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Hybrid flow shop scheduling problems are encountered in many real-world manufacturing operations such as computer assembly, TFT-LCD module assembly, and solar cell manufacturing. Most research considers the scheduling problem in regard to time requirements and the steps needed to improve production efficiency. However, the increasing amount of carbon emissions worldwide is contributing to the worsening global warming problem. Many countries and international organizations have started to pay attention to this problem, even creating mechanisms to reduce carbon emissions. Furthermore, manufacturing enterprises are showing growing interest in realizing energy savings. Thus, the present research study focuses on reducing energy costs and completion time at the manufacturing-system level. This paper proposed a multi-objective mixed-integer programming for energy-efficient hybrid flow shop scheduling with lot streaming in order to minimize both the production makespan and electric power consumption. Due to a trade-off between these objectives and the computational complexity of the proposed multi-objective mixed-integer program, this study adopts the genetic algorithm (GA) to obtain approximate Pareto solutions more efficiently. In addition, a multi-objective energy efficiency scheduling algorithm is also developed to calculate the fitness values of each chromosome in GA.

Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments.

As the Internet of Things (IOT) becomes more widely used in our everyday lives, an increasing number of wireless communication devices are required, meaning that an increasing number of signals are transmitted and received through antennas. Thus, the performance of antennas plays an important role in IOT applications, and increasing the efficiency of antenna design has become a crucial topic. Antenna designers have often optimized antennas by using an EM simulation tool. Although this method is feasible, a great deal of time is often spent on designing the antenna. To improve the efficiency of antenna optimization, this paper proposes a design of experiments (DOE) method for antenna optimization. The antenna length and area in each direction were the experimental parameters, and the response variables were antenna gain and return loss. Response surface methodology was used to obtain optimal parameters for the layout of the antenna. Finally, we utilized antenna simulation software to verify the optimal parameters for antenna optimization, showing how the DOE method can increase the efficiency of antenna optimization. The antenna optimized by DOE was implemented, and its measured results show that the antenna gain and return loss were 2.65 dBi and 11.2 dB, respectively.

BackgroundShift work is a workschedule, since industrial era and some employees work in shift. It causes a desynchronization of the biological clock with consequences on sleep amount and quality, such as insomnia and easy fatigue. Obstructive sleep apnea (OSA) is one of the sleep problems that are getting more and more attention, but studies on the association between shift work and OSA were rare. Herein, we aimed to conduct a systematic review and meta-analysis to investigate the association between shift work and possible OSA.MethodsThis study was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We queried PubMed, Embase, and Web of Science databases using a related set of keywords. The inclusion criteria were as follows: (1) participants were adult employees hired by a company or organization; (2) exposure was shift work; and (3) outcome was possible OSA according to examination or assessment.ResultsWe included six studies in the systematic review and five studies were selected for further meta-analysis. A random-effects model showed an association of shift work with a small, non-significant increase in possible OSA cases (pooled prevalence relative risk = 1.05; 95% CI 0.85–1.30; p = 0.65). This association occurred in both healthcare and non-healthcare workers group.ConclusionThe association between shift work and possible OSA remains inconclusive and could be small if not negligible. Future studies should assess the association between specific work schedules and specific OSA definitions.Trial registration numberPROSPERO ID: CRD42020156837

Response time reduction is a fundamental aspect of ambulance location management. To minimize patient mortality and disability, the response time of emergency medical services is critical. Therefore, real-time management is required to determine the location of an ambulance with a low response time or called or a dynamic allocation system. Dynamic allocation is moving the ambulance bases from low demand areas to high-demand areas that is useful in the operational level. However, the dynamic allocation model for real-time management requires re-allocation of ambulances, resulting in high costs and heavy workloads for the ambulance crews. This paper focuses on a covering model based on social media analysis. The model was used for developing an ambulance reallocation system. In addition to dynamic allocation, the proposed model considers real-time data from a social media application (Twitter) to minimize the response time and cost during emergencies and disasters. Twitter has been used in various ways to communicate during and manage emergencies. In this paper, we formulate the Maximal Covering Location Problem (MCLP), develop a solution procedure based on social media (Twitter application) and show the effect of the approach on the optimal solution by comparing it with the classical approach and also demonstrate our approach on Bangkok EMS.

Background Association between smoking and sleep apnea is well-known from previous studies. However, the influence of secondhand smoke (SHS), which is a potential risk factor of obstructive sleep apnea (OSA), remains unclear. Our aim was to investigate the relationship between SHS and OSA using a meta-analysis. Materials and methods For the meta-analysis, searches were performed in MEDLINE, EMBASE, and Web of Science databases on January 10, 2022, by combining various keywords including “SHS exposure” and “OSA”. Data were extracted using defined inclusion and exclusion criteria. Fixed-effects model meta-analyses were used to pool risk ratio (RR) estimates with their 95% confidence intervals (CI). I 2 was used to assess heterogeneity. Moreover, we performed subgroup meta-analyses of children-adults, and smoker fathers and mothers. Results In total, 267 articles were obtained through an electronic search. Twenty-six articles were included in our analysis according to the inclusion and exclusion criteria. We found evidence of an association between SHS exposure and possible OSA (RR 1.64, 95% CI 1.44–1.88). The results of the subgroup analyses showed that children passive smokers (RR 1.84, 95% CI 1.60–2.13) were at greater risks of possible OSA than adult passive smokers (RR 1.35, 95% CI 1.21–1.50). Also, significant differences were observed in mothers with smoking exposure (RR 2.61, 95% CI 1.62–4.21, p  < 0.0001), as well as in fathers with smoking exposure (RR 2.15, 95% CI 0.98–4.72, p  = 0.06). Short conclusion. Our meta-analysis confirmed that SHS exposure is significantly associated with OSA. In the subgroup analyses, the association of SHS and possible OSA was significant in both children and adults, as well as in smoker mothers and fathers. Highlights 1. This is a meta-analysis to evaluate the relationship between secondhand smoke (SHS) exposure and obstructive sleep apnea (OSA) in both adults and children. 2. Our meta-analysis revealed a significantly positive association between SHS exposure and possible OSA in children and adults. 3. Both smoking in mothers and fathers are associated with significantly higher risk of OSA in children.

Missing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis. In the present study, we focus on missing data imputation using classification and regression trees (CART). We consider a new perspective on missing data in a CART imputation problem and realize the perspective through some resampling algorithms. Several existing missing data imputation methods using CART are compared through simulation studies, and we aim to investigate the methods with better imputation accuracy under various conditions. Some systematic findings are demonstrated and presented. These imputation methods are further applied to two real datasets: Hepatitis data and Credit approval data for illustration. The method that performs the best strongly depends on the correlation between variables. For imputing missing ordinal categorical variables, the package with surrogate variables is recommended under correlations larger than 0 with missing completely at random (MCAR) and missing at random (MAR) conditions. Under missing not at random (MNAR), chi-squared test methods and the package with surrogate variables are suggested. For imputing missing quantitative variables, the iterative imputation method is most recommended under moderate correlation conditions.

Background Well-being is an important issue in workplace. One of these assessment tools of well-being, Workplace PERMA Profiler, is based on Seligman’s five dimensions well-being. Prolonged fatigue may last for a long time, leading a great impact on both employees and enterprises. However, rare studies about the association between well-being and fatigue had been investigated. Our aim is to establish the Chinese version Profiler, and to discovery the association between workplace well-being and fatigue. Methods The Chinese version was established according to International Society of Pharmacoeconomics and Outcomes Research (ISPOR) task force guidelines. In the study, researchers employed simple random sampling by approaching individuals undergoing health checkups or receiving workplace health services, inviting them to participate in a questionnaire-based interview. Prolonged Fatigue was evaluated by Checklist Individual Strength (CIS). The reliability was evaluated by Cronbach’s alphas, Intra-class Correlation Coefficients (ICCs), and measurement errors. Moreover, confirmatory factor analysis and correlational analyses were assessed for the validity. Results The analyses included 312 Chinese workers. Cronbach’s alphas of the Chinese version ranged from 0.69 to 0.93, while the ICC ranged from 0.70 to 0.92. The 5-factor model of confirmatory factor analysis revealed a nearly appropriate fit (χ 2 (82) = 346.560, Comparative Fit Index [CFI] = 0.887, Tucker-Lewis Index [TLI] = 0.855, Root Mean Square Error of Approximation [RMSEA] = 0.114, Standardized Root Mean Square Residual [SRMR] = 0.060). Moreover, the CIS and its four dimensions were significantly and negatively associated with the Positive Emotion, while they are positively associated with Engagement dimension except CIS-Motivation dimension. Conclusion The Chinese version Workplace PERMA-Profiler indicate nice reliability and validity. Furthermore, all CIS dimensions were negatively influenced by Positive Emotion, while commonly positively associated with Engagement.

As automated equipment in PCB manufacturing becomes increasingly reliant on precision hot-air ovens, ensuring operational stability and reducing downtime have become critical challenges. Existing anomaly detection methods, such as Support Vector Machines (SVMs), Deep Neural Networks (DNNs), and Long Short-Term Memory (LSTM) Networks, struggle with high-dimensional dynamic data, leading to inefficiencies and overfitting. To address these issues, this study proposes an innovative anomaly detection system specifically designed for fault diagnosis in PCB hot-air ovens. The motivation is to improve accuracy and efficiency while adapting to dynamic changes in the manufacturing environment. The core innovation lies in the introduction of the Adaptive Temporal Feature Map (ATFM), which dynamically extracts and adjusts key temporal features in real time. By combining ATFM with Bi-Directional Dimensionality Reduction (BDDR) and eXtreme Gradient Boosting (XGBoost), the system effectively handles high-dimensional data and adapts its parameters based on evolving data patterns, significantly enhancing fault detection accuracy and efficiency. The experimental results show a fault prediction accuracy of 99.33%, greatly reducing machine downtime and product defects compared to traditional methods.

Exposure to heavy metals could lead to adverse health effects by oxidative reactions or inflammation. Some essential elements are known as reactors of anti-inflammatory enzymes or coenzymes. The relationship between tumor necrosis factor alpha (TNF-α) and heavy metal exposures was reported. However, the interaction between toxic metals and essential elements in the inflammatory response remains unclear. This study aimed to explore the association between arsenic (As), cadmium (Cd), lead (Pb), cobalt (Co), copper (Cu), selenium (Se), and zinc (Zn) in blood and TNF-α as well as kidney function. We enrolled 421 workers and measured the levels of these seven metals/metalloids and TNF-α in blood; kidney function was calculated by CKD-EPI equation. We applied weighted quantile sum (WQS) regression and group WQS regression to assess the effects of metal/metalloid mixtures to TNF-α and kidney function. We also approached the relationship between metals/metalloids and TNF-α by generalized additive models (GAM). The relationship of the exposure–response curve between Pb level and TNF-α in serum was found significantly non-linear after adjusting covariates (p < 0.001). Within the multiple-metal model, Pb, As, and Zn were associated with increased TNF-α levels with effects dedicated to the mixture of 50%, 31%, and 15%, respectively. Grouped WQS revealed that the essential metal group showed a significantly negative association with TNF-α and kidney function. The toxic metal group found significantly positive associations with TNF-α, serum creatinine, and WBC but not for eGFR. These results suggested Pb, As, Zn, Se, and mixtures may act on TNF-α even through interactive mechanisms. Our findings offer insights into what primary components of metal mixtures affect inflammation and kidney function during co-exposure to metals; however, the mechanisms still need further research.