Joint monitoring of time and magnitude is an important issue in many industrial and non-industrial fields and several proposals exist in the literature. The aim of this article is to propose a new ...maximum exponentially weighted moving average (Max-EWMA) chart assuming generalized exponential distribution for time as well as magnitude. As the test-statistic of Max-EWMA chart consists of two EWMA statistics, it is easy to identify the out-of-control signal raised by the chart. The performance of the chart is evaluated using different run length characteristics including, average run length (ARL), standard deviation of run length (SDRL), and quantiles of the run length distribution. Besides extensive simulation studies, a real data set is also used to show the practicality of the proposed chart. The results show that the proposed chart is efficient in detecting different size of small to moderate shifts.
Some plant growth-promoting bacteria encode for 1-aminocyclopropane-1-carboxylate (ACC) deaminase, which facilitates plant growth and development by lowering the level of stress ethylene under ...waterlogged conditions. The substrate ACC is the immediate precursor for ethylene synthesis in plants; while bacterial ACC deaminase hydrolyzes this compound into α-ketobutyrate and ammonia to mitigate the adverse effects of the stress caused by ethylene exposure. Here, the structure and function of ACC deaminase, ethylene biosynthesis and waterlogging response, waterlogging and its consequences, role of bacterial ACC deaminase under waterlogged conditions, and effect of this enzyme on terrestrial and riparian plants are discussed.
Analyses of large-scale population structure of pathogens enable the identification of migration patterns, diversity reservoirs or longevity of populations, the understanding of current evolutionary ...trajectories and the anticipation of future ones. This is particularly important for long-distance migrating fungal pathogens such as Puccinia striiformis f.sp. tritici (PST), capable of rapid spread to new regions and crop varieties. Although a range of recent PST invasions at continental scales are well documented, the worldwide population structure and the center of origin of the pathogen were still unknown. In this study, we used multilocus microsatellite genotyping to infer worldwide population structure of PST and the origin of new invasions based on 409 isolates representative of distribution of the fungus on six continents. Bayesian and multivariate clustering methods partitioned the set of multilocus genotypes into six distinct genetic groups associated with their geographical origin. Analyses of linkage disequilibrium and genotypic diversity indicated a strong regional heterogeneity in levels of recombination, with clear signatures of recombination in the Himalayan (Nepal and Pakistan) and near-Himalayan regions (China) and a predominant clonal population structure in other regions. The higher genotypic diversity, recombinant population structure and high sexual reproduction ability in the Himalayan and neighboring regions suggests this area as the putative center of origin of PST. We used clustering methods and approximate Bayesian computation (ABC) to compare different competing scenarios describing ancestral relationship among ancestral populations and more recently founded populations. Our analyses confirmed the Middle East-East Africa as the most likely source of newly spreading, high-temperature-adapted strains; Europe as the source of South American, North American and Australian populations; and Mediterranean-Central Asian populations as the origin of South African populations. Although most geographic populations are not markedly affected by recent dispersal events, this study emphasizes the influence of human activities on recent long-distance spread of the pathogen.
Electricity demand and price forecasting are key components for the market participants and system operators as precise forecasts are necessary to manage power systems effectively. However, ...forecasting electricity demand and prices are challenging due to their specific features, such as high frequency, volatility, long trend, nonconstant mean and variance, mean reversion, multiple seasonalities, calendar effects, and spikes/jumps. Thus, the main aim of this study is to propose models that can efficiently forecast electricity demand and prices. To this end, the time series (demand/price) is divided into two components. The first component is considered a deterministic component that includes a trend, yearly, seasonal, and weekly periodicities, calendar effects, and lagged exogenous information and is modeled by parametric and nonparametric approaches. The second component is known as a stochastic (residual) component that is estimated using univariate autoregressive (AR) and multivariate vector autoregressive (VAR) models. The estimation of these models is carried out by four different estimation methods, including ordinary least squares (O), Lasso (L), Ridge (R), and Elastic-net (E). The proposed modeling scheme is applied to Nordic electricity demand and price time series, and one-day-ahead out-of-sample forecasts are obtained for a whole year. Besides descriptive statistics, a statistical significance test is also used to evaluate the models’ forecasting accuracy. The results suggest that the proposed methodology effectively forecasts the price and demand for electricity. In addition, the choice of the estimation procedure used for both deterministic and stochastic components has a significant effect on the forecasting results. Furthermore, multivariate vector autoregressive gives superior performance compared to univariate autoregressive models.
Generalized linear model based gamma control chart Ali, Sajid; Asghar, Maria; Shah, Ismail
Quality and reliability engineering international,
February 2024, 2024-02-00, 20240201, Letnik:
40, Številka:
1
Journal Article
Recenzirano
Traditional monitoring techniques are frequently used for monitoring a response variable, while ignoring the other important variables. A simple linear regression model to introduce covariates‐based ...charts has received a lot of attention in the recent publications. When the response variable belongs to the exponential family, the generalized linear model (GLM) is a flexible approach to model a phenomenon. This study uses gamma distribution to introduce GLM‐based Shewhart‐type control charts. The monitoring statistic is developed using the Pearson residuals (PRs) obtained from the gamma regression model. The suggested charts' performance is evaluated using the run‐length properties and extensive Monte Carlo simulations. A comparison of Pearson‐residual to the deviance‐residual charts is also discussed in this article. Finally, to emphasize the significance of the study, the proposed control charts are implemented on a real‐life data set.
Abiotic stresses lead to excessive crop yield losses and are a major threat to agriculture. It is essential to equip crops with multi-stress tolerance to mitigate the adverse effects of abiotic ...stressors and meet the demands of the increasing global population. The association between plants and symbiotic microorganisms is involved in key functions at the ecosystem and plant levels, and the application of microbial plant biostimulants (MPBs) is a sustainable strategy to augment plant growth and productivity, even under abiotic stress conditions. Several different microorganisms can be used as MPBs to enhance plant growth and produce progressive and reproducible effects on crops. In the present review, we assessed the current knowledge on the use of MPBs, discuss the diversity and characteristics of MPBs, and provide a meticulous assessment of the possible applications of MPBs in abiotic stress relief in crops.
Membrane desalination (MD) is preferred over other desalination techniques since it requires a lower temperature gradient. Its performance can be further enhanced by preheating the intake of saline ...water. In this context, a novel solar-assisted air gap membrane desalination (AGMD) system was hypothesized. The motivation was derived from the fact that the use of solar energy to provide power and a pre-heating source for the intake of saline water can offer a sustainable alternative that can further enhance the acceptance of MD systems. Since solar panels suffer from a loss of efficiency as they heat up during operation, a solar-assisted air gap membrane desalination (AGMD) system can help to improve the overall system performance by (1) providing the necessary pumping power to operate the system and (2) improving solar panel performance by exchanging heat using water that is (3) used to pre-heat the saline water necessary for increased performance of the AGMD system. To verify the hypothesis, a solar-assisted AGMD system for freshwater production was theoretically designed, fabricated locally, and then tested experimentally. The effect of the process operating parameters and the ambient conditions on the overall performance of the proposed solar-assisted AGMD desalination unit is presented in detail, both theoretically and experimentally. The results indicated a direct correlation between the permeate flux, saline hot feed temperature, and hot feed flow rate. In addition, an inverse relationship between the cold feed temperature, cold feed flow rate, and the air gap thickness of the module was also observed and reported, thus, validating the hypothesis that a solar-assisted air gap membrane desalination (AGMD) system can help to boost performance.
Current development of Pakistan's economy, transportation and industry with the improvement of urbanization, environmental pollution problems have gradually become prominent, but this is contrary to ...people's vision of pursuing a high-quality life. Now the problem of haze, photochemical problems in the air, and global warming is already a key issue of global concern. This is focused on the ambient air quality of Lahore city of Pakistan. The study reveals that the particulate matter in the Lahore season (PM 2.5 , PM 10 ) exceeds Pakistan's National Environmental Quality Standards (NEQS). Correlation study suggests the positive correlation between the particulate matter and other mass concentration particles like Ozone (O 3 ), Nitrogen Oxide (NO), Sulphur Dioxide (SO 2 ). Higher values of CO/NO suggest that mobile sources are one of the major factors of this increase in NO. Further estimation of backward trajectory is done by the Hybrid-Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model which provides the path of those particles in the last year period and the source of origin is from Afghanistan. This study provides in depth analysis of all factors of air pollutants by correlation between those factors. Prediction of future concentration of PM 2.5 is predicted using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model which gives the increasing value of PM 2.5 in next year and provides the lowest and highest predicts (more than <inline-formula> <tex-math notation="LaTeX">100~\mu \text{g}/\text{m}^{3} </tex-math></inline-formula>).
Abstract
Several control charts have been developed in the literature to monitor zero inflation using classic and simple linear regression models with covariates. Simple linear regression models may ...not be appropriate, especially when the response variable is skewed or count. When the response distribution follows the exponential family; however, the generalized linear model (GLM) gives a more flexible approach. The goal of this study is to use the Poisson hurdle model to provide GLM‐based Shewhart‐type control charts. To calculate monitoring statistics, the Pearson residuals (PRs) are obtained using the Poisson hurdle model. A detailed Monte Carlo simulation analysis is used to analyze the attributes of the suggested charts. Besides this, a real‐life data set is used to show the practical application of the charts.
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