Bayesian Two-sided Complete Group Chain Sampling Plan for Binomial Distribution using Beta Prior through Quality Regions

Acceptance sampling is a technique for statistical quality assurance based on the inspection of a random sample to decide the lot disposition: accept or reject. Producer’s risk and consumer’s risk are inevitable in acceptance sampling. Most conventional plans only focus on minimizing the consumer’s risk. This study focused on minimizing both producer’s and consumer’s risks through the quality region. Experts from available historical knowledge concurred that Bayesian is the best approach to make the correct decision. In this study, a Bayesian two-sided complete group chain sampling plan (BTSCGChSP) was proposed for the average probability of acceptance. The binomial distribution was used to derive the probability of lot acceptance, and the beta distribution was used as the prior distribution. For selected design parameters in BTSCGChSP, the acceptable quality level and limiting quality level were considered to estimate quality regions that were directly associated with producer’s and consumer’s risks, respectively. Four quality regions: (i) quality decision region , (ii) probabilistic quality region (PQR), (iii) limiting quality region, and (iv) indifference quality region, were evaluated. To compare with the existing Bayesian group chain sampling plan (BGChSP), operating characteristic curves were used for the same parameter values and probability of lot acceptance. The findings explained that BTSCGChSP provided a smaller proportion of defectives than BGChSP for the same probability of acceptance. If quality regions were found for the same values of consumer and producer risks, then the BTSCGChSP region would contain fewer defectives than in the BGChSP region. Therefore, for industrial practitioners, the proposed plan is a better substitute for existing BGChSP and other conventional plans.

With a Hint of Sudachi: Food Plating Can Facilitate the Fondness of Food

Among the senses of food, our subjective sense of taste is significantly influenced by our visual perception. In appetite science, previous research has reported that when we estimate quality in daily life, we rely considerably on visual information. This study focused on the multimodal mental imagery evoked by the visual information of food served on a plate and examined the effect of the peripheral visual information of garnish on the sensory impression of the main dish. A sensory evaluation experiment was conducted to evaluate the impressions of food photographs, and multivariate analysis was used to structure sensory values. It was found that the appearance of the garnish placed on the plates close to the main dish contributes to visual appetite stimulants. It is evident that color, moisture, and taste (sourness and spiciness) play a major role in the acceptability of food. To stimulate one’s appetite, it is important to make the main dish appear warm. These results can be used to modulate the eating experience and stimulate appetite. Applying these results to meals can improve the dining experience by superimposing visual information with augmented reality technology or by presenting real appropriate garnishes.

Generalized Relational Tensors For Irregularly Sampled Time Series: Methods To Store, Re-generate, And Predict

The paper deals with a generalized relational tensor, a novel discrete structure to store information about a time series, and algorithms (1) to fill the structure, (2) to generate a time series from the structure, and (3) to predict a time series, for both regularly and irregularly sampled time series. The algorithms combine the concept of generalized z-vectors with ant colony optimization techniques. In order to estimate quality of the storing/re-generating procedure, a difference between characteristics of the initial and regenerated time series is used. The structure allows working with a multivariate time series, with an irregularly sampled time series, and with a number of series as well. For chaotic time series, a difference between characteristics of the initial time series (the highest Lyapunov exponent, the auto-correlation function) and those of the time series re-generated from a structure is used to assess the effectiveness of the algorithms in question. The approach has shown fairly good results for periodic and benchmark chaotic time series and satisfactory results for real-world chaotic data.

Analyzing, Modeling, and Utilizing Observation Series Correlation in Capital Markets

In this paper, we consider the task of the analysis, modeling, and application of dependencies between asset quotes at various capital markets. As an example, we study the dependency between financial instrument observation series in the currency and stock markets. Our work intends to give a theoretical basis to asset management strategies that estimate an asset’s price via regression, taking into account its correlated assets in various markets. Furthermore, we provide a way to increase the estimate quality using an evolutionary algorithm.

Influence of dental treatment on quality of life and self-esteem of hemodialysis-therapy patients

Background: Patients undergoing dialysis treatment have a worse quality of life than the general population, experience decreased quality of life and have higher rates of malnutrition, inflammation and hospitalization compared to the general population. Objective: The objective of this study was to investigate the influence of dental treatment on quality of life and self-esteem of hemodialysis-therapy patients. Materials and Methods: An interventional study was developed with 27 patients undergoing hemodialysis in Diamantina, Brazil. Initially, patients received clinical evaluation of a dentist and responded to questionnaires to estimate quality of life (Medical Outcomes Study 36-Item Short Form Health Questionnaire (SF-36) and Oral Health Impact Profile (OHIP) version 14) and self-esteem (Scale Self-Esteem of Rosenberg). After 45 days, the questionnaires were reapplied and the patients received dental treatment. After the treatment, 45 days were awaited to reapply the questionnaires. Descriptive analyzes were performed, Wilcoxon and Friedman tests were done. The level of significance was set at 5% (p <0.05). Results: There was statistical significance in SF-36 and OHIP-14 dimensions and improvement of self-esteem. Conclusion: Dental treatment interfered positively with the quality of life and self-esteem of patients undergoing hemodialysis.

Dacomitinib in first-line treatment of advanced EGFR-mutated non-small-cell lung cancer: a cost–effectiveness analysis

Aim: To assess the cost–effectiveness of first-line treatment with dacomitinib compared with gefitinib in patients newly diagnosed with advanced NSCLC EGFR-positive in the context of Spain. Materials & methods: A partitioned survival model was developed including costs, utilities and disutilities to estimate quality-adjusted life-year (QALY) and incremental cost–effectiveness ratio when treating with dacomitinib versus gefitinib. Results: Dacomitinib presented higher QALYs (0.51) compared with gefitinib (0.45). Dacomitinib costs were €33,061 in comparison with €26,692 for gefitinib arm. An incremental cost–effectiveness ratio of €111,048 was obtained for dacomitinib. Conclusion: Dacomitinib was more effective in terms of QALYs gained than gefitinib. However, to obtain a cost–effectiveness alternative, a discount greater than 25% in dacomitinib acquisition cost is required.

Use of Quality-Adjusted Tooth Years as an Outcome Measure of Periodontal Treatment: A Quasi Experimental Study

Aim: To develop a simple approach to estimate quality-adjusted tooth years of teeth (QATY) treated for periodontitis and determine gains in QATY after undergoing cause-related periodontal therapy (CRPT). Quality-adjusted life years (QALY) gained for these patients were also determined. Materials and Methods: Patients newly diagnosed with periodontitis ( n = 165, 58.8% females, mean age 43.3 years) were recruited from periodontal specialist clinics. They received CRPT within a period of one year. We used the EuroQoL 5 Dimension (EQ-5D) and the Malaysian Oral Health Impact Profile (OHIP-14) indexes to measure changes in quality-of-life after one year of periodontal treatment. We then used these two scores to calculate their respective utilities and subsequently calculated QALY and QATY. Results: Proportions of deep periodontal sites (≥ 4 mm) decreased from 32.7% to 23.2% and patients gained an average of 0.3 mm of clinical attachment level per tooth at post-treatment follow-up. Improvements in means of OHIP-14 scores from 20.30 to 12.7 were consistent with changes in EQ-5D utilities from 0.81 to 0.91. Treated teeth gained 20.4 years for longevity while patients gained 17 QATYs. This is in line with the increase of 3.8 QALYs. Both treatment outcomes and clinical parameters were statistically significant at P < .0001. Conclusions: The method of estimating QATY using utilities derived from OHIP-14 combined with tooth life expectancy demonstrated that it may be used to measure effective treatment outcomes and is found to be consistent with improvements in QALY scores as calculated using EQ-5D-3L.

Building DEVS Models from the Functional Design of Software Architecture Components to Estimate Quality

Software architectures can be used as a vehicle to improve the study of quality properties in the early stages of development. This paper proposes an automatic mapping between the design of architectural components and the specification of DEVS atomic models with aims to evaluate all-purpose quality metrics. Then, we use the functional description of architectural components (that address functional requirements) to estimate the architecture adjustment to non-functional requirements. The guidelines for structuring the simulation models are defined starting from the design of high-level components. To illustrate the proposal, web-based architecture is used as proof of concepts.