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The purpose of this study is to explore how human resource management can be digitally transformed in the context of the digital economy. The drivers, directions, and impacts of human resource management digital transformation constitute the major study content. The study proposes that five factors—internal customer digital needs, industry digital innovation, competitor challenges, digital innovation governance, and digital era needs—drive human resource management digital transformation. It analyzes the essence of the human resource management digital transformation, such as digital workplace, digital human resource management processes, and digital employee services. Particularly, the study points out that digital human resource management processes refer to the implementation of selection, training and development, and assessment functions leveraging state-of-the-art digital technologies. It is emphasized that although digital transformation brings benefits for business development, its potential impacts cannot be ignored, including how the old and new human resource management systems are converted and the negative effects of the new system. [ABSTRACT FROM AUTHOR]

Distribution grid network has problems such as long mileage, large scale, complex surrounding environment, and aging of equipment. It is the development trend of power distribution network operation and maintenance to use unmanned aerial vehicles to patrol and combine with image processing technology for intelligent detection of equipment status. Image segmentation is well-known technique for extracting defect regions of equipment from distribution network inspection images. Therefore, this paper proposes an efficient a novel multilevel thresholding segmentation method to improve the fault diagnosis process with an improved weighted mean of vectors optimization (IINFO) algorithm. The IINFO algorithm adopts various measures to improve the optimization results, including Gaussian mutation to increase the local search ability and range of the optimal individual, Cauchy mutation to enhance the global search ability of its vector individual, reflective learning operators to strengthen self-learning and avoid local optimal solutions, and parallel operation to improve the utilization of computational resources. Moreover, two-dimensional Kapur cross-entropy is used as an objective function to solve the multilevel thresholding problem. The proposed method is evaluated using benchmark functions and distribution network inspection image datasets and is compared with 12 other metaheuristic algorithms. The results demonstrate that the proposed method has better performance and a higher ability to find optimal solutions compared to the other algorithms. These findings suggest that our method may be useful in improving the accuracy and efficiency of distribution network inspections and have significant potential for practical applications. [ABSTRACT FROM AUTHOR]

Space exploration has drawn increasing attention to space control technology. For debris removal missions and on-orbit servicing, accurate pose estimation of a noncooperative target is critical. This article introduces the satellite relative pose estimation network (SR-Net) two-stage training method for a noncooperative target via RGB images. As the first stage in regressing the 3D translation, we combined the detection and translation regression modules into a single model. SR-Net decouples the translation and rotation information in stage two by utilizing classification instead of regression, using the detected picture as input and fitting a rotation by minimizing the weighted least squares. Furthermore, a large-scale dataset for 6-DoF pose estimation is introduced, which can be utilized as a benchmark for various state-of-the-art monocular vision-based 6-DoF pose estimation methods. Ablation studies are used to verify the effectiveness and scalability of each module. SR-Net can be added to a baseline model as a separate module to improve the 6-DoF pose estimation accuracy for noncooperative targets. The results are extremely encouraging since they show that using only vision data, it is feasible to accurately estimate the 6-DoF pose of a noncooperative target. [ABSTRACT FROM AUTHOR]

First, the benefits of AI-based training for business development are presented. Then, the theoretical literature on various stages from traditional training to AI-based training is analyzed. Finally, we analyze AI applications in the training process and managerial response. This study presents a framework for the application of artificial intelligence (AI) technology to training and managerial challenges. AI tools can be applied to the training process, including knowledge management (KM), needs analysis, training organization, and results feedback. AI-based training transforms organizations into knowledge organizations that can meet the demands of personalized training and improve learning quality. AI tools bring about a shift in the training phases of knowledge base creation, needs surveys, the organization of training, and feedback on results. [ABSTRACT FROM AUTHOR]

Since the steel plate surface defect image often has complicated background and lots of noise, the segmentation accuracy is low when using the single threshold Otsu method. Therefore, this paper introduces the whale optimization algorithm (WOA) to optimize the threshold of the dual-threshold image segmentation. To avoid the premature convergence, slow convergence speed and easy fall into the local optimum of the original WOA, an improved WOA is proposed. Firstly, the WOA is discretized by using round function; secondly, the sin mapping generation chaotic sequence is used to replace the randomly generated initial population in the initialization process of the WOA to enhance the multiformity of population; thirdly, the global search and local development capabilities are balanced and improved by nonlinear time-varying factors and inertia weights in the position updating mechanism; finally, the improved WOA is applied to the two-dimensional Otsu (2D-Otsu) algorithm to select the optimal threshold for image segmentation. The simulation results of 8 classic benchmark functions show that the improved WOA can obtain the optimal value of the function 0, − 12,569.5. The improved WOA can raise convergence speed and improve the global search ability and get rid of the local optimum. The experimental results show that the proposed algorithm outperforms the Otsu algorithm and can achieve more accurate segmentation of steel plate surface defect image. Compared with 2D-Otsu algorithm, the proposed algorithm reduces running time by 0.34 s and has the highest segmentation efficiency for rolled-in scale defects. [ABSTRACT FROM AUTHOR]

In the global war for talent, traditional recruiting methods are failing to cope with the talent competition, so employers need the right recruiting tools to fill open positions. First, we explore how talent acquisition has transitioned from digital 1.0 to 3.0 (AI-enabled) as the digital tool redesigns business. The technology of artificial intelligence has facilitated the daily work of recruiters and improved recruitment efficiency. Further, the study analyzes that AI plays an important role in each stage of recruitment, such as recruitment promotion, job search, application, screening, assessment, and coordination. Next, after interviewing with AI recruitment stakeholders (recruiters, managers, and applicants), the study discusses their acceptance criteria for each recruitment stage; stakeholders also raised concerns about AI recruitment. Finally, we suggest that managers need to be concerned about the cost of AI recruitment, legal privacy, recruitment bias, and the possibility of replacing recruiters. Overall, the study answers the following questions: (1) How artificial intelligence is used in various stages of the recruitment process. (2) Stakeholder (applicants, recruiters, managers) perceptions of AI application in recruitment. (3) Suggestions for managers to adopt AI in recruitment. In general, the discussion will contribute to the study of the use of AI in recruitment, as well as providing recommendations for implementing AI recruitment in practice. [ABSTRACT FROM AUTHOR]

In this paper, the problem of the robust tracking for two-arm condenser cleaning crawler-type mobile manipulators (CCCMM) with delayed angle-velocity uncertainties is original investigated. The two-arm condenser cleaning crawler-type mobile manipulators are composed of a crawler-type mobile platform and two-arm industrial manipulators.The uncertainty is nonlinear time-varying and does not require a matching condition. A wavelet transform and probabilistic neural network (WTPNN) system is used to approximate an unknown controlled system from the strategic manipulation of the model following tracking errors. Based on the Lyapunov method and the linear matrix inequality (LMI) approach, several sufficient conditions, which guarantee the state variables of the closed loop system to converge, globally, uniformly and exponentially, to a ball in the state space with any pre-specified convergence rate, are derived. Experiment results are given to illustrate the superior control performance of the proposed intelligent control method. [ABSTRACT FROM AUTHOR]

Foot-and-mouth disease virus (FMDV) is one of the most important animal pathogens in the world. FMDV naturally infects swine, cattle, and other cloven-hoofed animals. FMD is not adequately controlled by vaccination. An alternative strategy is to develop swine that are genetically resistant to infection. Here, we generated FMDV-specific shRNA transgenic cells targeting either nonstructural protein 2B or polymerase 3D of FMDV. The shRNA-positive transgenic cells displayed significantly lower viral production than that of the control cells after infection with FMDV (P < 0.05). Twenty-three transgenic cloned swine (TGCS) and nine non-transgenic cloned swine (Non-TGCS) were produced by somatic cell nuclear transfer (SCNT). In the FMDV challenge study, one TGCS was completely protected, no clinical signs, no viremia and no viral RNA in the tissues, no non-structural antibody response, another one TGCS swine recovered after showing clinical signs for two days, whereas all of the normal control swine (NS) and Non-TGCS developed typical clinical signs, viremia and viral RNA was determined in the tissues, the non-structural antibody was determined, and one Non-TGCS swine died. The viral RNA load in the blood and tissues of the TGCS was reduced in both challenge doses. These results indicated that the TGCS displayed resistance to the FMDV infection. Immune cells, including CD3+, CD4+, CD8+, CD21+, and CD172+ cells, and the production of IFN-γ were analyzed, there were no significant differences observed between the TGCS and NS or Non-TGCS, suggesting that the FMDV resistance may be mainly derived from the RNAi-based antiviral pathway. Our work provides a foundation for a breeding approach to preventing infectious disease in swine. [ABSTRACT FROM AUTHOR]

The Steiner minimal tree (SMT) problem is an NP-hard problem, which is the best connection model for a multi-terminal net in global routing problem. This paper presents a unified algorithm for octagonal and rectilinear SMT construction based on hybrid transformation strategy (HTS) and self-adapting particle swarm optimization. Firstly, an effective HTS is proposed to enlarge the search space and improve the convergence speed. Secondly, the proposed HTS in the evolutionary process may produce an ineffective solution, and consequently the crossover and mutation operators of genetic algorithm (GA) based on union-find sets is proposed. Thirdly, a self-adapting strategy that can adjust the acceleration coefficients is proposed to further improve the convergence and the quality of the proposed algorithm. Finally, the hybrid transformation can be applied to GA and the proposed algorithm can be applied to rectilinear architecture. To our best knowledge, the proposed algorithm is the first unified algorithm to solve the SMT construction under both octagonal and rectilinear architecture. The experimental results show that the proposed algorithm can efficiently provide a better solution for SMT problem both in octagonal and rectilinear architectures than others. Moreover, the algorithm can obtain several topologies of SMT, which is beneficial for optimizing congestion in VLSI global routing stage. [ABSTRACT FROM AUTHOR]