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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]

Herein, we have developed a novel single-molecular probe (NORP) for selective and accurate determination of NTR in living cells. It was discovered that up-regulation of endogenous NTR occurred in response to hypoxic stimulation, and there was a dependence between the NTR levels and the degree of hypoxia. [ABSTRACT FROM AUTHOR]

Highlights: Fe3+, Co2+, H3BTC, and collagen peptide are used to achieve a one-step assembly of stable FeCo-MOG/CP by manipulating the complexation effect and solution polarity. By optimizing pyrolysis, two kinds of nitrogen-doped carbon aerogels loaded with virus-shaped and nanospherical magnetic particles are obtained. FeCo/Fe3O4/NC and FeCo/NC aerogels exhibit excellent electromagnetic wave absorbing and radar stealth performances. Metal–organic gel (MOG) derived composites are promising multi-functional materials due to their alterable composition, identifiable chemical homogeneity, tunable shape, and porous structure. Herein, stable metal–organic hydrogels are prepared by regulating the complexation effect, solution polarity and curing speed. Meanwhile, collagen peptide is used to facilitate the fabrication of a porous aerogel with excellent physical properties as well as the homogeneous dispersion of magnetic particles during calcination. Subsequently, two kinds of heterometallic magnetic coupling systems are obtained through the application of Kirkendall effect. FeCo/nitrogen-doped carbon (NC) aerogel demonstrates an ultra-strong microwave absorption of − 85 dB at an ultra-low loading of 5%. After reducing the time taken by atom shifting, a FeCo/Fe3O4/NC aerogel containing virus-shaped particles is obtained, which achieves an ultra-broad absorption of 7.44 GHz at an ultra-thin thickness of 1.59 mm due to the coupling effect offered by dual-soft-magnetic particles. Furthermore, both aerogels show excellent thermal insulation property, and their outstanding radar stealth performances in J-20 aircraft are confirmed by computer simulation technology. The formation mechanism of MOG is also discussed along with the thermal insulation and electromagnetic wave absorption mechanism of the aerogels, which will enable the development and application of novel and lightweight stealth coatings. [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]

The rapid progress of artificial intelligence expands its wide applicability in Internet of Things (IoT). Meanwhile, data insufficient and data source privacy are key supply chain challenges facing IoT especially in the healthcare industry. To address this problem in healthcare IoT, in this article, we propose a skin cancer detection model based on federated learning integrated with deep generation model. First, we employ dual generative adversarial networks to address the problem of insufficient data. In addition, to improve the quality of generated images, we synchronously optimize the sharpness of images, Frechet inception distance, image diversity, and loss using knee point-driven evolutionary algorithm (KnEA). Then, we protect patient information privacy by training federated skin cancer framework. Finally, we employ the ISIC 2018 dataset to test the performance of the proposed training model under different situations, including using identically distributed data, nonidentically distributed data, a sparse convolutional neural network, and a fully connected convolutional neural network. The experiment results demonstrate that the accuracy and area under the curve reach 91% and 88%, respectively. This model can help resolve problems of insufficient data in smart medicine of IoT and protect the privacy of user data while also providing an excellent detection rate. [ABSTRACT FROM AUTHOR]