Estimating change point in multivariate processes via simultaneous mean vector and covariance matrix

In many industrial processes, several quality characteristics are inevitably related. In this situation, the mean vector and covariance matrix must be simultaneously monitored and controlled to determine whether a multivariate process is in control. With the increase in the number of variables, the performance of control charts is significantly reduced, and the time delay between the actual time of change in the process and the warning time of the control chart increases, which is one of the main challenges when using multivariable control charts. Between the real-time and the change time (called the change-point - CP), especially during the simultaneous monitoring and controlling of the parameters, the mean vector, and the covariance matrix cause problems such as delay or stoppage of the production lines or services, as well as inconsistent production of products or services. To improve this, a new way of estimating the CP will help statistical process control (SPC) professionals identify the cause(s) of out-of-control (OC) conditions, thus providing better feedback for process improvement. This study presented a new method based on an artificial neural network (ANN), which first examined the OC conditions for a multivariate process using the multivariate exponentially weighted moving average (MEWMA) and multivariate exponentially weighted mean square (MEWMS) control charts. Then, the ANN-fitting method was used to diagnose the cause(s) of OC conditions using the machine learning (ML)-classifier and estimating the length of delay time. Finally, the change point (CP) was estimated by integrating all these methods. The performance of the new approach was validated by comparing it with the results from another study. It also validated the proposed method developed by evaluating the accuracy and precision of this research. As a conclusion, the MEWMS chart was the best for detecting the OC condition while the support vector machines (SVM) gaussian model best to diagnoses the cause(s) o f the OC condition. The model provided has estimated the change point on one sample with difference over 10,000 tested cases (simulated) with a probability of 99%, which is an accurate and reliable model for a practical approach

Multivariate change point estimation in covariance matrix using ANN

In statistical process control, change point estimation is an essential requirement for diagnosing the source of a deviation when a process is out of control. In this study, an ANN- based method is proposed to estimate the change point in the multivariate normal process which is subjected to covariance variation. Since in a physical system parameter is correlated, correlation is kept constant to obtain realistic simulated data. Employing statistical simulation, different out of control scenarios are simulated and statistics are calculated for each scenario. This study is to predict the change point in the control chart using the simulated set and corresponding statistical sets, an ANN is adopted. The resulting model achieved a high accuracy of 90% in training and 80% testing with a reasonable level of confidence in the prediction. Also, results show that Bayesian reaches a higher accuracy than Levenberg in ANN training

Harnessing the Power of Smart and Connected Health to Tackle COVID-19:IoT, AI, Robotics, and Blockchain for a Better World

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), Artificial Intelligence (AI) — including Machine Learning (ML) and Big Data analytics — as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This paper provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas where IoT can contribute are discussed, namely, i) tracking and tracing, ii) Remote Patient Monitoring (RPM) by Wearable IoT (WIoT), iii) Personal Digital Twins (PDT), and iv) real-life use case: ICT/IoT solution in Korea. Second, the role and novel applications of AI are explained, namely: i) diagnosis and prognosis, ii) risk prediction, iii) vaccine and drug development, iv) research dataset, v) early warnings and alerts, vi) social control and fake news detection, and vii) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including i) crowd surveillance, ii) public announcements, iii) screening and diagnosis, and iv) essential supply delivery. Finally, we discuss how Distributed Ledger Technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19

Investigation of Pb(II) Removal from Aqueous Solutions Using Modified Nano Zero-Valent Iron Particles

This research was conducted in experimental scale with the aim of investigation effect of polyacrylic acid-stabilized zero-valent iron nanoparticles (PAA-nZVI) on lead removal from aqueous solution. In this regards, NZVI was synthesized with polyacrylic acid and their size and morphological characteristics were examined via X-ray diffraction (XRD), Scanning Electron Microscopy (SEM) and Fourier Transmission Infrared Spectroscopy (FTIR). To study the effect of PAA-nZVI on lead removal, pH of aqueous solution, contact time, PAA-NZVI concentration  and initial Pb(II) concentration were considered as variables. Furthermore, the experimental data of Pb(II)  removal were fitted using three kinetic models, namely Zero-order, First-order and Second-order.The results of experiments showed that maximum Pb(II) removal efficiency was observed at pH=5, 15 min contact time and 5 g/L PAA-nZVI concentration. Moreover, the results of kinetic studies indicated that among all applied kinetic models, First-order kinetic model had more better prediction than other kinetic models ofPb(II) removal. Based on the results of present research, PAA-NZVI is an efficient agent to remove Pb(II) from aqueous solutions

Comparison of the Efficiencies of Zero-Valent Iron Nanoparticles and Stabilized Iron Nanoparticles for Nitrate Reduction from Polluted Waters

The present study was conducted to evaluate the feasibility of zero-valent iron nanoparticles (ZVIN) for the removal of nitrate from aqueous solutions. For this purpose, bare zero-valent iron nanoparticles (bare-ZVIN) and CMC-ZVIN were synthesized using the borohydride reduction method and their morphological characteristics were examined via scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier Transmission Infrared Spectroscopy (FTIR). The effects of pH of the aqueous solution, initial nitrate concentration, ZVIN concentration, and contact time on nitrate reduction were investigated as operational parameters and the kinetics of nitrate reduction was studied in batch experiments. The results showed that 93.65% of nitrate was removed by stabilized nanoparticles at pH=6 while non-stabilized nanoparticles at pH=2 were able to remove 85.55% of the nitrate.Furthermore, nitrate reduction was enhanced by increasing ZVIN concentration and contact time while it was decreased as a result of increasing initial nitrate concentration. The major product of nitrate reduction at an acidic pH was found to be ammonium; at an alkaline pH, however, nitrate was converted to nitrogen and nitrite production dropped to less than 2%. Kinetic analysis demonstrated that denitrification of nitrate by the nanoparticles fitted well with first-order and second-order reaction models. The results also demonstrated that the stabilized ZVI nanoparticles were more effective than bare-ZVIN for nitrate reduction in aqueous solutions

Theoretical and experimental investigation of estimating change point in multivariate processes via simultaneous covariance matrix and mean vector

The identification of change points in statistical process control (SPC) data is the critical criterion for multivariate techniques when output is out-of-control condition. Therefore, monitoring all independent variables is essential and demands targeted attention to avoid errors at the systems control stage. However, estimating change-point in multivariate control charts is the main problem when these correlated quality characteristics monitor together. Therefore, we proposed a combination of an ensemble learning-based model of artificial neural networks with support vector machines to monitor process mean vector and covariance matrix shifts simultaneously to estimate the change point in a multivariable system. The performance of the final model indicated an estimated changing point with one sample over 6,000 simulated cases with a probability of 98 percent, which is a significantly high accuracy rating. Finding suggests the outcome of the project confirms that the proposed model can provide a precise estimating the change point by monitoring the mean vector and the covariance matrix simultaneously and, helps to identify those variable(s) responsible for an out-of-control condition. For further validation of the model, the performance of the proposed model has been compared with previous reported which confirms a better performance of the proposed model. Finally, the model was applied to monitor the performance of the solar hydrogen production system and the model identify the variables which have negative effects on the performance of the system

The value of coronary artery calcium score assessed by dual-source computed tomography coronary angiography for predicting presence and severity of coronary artery disease

Background: Measuring coronary artery calcium score (CACS) using a dual-source CT scanner is recognized as a major indicator for assessing coronary artery disease. The present study aimed to validate the clinical significance of CACS in predicting coronary artery stenosis and its severity. Material and Methods: This prospective study was conducted on 202 consecutive patients who underwent both conventional coronary angiography and dual-source (256-slice) computed tomography coronary angiography (CTA) for any reason in our cardiac imaging center from March to September 2013. CACS was measured by Agatston algorithm on non-enhanced CT. The severity of coronary artery disease was assessed by Gensini score on conventional angiography. Results: There was a significant relationship between the number of diseased coronary vessels and mean calcium score, i.e. the mean calcium score was 202.25±450.06 in normal coronary status, 427.50±607.24 in single-vessel disease, 590.03±511.34 in two-vessel disease, and 953.35±1023.45 in three-vessel disease (p<0.001). There was a positive association between calcium score and Gensini score (r=0.636, p<0.001). In a linear regression model, calcium score was a strong determinant of the severity of coronary artery disease. Calcium scoring had an acceptable value for discriminating coronary disease from normal condition with optimal cutoff point of 350, yielding a sensitivity and specificity of 83% and 70%, respectively. Conclusions: Our study confirmed the strong relationship between the coronary artery calcium score and the presence and severity of stenosis in coronary arteries assessed by both the number of diseased coronary vessels and also by the Gnesini score

Study the Effect of Endocannabinoid System on Rat Behavior in Elevated Plus-Maze

Introduction: Previous studies have shown that cannabinoidergic system is involved in anxiety. However, there are controversial reports in the experimental studies. The aim of this study is to evaluate the effect of pharmacological stimulation or blocking of CB1 receptors and inhibition of endocannabinoid degradation in anxiety like behavior in elevated plus-maze (EPM) test in rat. The EPM is one of the most widely used animal models of anxiety.&nbsp; Methods: Male Wistar rats were randomly allocated to ten groups. Different groups of animals intraperitoneally received Win-55212 (0.3, 1 and 5 mg/kg) as CB1 receptor agonist, AM- 251 (0.3, 1 and 5 mg/kg) as CB1 receptor antagonist, URB-597 (0.03, 0.1 and 0.3 mg/kg) as endocannabinoid breakdown inhibitor or saline (as control group) 30 min before submitting into EPM test.&nbsp; Results: The results showed that compared to the control group, Win-55212 (1 and 5 mg/kg) and URB-597 (0.1 and 0.3 mg/kg) significantly increased both of the time and percentage of entries into open arms. AM-251 (1 and 5 mg/kg) significantly decreased the time and percentage of entries into open arms in the EPM test. These substances have no effects on the total distance covered by animals and number of closed arm entries.&nbsp; Discussion: It is concluded that activation of cannabinoid receptor exert anxiolytic effect while blocking of cannabinoid receptor resulted in anxiety behavior. The locomotor activity was not significantly changed by cannabinoid system. It is suggested that potentiation of cannabinoid system may be therapeutic strategy for the anxiety behavior

The Epidemiological Profile of Injuries in Children up to 59 Months Old in Kermanshah Province in 2015

Abstract Background: Injuries have always been among the main factors threatening human life and health, and Children are one of the vulnerable population groups in this field. The purpose of this study is to survey incident and examine the distribution of injuries in children 0 to 59 months old in Kermanshah Province in 2015. Material and Methods: This survey is a cross-sectional study, gathering its data from Health Vice-presidency of Kermanshah University of Medical Sciences. It covers all the injured whose reason of injury was referring to private and public hospitals under the supervision of Health Vice-presidency of Kermanshah University of Medical Sciences, during 2015. Data was collected from the injury software package of Ministry of Health. All registered individuals in this software were included in the study. Also, all the injuries, including electric shock, road traffic accidents, animal attacks, violence, fall, burn, hit, scorpion and snake sting, drowning, poisoning were entered in our research. Results: The number of injuries in children was 3499 cases: 2216 (63.3%) boys and 1283 (36.7%) girls. Incidence of injuries was 23.3 cases per 1000 children aged 0-59 months old. The highest frequency of injuries was in children aged 0 to 11 months (42%) and majority of them occurred at home (58.5%). Most incidents was in summer (especially in September). The most common causes of injuries included fall (52.7%), road traffic injuries (29.6%), and poisoning (14.1%). Conclusion: Based on the results of this study, most of injuries occur in the home, it is recommended that certain instructions be given to parents about child developmental stages to enable them to make the home a safe environment for children, as children less than 5 years old spend most of their time there. &nbsp