Modified hotelling’s T2 control charts using modified mahalanobis distance

This paper proposed new adjusted Hotelling’s T^2 control chart for individual observations. For this objective, bootstrap method for producing the individual observations were employed. To do so, both arithmetic mean vector and the covariance matrix in the traditional Hotelling’s T^2 chart were substituted by the trimmed mean vector and the covariance matrix of the robust scale estimators〖 Q〗_n, respectively which, in turn, its performance is carried out by simulated. In fact, the calculation of false alarms and the probability of detection outlier is used for determining the validity of this modified chart. The findings revealed a considerable significance in its performance

Statistical Process Control Using Modified Robust Hotelling's T² Control Charts

Hotelling’s T² chart is a popular tool for monitoring statistical process control. However, this chart is sensitive to outliers. To alleviate the problem, three approaches to the robust Hotelling’s T² chart namely trimming, Winsorizing and median based were proposed. These approaches used robust location and scale estimators to substitute for the usual mean and covariance matrix, respectively. For each approach, three robust scale estimators: MADn, Sn and Tn were introduced, and these estimators functioned accordingly to the approach. The first approach, denoted as T²t, applied the concept of trimming via Mahalanobis distance. The robust scale estimator was used to replace the covariance matrix in Mahalanobis distance. The trimmed mean and trimmed covariance matrix were the location and scale estimators for the T²t chart. The second approach,, T²w, employed each scale estimator as the Winsorized criterion. This approach applied Winsorized modified one step M-estimator and its corresponding Winsorized covariance as the location and the scale matrix for T²w chart, respectively. Meanwhile, in the third approach, T²н, the robust scale estimator took the role of the scale matrix with Hodges-Lehman as the location estimator. This approach worked with the original data without any trimming or Winsorizing. Altogether, nine robust control charts were proposed. The performance of each robust control chart was assessed based on false alarm rates and probability of detection. To investigate on the strengths and weaknesses of the proposed charts, various conditions were created by manipulating four variables, namely number of quality characteristics, proportion of outliers, degree of mean shifts, and nature of quality characteristics (independent and dependent). In general, the proposed charts performed well in terms of false alarm rates. With respect to probability of detection, all the proposed charts outperformed the traditional Hotelling's T² charts. The overall findings showed that, the proposed robust Hotelling's T² control charts are viable alternatives to the disputed traditional charts

Bivariate modified hotelling’s T2 charts using bootstrap data

The conventional Hotelling’s  charts are evidently inefficient as it has resulted in disorganized data with outliers, and therefore, this study proposed the application of a novel alternative robust Hotelling’s  charts approach. For the robust scale estimator , this approach encompasses the use of the Hodges-Lehmann vector and the covariance matrix in place of the arithmetic mean vector and the covariance matrix, respectively.  The proposed chart was examined performance wise. For the purpose, simulated bivariate bootstrap datasets were used in two conditions, namely independent variables and dependent variables. Then, assessment was made to the modified chart in terms of its robustness. For the purpose, the likelihood of outliers’ detection and false alarms were computed. From the outcomes from the computations made, the proposed charts demonstrated superiority over the conventional ones for all the cases tested

Vitamins D, C, and E in the prevention of type 2 diabetes mellitus: modulation of inflammation and oxidative stress

The incidence of type 2 diabetes mellitus (T2DM) is increasing worldwide, and certain population subgroups are especially vulnerable to the disease. To reduce T2DM risk and progression at the population level, preventative strategies are needed that can be implemented on a population-wide scale with minimal cost and effort. Chronic low-grade inflammation resulting from oxidative stress and imbalances in the innate immune system has been associated with obesity, metabolic syndrome, and insulin resistance – critical stages in the development and progression of T2DM. Therefore, inflammation may play a causal role in the pathogenesis of T2DM, and reducing it via modulation of oxidative stress and the innate immune response could lead to a status of improved insulin sensitivity and delayed disease onset. Dietary supplementation with anti-inflammatory and antioxidant nutritional factors, such as micronutrients, might present a novel strategy toward the prevention and control of T2DM at the population level. This review examines current knowledge linking oxidation, inflammatory signaling pathways, and vitamin supplementation or intake to the risk of T2DM. The concept that micronutrients, via attenuation of inflammation, could be employed as a novel preventive measure for T2DM is evaluated in the context of its relevance to public health

Evaluation of the Effect of Nano and Micro Hydroxyapatite Particles on the Impact Strength of Acrylic Resin: In Vitro Study

Poly (methylmethacrylate) is considered the basis material for a denture base. However, such substance has some drawbacks such as poor impact resistance, which is thought to be the primary cause of fracture of denture base resins. The purpose of the study was to determine how Nano and Micro hydroxyapatite particles affected the impact strength of acrylic resin. Thirty specimens were constructed of heat-cured acrylic resin and were divided into three groups: Ten specimens for the control, 10 for 1%nano hydroxyapatite, and 10 for micro hydroxyapatite. Acrylic samples were subjected to an impact strength test via a Charpy-type. Data were then analysed using SPSS v20. The ANOVA test was used for comparison among the groups. Highly statistically significant differences among all studied groups (P-value <0.0001). Both 5% Micro hydroxyapatite and 1% Nano hydroxyapatite had a higher mean value than the control. Incorporating Nano and Micro hydroxyapatite into PMMA improved the impact strength of acrylic resins

Applying the big bang-big crunch metaheuristic to large-sized operational problems

In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature

Baseline characteristics and treatment pattern of type 2 diabetes patients in Jordan: analysis from the DISCOVER patient population

Introduction: Jordan has limited published data on T2DM and its treatment patterns. This analysis of the DISCOVER study, focusing on Jordan, is aimed at describing the characteristics of patients and treatment patterns according to the real-world setting in T2DM patients initiating a second-line antidiabetic treatment Methods: The DISCOVER study is an ongoing, multi-country, multicenter, observational, prospective, and longitudinal cohort study. The baseline data of patients’ characteristics, clinical and laboratory variables, micro- and macro-complications, and treatment choices were captured on a standardized case report form. Results: Two hundred and seventy-one patients were enrolled from 13 different clinical sites in Jordan. Sixty percent of the patients were male. The participants overall mean age was 53.8 ± 11.3 years with a mean BMI 30.8 ± 5.0 kg/m 2. The mean duration of T2DM was almost 6 years and the mean documented HbA1c and fasting plasma glucose were e 8.4% ± 1.6 and 180.9 ± 63.7 mg/dL, respectively, at the initiation of second-line antidiabetic treatment. Almost 25% of the participants were reported to be either current smokers or ex-smokers. More than 40% of patients had comorbidities such as hypertension or dyslipidemia. Diabetes related microvascular and macrovascular complications were documented in 10.3% and 12.5% of patients, respectively. Metformin (MET) alone was used as a first-line therapy in almost one-half of the patients and in combination with sulfonylurea (SU) in approximately one-third of the patients. The most commonly used second-line therapy was the combination of MET and dipeptidyl peptidase-4 inhibitors (DPP-4i) with 29.9% followed by the triple therapy of MET, SU, and DPP-4i with 28%. Conclusion: A substantial number of patients were young with uncontrolled diabetes and at high risk for micro- and macrovascular complications. Therefore, a comprehensive management with early treatment intensification and risk factors modifications are required to achieve target goals

Evaluation of a genomic classifier in radical prostatectomy patients with lymph node metastasis.

OBJECTIVE: To evaluate the performance of the Decipher test in predicting lymph node invasion (LNI) on radical prostatectomy (RP) specimens. METHODS: We identified 1,987 consecutive patients with RP who received the Decipher test between February and August 2015 (contemporary cohort). In the contemporary cohort, only the Decipher score from RP specimens was available for analysis. In addition, we identified a consecutive cohort of patients treated with RP between 2006 and 2012 at the University of California, San Diego, with LNI upon pathologic examination (retrospective cohort). The retrospective cohort yielded seven, 22, and 18 tissue specimens from prostate biopsy, RP, and lymph nodes (LNs) for individual patients, respectively. Univariable and multivariable logistic regression analyses were used to evaluate the performance of Decipher in the contemporary cohort with LNI as the endpoint. In the retrospective cohort, concordance of risk groups was assessed using validated cut-points for low (0.60) Decipher scores. RESULTS: In the contemporary cohort, 51 (2.6%) patients had LNI. Decipher had an odds ratio of 1.73 (95% confidence interval, 1.46-2.05) and 1.42 (95% confidence interval, 1.19-1.7) per 10% increase in score on univariable and multivariable (adjusting for pathologic Gleason score, extraprostatic extension, and seminal vesicle invasion), respectively. No significant difference in the clinical and pathologic characteristics between the LN positive patients of contemporary and retrospective cohorts was observed (all P\u3e0.05). Accordingly, among LN-positive patients in the contemporary cohort and retrospective cohort, 80% and 77% had Decipher high risk scores (P=1). In the retrospective cohort, prostate biopsy cores with the highest Gleason grade and percentage of tumor involvement had 86% Decipher risk concordance with both RP and LN specimens. CONCLUSION: Decipher scores were highly concordant between pre- and post-surgical specimens. Further, Decipher scores from RP tissue were predictive of LNI at RP. If validated in a larger cohort of prostate biopsy specimens for prediction of adverse pathology at RP, Decipher may be useful for improved pre-operative staging