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Abstract Insider threats pose a critical risk to organisations, impacting their data, processes, resources, and overall security. Such significant risks arise from individuals with authorised access and familiarity with internal systems, emphasising the potential for insider threats to compromise the integrity of organisations. Previous research has addressed the challenge by pinpointing malicious actions that have already occurred but provided limited assistance in preventing those risks. In this research, we introduce a novel approach based on bidirectional long short-term memory (BiLSTM) networks that effectively captures and analyses the patterns of individual actions and their sequential dependencies. The focus is on predicting whether an individual would be a malicious insider in a future day based on their daily behavioural records over the previous several days. We analyse the performance of the four supervised learning algorithms on manual features, sequential features, and the ground truth of the day with different combinations. In addition, we investigate the performance of different RNN models, such as RNN, LSTM, and BiLSTM, in incorporating these features. Moreover, we explore the performance of different predictive lengths on the ground truth of the day and different embedded lengths for the sequential features. All the experiments are conducted on the CERT r4.2 dataset. Experiment results show that BiLSTM has the highest performance in combining these features.

Phase Change Material-based Battery Thermal Management System (PCM-based BTMS) has become a current research hotspot due to its high efficiency, thermal stability, and compactness. Regrettably, most existing research on PCM-based BTMS neglects the existence of mechanical vibrations, despite the inevitable involvement of such vibrations in the operating conditions of BTMS in electric vehicles. Therefore, in this study, a PCM-based BTMS is applied to a 6-cell lithium-ion battery (LIB) module, and then numerical simulation is employed to comprehensively evaluate the BTMS's performance in the existence of mechanical vibration. The findings indicate that, under mechanical vibration condition, mechanical vibration's influence on the performance of the BTMS is negligible at lower discharge rates, but becomes significant when the discharge rate surpasses a certain threshold, particularly at extremely fast discharge level, resulting in a decrease of 2.28 K in the LIB module’s maximum temperature and a more uniform temperature distribution upon completion of an 8 C discharge in contrast to its stationary equivalent. Furthermore, mechanical vibration only effectively enhances the BTMS’s thermal absorption capability when the PCM thickness surpasses a certain value, and this vibration also improves the capability of the BTMS to achieve uniform temperature distribution in the LIB module, especially for larger PCM thicknesses. Lastly, the BTMS's performance is able to strengthened by raising vibrational amplitude, but the impact is negligible when the amplitude is equal to or greater than 50 mm, and the thermal absorption capability of the BTMS can be augmented by raising the vibrational frequency, but there exists an enhancement limit. This work promotes the use of PCM-based BTMS in real-world applications and contributes to the advancement of LIB towards higher discharge rates.

The current global climate is complex with an increasing frequency of extreme weather events. The randomness, variability, and intermittency of new energy sources pose significant challenges for the balance between power generation and consumption in electricity grids. This challenge is particularly pronounced during extreme weather events such as cold waves, which aggravate the challenges of power supply stability. Accurate prediction of the wind power output can reduce the need for system backup capacity, ensuring the stable operation and reliability of the power system. However, current prediction models do not effectively consider the impact of extreme weather, leading to low prediction accuracy and large deviations. Additionally, the mechanisms by which extreme weather affects wind power differ from those under normal weather conditions. Extreme weather events such as cold waves are rare, and sample data are scarce, making it difficult to establish precise prediction models. To address these issues, this study proposes a wind power output prediction correction method based on time-series generative adversarial networks. First, a time-series generative adversarial network algorithm was used to generate samples from meteorological and power data, constructing an extreme weather meteorological and power database to effectively address the issue of sample scarcity. Second, the improved particle swarm algorithm (IPSO) and Bayesian optimization (BO) algorithm were used to optimize the parameters of single-prediction algorithms such as eXtreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO). Subsequently, by combining the algorithm performance and principle of maximum diversity, the optimal algorithm combination was determined using the evaluation indicators of the prediction error and Pearson correlation coefficients to construct an extreme weather power prediction model under the stacking ensemble learning framework, overcoming the limitations of single algorithms and effectively improving the prediction accuracy. Finally, Support Vector Regression (SVR) was used to construct a power prediction correction model for similar days, correcting the predictions. Through verification using an actual cold wave case study, the proposed method demonstrated significantly improved prediction accuracy and performance compared to single algorithms and conventional prediction models.

The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks (SHNs). To enhance the precision of diagnosis, different participants in SHNs share health data that contain sensitive information. Therefore, the data exchange process raises privacy concerns, especially when the integration of health data from multiple sources (linkage attack) results in further leakage. Linkage attack is a type of dominant attack in the privacy domain, which can leverage various data sources for private data mining. Furthermore, adversaries launch poisoning attacks to falsify the health data, which leads to misdiagnosing or even physical damage. To protect private health data, we propose a personalized differential privacy model based on the trust levels among users. The trust is evaluated by a defined community density, while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential privacy. To avoid linkage attacks in personalized differential privacy, we design a noise correlation decoupling mechanism using a Markov stochastic process. In addition, we build the community model on a blockchain, which can mitigate the risk of poisoning attacks during differentially private data transmission over SHNs. Extensive experiments and analysis on real-world datasets have testified the proposed model, and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.

The high-temperature mechanical properties and failure mechanisms of Si, BSAS and Yb2SiO5 ceramic materials are important for environmental barrier coatings. The Si coating, BSAS coating and Yb2SiO5 coating were prepared by the atmospheric plasma spray (APS) technique. The elastic modulus, fracture strength and fracture strain are determined by three-point bending combined with the digital image correlation (DIC) method. The results show that as the test temperature increases from 25 °C to 1000 °C, the elastic modulus of the Si coating and Yb2SiO5 coating decreases while the elastic modulus of the BSAS coating increases. And the fracture strength and fracture strain of the Si coating, BSAS coating and Yb2SiO5 coating increase. The elastic modulus is related to the interatomic distance and bonding strength of atoms. The fracture strength and fracture strain increase with the densification of the coating.

Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in image processing, signal recognition, and object detection, has facilitated scientific research in EDA. This paper analyses 204 articles through a systematic literature review to investigate the status quo, development, and challenges of DL for EDA. The paper first examines the distribution characteristics and trends of the two categories of EDA assessment objects, including earthquakes and secondary disasters as disaster objects, buildings, infrastructure, and areas as physical objects. Next, this study analyses the application distribution, advantages, and disadvantages of the three types of data (remote sensing data, seismic data, and social media data) mainly involved in these studies. Furthermore, the review identifies the characteristics and application of six commonly used DL models in EDA, including convolutional neural network (CNN), multi-layer perceptron (MLP), recurrent neural network (RNN), generative adversarial network (GAN), transfer learning (TL), and hybrid models. The paper also systematically details the application of DL for EDA at different times (i.e., pre-earthquake stage, during-earthquake stage, post-earthquake stage, and multi-stage). We find that the most extensive research in this field involves using CNNs for image classification to detect and assess building damage resulting from earthquakes. Finally, the paper discusses challenges related to training data and DL models, and identifies opportunities in new data sources, multimodal DL, and new concepts. This review provides valuable references for scholars and practitioners in related fields.

Anthraquinone dyes are the second most important type of dyes after azo dyes. In particular, 1-aminoanthraquinone has been extensively utilized in the preparation of diverse anthraquinone dyes. This study employed a continuous-flow method to synthesize 1-aminoanthraquinone safely and efficiently through the ammonolysis of 1-nitroanthraquinone at high temperatures. Various conditions (reaction temperature, residence time, molar ratio of ammonia to 1-nitroanthraquinone (M-ratio), and water content) were investigated to explore the details of the ammonolysis reaction behavior. Operation conditions for the continuous-flow ammonolysis were optimized using Box–Behnken design in the response surface methodology, and ~88% yield of 1-aminoanthraquinone could be achieved with an M-ratio of 4.5 at 213 °C and 4.3 min. The developed process’s reliability was evaluated by performing a 4 h process stability test. The kinetic behavior for the preparation of 1-aminoanthraquinone was investigated under continuous-flow mode to guide the reactor design and to gain a deeper understanding of the ammonolysis process.

Deoxyribosyl transferases and functionally related purine nucleoside phosphorylases are used extensively for synthesis of non-natural deoxynucleosides as pharmaceuticals or standards for characterizing and quantitating DNA adducts. Hence exploring the conformational tolerance of the active sites of these enzymes is of considerable practical interest. We have determined the crystal structure at 2.1 Å resolution of Lactobacillus helveticus purine deoxyribosyl transferase (PDT) with the tricyclic purine 8,9-dihydro-9-oxoimidazo[2,1-b]purine (N2,3-ethenoguanine) at the active site. The active site electron density map was compatible with four orientations, two consistent with sites for deoxyribosylation and two appearing to be unproductive. In accord with the crystal structure, Lactobacillus helveticus PDT glycosylates the 8,9-dihydro-9-oxoimidazo[2,1-b]purine at N7 and N1, with a marked preference for N7. The activity of Lactobacillus helveticus PDT was compared with that of the nucleoside 2'-deoxyribosyltransferase enzymes (DRT Type II) from Lactobacillus leichmannii and Lactobacillus fermentum, which were somewhat more effective in the deoxyribosylation than Lactobacillus helveticus PDT, glycosylating the substrate with product profiles dependent on the pH of the incubation. The purine nucleoside phosphorylase of Escherichia coli, also commonly used in ribosylation of non-natural bases, was an order of magnitude less efficient than the transferase enzymes. Modeling based on published active-site structures as templates suggests that in all cases, an active site Phe is critical in orienting the molecular plane of the purine derivative. Adventitious hydrogen bonding with additional active site residues appears to result in presentation of multiple nucleophilic sites on the periphery of the acceptor base for ribosylation to give a distribution of nucleosides. Chemical glycosylation of O9-benzylated 8,9-dihydro-9-oxoimidazo[2,1-b]purine also resulted in N7 and N1 ribosylation. Absent from the enzymatic and chemical glycosylations is the natural pattern of N3 ribosylation, verified by comparison of spectroscopic and chromatographic properties with an authentic standard synthesized by an unambiguous route.