol. The experimental results demonstrate the low latency and energy-saving advantages of our proposal. When the payment is executed 250 times, the delay of our proposal is only 1.54% of the normal on-chain payment time.

The redox-active metal ions, especially Cu2+, are highly correlated to Alzheimer’s disease (AD) by causing metal ion-mediated oxidative stress and toxic metal-bound β-amyloid (Aβ) aggregates. Numerous pieces of evidence have revealed that the regulation of metal homeostasis could be an effective therapeutic strategy for AD. Herein, in virtue of the interaction of both amino-containing silane and ethylenediaminetetraacetic acid disodium salt for Cu2+, the silicon–carbon dots (SiCDs) are deliberately prepared using these two raw materials as the cocarbon source; meanwhile, to realize the local enrichment of SiCDs and further maximize the chelating ability to Cu2+, the SiCDs are feasibly loaded to the biocompatible mesoporous silica nanoparticles (mSiO2) with the interaction between residual silane groups on SiCDs and silanol groups of mSiO2. Thus-obtained nanocomposites (i.e., mSiO2@SiCDs) could serve as an efficient Cu2+chelator with satisfactory metal selectivity and further modulate the enzymic activity of free Cu2+and the Aβ42–Cu2+complex to alleviate the pathological oxidative stress with an anti-inflammatory effect. Besides, mSiO2@SiCDs show an inspiring inhibitory effect on Cu2+-mediated Aβ aggregation and further protect the neural cells against the toxic Aβ42–Cu2+complex. Moreover, the transgenic Caenorhabditis elegansCL2120 assay demonstrates the protective efficacy of mSiO2@SiCDs on Cu2+-mediated Aβ toxicity in vivo, indicating its potential for AD treatment.

Sulphate (SO42−) is a main component of PM2.5in China. The chemical formation mechanisms of sulphate are complex, and many air quality models have been used to analyse these mechanisms. To improve the accuracy of Weather Research Forecast-Chemistry (WRF-Chem) on sulphate, an alternative method is proposed in this paper. Moreover, data assimilation is performed to adjust the chemical reaction rates of sulphate. Based on the original reactions, a new sulphate parameterisation scheme, which includes two hypothetical reactions and six undetermined parameters, was added. Based on the WRF-Chem/DART (Data Assistance Research Testbed) system, the near-ground concentrations of SO42−, SO2, NO2, O3and particulate matter are assimilated to adjust the six parameters. After adjusting the parameters, the new scheme can effectively solve the underestimation of SO42−and overestimation of SO2. The simulation of SO42−improved as the mean bias changed from −13.1 µg m−3to 3.5 µg m−3while SO2improved from 17.0 µg m−3to 6.3 µg m−3. The temporal and spatial variation characteristics predicted by the new scheme are consistent with the theoretical research results, indicating that the complex mechanism of sulphate formation could be replaced by the temporal and spatial variation characteristics predicted by the new scheme and that the parameters can be adjusted by data assimilation. Furthermore, the reaction rates of the SO42−parameterisation scheme of the WRF-Chem model are improved in this study, and a new method for improving the accuracy of the air quality model is provided.

Nowadays, profile data mining techniques facilitate effective process monitoring, quality control, fault diagnosis, etc., with considerable benefits to manufacturing industry. However, regarding the complex system in modern manufacturing industry, there are two significant challenges for application development based on profile data mining. First, the staggering data volume leads to high memory and computational requirements. Second, the noisy signals in collected data may deteriorate useful information and model performance. This article proposes a novel algorithm for profile data mining called profile abstract, which simultaneously enables profile data compression and segmentation. The proposed algorithm mainly considers the scenario of fault diagnosis and can be utilized as a pre-processing step to address the above challenges. Profile abstract seeks to find a subset of raw data or a group of models as representatives that preserve the essential characteristics of raw data. Finding the data representatives helps reduce data redundancy while maintaining the model performance. Model representatives assist in describing the complex pattern of the profile, which can be used for pattern-based data segmentation. After data segmentation, information gain is adopted to determine the critical primitives for model improvement. In this article, validation of the proposed method's superiority is performed with two datasets from a real production line and one simulation dataset.

ABSTRACTObjectiveLittle is known about the morbidity and mortality of infusion Adverse drug reactions (ADRs) in the emergency department. We sought to evaluate the epidemiology of emergency infusion ADRs.Materials and MethodsThis was a prospective study of infusion ADRs in the emergency infusion unit (EIU) of a tertiary hospital from 1 January 20201 January 2020, to 31 December 2021w31 December 2021. Emergency infusion ADRs were identified as intravenous drug-related ADRs that the causality was determined using the Naranjo algorithm. The incidence, severity and preventability of these ADRs were assessed using other standard criteria.ResultsA total of 327 ADRs were recorded for 320 participants, antibiotics were the class of drugs most commonly involved, and 76.15% of ADRs occurred within the first hour. The most common symptoms observed were skin manifestations, accounting for 46.04% of ADRs. Mild reactions accounted for 85.32% based on the Hartwig and Siegel scale. In 89.30% of the reports, the ADRs were evaluated as not preventable based on the modified Schumock and Thornton scale. The causality and severity of ADRs were related to Charlson Comorbidity Index score and age (P < 0.05).ConclusionThis epidemiological study described the pattern of emergency infusion ADRs in East China in detail. These findings may be useful to compare patterns among different centers.

Most classical accelerated degradation test (ADT) planning models implicitly overlook the errors when measuring the degradation levels of the test units. However, the sensor measurement errors are inevitable and the magnitude of the errors may have a trend to increase over time due to sensor degradation. As a consequence improperly overlooking the sensor degradation in ADT planning could result in a test plan with unsatisfactory performance. This article addresses this issue by proposing a sequential ADT planning model that factors in sensor degradation. The system degradation level is periodically measured, based on which we dynamically adjust the stress level during ADT. We adopt a Bayesian framework that periodically updates the posterior distribution of model parameters considering the sensor degradation. An approximate Bayesian computation algorithm is developed to circumvent the difficulty of directly evaluating the complicated likelihood function in our problem. Numerical studies on a gas turbine reveal that our sequential model outperforms several traditional ADT designs that overlook the sensor degradation.

Reliability-based design optimization (RBDO) is one of the most crucial techniques in complex and reliability-critical engineering systems. This has been a research hotspot over the past few decades. RBDO allows us to take into consideration the uncertainties from various sources, during the early design stage. It provides a design that satisfies various constraints as well as the reliability of the designed system performing the expected functions. Such capabilities can serve as countermeasures against the foreseeable uncertainties in the manufacturing or application process. Following a short preliminary overview of RBDO as well as the canonical strategies, this article sets out to review how surrogate models have been explored to streamline RBDO. This is done through a systematic study, outlining their respective advantages as well as disadvantages, and discussing the problems that need to be solved.

Through the incorporation of phosphorus (P) as a third component, an in-situ formation of a magnetic phase with a nanotwin structure was achieved. The utilization of selective laser melting (SLM) enabled the synthesis of a Cu–Fe–P immiscible alloy that exhibits an integrated structure and function. The microstructure predominantly consists of a magnetic heterogeneous Fe–P-rich phase, comprising a mixture of Fe2P and a small quantity of Fe3P, which is distributed within the ε-Cu Cu-rich matrix in alternating “fiber-layer” and “particle-shape” forms. Additionally, the fibrous Fe2P phase exhibited precipitation of α-Fe, while the Fe–P-rich phase experienced the precipitation of high-density nanotwinned Cu (nt-Cu) particles. The microstructure density and structural characteristics of Cu–Fe–P immiscible alloys were enhanced through the adjustment of laser energy density, leading to improvements in their mechanical and magnetic properties. It was observed that the microstructure density initially increased and then decreased with increasing laser energy density. The highest relative density of the Cu–Fe–P immiscible alloy microstructure (98%) was achieved at a laser energy density of 53 J/mm3. Furthermore, the influence of laser energy density on the mechanical and soft magnetic properties of Cu–Fe–P immiscible alloys was examined. The findings indicate that the mechanical and soft magnetic properties of these alloys are affected by the laser energy density. The findings indicate that the compact Cu–Fe–P immiscible alloys exhibit favorable compression mechanical properties and soft magnetic properties. Specifically, the ultimate compressive stress can reach 890.5 ± 20 MPa, while the failure strain can reach 20.5 ± 2%. Additionally, the coercivity is observed to be as low as 20.0 Oe, the magnetic saturation strength as high as 93.5 emu/g, and the residual magnetization at 1.1 emu/g.

Based on cognitive radio (CR) networks, this paper considers event-triggered control for unmanned aerial vehicle (UAV) systems subject to deception attacks. Two states (ON and OFF) are switched in CR networks. For the switched model, a new Quality of Service (QoS)-dependent event-triggered scheme (ETS) is proposed over CR networks under deception attacks. For a non-ideal QoS, more sensitive trigger parameters are required against deception attacks. For an ideal QoS, less sensitive trigger parameters are adjusted to reduce data transmissions. Based on the QoS-dependent ETS, a new switched system is modeled subject to deception attacks. By constructing the Lyapunov functional, the criteria are obtained to guarantee the exponential mean-square stability of UAV systems and the desired $\mathcal {L}_{\infty }$ performance. Moreover, an algorithm is developed to adjust the parameters of QoS-dependent ETS according to the QoS of CR network. Finally, an UAV system is utilized to show the effectiveness of the proposed approach.

Suspension systems are significant for safe and comfort operation of high-speed trains. Health assessment is a useful tool to schedule maintenance plans of suspension systems, and furthermore ensure safety operation of high-speed railway transportation. In real operating condition, two problems, i.e. data imbalance and shortage of labelled data, result in difficult for health assessment of the suspension system using deep learning. In this work, a multi-sensor information fusion method, called as multi-sensor graph transfer network (MSGTN), is proposed in basis of deep transfer learning and graph neural network. In the proposed method, a domain-share multi-sensor graph neural network (MSGNN) is firstly proposed to extract features from vibration signals collected from three different positions in train vehicles. A graph-based fusion layer in MSGNN is proposed to fuse multi-sensor information by combining frequency response curves of the suspension system. The MSGTN mainly includes two parts in source and target domains respectively. In the source domain, a simple physical dynamic model of high-speed rail suspension system is built to generate labelled simulation datasets to pre-train MSGNN. In the target domain, the initial hyper-parameters of MSGNN are that of the pre-train model in the source domain. The labelled data in the target domain is fed to fine-tune MSGNN and then the final model for health assessment can be obtained by minimizing the loss function. The effectiveness of the proposed method was verified using real-work operation data.

We tested a series of SQ109 analogues against Mycobacterium tuberculosisand M. smegmatis, in addition to determining their uncoupling activity. We then investigated potential protein targets, involved in quinone and cell wall biosynthesis, using “rescue” experiments. There was little effect of menaquinone on growth inhibition by SQ109, but there were large increases in the IC50of SQ109 and its analogues (up to 20×) on addition of undecaprenyl phosphate (Up), a homologue of the mycobacterial decaprenyl (C50) diphosphate. Inhibition of an undecaprenyl diphosphate phosphatase, an ortholog of the mycobacterial phosphatase, correlated with cell growth inhibition, and we found that M. smegmatiscell growth inhibition could be well predicted by using uncoupler and Up-rescue results. We also investigated whether SQ109 was metabolized inside Mycobacterium tuberculosis, finding only a single metabolite, previously shown to be inactive. The results are of general interest since they help explain the mechanism of SQ109 in mycobacteria.

Maternal–fetal immune tolerance is a process that involves complex interactions of the immune system, and myeloid-derived suppressor cells have emerged as one of the novel immunomodulator in the maintenance of maternal-fetal immune tolerance. Myeloid-derived suppressor cells are myeloid progenitor cells with immunosuppressive activities on both innate and adaptive cells through various mechanisms. Emerging evidence demonstrates the accumulation of myeloid-derived suppressor cells during healthy pregnancy to establish maternal–fetal immune tolerance, placentation, and fetal–growth process. By contrast, the absence or decreased myeloid-derived suppressor cells in pregnancy complications like preeclampsia, preterm birth, stillbirth, and recurrent spontaneous abortion have been reported. Here, we have summarized the origin, mechanisms, and functions of myeloid-derived suppressor cells during pregnancy along with the recent advancements in this dynamic field. We also shed light on the immunomodulatory activity of myeloid-derived suppressor cells, which can be a foundation for potential therapeutic manipulation in immunological pregnancy complications.Myeloid-derived suppressor cells have emerged as a novel immunomodulator at the maternal–fetal interface, thus targeting myeloid-derived suppressor cells functions could positively influence the outcome of immunological pregnancy complications.