Economic Design of X-bar Control Chart Using Gravitational Search Algorithm

Control chart is a major and one of most widely used statistical process control (SPC) tools. It is used to statistically monitor the process through sampling inspection. Control chart tells us when to allow the process to continue or avoid unnecessary adjustments with machine and when to take the corrective action. On to same problem either on the material side or from the operator side it is quite possible that either targeted value X-bar has changed or process dispersion has changed. These changes must be reflected on the control chart so that the corrective action can be taken. The use of control chart requires selection of three parameters namely sample size n, sampling interval h, and width of control limits k for the chart. Duncan developed a loss cost function for X-bar control chart with single assignable cause. The function has to be optimized using metaheuristic optimization technique. In the present project, the economic design of the X-bar control chart using Gravitational Search Algorithm (GSA) has been developed MATLAB software to determine the three parameters i.e. n , h and k such that the expected total cost per hour is minimized. The results obtained are found to be better than that reported in literature

Automatic detection of sensor calibration errors in mining industry

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Sensor errors cost the mining industry millions of dollars in losses each year. Unlike gross errors, "calibration errors" are subtle, develop over time, and are difficult to identify. Economic losses start accumulating even when errors are small. Therefore, the aim of this research was to develop methods to identify calibration errors well before they become obvious. The goal in this research was to detect errors at a bias as low as 2% in magnitude. The innovative strategy developed relied on relationships between a variety of sensors to detect when a given sensor started to stray. Sensors in a carbon stripping circuit at a gold processing facility (Pogo Mine) in Alaska were chosen for the study. The results from the initial application of classical statistical methods like correlation, aggregation and principal component analysis (PCA), and the signal processing methods (FFT), to find bias (±10%) in "feed" sensor data from a semi-autogenous (SAG) grinding mill operation (Fort Knox mine, Alaska) were not promising due to the non-linear and non-stationary nature of the process characteristics. Therefore, those techniques were replaced with some innovative data mining techniques when the focus shifted to Pogo Mine, where the task was to detect calibration errors in strip vessel temperature sensors in the carbon stripping circuit. The new techniques used data from two strip vessel temperature sensors (S1 and S2), four heat exchanger related temperature sensors (H1 through H4), barren flow sensor (BARNFL) and a glycol flow sensor (GLYFL). These eight sensors were deemed to be part of the same process. To detect when the calibration of one of the strip vessel temperature sensors, S1, started to stray, tests were designed to detect changes in relationship between the eight temperature sensors. Data was filtered ("threshold") based on process characteristics prior to being used in tests. The tests combined basic concepts such as moving windows of time, ratios (ratio of one sensor data to data from a set of sensors), tracking of maximum values, etc. Error was triggered when certain rules were violated. A 2% error was randomly introduced into one of the two strip vessel temperature data streams to simulate calibration errors. Some tests were less effective than others at detecting the simulated errors. The tests that used GLYFL and BARNFL were not very effective. On the other hand, the tests that used total "Heat" of all the heat exchanger sensors were very effective. When the tests were administered together ("Combined test"), they have a high success rate (95%) in terms of True alarms, i.e., tests detecting bias after it is introduced. In those True alarms, for 75% of the cases, the introduction of the error was detected within 39.5 days. A -2% random error was detected with a similar success rate

Economic Design of X-bar Control Chart Using Gravitational Search Algorithm

Control chart is a major and one of most widely used statistical process control (SPC) tools. It is used to statistically monitor the process through sampling inspection. Control chart tells us when to allow the process to continue or avoid unnecessary adjustments with machine and when to take the corrective action. On to same problem either on the material side or from the operator side it is quite possible that either targeted value X-bar has changed or process dispersion has changed. These changes must be reflected on the control chart so that the corrective action can be taken. The use of control chart requires selection of three parameters namely sample size n, sampling interval h, and width of control limits k for the chart. Duncan developed a loss cost function for X-bar control chart with single assignable cause. The function has to be optimized using metaheuristic optimization technique. In the present project, the economic design of the X-bar control chart using Gravitational Search Algorithm (GSA) has been developed MATLAB software to determine the three parameters i.e. n , h and k such that the expected total cost per hour is minimized. The results obtained are found to be better than that reported in literature

BIO-ANALYTICAL METHOD DEVELOPMENT AND VALIDATION OF AVELUMAB, AXITINIB AND ITS APPLICATION TO PHARMACOKINETIC STUDIES IN RABBIT PLASMA BY USING LCMS/MS

Objective: An easy, quick, precise, active and reproducible LC-MS/MS technique was developed for the bioanalytical method of Avelumab and Axitinib using Cytarabine as an internal standard. Methods: This article summarizes the recent progress on bioanalytical LC-MS/MS methods using waters x-bridge phenyl column (150x4.6 mm, 3.5µ) column and organic mobile phase of 0.1% Tri fluoro acetic acid and Acetonitrile in 50:50 ratio. Results: The calibration curve was linear in the range of 2-40 ng/ml for avelumab and 0.5-10 ng/ml axitnib. Accuracy, precision, recovery, matrix effect and stability results were found to be within the suitable limits. Simple and efficient method was developed and utilized in pharmacokinetic studies to see the investigated analyte in body fluids. Conclusion: The application denotes all the parameters of system suitability, specificity, linearity and accuracy are in good agreement with USFDA guidelines and applied effectively for the investigation of pharmacokinetic studies in rabbit

High Prevalence of Neuropathic Pain Component in Patients with Low Back Pain : Evidence from Meta-Analysis

BACKGROUND: Low back pain (LBP) is a complex syndrome which includes a nociceptive (NcP) component, a neuropathic (NeP) component, or a mixture of components (mixed pain). The NeP component (NePC) in LBP is defined as the presence of NeP with or without an NcP. OBJECTIVE: This meta-analysis aimed at assessing the pooled prevalence of NePC in patients with LBP and at identifying the factors causing significant heterogeneity in reported prevalence. STUDY DESIGN: Meta-analysis. METHODS: A systematic literature search was carried out, with inclusion of all epidemiological studies describing the NeP prevalence levels in LBP patients while using standard diagnostic methods. The "pooled prevalence rate (PPR)" of NePC, either on its own or in combination with NcP, was calculated. A pre-specified subgroup analysis was carried out, considering LBP duration, presence of leg pain, diagnostic method(s), and questionnaire(s) used. RESULTS: The meta-analysis included 20 studies relating to a total of 14,269 LBP patients, of whom 7,969 patients (55.8%) were identified as presenting with NePC. The pooled PR (95% CI) of NePC in patients with LBP was 0.47 (0.40 - 0.54), while the pooled PR of NcP was 0.56 (0.48 - 0.63). Higher NePC pooled PR values were identified in LBP with leg pain as compared to uncomplicated LBP (respectively: 0.60; 0.47 - 0.73 vs 0.27; 0.23 - 0.31; Pinteraction < 0.01). LIMITATIONS: The quality of the included studies was assessed using ad-hoc criteria. Due to the limited number of available studies, one may need to be cautious in reaching conclusions about the impact of disease duration on NePC prevalence values. We pooled studies which used a range of different diagnostic methods, with putatively different sensitivity/specificity diagnosing levels. CONCLUSIONS: Overall, high NePC prevalence levels were here identified in LBP patients. As the pain is a subjective phenomenon and there is no gold standard for the diagnosis of NePC, there is the possibility that the pooled effect estimate may alter depending upon the diagnostic method used. KEY WORDS: Neuropathic pain, nociceptive pain, low back pain, symptom-based questionnaire, chronicity.Peer reviewedFinal Published versio

Prediction of Safety Performance by Using Machine Learning Algorithms: Evidence from Indian Construction Project Sites

The construction industry in India happens to be the second most contributor to its gross domestic product (GDP) but high rates of accidents and fatalities have tarnished the image of the industry in India. To enhance the importance and alertness among the stakeholders in construction project sites, the present study proposes a framework for predicting safety performance. In this retrospective study, the data pertaining to the 69 construction project sites across India from January, 2021, to July, 2022 was analysed. The data analysis was conducted in two phases, in the first phase of the study the efficiency of project sites was computed by implementing data envelopment analysis (DEA). In the second phase, the results of the first phase are utilized to predict the safety performance of construction sites by applying four machine learning (ML) algorithms. In the first phase of the study, three input and three output variables were considered to compute the efficiency of the project sites. Results of four ML classifiers revealed that the random forest classifier with high recall percentage of 95.0 is considered the best in predicting the safety performance. Finally, the results indicate that the ML classifiers enable a good accuracy level in predicting the safety performance of project sites. Among the four ML classifiers, notably the Random Forest Classifier enables identifying the inefficient project sites and advising the site management to implement control measures. Finally, a safety performance prediction tool was developed to understand the results

Improved Performance of CaCl2 Incorporated Polyethersulfone Ultrafiltration Membranes

Poly(ethersulfone) (PES) / poly(vinylpyrrolidone) (PVP) blend membranes modified with calcium chloride (CaCl2) were prepared by phase inversion method. Effect of CaCl2 on the morphology, filtration and performance characteristics of the PES/PVP membranes was studied in detail. Results indicated that CaCl2 blend membranes possessed better porosity and flux than the pristine PES membrane. Dye separation efficiency of CaCl2 blend membranes was also increased considerably. Especially, the PES/PVP blend membrane with 1 wt% CaCl2 showed highest permeate flux and improved dye rejection. Fouling analysis carried out on CaCl2 blend membranes clearly showed that these membranes possessed better antifouling effect than pure PES membrane. Thus the CaCl2 blended PES/PVP membranes are more promising for the treatment of dye polluted wastewater

Abnormal uterine bleeding in women of peri-menopausal age: a retrospective study

Background: Abnormal uterine bleeding (AUB) is a phenomenon which refers to menstrual bleeding of abnormal frequency, duration or quantity. It is a common gynaecological complaint caused by wide variety of organic or non-organic causes. The objective of the study was to determine the incidence of abnormal uterine bleeding with respect to aetiopathology, demographic variables, treatment options and other medical disorders.Methods: A retrospective study of randomly selected 200 cases of abnormal uterine bleeding between 40–55 years of age during January 2018 to January 2019, in the Dept. of Obstetrics and Gynaecology, in a tertiary care hospital. Demographic details of each patient were recorded and analysed. Patients were evaluated with menstrual history, physical examination, laboratory tests and histological examinations. Patients were followed up from 3 to 8 months.Results: Most common age group presenting with AUB was 40–45 years (65.55%) and mostly (68.33%) belonged to low socioeconomic status. Most of the women were multiparous and menorrhagia was most common presentation. In 60% cases, cause was non-organic (dysfunctional uterine bleeding) and among organic causes fibroid (21%) uterus was most common. Maximum number of patients (75%) was treated surgically and 20% got medical treatment.Conclusions: Abnormal uterine bleeding (AUB) is a common gynaecological manifestation allied with considerable morbidity and significantly affects the patient's family, personal and social life. Perimenopausal women’s health and quality of life can be maintained and improved through preventive care, life style modification, early diagnosis of risk factor and appropriate treatment