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Purpose: Predicting the number of novel Coronavirus Disease 2019 (COVID-19) new-suspected or confirmed cases is crucial in the prevention and control of the COVID-19 outbreak. Methods: Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from December 31, 2019, to February 9, 2020. The new-suspected cases of COVID-19 data were collected from January 20, 2020, to February 9, 2020. We used the lagged series of SMSI to predict new-suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new-suspected COVID-19 case numbers over the subsequent 20 days. We further validated the optimal method for new-confirmed cases of COVID-19 from December 31, 2019, to February 17, 2020. Results: The new-suspected COVID-19 case numbers were significantly correlated with the lagged series of SMSI. SIMI could be earlier detected 6–9 days than new-suspected cases of COVID-19. The optimal method was subset select, which had the lowest estimation error and a moderate number of predictors. The subset selection method was also significantly correlated with the new-confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new-confirmed COVID-19 cases. Conclusions: SMSI could be a significant predictor of the number of COVID-19 infections. Funding Statement: Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, supports SzuYuan Wu’s work (Funding Number: 10908 and 10909). Lei Qin's work is supported by University of International Business and Economics Huiyuan outstanding young scholars research funding (17YQ15), "the Fundamental Research Funds for the central Universities" in UIBE (CXTD10-10). Ben-Chang Shia’s work was also supported by an institutional grant from Taipei Medical University (Taipei, Taiwan) for New Faculty Research (TMU103-AE1-B22). Declaration of Interests: The authors have no potential conflicts of interest to declare.

This research develops a new performance indicator for material flow effectiveness in production systems. The so-called flow value can represent the smoothness of material flow in a production system, thus can be used as real time indicator to instantly reveal the performance of a production system. A simulation study on a machining equipment manufacturing company in Taiwan validates the usefulness of using flow value as an indicator of material flow performance. This flow value is also capable of revealing areas where improvement can be made to effectively improve total system's performance in terms of material flow.

This paper studies the optimal redundant policies for a manufacturing system processing jobs with random working times. The redundant units of the parallel systems and standby systems are subject to stochastic failures during the continuous production process. First, a job consisting of only one work is considered for both redundant systems and the expected cost functions are obtained. Next, each redundant system with a random number of units is assumed for a single work. The expected cost functions and the optimal expected numbers of units are derived for redundant systems. Subsequently, the production processes of N tandem works are introduced for parallel and standby systems, and the expected cost functions are also summarized. Finally, the number of works is estimated by a Poisson distribution for the parallel and standby systems. Numerical examples are given to demonstrate the optimization problems of redundant systems.

Some systems work for a job with random working times. It would be useful for such systems that they are checked at the completion of working times to detect their failures, which is called a random inspection policy. Two inspection policies, where a system is checked at periodic or successive times and also at every completion of working times, are considered. The total expected costs until the detection of failure are obtained, and when the random working time is exponential, optimal inspection policies which minimize them are derived analytically. Furthermore, the inspection policy where the system is checked only at every completion of N th working time is proposed. Finally, as one of modified random inspection models, the backup model where the system goes back to the latest checking time when it has failed is taken up and analyzed, by using the inspection policy.

Face recognition has been a popular research topic in computer vision. Numerous different face recognition techniques have been developed owing to the growing number of real world applications. This study presents a face recognition method based on the stereo matching and eigenface techniques under a stereovision system. The proposed method aims to improve the performance of the 2D eigenface system by adding 3D information. 3D information (called disparity face) was derived by taking two faces of a subject simultaneously in different positions, and then the two faces were further matched using a scanlined-based asynchronous Hopfield neural network. After deriving 2D and 3D faces, we applied principal component analysis (PCA) to both faces to extract effective features for recognition. An experiment was conducted acquiring the facial images of 100 individuals. Each subject provides three pairs of faces with different expressions for training and testing. At the finals, the performance of face recognition using 2D faces, disparity faces, and a combination of the two was evaluated and compared. The experimental results reveal that a 3-5% improvement in recognition rate is achieved by using the additional depth information.

This paper examines the problem of scheduling two-machine no-wait open shops to minimize makespan. The problem is known to be strongly NP-hard. An exact algorithm, based on a branch-and-bound scheme, is developed to optimally solve medium-size problems. A number of dominance rules are proposed to improve the search efficiency of the branch-and-bound algorithm. An efficient two-phase heuristic algorithm is presented for solving large-size problems. Computational results show that the branch-and-bound algorithm can solve problems with up to 100 jobs within a reasonable amount of time. For large-size problems, the solution obtained by the heuristic algorithm has an average percentage deviation of 0.24% from a lower bound value.

This paper addresses the open shop scheduling problem to minimize the total completion time, provided that one of the machines has to process the jobs in a given sequence. The problem is NP-hard in the strong sense even for the two-machine case. A lower bound is derived based on the optimal solution of a relaxed problem in which the operations on every machine may overlap except for the machine with a given sequence of jobs. This relaxed problem is NP-hard in the ordinary sense, however it can be quickly solved via a decomposition into subset-sum problems. Both heuristic and branch-and-bound algorithm are proposed. Experimental results show that the heuristic is efficient for solving large-scaled problems, and the branch-and-bound algorithm performs well on small-scaled problems.

In this paper, we study a modified minimal repair/replacement problem that is formulated as a Markov decision process. The operating cost is assumed to be a nondecreasing function of the system's age. The specific maintenance actions for a manufacturing system to be considered are whether to have replacement, minimal repair or keep it operating. It is shown that a control limit policy , or in particular a ( t , T ) policy, is optimal over the space of all possible policies under the discounted cost criterion. A computational algorithm for the optimal ( t , T ) policy is suggested based on the total expected discounted cost.