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A few recent publications on interrupted time series analysis only conduct preintervention modelling and use it to illustrate postintervention deviation without quantifying the amount lost during the intervention period. Thus, this study aims to illustrate how to estimate and quantify the actual amounts (in South African Rands—ZAR) that the negative impact of the intervention effects of the COVID-19 pandemic had on the South African total monthly wholesale and retail sales using the seasonal autoregressive integrated moving average (SARIMA) with exogenous components (SARIMAX) model. In addition, the SARIMAX model is supplemented with three approaches for interrupted time series fitting (also known as a pulse function covariate vector), which are: (i) trial and error, (ii) quotient of fitted values and actual values, and (iii) a constant value of 1 throughout the intervention period. Model selection and adequacy metrics indicate that fitting a pulse function with a trial-and-error approach produces estimates with the minimum errors on both datasets, so a more accurate loss in revenue in the economy can be approximated. Consequently, using the latter method, the pandemic had an immediate, severe negative impact on wholesale trade sales, lasting for 15 months (from March 2020 to May 2021) and resulted in a loss of ZAR 302,339 million in the economy. Moreover, the retail sales were also negatively affected, but for 8 months (from March 2020 to October 2020), with a 1-month lag or delay, suggesting the series felt the negative effects of the pandemic one month into the intervention period and resulted in a loss of ZAR 87,836 million in the economy. [ABSTRACT FROM AUTHOR]

In many situations, the quality of a process or product can be characterized by a functional relationship or profile. It is well-known that the independence assumptions of the error terms within or between profiles are not always valid and could be violated due to within or between profile autocorrelation. Since most of the process capability indices (PCIs) have been developed for simple linear profiles (SLPs) without considering autocorrelation, this paper provides some novel methods to analyze the capability of SLP under each of the two different autocorrelation effects separately, as well as the case where both autocorrelation effects are present. We assume that the first-order autoregressive AR(1) model explains the within- and between-profile autocorrelation in error terms. To evaluate the process capability, a new functional index called C p ‴ (P r o f i l e) is introduced for SLP with independent errors, and then it is modified to include the three possible cases of within, between, and simultaneous autocorrelation. The simulation results demonstrate that the proposed schemes outperform existing schemes regarding bias and mean square error (MSE) criteria. Moreover, bootstrap confidence intervals for the proposed index are obtained. Finally, an illustrative example in the chemical industry is used to demonstrate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Control charts, which are one of the major tools in the Statistical Process Control (SPC) domain, are used to monitor a process over time and improve the final quality of a product through variation reduction and defect prevention. As a novel development of control charts, referred to as profile monitoring, the study variable is not defined as a quality characteristic; it is a functional relationship between some explanatory and response variables which are monitored in such a way that the major aim is to check the stability of this model (profile) over time. Most of the previous works in the area of profile monitoring have focused on the development of different theories and assumptions, but very little attention has been paid to the practical application in real-life scenarios in this field of study. To address this knowledge gap, this paper proposes a monitoring framework based on the idea of profile monitoring as a data-driven method to monitor the internet usage of a telecom company. By definition of a polynomial model between the hours of each day and the internet usage within each hour, we propose a framework with three monitoring goals: (i) detection of unnatural patterns, (ii) identifying the impact of policies such as providing discounts and, (iii) investigation of general social behaviour variations in the internet usage. The results shows that shifts of different magnitudes can occur in each goal. With the aim of different charting statistics such as Hoteling T2 and MEWMA, the proposed framework can be properly implemented as a monitoring scheme under different shift magnitudes. The results indicate that the MEWMA scheme can perform well in small shifts and has faster detection ability as compared to the Hoteling T2 scheme. [ABSTRACT FROM AUTHOR]

Control charts play a beneficial role in the manufacturing process by reduction of non-compatible products and improving the final quality. In line with these aims, several adaptive methods in which samples can be taken with variable sampling rates and intervals have been proposed in the area of statistical process control (SPC). In some SPC applications, it is important to monitor a relationship between the response and independent variables—this is called profile monitoring. This article proposes adaptive generalized likelihood ratio (GLR) control charts based on variable sampling interval (VSI) and sequential sampling (SS) techniques for monitoring simple linear profiles. Because in some real-life problems, it may be possible that the user cannot control the values of explanatory variables; thus, in this paper, we focus on such a scenario. The performance of the proposed method is compared under three different situations, i.e., the fixed sampling rate (FSR), VSI, and SS, based on average time to signal (ATS) criteria for phase II analysis. Since the SS approach uses a novel sampling procedure based on the statistic magnitude, it has a superior performance over other competing charts. Several simulation studies indicate the superiority as the SS approach yields lower ATS values when there are single-step changes in the intercept, slope, standard deviation of the error term, and explanatory variables. In addition, some other related sensitivity analysis indicates that other aspects of the proposed methods, such as computational time, comparison with other control charts, and consideration of fixed explanatory variables. Furthermore, the results are supported by a real-life illustrative example from the adhesive manufacturing industry. [ABSTRACT FROM AUTHOR]

There is evidence to suggest that restoration of major/rare biominerals by supplementation can produce osteogenic and anti-resorptive effects in humans. LithoLexal® is a natural extract harvested from a marine alga, Lithothamnion sp., with a porous microstructure and multimolecular composition rich in calcium (32% w/w) and magnesium (2.2% w/w) together with ~72 trace bioelements. In vitro, LithoLexal® demonstrated cellular-level osteogenic efficacy through enhancing the maturation and activity of pre-osteoblasts. This extract also expressed the ability to suppress osteoclastogenesis by downregulating the pro-resorptive cytokines TNF-α and IL-1β and the master regulator of inflammation NF-κB. Parathyroid hormone inhibition of parathyroid hormone secretion is another bioactivity of LithoLexal® Bone reported with both short- and long-term administration at a longer duration and higher magnitude than what calcium carbonate could induce. Due to these bioactivities that affect pathogenetic factors of osteoporosis, LithoLexal® Bone is referred to as a disease-modifying adjunctive therapy (DMAT). In postmenopausal animal models, LithoLexal® monotherapy preserved bone mineral density, microarchitecture, and biomechanical properties, while calcium carbonate failed to produce significant outcomes. The pro-resorptive effect of a high-fat diet was also efficiently counteracted in vivo by supplementary LithoLexal®. A large clinical trial on postmenopausal women verified the mitigating effects of LithoLexal® Bone on bone resorption and turnover rate. The characteristic composition of LithoLexal® together with its lattice microstructure are suggested to underlie its in vivo bioactivities. In conclusion, adjunctive therapy with LithoLexal® Bone is an attractive option for clinical prevention and treatment of osteopenia/osteoporosis. [ABSTRACT FROM AUTHOR]