Using the Change Point Model (CPM) Framework to Identify Windows for Water Resource Management Action in the Lower Colorado River Basin of Texas, USA

In water-stressed river basins with growing urban populations, conflicts over water resources have emerged between urban and agricultural interests, as managerial interventions occur with little warning and tend to favor urban over agricultural water uses. This research documents changes in water use along an urban-to-agricultural gradient to examine whether it is possible to leverage temporal fluctuations in key quantitative data indicators to detect periods in which we could expect substantive managerial interventions in water resource management. We employ the change point model (CPM) framework to locate shifts in water use, climate-related indicators, lake and river characteristics, and agricultural trends across urban and agricultural counties in the lower Colorado River basin of Texas. Three distinctive groupings of change points appear. Increasing water use by urban counties and a shift in local climate conditions characterize the first period. Declines in agricultural counties’ water use and crop production define the second. Drops in lake levels, lower river discharge, and an extended drought mark the third. We interpret the results relative to documented managerial intervention events and show that managerial interventions occur during and after significant change points. We conclude that the CPM framework may be used to monitor the optimal timing of managerial interventions and their effects to avoid negative outcomes.

Investigation of the compact torus plasma motion in the KTX-CTI device based on circuit analyses

Compact torus (CT) injection is one of the most promising methods for the central fuelling of next-generation reactor-grade fusion devices due to its high density, high velocity, and self-contained magnetised structure. A newly compact torus injector (CTI) device in Keda Torus eXperiment (KTX), named KTX-CTI, was successfully developed and tested at the University of Science and Technology in China. In this study, first, we briefly introduce the basic principles and structure of KTX-CTI, and then, present an accurate circuit model that relies on nonlinear regression analysis (NRA) for studying the current waveform of the formation region. The current waveform, displacement, and velocity of CT plasma in the acceleration region are calculated using this NRA-based one-dimensional point model. The agreement between the model results and the experimental results is better than in the previous general model results estimated by the device dimensions in previous. The next-step upgrading reference scheme of the KTX-CTI device is preliminarily investigated using this NRA-based point model. This research can provide insights for the development of experiments and future upgrades of the device.

Item Pool Quality Control in Educational Testing: Change Point Model, Compound Risk, and Sequential Detection

In standardized educational testing, test items are reused in multiple test administrations. To ensure the validity of test scores, the psychometric properties of items should remain unchanged over time. In this article, we consider the sequential monitoring of test items, in particular, the detection of abrupt changes to their psychometric properties, where a change can be caused by, for example, leakage of the item or change of the corresponding curriculum. We propose a statistical framework for the detection of abrupt changes in individual items. This framework consists of (1) a multistream Bayesian change point model describing sequential changes in items, (2) a compound risk function quantifying the risk in sequential decisions, and (3) sequential decision rules that control the compound risk. Throughout the sequential decision process, the proposed decision rule balances the trade-off between two sources of errors, the false detection of prechange items, and the nondetection of postchange items. An item-specific monitoring statistic is proposed based on an item response theory model that eliminates the confounding from the examinee population which changes over time. Sequential decision rules and their theoretical properties are developed under two settings: the oracle setting where the Bayesian change point model is completely known and a more realistic setting where some parameters of the model are unknown. Simulation studies are conducted under settings that mimic real operational tests.

Fatigue mitigation of wind turbine system using multiple point model predictive control

<span lang="EN-US">A multi-point model predictive control (MPMPC) is widely used for many applications, including wind energy system (WES), notably enhanced power characteristics and oscillation regulation. In this work, MPMPC is adapted to condense the fatigue load of the WES and improve the lifetime of the turbine assembly. The lifetime examination is carried out by considering the three chief parameters: basic lifetime until failure, short-time damage equivalent loads (DELs), and lifetime DELs. The simulation study is performed for two cases: blade root bending moments and tower top bending. Further, fatigue load examination is demonstrated to analyze the effectiveness of the proposed controller. The observed results show that the lifetime analysis of the wind turbine system displayed more excellent characteristics, i.e., 49.50% greater than MPC. Also, the fatigue load mitigation showed greater magnitude due to the control action of the proposed controller, about 37.38% grander than MPC. Therefore, the attained outcomes exhibit outstanding performance compared with conventional controllers.</span>

Physical properties of mesoporous scoria and pumice volcanic rocks

In this study, the chemical composition, crystal structure, texture properties, and thermal properties of five powdered samples of scoria and pumice volcanic rock from different Harrats were investigated. It was observed that volcanic rocks show variations in chemical compositions, crystal structure, texture, and thermal properties. All samples comprised SiO2, Al2O3, CaO, and Fe2O3 as the major elements and contained both amorphous and crystalline phases. Textural parameters such as surface area and porosity were determined using various calculation models. The surface area of scoria samples was between 0.85 and 1.71 m2/g (Brunauer–Emmett–Teller and Single point model), 0.293-1.028 m2/g (Barrett–Joyner–Halenda model), and 1.02- 2.35 m2/g (Langmuir model). While for pumice, the calculated values of the surface area were 1.67 m2/g (Brunauer–Emmett–Teller and Single point model), 0.763 m2/g (Barrett–Joyner–Halenda model), and 2.24 m2/g (Langmuir model). The adsorption-desorption isotherm curves reveal that the scoria and pumice particles under study have mesoporous sizes between 7.89 and 9.81 nm, respectively. The differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) results of scoria and pumice samples illustrate a thermally stable material at high temperatures. TGA results show a weight gain by about 1.0% has been observed in the scoria samples in the region beyond 600 ℃ that may indicate a probable oxidation phenomenon with change color. While the DSC results of the red scoria and pumice did not show any recrystallization or oxidation, but only showed a small loss weight in the TGA result. The diversity in molecular composition, texture, and structure of scoria and pumice volcanic rock samples provide for promising natural stable mesopore materials that can be used in various mesopore technologies or applications such as solar cells.

The Choice Point Model of Acceptance and Commitment Therapy With Inpatient Substance Use and Co-occurring Populations: A Pilot Study

Objectives: Acceptance and Commitment Therapy (ACT) is an empirically supported treatment which aims to enhance self-acceptance and a commitment to core values. The present study examined the effectiveness of the Choice Point model of ACT in a residential substance use disorder (SUD) setting. Choice Point is a contemporary approach to ACT and targets transdiagnostic processes.Methods: This uncontrolled quasi-experimental design assessed 47 participants taking part in Choice Point for Substances (CHOPS) in order to investigate its influence on psychological inflexibility, values-based action, and self-compassion over time. The study additionally assessed for sleeper effects and associations between transdiagnostic processes and warning signs of relapse.Results: Findings demonstrated a decrease in psychological inflexibility and increases in values-based action and self-compassion over time. Gains were maintained at follow-up, and sleeper effects were observed for psychological inflexibility and mindfulness. Correlational analysis suggested that all transdiagnostic processes were related to warning signs of relapse at follow-up.Conclusion: These results provide preliminary evidence for the feasibility, acceptability, and effectiveness of CHOPS for SUD. Observed sleeper effects in psychological inflexibility and mindfulness indicate that CHOPS may provide longer-term benefits critical to a population where relapse is common. While encouraging, these findings should be interpreted with caution. Future research should utilize comparison groups when investigating CHOPS.

bmggum: An R Package for Bayesian Estimation of the Multidimensional Generalized Graded Unfolding Model With Covariates

Over the past couple of decades, there has been an increasing interest in adopting ideal point models to represent noncognitive constructs, as they have been demonstrated to better measure typical behaviors than traditional dominance models do. The generalized graded unfolding model ( GGUM) has consistently been the most popular ideal point model among researchers and practitioners. However, the GGUM2004 software and the later developed GGUM package in R can only handle unidimensional models despite the fact that many noncognitive constructs are multidimensional in nature. In addition, GGUM2004 and the GGUM package often yield unreasonable estimates of item parameters and standard errors. To address these issues, we developed the new open-source bmggum R package that is capable of estimating both unidimensional and multidimensional GGUM using a fully Bayesian approach, with supporting capabilities of stabilizing parameterization, incorporating person covariates, estimating constrained models, providing fit diagnostics, producing convergence metrics, and effectively handling missing data.

An Automatic Algorithm to Date the Reference Cycle of the Spanish Economy

This paper provides an accurate chronology of the Spanish reference business cycle adapting a multiple change-point model. In that approach, each combination of peaks and troughs dated in a set of economic indicators is assumed to be a realization of a mixture of bivariate Gaussian distributions, whose number of components is estimated from the data. The means of each of these components refer to the dates of the reference turning points. The transitions across the components of the mixture are governed by Markov chain that is restricted to force left-to-right transition dynamic. In the empirical application, seven recessions in the period from 1970.2 to 2020.2 are identified, which are in high concordance with the timing of the turning point dates established by the Spanish Business Cycle Dating Committee (SBCDC).