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Solid propellant fires, characterized by uncontrolled combustion behaviors, are a common occurrence in industries that utilize solid propellants and can lead to significant consequences. However, there has been a lack of comprehensive studies on modeling and simulating the flame characteristics of NEPE solid propellant fires. This research aims to investigate the flame behaviors of large NEPE solid propellants at different altitudes using computational fluid dynamics (CFD). The flame's shape and temperature were examined based on experimental data under atmospheric conditions to validate the numerical model. The findings reveal that the simulated flame temperature slightly surpasses the experimental values, while the simulated flame length and width align well with the experimental measurements. Morewover, increasing altitudes result in shorter flame lengths and lower combustion temperatures. Furthermore, the study scrutinizes the influence of turbulence models on the flame behavior of NEPE propellant fires.

The structure containing explosives may encounter different fire scenarios in application. The influence factors of flame radiant heat flow are numerous and the control mechanism is complex due to the complexity and diversity of fire scenes. It is necessary to identify key physical quantities, analyze the controlling mechanism, and establish boundary heat flow load model. Several factors were considered to comprehend and assess the thermal response characteristics of ammunition in a real fire environment, including the boundary heat flow load, the combined heat transfer effect of the air inter-layer, and the thermal decomposition of energetic explosive. Numerical model and calculation method were developed, and the thermal response characteristics of ammunition exposed to fire were investigated. The results demonstrate that the ammunition's temperature rise increases non-linearly as the thickness of flame engulfment increases. Conversely, the temperature rise decreases non-linearly as distance from the fire source increases. In terms of initiation delay time, it decreases non-linearly as the thickness of flame engulfment increases. However, initiation delay time increases non-linearly as distance from the fire source increases. A comprehensive model that considers real fire environment is crucial for prompt and accurate interventions, when assessing the thermal safety of ammunition in complex fire scenarios.

The widespread application of mobile robots holds significant importance for advancing social intelligence. However, as the complexity of the environment increases, existing Obstacle Avoidance Path Planning (OAPP) methods tend to fall into local optimal paths, compromising reliability and practicality. Therefore, based on the Spider-Wasp Optimizer (SWO), this paper proposes an improved OAPP method called the LMBSWO to address these challenges. Firstly, the learning strategy is introduced to enhance the diversity of the algorithm population, thereby improving its global optimization performance. Secondly, the dual-median-point guidance strategy is incorporated to enhance the algorithm’s exploitation capability and increase its path searchability. Lastly, a better guidance strategy is introduced to enhance the algorithm’s ability to escape local optimal paths. Subsequently, the LMBSWO is employed for OAPP in five different map environments. The experimental results show that the LMBSWO achieves an advantage in collision-free path length, with 100% probability, across five maps of different complexity, while obtaining 80% fault tolerance across different maps, compared to nine existing novel OAPP methods with efficient performance. The LMBSWO ranks first in the trade-off between planning time and path length. With these results, the LMBSWO can be considered as a robust OAPP method with efficient solving performance, along with high robustness.

Time series classification exists in widespread domains such as EEG/ECG classification, device anomaly detection, and speaker authentication. Although many methods have been proposed, efficient selection of intuitive temporal features to accurately classify time series remains challenging. Therefore, this paper presents TSC-RTF, a new time series classification method using random temporal features. First, to ensure the intuitiveness of the features, TSC-RTF selects subsequences containing important data points as candidates for intuitive temporal features. Then, TSC-RTF uses random sampling to reduce the number of candidates significantly. Next, TSC-RTF selects the final temporal features using a random forest to ensure the validity of the final temporal features. Finally, a deep learning classifier is trained by TSC-RTF to achieve high accuracy. The experimental results show that the proposed method can compete with the state-of-the-art methods.

Ship trajectory prediction is essential for ensuring safe route planning and to have advanced warning of the dangers at sea. With the development of deep learning, most of the current research has explored advanced prediction methods based on historical spatio-temporal Automatic Identification System (AIS) data. However, environmental factors such as sea wind and visibility also affect ship navigation in real-world maritime shipping. Therefore, developing reliable models utilizing multimodal data, such as AIS and environmental data, is challenging. In this research, we design an adaptive multimodal vessel trajectory data prediction model (termed AMD) based on satellite AIS and environmental data. The AMD model mainly consists of an AIS-based extraction network, an environmental-based extraction network, and a fusion block. In particular, this work considers multimodal data such as historical spatio-temporal information and environmental factors. Time stamps and distances are correlated with AIS and environmental data, and a multilayer perceptron and gated recurrent unit networks are used to design multimodal feature extraction networks. Finally, the fusion block realizes the fusion output of multimodal features to improve the reliability of the AMD model. Several quantitative and qualitative experiments are conducted using real-world AIS and multimodal environmental datasets. Numerous experimental results prove that prediction performance using multimodal data can ensure satisfactory accuracy and reliability while exhibiting a positive impact on improving maritime transport services.

The prop-based 3D virtual object manipulation method is widely used for interaction in Augmented Reality (AR) due to its convenience and flexibility. However, when the represented virtual object is different from the physical prop, the look and feel of the object are not well aligned. To address this problem, we present a dynamic finger remapping approach to creating a visuo-haptic illusion that dynamically adjusts the presented virtual hand posture to fit different sizes and shapes of virtual objects in AR. The finger movement toward a physical prop is synchronously remapped to the movement of the virtual fingers towards the corresponding virtual object. We developed a system that enables users to perceive consistent visual and tactile feedback while grasping and releasing various virtual objects represented by a physical prop. We conducted a user study to explore the effect of this visuo-haptic illusion on the perceived size of virtual objects, setting the sizes of the rendered virtual object and the physical prop as independent variables. We found that the perceived size of a virtual object varied with its rendered size in an almost linear fashion, while the physical prop size did not significantly affect the perception. We also conducted a second study to compare our system with a current prop-based method on virtual object manipulation. The results indicated that the remapped hands could effectively improve the realism and naturalness of the experience.

Based on the investigation result on the training program of information security to undergraduates in Hunan university,an analysis from four aspects was made:domestic and international universities,external specialists,teachers in the college and students in the college.Aiming at issues being found,the training program based on existing experiences and achievements on university-enterprise cooperation were improved.The new training program features university-enterprise joint cultivation.

Owing to the high availability and space-efficiency of erasure codes, they have become the de facto standard to provide data durability in large scale distributed storage systems. The update-intensive workloads of erasure codes lead to a large amount of data transmission and I/O. As a result, it becomes a major challenge to reduce the amount of data transmission and optimize the use of existing network resources so that the update efficiency of the erasure codes could be improved. However, very little research has been done to optimize the update efficiency of the erasure codes under multiple QoS metrics. In this paper, our proposed update scheme, the Ant Colony Optimization based multiple data nodes Update Scheme (ACOUS) employs a two-stage rendezvous data update procedure to optimize the multiple data nodes updates. Specifically, the two-stage rendezvous data update procedure performs the data delta collection and the parity delta distribution based on a multi-objective update tree which is built by the ant colony optimization routing algorithm. Under typical data center network topologies, extensive experimental results show that, compared to the traditional TA-Update scheme, our scheme is able to achieve a 26% to 37% reduction of update delay with convergence guarantee at the cost of negligible computation overhead.

Field-effect mobility (μFET) of organic semiconductor films plays a key role in the performance of field-effect transistors (FETs). Numerical extraction of μFET from organic FET characteristics is not only time-consuming due to patterning of source/drain electrodes, but also frequently unreliable because of the contact resistances (RC) between source/drain electrodes and semiconductors. Here, we propose an approach to rapidly evaluate μ by a point-contact four-probes method (μPFP) for organic semiconductor films. Four tip-like probes quickly contact the semiconductor film surface directly, without deposition of the conventional source/drain electrodes, to simultaneously inject current and measure the electric potential. The charge density and thus conductance of the film is manipulated upon scanning gate voltage, from which the extraction of μPFP, in good agreement with μFET, could be realized in a few seconds. This method, with easily accessible setup and numerical model, substantially accelerates the evaluation of μPFP, and thus could help screen materials and optimize film morphology for organic FETs applications.

Along with the coming of Internet of Everything era, massive numbers of pervasive connected devices in various fields are continuously producing oceans of time series stream data. In order to carry out different kinds of data mining tasks (similarity search, classification, clustering, and prediction) based on streaming time series efficiently and effectively, segmentation and representation which segment a streaming time series into several subsequences and provide approximative representation for the raw data, should be done as the first step. With the virtue of solid theoretical foundations, piecewise linear representation (PLR) has been gained success in yielding more compact representation and fewer segments. However, the current state of art PLR methods have their own flaws: For one thing, most of current PLR methods focus on the guaranteed error bound instead of the holistic approximation error, which may lead to excessive fitting errors of segments and loss of factual research significance. For another, most of current PLR methods process streaming time series with some fixed criteria, which cannot provide a more flexible way to represent streaming time series. Motivated by the above analysis, we propose a novel continuous segmentation and multi-resolution representation approach based on turning points, which subdivides the streaming time series by a set of temporal feature points and represents the time series flexibly. Our method can not only generate more accurate approximation than the state-of-the-art of PLR algorithm, but also represent the streaming time series in a more flexible way to meet different needs of users. Extensive experiments on different kinds of typical time series datasets have been conducted to demonstrate the superiorities of our method.

Intumescent coating is able to provide effective fire protection with both practicality and aesthetics. In this study, expansion performance and thermal physical property experiments are firstly carried out to obtain the basic parameters at different temperatures. Then, the thermal response model of the concrete-filled steel tubular (CFST) structure under the protection of intumescent coating in a fire is established. Finally, based on the experimental data and thermal response model, the effects of initial thickness, expansion rate, intra-pore emissivity and reaction heat on the structure temperature are discussed in detail. The results of this study can provide guidance on intumescent coating formulation design, as well as fire protection design.

Natural convection heat transfer in a porous annulus filled with a Cu nanofluid has been investigated numerically. The Darcy–Brinkman and the energy transport equations are employed to describe the nanofluid motion and the heat transfer in the porous medium. Numerical results including the isotherms, streamlines, and heat transfer rate are obtained under the following parameters: Brownian motion, Rayleigh number (103–105), Darcy number (10−4–10−2), nanoparticle volume fraction (0.01–0.09), nanoparticle diameter (10–90 nm), porosity (0.1–0.9), and radius ratio (1.1–10). Results show that Brownian motion should be considered. The nanoparticle volume fraction has a positive effect on the heat transfer rate, especially with high Rayleigh number and Darcy number, while the nanoparticle diameter has an inverse influence. The heat transfer rate is enhanced with the increase of porosity. The radius ratio has a significant influence on the isotherms, streamlines, and heat transfer rate, and the rate is greatly enhanced with the increase of radius ratio.

The relationship between microstructure and mechanical properties of multi-component solid waste low-carbon cementitious materials has been widely pay attention to. However, industrial solid waste is a complex multi-component system with many variable factors, which makes it difficult to design the formulation of cementitious materials. This paper pioneered the application of machine learning (ML) models, algorithms and error rates to analyze the compressive and flexural strength of fly ash-based pastes. Coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE) and a20-index were used to evaluate robustness. X-ray diffraction (XRD), scanning electron microscope (SEM) and Brunauer-Emmett-Taylor (BET) were carried out to analyze evolution of cementitious materials. The evaluation results of ML models exhibited that the Gradient boosting regression (GBR) model had the best determination parameters and a steep normal distribution fitting curve with an a20-index of 0.861. GBR model exhibited the best robustness. The key factors of fly ash-based cementitious materials were identified by Pearson's coefficient, which was benefit to determine the formulation of multi-component solid waste low-carbon cementitious materials. Furthermore, experiments also demonstrated that the optimum ratio of multi-component solid waste low carbon cementitious material was 10 % gypsum, 10 % metakaolin, 45 % fly ash, 15 % slag and 20 % cement, respectively. It was worth noting that the compressive strength of this kind of multi-component solid waste low-carbon cementitious materials reached 35 MPa, which was superior to the mechanical properties of P·O 32.5 cement. The results of phase, SEM images and pore structure distribution showed that the synergistic effect of the multi-component solid waste materials effectively filled the material voids and also facilitated the formation of a variety of gelatinous materials through gelling reactions in the late stage (14–28 d). This work will promote the resource utilization of industrial solid waste, contribute to carbon reduction, and can accelerate the green revolution of concrete.

Abstract Background Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by synovial inflammation, causing substantial disability and reducing life quality. While macrophages are widely appreciated as a master regulator in the inflammatory response of RA, the precise mechanisms underlying the regulation of proliferation and inflammation in RA-derived fibroblast-like synoviocytes (RA-FLS) remain elusive. Here, we provide extensive evidence to demonstrate that macrophage contributes to RA microenvironment remodeling by extracellular vesicles (sEVs) and downstream miR-100-5p/ mammalian target of rapamycin (mTOR) axis. Results We showed that bone marrow derived macrophage (BMDM) derived-sEVs (BMDM-sEVs) from collagen-induced arthritis (CIA) mice (cBMDM-sEVs) exhibited a notable increase in abundance compared with BMDM-sEVs from normal mice (nBMDM-sEVs). cBMDM-sEVs induced significant RA-FLS proliferation and potent inflammatory responses. Mechanistically, decreased levels of miR-100-5p were detected in cBMDM-sEVs compared with nBMDM-sEVs. miR-100-5p overexpression ameliorated RA-FLS proliferation and inflammation by targeting the mTOR pathway. Partial attenuation of the inflammatory effects induced by cBMDM-sEVs on RA-FLS was achieved through the introduction of an overexpression of miR-100-5p. Conclusions Our work reveals the critical role of macrophages in exacerbating RA by facilitating the transfer of miR-100-5p-deficient sEVs to RA-FLS, and sheds light on novel disease mechanisms and provides potential therapeutic targets for RA interventions. Graphical abstract

In recent years, with rapid technological advancement in both computing hardware and algorithm, Artificial Intelligence (AI) has demonstrated significant advantage over human being in a wide range of fields, such as image recognition, education, autonomous vehicles, finance, and medical diagnosis. However, AI-based systems are generally vulnerable to various security threats throughout the whole process, ranging from the initial data collection and preparation to the training, inference, and final deployment. In an AI-based system, the data collection and pre-processing phase are vulnerable to sensor spoofing attacks and scaling attacks, respectively, while the training and inference phases of the model are subject to poisoning attacks and adversarial attacks, respectively. To address these severe security threats against the AI-based systems, in this article, we review the challenges and recent research advances for security issues in AI, so as to depict an overall blueprint for AI security. More specifically, we first take the lifecycle of an AI-based system as a guide to introduce the security threats that emerge at each stage, which is followed by a detailed summary for corresponding countermeasures. Finally, some of the future challenges and opportunities for the security issues in AI will also be discussed. [ABSTRACT FROM AUTHOR]

Expansion of renewable energy could help realize the goals of peaking carbon dioxide emissions and carbon neutralization. Some existing grid dispatching methods integrating short-term renewable energy prediction and reinforcement learning (RL) have been proven to alleviate the adverse impact of energy fluctuations risk. However, these methods omit long-term output prediction, which leads to stability and security problems on optimal power flow. This paper proposes a confidence estimation Transformer for long-term renewable energy forecasting in reinforcement learning-based power grid dispatching (Conformer-RLpatching). Conformer-RLpatching predicts long-term active output of each renewable energy generator with an enhanced Transformer to ensure stable operation of the hybrid energy grid and improve the utilization rate of renewable energy, thus boosting dispatching performance. Furthermore, a confidence estimation method is proposed to reduce the prediction error of renewable energy. Meanwhile, a dispatching necessity evaluation mechanism is put forward to decide whether the active output of a generator needs to be adjusted. Experiments carried out on the SG-126 power grid simulator show that Conformer-RLpatching achieves great improvement over the second best algorithm DDPG in security score by 25.8% and achieves a better total reward compared with the golden medal team in the power grid dispatching competition sponsored by State Grid Corporation of China under the same simulation environment. Codes are outsourced in https://github.com/BUPT-ANTlab/Conformer-RLpatching.

As the number of power terminals continues to increase and their usage becomes more widespread, the security of power systems is under great threat. In response to the lack of effective trust evaluation methods for terminals, we propose a trust evaluation model based on equipment portraits for power terminals. First, we propose an exception evaluation method based on the network flow order and evaluate anomalous terminals by monitoring the external characteristics of network traffic. Second, we propose an exception evaluation method based on syntax and semantics. The key fields of each message are extracted, and the frequency of keywords in the message is statistically analyzed to obtain the keyword frequency and time-slot threshold for evaluating the status of the terminal. Thus, by combining the network flow order, syntax, and semantic analysis, an equipment portrait can be constructed to guarantee security of the power network terminals. We then propose a trust evaluation method based on an equipment portrait to calculate the trust values in real time. Finally, the experimental results of terminal anomaly detection show that the proposed model has a higher detection rate and lower false detection rate, as well as a higher real-time performance, which is more suitable for power terminals.

In recent years, with rapid technological advancement in both computing hardware and algorithm, Artificial Intelligence (AI) has demonstrated significant advantage over human being in a wide range of fields, such as image recognition, education, autonomous vehicles, finance, and medical diagnosis. However, AI-based systems are generally vulnerable to various security threats throughout the whole process, ranging from the initial data collection and preparation to the training, inference, and final deployment. In an AI-based system, the data collection and pre-processing phase are vulnerable to sensor spoofing attacks and scaling attacks, respectively, while the training and inference phases of the model are subject to poisoning attacks and adversarial attacks, respectively. To address these severe security threats against the AI-based systems, in this article, we review the challenges and recent research advances for security issues in AI, so as to depict an overall blueprint for AI security. More specifically, we first take the lifecycle of an AI-based system as a guide to introduce the security threats that emerge at each stage, which is followed by a detailed summary for corresponding countermeasures. Finally, some of the future challenges and opportunities for the security issues in AI will also be discussed. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) technology, as a non-destructive testing methodology, is extensively utilized to monitor various materials’ structural integrity. However, AE signals captured during experimental processes are often tainted with assorted noise factors that degrade the signal clarity and integrity, complicating precise analytical evaluations of the experimental outcomes. In response to these challenges, this paper introduces an unsupervised deep learning-based denoising model tailored for AE signals. It juxtaposes its efficacy against established methods, such as wavelet packet denoising, Hilbert transform denoising, and complete ensemble empirical mode decomposition with adaptive noise denoising. The results demonstrate that the unsupervised skip autoencoder model exhibits substantial potential in noise reduction, marking a significant advancement in AE signal processing. Subsequently, the paper focuses on applying this advanced denoising technique to AE signals collected during the tensile testing of steel fiber-reinforced concrete (SFRC), the tensile testing of steel, and flexural experiments of reinforced concrete beam, and it meticulously discusses the variations in the waveform and the spectrogram of the original signal and the signal after noise reduction. The results show that the model can also remove the noise of AE signals.

Abstract Broadband near‐infrared (NIR) photonic materials have wide applications. Although extensive studies on rare‐earth, transition‐metal, and even semiconductor‐activated materials have enabled the development of a rich NIR material pool, developing broadband and efficient photonic candidates covering the NIR I and II regions from 750 to 1500 nm has been met with limited success. Here, it is reported that a subnano Te cluster with a characteristic configuration different from that of the ion state may fill the aforementioned gap. Further, a strategy is proposed for the in situ generation and stabilization of Te clusters by tuning the cluster evolution in glass. A novel active photonic glass embedded with a Te cluster is fabricated; it exhibits intense and broadband short‐wave NIR luminescence with a central wavelength at 1030 nm and a bandwidth exceeding 330 nm. Interestingly, the glass exhibited a full visible‐spectrum conversion ability from 300 to 800 nm. The application of this unique broadband excitation feature for night vision and tissue penetration is demonstrated using a smartphone as the excitation source. These findings demonstrate a fundamental principle of cluster design in glass for creating new properties and provide a new direction for developing novel cluster‐derived functional composite materials.

Abstract Light initiated multi-gate semiconductor switch (LIMS) is a kind of power electronic device which has many differences from traditional thyristor triggered by electric pulse. LIMS is triggered by laser, the turn-on time is smaller, and the anti-electromagnetic interferences is strong. The opening mode of LIMS is obviously different to traditional thyristor. After the laser into the gate area, a large number of electrons and holes will appear in P-base region, holes gather in the area of P-base in PN junction J2, and electrons gather in N-drift region around the PN junction J2. PN junction J2 will open first, then PN junction J3 opens. The delay time of the NPN and PNP thyristors is close to zero when the laser pulse is narrow and the peak power is high, so the turn-on velocity is fast. To optimize the characteristics of the LIMS at high temperatures, we propose a new structure of the LIMS with the optimization of the n+ layer, circular light gate, and the new-style edge termination. The diameter of the LIMS is 23 mm. The experiment results show that the leakage current of the proposed LIMS has been decreased from more than 1 mA to 500 μA at 125 °C, the output current of the LIMS is 10.2 kA with a voltage of 4 kV at 85 °C, and the output current of the LIMS is 12.1 kA with a voltage of 4 kV at − 55 °C. Additionally, di/dt is larger than 30 kA/μs.