The Clinical Observation of Inflammation Theory for Depression: The Initiative of the Formosa Long COVID Multicenter Study (FOCuS)

Shu-Tsen Liu(Shu-Tsen Liu),Sheng-Che Lin(Sheng-Che Lin),Jane Pei-Chen Chang(Jane Pei-Chen Chang),Kai-Jie Yang(Kai-Jie Yang),Che-Sheng Chu(Che-Sheng Chu),Chia-Chun Yang(Chia-Chun Yang),Chih-Sung Liang( 대한정신약물학회 2023 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.21 No.1

There is growing evidence that the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is associated with increased risks of psychiatric sequelae. Depression, anxiety, cognitive impairments, sleep disturbance, and fatigue during and after the acute phase of COVID-19 are prevalent, long-lasting, and exerting negative consequences on well-being and imposing a huge burden on healthcare systems and society. This current review presented timely updates of clinical research findings, particularly focusing on the pathogenetic mechanisms underlying the neuropsychiatric sequelae, and identified potential key targets for developing effective treatment strategies for long COVID. In addition, we introduced the Formosa Long COVID Multicenter Study (FOCuS), which aims to apply the inflammation theory to the pathogenesis and the psychosocial and nutrition treatments of post-COVID depression and anxiety.

An Evaluation of Multiple-input Dual-output Run-to-Run Control Scheme for Semiconductor Manufacturing

Shu-Kai S. Fan,Yen Lin 대한산업공학회 2005 Industrial Engineeering & Management Systems Vol.4 No.1

The Design and Implementation of Collaboration Service Integration Platform Based on Context-Aware Role Based Access Model

Shu-Ping Lu,Kuei-Kai Shao,Yu-Nung Chao,Kuo-Shu Luo,Chi-Hua Chen 보안공학연구지원센터 2014 International Journal of Security and Its Applicat Vol.8 No.1

Optimization of Triple Response Systems by Using the Dual Response Approach and the Hooke-Jeeves Search Method

Shu-Kai S. Fan,Chia-Fen Huang,Ko-Wei Chang,Yu-Chiang Chuang 대한산업공학회 2010 Industrial Engineeering & Management Systems Vol.9 No.1

This paper presents an extended computing procedure for the global optimization of the triple response system (TRS) where the response functions are nonconvex (nonconcave) quadratics and the input factors satisfy a radial region of interest. The TRS arising from response surface modeling can be approximated using a nonlinear mathematical program involving one primary (objective) function and two secondary (constraints) functions. An optimization algorithm named triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the nondegenerate TRS. In TRSALG, the Lagrange multipliers of target (secondary) functions are computed by using the Hooke-Jeeves search method, and the Lagrange multiplier of the radial constraint is located by using the trust region (TR) method at the same time. To ensure global optimality that can be attained by TRSALG, included is the means for detecting the degenerate case. In the field of numerical optimization, as the family of TR approach always exhibits excellent mathematical properties during optimization steps, thus the proposed algorithm can guarantee the global optimal solution where the optimality conditions are satisfied for the nondegenerate TRS. The computing procedure is illustrated in terms of examples found in the quality literature where the comparison results with a gradient-based method are used to calibrate TRSALG.

Recursive Least Squares Run-to-Run Control with Time-Varying Metrology Delays

Shu-Kai Fan,Yuan-Jung Chang 대한산업공학회 2010 Industrial Engineeering & Management Systems Vol.9 No.3

This article investigates how to adaptively predict the time-varying metrology delay that could realistically occur in the semiconductor manufacturing practice. Metrology delays pose a great challenge for the existing run-to-run (R2R) controllers, driving the process output significantly away from target if not adequately predicted. First, the expected asymptotic double exponentially weighted moving average (DEWMA) control output, by using the EWMA and recursive least squares (RLS) prediction methods, is derived. It has been found that the relationships between the expected control output and target in both estimation methods are parallel, and six cases are addressed. Within the context of time-varying metrology delay, this paper presents a modified recursive least squares-linear trend (RLS-LT) controller, in combination with runs test. Simulated single input-single output (SISO) R2R processes subject to various time-varying metrology delay scenarios are used as a test-bed to evaluate the proposed algorithms. The simulation results indicate that the modified RLS-LT controller can yield the process output more accurately on target with smaller mean squared error (MSE) than the original RLSLT controller that only deals with constant metrology delays.

An Evaluation of Multiple-input Dual-output Run-to-Run Control Scheme for Semiconductor Manufacturing

Fan, Shu-Kai-S.,Lin, Yen Korean Institute of Industrial Engineers 2005 Industrial Engineeering & Management Systems Vol.4 No.1

This paper provides an evaluation of an optimization-based, multiple-input double-output (MIDO) run-to-run (R2R) control scheme for general semiconductor manufacturing processes. The controller in this research, termed adaptive dual response optimizing controller (ADROC), can serve as a process optimizer as well as a recipe regulator between consecutive runs of wafer fabrication. In evaluation, it is assumed that the equipment model could be appropriately described by a pair of second-order polynomial functions in terms of a set of controllable variables. Of practical relevance is to consider a drifting effect in the equipment model since in common semiconductor practice the process tends to drift due to machine aging and tool wearing. We select a typical application of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing in this evaluation, and there are five different CMP process scenarios demonstrated, including mean shift, variance increase, and IMA disturbances. For the controller, ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual response approach, arising from the response surface methodology (RSM) literature, is used to seek the optimum recipe within the acceptability region for the execution of next run. The main components of ADROC are described and its control performance is assessed. It reveals from the evaluation that ADROC can provide excellent control actions for the MIDO R2R situations even though the process exhibits complicated, nonlinear interaction effects between control variables, and the drifting disturbances.

Recursive Least Squares Run-to-Run Control with Time-Varying Metrology Delays

Fan, Shu-Kai,Chang, Yuan-Jung Korean Institute of Industrial Engineers 2010 Industrial Engineeering & Management Systems Vol.9 No.3

This article investigates how to adaptively predict the time-varying metrology delay that could realistically occur in the semiconductor manufacturing practice. Metrology delays pose a great challenge for the existing run-to-run (R2R) controllers, driving the process output significantly away from target if not adequately predicted. First, the expected asymptotic double exponentially weighted moving average (DEWMA) control output, by using the EWMA and recursive least squares (RLS) prediction methods, is derived. It has been found that the relationships between the expected control output and target in both estimation methods are parallel, and six cases are addressed. Within the context of time-varying metrology delay, this paper presents a modified recursive least squares-linear trend (RLS-LT) controller, in combination with runs test. Simulated single input-single output (SISO) R2R processes subject to various time-varying metrology delay scenarios are used as a testbed to evaluate the proposed algorithms. The simulation results indicate that the modified RLS-LT controller can yield the process output more accurately on target with smaller mean squared error (MSE) than the original RLSLT controller that only deals with constant metrology delays.

Optimization of Triple Response Systems by Using the Dual Response Approach and the Hooke-Jeeves Search Method

Fan, Shu-Kai S.,Huang, Chia-Fen,Chang, Ko-Wei,Chuang, Yu-Chiang Korean Institute of Industrial Engineers 2010 Industrial Engineeering & Management Systems Vol.9 No.1

This paper presents an extended computing procedure for the global optimization of the triple response system (TRS) where the response functions are nonconvex (nonconcave) quadratics and the input factors satisfy a radial region of interest. The TRS arising from response surface modeling can be approximated using a nonlinear mathematical program involving one primary (objective) function and two secondary (constraints) functions. An optimization algorithm named triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the nondegenerate TRS. In TRSALG, the Lagrange multipliers of target (secondary) functions are computed by using the Hooke-Jeeves search method, and the Lagrange multiplier of the radial constraint is located by using the trust region (TR) method at the same time. To ensure global optimality that can be attained by TRSALG, included is the means for detecting the degenerate case. In the field of numerical optimization, as the family of TR approach always exhibits excellent mathematical properties during optimization steps, thus the proposed algorithm can guarantee the global optimal solution where the optimality conditions are satisfied for the nondegenerate TRS. The computing procedure is illustrated in terms of examples found in the quality literature where the comparison results with a gradient-based method are used to calibrate TRSALG.

INVESTIGATING THE DEVELOPMENT OF BRAND LOYALTY IN BRAND COMMUNITIES FROM A POSITIVE PSYCHOLOGY PERSPECTIVE

Chia-Wu Lin,Kai-Yu Wang,Shu-Hao Chang,Jin-An Lin 글로벌지식마케팅경영학회 2016 Global Marketing Conference Vol.2016 No.7

Brand communities have been increasingly used by marketers to build brands. A brand community can be defined as a “…group of consumers with a shared enthusiasm for the brand and a well-developed social identity, whose members engage jointly in group actions to accomplish collective goals and/or express mutual sentiments and commitments” (Bagozzi and Dholakia, 2006, p. 45). Recent research on brand communities has begun to identify the importance of consumers’ psychological processes in regard to developing successful brand communities. Based on the flow theory from positive psychology, we propose that flow could be generated by brand community characteristics and plays an important role in influencing brand community members’ attitudes toward the brand. Specifically, we propose a model that identifies brand community characteristics (i.e., community cohesiveness and information quality) that produce flow experience and how the flow experience impacts brand identification and brand loyalty. Members from 31 automobile brand communities participated in this survey study, and 580 validated questionnaires were returned. Structural Equation Model was used to test the research hypotheses. The results show that community cohesiveness and information quality positively directly influence brand identification and also indirectly influence brand identification via flow. Flow also positively influences members’ brand identification and, subsequently, impacts brand loyalty. The results from our research contribute to the branding, brand community, and flow theory literature.

Multi-Attribute Data Fusion for Energy Equilibrium Routing in Wireless Sensor Networks

( Kai Lin ),( Lei Wang ),( Keqiu Li ),( Lei Shu ) 한국인터넷정보학회 2010 KSII Transactions on Internet and Information Syst Vol.4 No.1

Data fusion is an attractive technology because it allows various trade-offs related to performance metrics, e.g., energy, latency, accuracy, fault-tolerance and security in wireless sensor networks (WSNs). Under a complicated environment, each sensor node must be equipped with more than one type of sensor module to monitor multi-targets, so that the complexity for the fusion process is increased due to the existence of various physical attributes. In this paper, we first investigate the process and performance of multi-attribute fusion in data gathering of WSNs, and then propose a self-adaptive threshold method to balance the different change rates of each attributive data. Furthermore, we present a method to measure the energy-conservation efficiency of multi-attribute fusion. Based on our proposed methods, we design a novel energy equilibrium routing method for WSNs, viz., multi-attribute fusion tree (MAFT). Simulation results demonstrate that MAFT achieves very good performance in terms of the network lifetime.