Evaluating the Impact of Construct Shift on Item Parameter Invariance, Test Equating and Proficiency Estimates

Common Core State Standards in English Language Arts and Mathematics at grades K to 12 were introduced in 2009 and at one time had been accepted by 45 of the states in the U.S. The new standards have created national curricula in these two subject areas. Along with this reform, new assessment systems have been developed too. Many of these new tests are showing signs of being more multidimensional than the tests they were replacing because of the use of new item formats, and the assessment of higher level thinking skills and various performance skills. In the short term at least, the new testing programs will still be using unidimensional IRT models because multidimensional IRT models are not well developed for wide-scale operational use. Test equating and proficiency estimates are likely to be influenced due to the violation of the unidimensionality assumption in the test data, which posed a threat to test fairness and the validity of score interpretations. Looking at the consequences of unidimensional models with multidimensional data is an important problem to investigate. In the first study, the potential effect of multidimensionality on item parameter invariance and IRT model fit at the item level was examined. The finding highlighted the fact that very modest changes in construct shift in the operational items of a test would not be problematic. As an extension of the first study, the second study introduced multidimensionality to a much greater magnitude and evaluated impact on IRT equating and proficiency estimates. It was found when construct shift occurred in the operational items, the impact was minimal in most aspects. Whereas, when construct shift occurred in the anchor and when these equating items were included in the total test, the influence of construct shift was quite substantial. The present research has provided evidence of consequences caused by construct shift as a function of the correlations between pairs of dimensions, the amount of shift, and where the shift occurs (equating or operational items). In general, it was found that construct shift had increasing impact as the amount of shift increased and the correlations between dimensions decreased

Spatial network structure and driving factors of human settlements in three Northeastern provinces of China

IntroductionUrban human settlements' spatial network structures have emerged as crucial determinants impacting their health and sustainability. Understanding the influencing factors is pivotal for enhancing these settlements. This study focuses on 34 prefecture-level cities in Northeastern China from 2005 to 2020. It employs a modified gravitational model to establish spatial relationships among urban human settlements. Social network analysis techniques, including modularity and the quadratic assignment procedure (QAP) regression model, are introduced to analyze the network's characteristics and driving factors.MethodsA modified gravitational model is applied to create the spatial association network of urban human settlements. Social network analysis tools, along with modularity and the QAP regression model, are utilized to investigate the network's attributes and influencing elements. The study evaluates the evolution of spatial correlation, network cohesion, hierarchy, and efficiency.ResultsThroughout the study period, spatial correlation among urban human settlements in Northeastern China progressively intensified. However, the network exhibited relatively low density (0.217675), implying limited interconnectivity among cities. The average network hierarchy was 0.178225, indicating the need for optimization, while the average network efficiency was 0.714025, reflecting fewer redundant relationships. The analysis reveals the emergence of a polycentric network pattern with core and sub-core cities like Shenyang, Dalian, Changchun, Daqing, and Harbin. The urban network configuration has largely stabilized. The spatial association network showcases the intertwining of "small groups" and community organizations. Geographic proximity and merit-based linkages govern feature flow. Measures such as breaking administrative barriers, reducing flow time and distance, boosting resident income, and increasing government investment are identified to foster balanced network development and structural optimization.DiscussionThe research underscores the increasing spatial correlation and evolving network pattern among urban human settlements in Northeastern China. Despite the observed strengthening correlation, challenges related to network cohesion and hierarchy persist. The formation of a polycentric network signifies positive progress in urban development. The study highlights the importance of proximity and merit-based connections for feature flow. The proposed measures offer pathways to enhance network development and optimize structure, promoting holistic urban settlement growth and sustainability

Long-term Navigation Optimal Operation of Cascaded Reservoirs

Water Resources Planning and Managemen

Accurate Disparity Estimation Based on Integrated Cost Initialization

Disparity estimation is one of the most important research topics in computer vision. Numerous local-based approaches have been proposed to solve this problem. Among them, most state-of-the-art methods mainly focus on color information when initializing the cost volume. However, color signals are less robust and more easily affected by image noise, illumination variation and radiometric differences. In this paper, we develop a high quality disparity estimation system based on an integrated matching cost initialization algorithm. During the cost initialization step, three individual cost terms are utilized to construct the cost volume: Gradient-based Census Transform (GCT), Absolute Color Differences (ACD), and Gabor Pattern Differences (GPD). The proposed method produces impressive performance and ranks excellently in the Middlebury benchmark. Furthermore, we present that the proposed scheme is also capable for real-world outdoor scenes which contain many challenges. Both quantitative and qualitative evaluations demonstrate that our approach is currently one of the most accurate local-based stereo matching algorithms