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In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks. [ABSTRACT FROM AUTHOR]

The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient's remote healthcare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient's affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient's data/report while saving the travel and waiting time for the hospital services. The patient's data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network. [ABSTRACT FROM AUTHOR]

This special issue discusses the present and future of production in the Asia Pacific region, which drives the current world economy. Based on the region's massive working population, production in the Asia Pacific region has led to economic growth, and the GDP share of this region has increased from 20% to 30% in the past 40 years, almost equalling that of the other regions. Considering the above background, this special issue was planned for selecting and presenting interesting and meaningful research papers that deal with current and future production, not only just productions but also services, in the Asia Pacific region. [Extracted from the article]

Objective: To assess the associations of the Assessment of Spondyloarthritis International Society Health Index (ASAS HI) with gender and other factors in patients with ankylosing spondylitis (AS). Methods: From November 2017 to October 2018, we measured the Ankylosing Spondylitis Disease Activity Score (ASDAS), the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), the Bath Ankylosing Spondylitis Functional Index (BASFI), the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) and the ASAS HI score for AS patients at the Taichung Veterans General Hospital. After adjusting for disease activity (ASDAS-erythrocyte sedimentation rate [ESR], ASDAS- C-reactive protein [CRP], BASDAI+ESR or BASDAI+CRP), mSASSS and other potential confounders including medications, comorbidities, and laboratory data, any associations between gender and the sum score of ASDAS HI were assessed using multiple linear regression analysis, as well as any associations between gender and an ASAS HI score >5 using multivariable logistic regression analysis. Results: A total of 307 AS patients (62 [20.2%] females, mean age 46.4 years [S.D. 13.3], mean symptom duration 20.6 years [S.D. 12.1]) were included. Multiple linear regression analysis showed that the male gender was significantly associated with a lower ASAS HI (B = -1. 91, 95% confidence interval [CI], −2.82–−1.00, p <0.001). Multivariable logistic regression analysis revealed that males also had a lower risk of achieving scores of ASAS HI > 5 than females (odds ratio = 0.15, 95% CI, 0.07–0.36, p <0.001). Disease activity measures, including ASDAS-ESR, ASDAS-CRP and BASDAI, had positive correlations with ASAS HI. Conclusion: This single-center, cross-sectional study revealed that a higher ASAS HI score was significantly associated with female gender and higher disease activity measures. [ABSTRACT FROM AUTHOR]

An Agent-Based Manufacturing System (ABMS), represented as a model of future manufacturing systems, is introduced in this article. In an ABMS, computerized agents are autonomous and working collaboratively through the regulation of structured dialogues. The autonomy functions of an agent are demonstrated by four internal functions: internal resource management, a reflexivity mechanism, a goal adjustment mechanism, and collaboration management. The four functions assist an agent in interacting with other agents and with the environment and to adjust itself to the situation in the dynamic environment. Agents are similar to living systems. Based on the concept of living systems, autonomy and viability at the agent level are proposed and defined as new measures for manufacturing systems. A theory of coexistence and parasitism of ABMS is developed to identify the coexistent and parasitic characteristics of ABMS. Applications of the theory to real cases are also illustrated. Finally, a road map for the future research of distributed, collaborative manufacturing organizations is shown, based on the ABMS model developed. [ABSTRACT FROM AUTHOR]

Background Studies investigating reasons for the admission and the associated lengths of stay (LOSs) among cerebral palsy (CP) patients are limited. This study determined common reasons for acute hospitalizations and the LOSs among children, adolescents, and young adults with CP. Methods We performed a secondary analysis of data. CP patients aged 4–32.9 years were identified by CP registry in the catastrophic illness patient registry of the 2010 Taiwan National Health Insurance Research Database. Data of admission claims from 2010 to 2011 were analyzed. Reasons for admissions were identified according to International Classification of Diseases codes. Common reasons, frequencies of admissions for each reason, and LOSs were reported. Results Pneumonia, other respiratory problems, and epilepsy were the top three reasons for admissions in all groups. Other common reasons in all groups were sepsis, other respiratory infections, and gastrointestinal problems. The reasons specific to children included orthopedic issues; ear, nose, and throat problems; and urinary tract infections (UTIs). In youths, scoliosis, and contractures, were unique reasons. In young adults, UTIs, blood problems, and mental illness, were special reasons. Most admission reasons appeared to prolong LOS, and the LOS exhibited an increasing trend as age increased. Conclusion The results implied that patients with CP are more susceptible to most disease invasions. Our results also suggest that the current care system in Taiwan is unsuitable for patients with CP. These results can be used as guidance for planning effective multidisciplinary assessments in the future. [ABSTRACT FROM AUTHOR]

This paper implements deep learning methods of recurrent neural networks and short-term memory models. Two kinds of time-series data were used: air pollutant factors, such as O3, SO2, and CO2 from 2017 to 2019, and meteorological factors such as temperature, humidity, wind direction, and wind speed. A trained model was used to predict air pollution within an eight-hour period. Correlation analysis was applied using Pearson and Spearman correlation coefficients. The KNN method was used to fill in the missing values to improve the generated model's accuracy. The average absolute error percentage value was used in the experiments to evaluate the model's performance. LSTM had the lowest RMSE value at 1.9 than the other models from the experiments. CNN had a significant RMSE value at 3.5, followed by Bi-LSTM at 2.5 and Bi-GRU at 2.7. In comparison, the RNN was slightly higher than LSTM at a 2.4 value. [ABSTRACT FROM AUTHOR]

Stock market is considered too uncertain to be predictable. Many individuals have developed methodologies or models to increase the probability of making a profit in their stock investment. The overall hit rates of these methodologies and models are generally too low to be practical for real-world application. One of the major reasons is the huge fluctuation of the market. Therefore, the current research focuses in the stock forecasting area is to improve the accuracy of stock trading forecast. This paper introduces a system that addresses the particular need. The system integrates various data mining techniques and supports the decision-making for stock trades. The proposed system embeds the top-down trading theory, artificial neural network theory, technical analysis, dynamic time series theory, and Bayesian probability theory. To experimentally examine the trading return of the presented system, two examples are studied. The first uses the Taiwan Semiconductor Manufacturing Company (TSMC) data-set that covers an investment horizon of 240 trading days from 16 February 2011 to 23 January 2013. Eighty four transactions were made using the proposed approach and the investment return of the portfolio was 54% with an 80.4% hit rate during a 12-month period in which the TSMC stock price increased by 25% (from $NT 78.5 to $NT 101.5). The second example examines the stock data of Evergreen Marine Corporation, an international marine shipping company. Sixty four transactions were made and the investment return of the portfolio was 128% in 12 months. Given the remarkable investment returns in trading the example TSMC and Evergreen stocks, the proposed system demonstrates promising potentials as a viable tool for stock market forecasting. [ABSTRACT FROM AUTHOR]

Background: SSIs (surgical site infections) are associated with increased rates of morbidity and mortality. The traditional quality improvement strategies focusing on individual performance did not achieve sustainable improvement. This study aimed to implement the Six Sigma DMAIC method to reduce SSIs and to sustain improvements in surgical quality. The surgical procedures, clinical data, and surgical site infections were collected among 42,233 hospitalized surgical patients from 1 January 2019 to 31 December 2020. Following strengthening leadership and empowering a multidisciplinary SSI prevention team, DMAIC (Define, Measure, Analyze, Improve, and Control) was used as the performance improvement model. An evidence-based prevention bundle for reduction of SSI was adopted as performance measures. Environmental monitoring and antimicrobial stewardship programs were strengthened to prevent the transmission of multi-drug resistant microorganisms. Process change was integrated into a clinical pathway information system. Improvement cycles by corrective actions for the risk events of SSIs were implemented to ensure sustaining improvements. We have reached the targets of the prevention bundle elements in the post-intervention period in 2020. The carbapenem resistance rates of Enterobacteriaceae and P. aeruginosa were lower than 10%. A significant 22.2% decline in SSI rates has been achieved, from 0.9% for the pre-intervention period in 2019 to 0.7% for the post-intervention period in 2020 ( p = 0.004). Application of the Six Sigma DMAIC approach could significantly reduce the SSI rates. It also could help hospital administrators and quality management personnel to create a culture of patient safety.

Objectives: The Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) has been widely utilized to evaluate disease activity in patients with ankylosing spondylitis (AS) by an arbitrary cut-off of ≥4 to indicate high disease activity and initiate biological therapy. The Ankylosing Spondylitis Disease Activity Score (ASDAS) is a new composite index to assess AS disease activity states that have been defined and validated. ASDAS ≥2.1 was selected as a criterion to start biological therapy. The purpose of this study was to estimate the corresponding BASDAI and ASDAS cut-off in a Taiwanese AS cohort., Methods: From November 2016 to October 2018, we assessed the ASDAS and the BASDAI regularly and recorded demographic data for 489 AS patients in Taichung Veterans General hospital (TCVGH) using an electronic patient-reported data system linked to electronic medical records. We used receiver operating characteristic curves with Youden's J statistic to determine the BASDAI values that correspond to ASDAS disease activity cut-offs (i.e., 1.3, 2.1, and 3.5)., Results: In our population, the best trade-off BASDAI values corresponding to ASDAS -C-reactive protein (CRP) 1.3, 2.1, and 3.5 were 2.1, 3.1, and 3.7, respectively. The optimal BASDAI values corresponding to ASDAS-erythrocyte sedimentation rates 1.3, 2.1, and 3.5 were 2.0, 2.6, and 4.8, respectively., Conclusion: We propose a revised BASDAI cut-off based on our data, as BASDAI scores are commonly used globally. A more reasonable, lower BASDAI cut-off to initiate or change biological therapy will bring us closer to better decisions to treat AS patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Huang, Chen, Lai, Hsieh, Hung, Lin, Tseng, Tang, Chou, Wu, Huang, Hsieh, Chen, Liao and Chen.)

Background: Epidemiologic data supporting the epilepsy-asthma association are insufficient. Therefore, we examined this association in this study., Methods: By using claims data from the National Health Insurance Research Database (Taiwan), we executed a retrospective cohort analysis. Analysis 1 entailed comparing 150,827 patients diagnosed as having incident asthma during 1996-2013 with disease-free controls who were selected randomly during the same period, frequency matched in terms of age and sex. Similarly, analysis 2 entailed comparing 25,274 patients newly diagnosed as having epilepsy with sex- and age-matched controls who were selected randomly. At the end of 2013, we evaluated in analysis 1 the epilepsy incidence and risk and evaluated in analysis 2 the asthma incidence and risk. We applied Kaplan-Meier analysis to derive plots of the proportion of asthma-free seizures., Results: In analysis 1, the asthma group exhibited a higher epilepsy incidence than did the control group (3.05 versus 2.26 per 1,000 person-years; adjusted hazard ratio: 1.39, 95% CI [1.33-1.45]). We also noted a greater risk of subsequent epilepsy in women and girls. In analysis 2, we determined that the asthma incidence between the control and epilepsy groups did not differ significantly; however, some age subgroups including children and individuals in their 30s had an increased risk. A negative association was found in adolescents. The Kaplan-Meier analysis revealed epilepsy to be positively associated with subsequent onset of asthma within seven years of epilepsy diagnosis., Discussion: Asthma may be associated with high epilepsy risk, and epilepsy may be associated with high asthma risk among children and individuals in their 30s. Nevertheless, people with epilepsy in other age subgroups should be aware of the possibility of developing asthma within seven years of epilepsy diagnosis., Competing Interests: The authors declare there are no competing interests.

Background: The objectives of this study were to compare the risk factors for unplanned intensive care unit (ICU) transfer after emergency department (ED) admission in patients with infections and those without infections and to explore the feasibility of using risk stratification tools for sepsis to derive a prediction system for such unplanned transfer., Methods: The ICU transfer group included 313 patients, while the control group included 736 patients randomly selected from those who were not transferred to the ICU. Candidate variables were analyzed for association with unplanned ICU transfer in the 1049 study patients., Results: Twenty-four variables were associated with unplanned ICU transfer. Sixteen (66.7%) of these variables displayed association in patients with infections and those without infections. These common risk factors included specific comorbidities, physiological responses, organ dysfunctions, and other serious symptoms and signs. Several common risk factors were statistically independent., Conclusions: The risk factors for unplanned ICU transfer in patients with infections were comparable to those in patients without infections. The risk factors for unplanned ICU transfer included variables from multiple dimensions that could be organized according to the PIRO (predisposition, insult/infection, physiological response, and organ dysfunction) model, providing the basis for the development of a predictive system.