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Poor medication adherence leads to high hospital admission rate and heavy amount of health-care cost. To cope with this problem, various electronic pillboxes have been proposed to improve the medication adherence rate. However, most of the existing electronic pillboxes use time-based reminders which may often lead to ineffective reminding if the reminders are triggered at inopportune moments, e.g. user is sleeping or eating.

In this paper, the authors propose an AI-empowered context-aware smart pillbox system. The pillbox system collects real-time sensor data from a smart home environment and analyzes the user’s contextual information through a computational abstract argumentation-based activity classifier.

Based on user’s different contextual states, the smart pillbox will generate reminders at appropriate time and on appropriate devices.

This paper presents a novel context-aware smart pillbox system that uses argumentation-based activity recognition and reminder generation.

Shared leadership is an effective mechanism for managing project teams. Its performance-enhancing benefits have been demonstrated in many studies. Nonetheless, there is an obvious silence about how to promote shared leadership in Lean Six Sigma (LSS) project teams. To address this deficit, the purposes of this study are to investigate the influence of shared leadership on LSS project success and to explore how team psychological safety, project task complexity and project task interdependence influence shared leadership.

A multi-source, time-lagged survey design with a four-month interval was conducted. To do this, the authors collected data from 71 project teams (comprising 71 project managers and 352 project members) using LSS approaches in the manufacturing and service industries.

The findings show that shared leadership positively influences LSS project success. The authors also found that team psychological safety fosters the development of shared leadership and, more importantly, these effects are stronger when the tasks are more complex and more interdependent.

These findings advance our understanding of the factors that enable shared leadership and equip LSS project managers with practical techniques to improve shared leadership for the success of their projects.

This study extends the theory of shared leadership to the context of LSS project management and is among the first, to the best of the authors’ knowledge, to theoretically propose and empirically validate how to promote shared leadership in LSS project teams.

This paper aims to theoretically investigate a knowledge management model from the combined perspective of knowledge acquisition and knowledge application and its effect on organizational performance.

This study reviews prior research on knowledge acquisition and knowledge application, puts forward the concepts of “the extensiveness of knowledge acquisition” and “the concentration of knowledge application” and more importantly proposes an integrated model by combining these two dimensions. Four case examples of enterprises are subsequently described and analyzed to illustrate the sources of knowledge acquisition, the objects of knowledge application and their influences on organizational performance.

Four knowledge management modes and their impacts are confirmed in this study. Specifically, the organization of the turbojet engine mode (high extensiveness of knowledge acquisition and high concentration of knowledge application) can achieve good performance. The pipeline mode (high extensiveness of knowledge acquisition and low concentration of knowledge application) is the second, which has limited influence on good organizational performance. Organizations with the flashlight mode (low extensiveness of knowledge acquisition and high concentration of knowledge application) can achieve limited performance under the appropriate environment. The candle mode (low extensiveness of knowledge acquisition and low concentration of knowledge application) is the worst, performance of which is poor due to the break of the knowledge chain.

This paper holds that organizations should actively use the turbojet engine mode, adopt the pipeline mode and the flashlight mode cautiously, and avoid falling into the candle mode.

To the best of the authors’ knowledge, this study is among the first to propose the concepts of “the extensiveness of knowledge acquisition” and “the concentration of knowledge application,” and provides a combined model for analyzing differences in organizational performance from the perspective of knowledge.

The extensive applications of the industrial robots have made the optimization of assembly lines more complicated. The purpose of this paper is to develop a balancing method of both workstation time and station area to improve the efficiency and productivity of the robotic assembly lines. A tradeoff was made between two conflicting objective functions, minimizing the number of workstations and minimizing the area of each workstation.

This research proposes an optimal method for balancing robotic assembly lines with space consideration and reducing robot changeover and area for tools and fixtures to further minimize assembly line area and cycle time. Due to the NP-hard nature of the considered problem, an improved multi-objective immune clonal selection algorithm is proposed to solve this constrained multi-objective optimization problem, and a special coding scheme is designed for the problem. To enhance the performance of the algorithm, several strategies including elite strategy and global search are introduced.

A set of instances of different problem scales are optimized and the results are compared with two other high-performing multi-objective algorithms to evaluate the efficiency and superiority of the proposed algorithm. It is found that the proposed method can efficiently solve the real-world size case of time and space robotic assembly line balancing problems.

For the first time in the robotic assembly line balancing problems, an assignment-based tool area and a sequence-based changeover time are took into consideration. Furthermore, a mathematical model with bi-objective functions of minimizing the number of workstations and area of each station was developed. To solve the proposed problem, an improved multi-objective immune clonal selection algorithm was proposed and a special coding scheme is designed.

The literature on help-seeking at work has experienced significant growth in the past decades. However, our knowledge about this research domain remains fragmented and lacks sufficient theoretical integration. Therefore, this paper aims to comprehensively integrate the extant literature on help-seeking behavior at work and propose an overarching, organized framework to propel this field forward.

A state-of-the-art review and theoretical development on help-seeking at work are conducted.

First, the authors provide the conceptual clarity of its definitions, key characteristics, types and measurement techniques. Second, the authors develop a fine-grained and integrative process-based framework consisting of antecedents, proximal psychological mechanisms, subsequent influencing processes and distal outcomes to advance our understanding of seeking help in the workplace. Third, the authors offer a detailed agenda for future research to target opportunities within the field.

The current study is comprehensive in surveying the full body of knowledge on help-seeking at work. It uniquely provides a coherent overarching framework that organizes prior findings and channels future research. Additionally, this review paints a complete picture of what has been done and what needs to be done in the field. More research can be spurred based on our conceptual framework.

Due to the market trend of low-volume and high-variety, the manufacturing industry is paying close attention to improve the ability to hedge against variability. Therefore, in this paper the assembly line with limited resources is balanced in a robust way that has good performance under all possible scenarios. The proposed model allows decision makers to minimize a posteriori regret of the selected choice and hedge against the high cost caused by variability.

A generalized resource-constrained assembly line balancing problem (GRCALBP) with an interval data of task times is modeled and the objective is to find an assignment of tasks and resources to the workstations such that the maximum regret among all the possible scenarios is minimized. To properly solve the problem, the regret evaluation, an exact solution method and an enhanced meta-heuristic algorithm, Whale Optimization Algorithm, are proposed and analyzed. A problem-specific coding scheme and search mechanisms are incorporated.

Theory analysis and computational experiments are conducted to evaluated the proposed methods and their superiority. Satisfactory results show that the constraint generation technique-based exact method can efficiently solve instances of moderate size to optimality, and the performance of WOA is enhanced due to the modified searching strategy.

For the first time a minmax regret model is considered in a resource-constrained assembly line balancing problem. The traditional Whale Optimization Algorithm is modified to overcome the inferior capability and applied in discrete and constrained assembly line balancing problems.

The issue of project managers’ competencies has gained much traction in practice and more recently in academic debate. However, they have become analogous to extensive wish lists where a project manager is expected to have an exhaustive list of aptitudes and capabilities. Therefore, identifying and defining the most critical competencies for project success is urgently needed. Moreover, although the vast number of studies emphasize the significance of behavioral competencies, there is a dearth of empirical research and studies within the context of information systems (IS) are scarce. Consequently, the present study aims to investigate the influence of project manager's behavioral competencies for the successful delivery of IS projects.

This research conducted a systematic literature review (2009–2019) of 27 relevant studies incorporating 179 competencies. The authors also collected data from 121 professional IS project managers and used regression analysis and dominance analysis to test the hypotheses proposed.

The results confirm that behavioral competencies (including leadership, communication, result orientation, emotional intelligence, ethics, creativity and motivation) are significantly and positively related to IS project success. Furthermore, the findings show that emotional intelligence (resilience, stress management and self-control), creativity (resourcefulness, creativity thinking and imagination) and ethics (transparency, honesty and integrity) are the most influential behavioral competencies for IS project success.

To the best of the authors’ knowledge, this research is among the first to use a quantitative analysis to empirically investigate project manager's behavioral competencies for project success in the IS discipline. It brings much-needed empirical evidence for the most important competencies for IS project managers.

The balancing of robotic weld assembly lines has a significant influence on achievable production efficiency. This paper aims to investigate the most suitable way to assign both assembly tasks and type of robots to every workstation, and present an optimal method of robotic weld assembly line balancing (ALB) problems with the additional concern of changeover times. An industrial case of a robotic weld assembly line problem is investigated with an objective of minimizing cycle time of workstations.

This research proposes an optimal method for balancing robotic weld assembly lines. To solve the problem, a low bound of cycle time of workstations is built, and on account of the non-deterministic polynomial-time (NP)-hard nature of ALB problem (ALBP), a genetic algorithm (GA) with the mechanism of simulated annealing (SA), as well as self-adaption procedure, was proposed to overcome the inferior capability of GA in aspect of local search.

Theory analysis and simulation experiments on an industrial case of a car body welding assembly line are conducted in this paper. Satisfactory results show that the performance of GA is enhanced owing to the mechanism of SA, and the proposed method can efficiently solve the real-world size case of robotic weld ALBPs with changeover times.

The additional consideration of tool changing has very realistic significance in manufacturing. Furthermore, this research work could be modified and applied to other ALBPs, such as worker ALBPs considering tool-changeover times.

For the first time in the robotic weld ALBPs, the fixtures’ (tools’) changeover times are considered. Furthermore, a mathematical model with an objective function of minimizing cycle time of workstations was developed. To solve the proposed problem, a GA with the mechanism of SA was put forth to overcome the inferior capability of GA in the aspect of local search.

During the production process of steel strip, some defects may appear on the surface, that is, traditional manual inspection could not meet the requirements of low-cost and high-efficiency production. The purpose of this paper is to propose a method of feature selection based on filter methods combined with hidden Bayesian classifier for improving the efficiency of defect recognition and reduce the complexity of calculation. The method can select the optimal hybrid model for realizing the accurate classification of steel strip surface defects.

A large image feature set was initially obtained based on the discrete wavelet transform feature extraction method. Three feature selection methods (including correlation-based feature selection, consistency subset evaluator [CSE] and information gain) were then used to optimize the feature space. Parameters for the feature selection methods were based on the classification accuracy results of hidden Naive Bayes (HNB) algorithm. The selected feature subset was then applied to the traditional NB classifier and leading extended NB classifiers.

The experimental results demonstrated that the HNB model combined with feature selection approaches has better classification performance than other models of defect recognition. Among the results of this study, the proposed hybrid model of CSE + HNB is the most robust and effective and of highest classification accuracy in identifying the optimal subset of the surface defect database.

The main contribution of this paper is the development of a hybrid model combining feature selection and multi-class classification algorithms for steel strip surface inspection. The proposed hybrid model is primarily robust and effective for steel strip surface inspection.

This paper aims to study a high-temperature (up to 200 °C) data acquisition and processing circuit for logging.

With the decrease in thermal resistance by system-in package technology and exquisite power consumption distribution design, the circuit worked well at high temperatures environment from both theoretical analysis and real experiments evaluation.

In thermal simulation, considering on board chips’ power consumption as additional heat source, the highest temperature point reached by all the chips in the circuit is only 211 °C at work temperature of 200 °C. In addition, the proposed circuit was validated by long time high-temperature experiments. The circuit showed good dynamic performance during a 4-h test in a 200-°C oven, and maintained a signal-to-noise ratio of 92.54 dB, a signal-to-noise and distortion ratio of 91.81 dB, a total harmonic distortion of −99.89 dB and a spurious free dynamic range of 100.28 dB.

The proposed circuit and methodology showed great potential for application in deep-well logging systems and other high-temperature situations.