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Summary: Matrix factorization (MF)‐based recommender systems (RSs) as black‐box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi‐modal matrix factorization (ERS‐REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi‐modal matrix, ERS‐REFMMF utilizes the Funk‐singular value decomposition method at the foundational layer to decompose the multi‐modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi‐objective optimization algorithms. ERS‐REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many‐objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods. [ABSTRACT FROM AUTHOR]

Summary: With the rising adoption of the Internet of Things (IoT) across a variety of industries, malware is increasingly targeting the large number of IoT devices that lack adequate protection. Malware hunting is challenging in the IoT due to the variety of instruction set architectures of devices, as shown by the differences in the relevant characteristics of malware on different platforms. There are also serious concerns about resource utilization and privacy leaks in the development of conventional detection models. This study suggests a novel federated malware detection framework based on many‐objective optimization (FMDMO) for the IoT to overcome the problems. First, the framework provides a cross‐platform compatible basis with the federated mechanism as the backbone, while avoiding raw data sharing to improve privacy protection. Second, an intelligent optimization‐based client selection method is designed for four objectives: learning performance, architectural selection deviation, time consumption, and training stability, which leads malware detection to retain a high degree of cross‐architectural generalization while enhancing training efficiency. Based on a large IoT malware dataset we constructed, containing 62,515 malware samples across seven typical architectures, the FMDMO is evaluated comprehensively in three scenarios. The experimental results demonstrate the FMDMO substantially enhances the model's cross‐platform detection performance while preserving effective training and flexibility. [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of big data, the explosive growth of data promotes the progress of the Internet of Things (IoT). Because it is hard for traditional cloud computing to meet vast computing tasks, scholars propose mobile edge computing (MEC) for the IoT. However, the mobility of users results in the instability of MEC performance. Besides, the conflict of interest between users and service providers needs to be balanced. To solve these problems, this paper constructs a virtual machine migration model based on many‐objective optimization (MaOVMMM). In MaOVMMM, four objectives are considered simultaneously: communication expense, computing expense, delay, and energy consumption. A many‐objective evolutionary algorithm with double population confrontation (MaOEA‐DPC) is suggested to support the MaOVMMM that is proposed. First, the population confrontation strategy is designed to better simulate the relationship between users and service providers. Second, the dynamic probability integration selection strategy is used to ensure the evolution ability of the algorithm. Simulation results demonstrate the effectiveness and superiority of MaOEA‐DPC when compared with other algorithms. This proposed approach can provide a superior virtual machine migration scheme for decision‐makers. [ABSTRACT FROM AUTHOR]

Summary: In many‐objective optimization algorithms, it is very important to maintain significant convergence and diversity of the population. And with the increasing demand in various fields, the optimization problem also becomes gradually complicated. Some existing many‐objective optimization algorithms are faced with challenges such as domination resistance and dimensional crisis. To solve these challenges, a many‐objective optimization algorithm based on dual criteria and mixed distribution correction strategy (MaOEA‐CSMDC) is proposed in this paper. To be specific, a matching selection strategy based on dual criteria combined by pareto domination strategy and achievement scalar function, which alleviates the domination resistance phenomenon and enhances the selection pressure of the algorithm. After that, an environment selection strategy based on equal probability mixed distribution correction is designed to better balance convergence and diversity. In this strategy, normal distribution, exponential distribution, and Cauchy distribution are introduced to adjust the weight of convergence and diversity in evolution by means of equal probability, so as to alleviate the problem that the conflict between them is intensified in the later stage of the algorithm. The experimental results show that, MaOEA‐CSMDC not only has advantages in convergence and diversity indicators, but also is more competitive in solving many‐objective optimization problems. [ABSTRACT FROM AUTHOR]

Aim: The aim of the study was to analyse the correlations among organizational justice, knowledge‐hiding behaviour and nurses' innovation ability. Design: A descriptive and cross‐sectional design and the data were collected using questionnaires. Methods: Demographic information, professional data, innovation capacity scales, knowledge‐hiding scales and organizational justice scales were used in this study. Using descriptive statistics, t‐tests, one‐way analysis of variance and Pearson's or Spearman's correlation analyses, we compared the differences and examined the correlations between participants' demographic and innovation capacity scales, and knowledge‐hiding scales and organizational fairness scales. Results: We received 1486 valid responses, with an effective response rate of 96.68%. We found team role, nursing age, number of training, literature‐reading habits, organizational justice, information justice, fair distribution and deaf knowledge‐hiding as the influencing factors of nurses' innovation. Nurses' sense of organizational fairness negatively correlated with knowledge concealment and positively correlated with innovation ability. Moreover, knowledge hiding negatively correlated with nurses' innovation ability. Furthermore, knowledge‐hiding plays a partial intermediary role between organizational fairness and nurses' innovation ability. [ABSTRACT FROM AUTHOR]

Cell cycle studies in plants and algae are highly dependent on reliable methods for detecting cellular DNA replication. With its short growth cycle and ease of genetic transformation, Phaeodactylum tricornutum is an important model organism for the study of pennate diatoms. Here we explored two different methods to detect the cell cycle of P. tricornutum, one using SYBR‐green I to via flow cytometry, and the other using EdU labeling to observe cell cycle changes under fluorescence microscopy. Both EdU labeling fluorescence microscopy and SYBR‐green I staining flow cytometry accurately indicated that the cells of P. tricornutum enter the G2/M phase after 12 h of light exposure. The results indicate that SYBR Green I was an adequate detection method for nuclear DNA quantitation in cells of P. tricornutum using a flow cytometer and without RNase A treatment. In addition, EdU can be applied to P. tricornutum to reliably detect cell proliferation. Besides, Mg‐ProtoIX was able to reverse the cell cycle division inhibition of P. tricornutum and allow the nuclear DNA replication to proceed normally. Taken together, the photoperiodic division time point was clearly identified, which sheds light on the regulation of cell division mechanism in P. tricornutum. [ABSTRACT FROM AUTHOR]

Summary At present, irrelevant or redundant features in network traffic data occupy a lot of storage and computing resources, reducing the accuracy of network anomaly detection. Aiming at this problem, a many‐objective feature selection model is proposed in this article. The model takes the number of selected feature, false alarm rate, detection rate, precision and accuracy as the optimization objectives, and characterizes the performance of the feature selection method from different perspectives. At the same time, a many‐objective integration optimization algorithm (IN‐MaOEA) is designed to solve this model. First, the algorithm will build the evolution strategy pool and the dominance strategy pool, then a random probability strategy selection mechanism is designed to improve the algorithm's convergence and diversity. At the same time, an anomaly detection simulation was performed using the NSL‐KDD dataset. Experimental results show that the IN‐MaOEA algorithm can effectively improve the performance of detection. [ABSTRACT FROM AUTHOR]

Lipase‐catalyzed synthesis of lauroyl glucose ester was continuously performed in a biphasic system consisting of supercritical CO2 and an ionic liquid (IL). Fourier transform infrared spectroscopy and nuclear magnetic resonance spectroscopy are employed to identify the product lauroyl glucose ester. The effects of reaction pressure, temperature, substrate flow rate, the amount of enzyme, and the amount of IL on the glucose conversion rate are investigated. The highest glucose conversion of up to 95.5% could be achieved at the optimal condition, which is higher than that in the pure IL. The enzyme activity in the biphasic system was 18.19 μmol/g/min, which is much higher than that in the pure IL. The continuous reaction in the binary system could last for 10 h at high enzyme activity. The combination of supercritical CO2 and ILs could not only improve the reaction rate and yield, which are attributed to the fast mass transfer rate and enhanced enzyme activity and lifespan, but also make the product separation and enzyme/ILs recycling easier. [ABSTRACT FROM AUTHOR]

The study of various communication networks has attracted many researchers from various academic fields in recent years. It is a major research to identify the faulty links in communication networks. A dependent failure approach is presented to identify the faulty links in wireless communication networks. The node importance evaluation is introduced to overcome the limitations of traditional independent failure methods. Faulty nodes effect on their direct links in the network. The dependent failure topology graph (DFTG) model and faulty link identification method take into consideration the failure probability of different nodes. The management node can autonomously identify, in real time, the most probable failed network links through building the DFTG model. Experimental validation and actual application are carried out separately. The identification method of faulty links applies to satellite networks with the connection‐oriented network structure. From the simulation analyses and the actual satellite network, the test results demonstrate that this proposed method is valid, efficient, and suitable for wireless communication network applications. [ABSTRACT FROM AUTHOR]