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This study uses evidence from a longitudinal survey (PISA Plus, Germany) to examine the potential of bias in international large-scale assessments (ILSAs). In PISA Plus, participation was mandatory at the first measurement point, but voluntary at the second measurement point. The study provides evidence for relevant selection bias regarding student competencies and background variables when participation is voluntary. Sample dropout at the second measurement point was related to characteristics such as family background, achievement in mathematics, reading and science, and other student and school demographic variables at both the student and school levels, with lower performing students and those with less favorable background characteristics having higher dropout frequencies, from which higher dropout probabilities of such students can be inferred. We further contrast the possibilities for addressing non-response through weight adjustments in longitudinal surveys with those in cross-sectional surveys. Considering our results, we evaluate and confirm the validity and appropriateness of strict participation rate requirements in ILSAs. Likely magnitudes of bias in cross-sectional studies in varying scenarios are illustrated. Accordingly, if combined participation rates drop below 70%, a difference of at least one-fifth of a standard deviation in an achievement score between non-respondents and participants leads to relevant bias. When participation drops below 50%, even a very small difference (one-tenth of a standard deviation) will cause non-negligible bias. Finally, we conclude that the stringent participation rate requirements established in most ILSAs are fully valid, reasonable, and important since they ensure a relatively low risk of biased results. [ABSTRACT FROM AUTHOR]

With the significant increase in the scale of energy storage configuration in wind farms, improving the smoothing capability and utilization of energy storage has become a key focus. Therefore, a wind power fluctuation smoothing control strategy is proposed for battery energy storage systems (BESSs), considering the state of charge (SOC). First, a BESS smoothing wind power fluctuation system model based on model predictive control (MPC) is constructed. The objective function aims to minimize the deviation of grid-connected power from the target power and the deviation of the BESS's remaining capacity from the ideal value by comprehensively considering the smoothing effect and the SOC. Second, when the wind power's grid-connected power exceeds the allowable fluctuation value, the weight coefficients in the objective function are adjusted in real time using the first layer of fuzzy control rules combined with SOC partitioning. This approach smooths wind power fluctuations while preventing overcharging and overdischarging of the BESS. When the grid-connected power is within the allowable fluctuation range, the charging and discharging power of the BESS is further refined using a second layer of fuzzy control rules. This enhances the BESS's capability and utilization for smoothing future wind power fluctuations by preemptively charging and discharging. Finally, the proposed control strategy is simulated using MATLAB R2021b with actual operational data from a wind farm as a case study. Compared to the traditional MPC control method, the simulation results demonstrate that the proposed method effectively controls the SOC within a reasonable range, prevents the SOC from entering the dead zone, and enhances the BESS's ability to smooth wind power fluctuations. [ABSTRACT FROM AUTHOR]

Background and Objectives Patellofemoral pain syndrome (PFPS) is one of the most common forms of knee pain and is a term used for various pathologies or anatomical abnormalities that lead to anterior knee pain. The aim of the current research was to investigate the effect of different levels of weight adjustment on the motor function of women with PFPS. Subjects and Methods In this quasi-experimental study, 75 women with PFPS were selected by purposeful sampling and were randomly divided into 5 groups with equal numbers including weight adjustment (1- ¼ of body weight, 2- ½ of body weight, 3- ¾ of body weight), the group without weight adjustment, and the control group. The training protocol included selected resistance and functional exercises to strengthen hip and knee muscles, which were performed on stable and unstable surfaces. Analysis of covariance and Bonferroni's post hoc test were used for statistical analysis. ResultsThe exercise intervention reduced pain and improved movementfunction in the exercise groups compared to the control group (P<0.001). Corrective exercises involving weight adjustment with ½ and ¾ of body weight resulted in more effectiveness with regard to pain variables and movement function compared to the weight adjustment group with ¼ of body weight and the group without weight adjustment (P < 0.01). Conclusion Based on the findings of this research, weight adjustment during corrective exercise can improve the quality of exercise by reducing mechanical pressure on the knee and reducing pain, which results in improved movement function in patients with PFPS. [ABSTRACT FROM AUTHOR]

The flaws in the conventional teaching quality evaluation system that have always existed have not been fixed since the start of the new century. This study suggests a teaching assessment model for college education that utilizes the CIPP model since there is a pressing need for a model that can satisfy the demands of the modern educational environment. The investigation examines the relevance of both approaches to teaching quality evaluation in educational institutions by combining the advantages of CIPP and BP neural networks. The artificial neural network’s working mechanism is described, the weights within the BP learning algorithm are modified, and the first version of the teaching assessment model is inferred based on an analysis of the structure and function of neurons. The conceptual model of teaching quality assessment in higher education is intended to be completed by the model’s algorithm and framework. Investigate and analyze the teaching quality of S colleges and universities using the empirical analysis approach. The results of the empirical investigation show that the correlation coefficients of the 4 dimensions of teaching evaluation are above 0.6, with control between 0.3-0.8, and they have a strong correlation. The Cronbach’s alpha coefficients of the survey are all above 0.7, with very good reliability.

Underground cut-off walls are widely used in various geotechnical applications to hinder groundwater flow, contaminant transportation and possibly heat conduction. Cut-off walls were usually found defective due to construction errors during the installation phase, leading to significant leakages in subsequent operation phase. Existing physics-based leakage evaluation approaches, such as the finite element analysis and three-dimensional discretized algorithm, are computationally expensive and may not satisfy the need for instant on-site leakage risk assessment. In this regard, a more efficient mapping between construction errors and performance of cut-off walls is highly demanded. A natural option for such mapping is the artificial intelligence approach. Several novel physics-inspired neural network models are proposed based on the well-designed physical layers with varying complexity, to strike a balance between benefits of machine learning and physical approaches. The result shows that introducing physical layers with clearer physical meaning helps mitigating overfitting problems, improving prediction accuracy, result interpretability and model capacity, at the price of increasing the calculation efficiency during training. An optimized degree of physical meaning clarity can be achieved to strike a balance between fitting effect and training computation cost. [ABSTRACT FROM AUTHOR]

Genome assembly based on NGS reads usually includes the following two steps: (1) Convert the reads into k-mers of a certain length, construct a de Bruijn graph according to the overlaps between the k-mers, and finally traverse paths in the graph to generate contigs. (2) Determine the linear order of contigs according to the alignment between the paired-end reads and the contigs generated in the first step, and construct scaffolds based on the order and relative positions of contigs. The second step is usually described as scaffolding. ScaffMatch is a classic scaffolding algorithm, which uses the number of paired-end reads that can be aligned to two contigs and whose pairwise distance equals the insert size as the weight of their edge in the scaffold graph, and removes the low-weight edges from the graph by setting the lowest weight threshold. However, due to the sequencing bias, there are few paired-end reads in the low sequencing depth regions, and ScaffMatch often ignores the connections between those reads, which significantly affects the effective size of the final assemblies. To overcome the shortcomings of ScaffMatch, we proposed an optimized scaffolding algorithm, called ScaffMatch-ud, based on ScaffMatch, and strategies of weight adjustment and new gap estimation. The experimental results show that the scaffolding performance of the proposed algorithm is better than other similar methods, which is more suitable for unbalanced sequencing. [ABSTRACT FROM AUTHOR]

When validating a risk model in an independent cohort, some predictors may be missing for some subjects. Missingness can be unplanned or by design, as in case-cohort or nested case-control studies, in which some covariates are measured only in subsampled subjects. Weighting methods and imputation are used to handle missing data. We propose methods to increase the efficiency of weighting to assess calibration of a risk model (i.e. bias in model predictions), which is quantified by the ratio of the number of observed events, $\mathcal{O}$, to expected events, $\mathcal{E}$, computed from the model. We adjust known inverse probability weights by incorporating auxiliary information available for all cohort members. We use survey calibration that requires the weighted sum of the auxiliary statistics in the complete data subset to equal their sum in the full cohort. We show that a pseudo-risk estimate that approximates the actual risk value but uses only variables available for the entire cohort is an excellent auxiliary statistic to estimate $\mathcal{E}$. We derive analytic variance formulas for $\mathcal{O}/\mathcal{E}$ with adjusted weights. In simulations, weight adjustment with pseudo-risk was much more efficient than inverse probability weighting and yielded consistent estimates even when the pseudo-risk was a poor approximation. Multiple imputation was often efficient but yielded biased estimates when the imputation model was misspecified. Using these methods, we assessed calibration of an absolute risk model for second primary thyroid cancer in an independent cohort. [ABSTRACT FROM AUTHOR]

In this paper, a particle filter design scheme for a robust nonlinear control system of uncertain heat exchange process against noise and communication time delay is presented. The particle filter employs a cluster of particles and associated weights to approximate the posterior distribution of states and is capable of handling nonlinear and non-Gaussian issues. However, when the realistic given noise is much larger than that of the one modeled by the particle filter, the estimated posterior distribution is no longer reliable. Considering that, the exponential weights take the place of the original absolute particle weights in this paper, which act as an adjustment to the particle filter weights for a better state estimation. This adjustment for the weight of the particle filter takes into account the practical significance and can ensure the stability, tracking performance, and continuous operation of the control process incorporated with the particle filter. The simulation verifies the feasibility and usefulness of the method. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper aims to design an optimization control for tunnel boring machine (TBM) based on geological identification. For unknown geological condition, the authors need to identify them before further optimization. For fully considering multiple crucial performance of TBM, the authors establish an optimization problem for TBM so that it can be adapted to varying geology. That is, TBM can operate optimally under corresponding geology, which is called geology-adaptability. Design/methodology/approach – This paper adopted k-nearest neighbor (KNN) algorithm with modification to identify geological conditions. The modification includes adjustment of weights in voting procedure and similarity distance measurement, which at suitable for engineering and enhance accuracy of prediction. The authors also design several key performances of TBM during operation, and built a multi-objective function. Further, the multi-objective function has been transformed into a single objective function by weighted-combination. The reformulated optimization was solved by genetic algorithm in the end. Findings – This paper provides a support for decision-making in TBM control. Through proposed optimization control, the advance speed of TBM has been enhanced dramatically in each geological condition, compared with the results before optimizing. Meanwhile, other performances are acceptable and the method is verified by in situ data. Originality/value – This paper fulfills an optimization control of TBM considering several key performances during excavating. The optimization is conducted under different geological conditions so that TBM has geological-adaptability.

Naive Bayes (NB) is an extremely simple and remarkably effective approach to classification learning, but its conditional independence assumption rarely holds true in real-world applications. Attribute weighting is known as a flexible model via assigning each attribute a different weight discriminatively to improve NB. Attribute weighting approaches can fall into two broad categories: filters and wrappers. Wrappers receive a bigger boost in terms of classification accuracy compared with filters, but the time complexity of wrappers is much higher than filters. In order to improve the time complexity of a wrapper, a filter can be used to optimize the initial weight of all attributes as a preprocessing step. So a hybrid attribute weighting approach is proposed in this paper, and the improved model is called correlation-based weight adjusted naive Bayes (CWANB). In CWANB, the correlation-based attribute weighting filter is used to initialize the attribute weights, and then each weight is optimized by the attribute weight adjustment wrapper where the objective function is designed based on dynamic adjustment of attribute weights. Extensive experimental results show that CWANB outperforms NB and some other existing state-of-the-art attribute weighting approaches in terms of the classification accuracy. Meanwhile, compared with the existing wrapper, the CWANB approach reduces the time complexity dramatically.

Monocular depth estimation is a fundamental problem for various vision applications, and is therefore gaining increasing attention in the field of computer vision. Though a great improvement has been made thanks to the rapid progress of deep convolutional neural networks, depth estimation of the object at finer details remains an unsatisfactory issue, especially in complex scenes that has rich structure information. In this article, we proposed a deep end-to-end learning framework with the combination of multi-scale convolutions and joint attention mechanisms to tackle this challenge. Specifically, we firstly elaborately designed a lightweight up-convolution to generate multi-scale feature maps. Then we introduced an attention-based residual block to aggregate different feature maps in joint channel and spatial dimension, which could enhance the discriminant ability of feature fusion at finer details. Furthermore, we explored an effective adaptive weight adjustment strategy for the loss function to further improve the performance, which adjusts the weight of each loss term during training without additional hyper-parameters. The proposed framework was evaluated using challenging NYU Depth v2 and KITTI datasets. Experimental results demonstrated that the proposed approach is superior to most of the state-of-the-art methods.

Methods for indirect comparisons and network meta‐analysis use aggregate level data from multiple studies. A very common, and closely related, scenario is where a company has individual patient data (IPD) from its own trial, but only has published aggregate data from a competitor's trial, and an indirect comparison of the treatments evaluated in these two trials is required. Matching‐Adjusted Indirect Comparison (MAIC) has been developed for this situation, where we use the available IPD to adjust for between‐trial imbalances in the distributions of observed baseline covariates between the two trials. We extend the current MAIC methodology, where we compute the weights that satisfy the conventional method of moments and result in the largest possible effective sample size (ESS). We show that the approach proposed by Zubizarreta in a previous study can be used for this purpose. We derive a new analytical result that shows why this alternative approach provides a larger ESS than a conventional MAIC. We also derive a new formula for the maximum ESS that can be achieved, even when permitting negative weights, when adjusting for one covariate. This can be used as an easily computed new metric that quantifies the difficulty in adjusting for covariates. What is already known: MAIC is an established way to perform population adjustment in the situation where IPD is available from one trial but only aggregate level data is available from another trial, and an indirect comparison is required. However the effective sample size (ESS) can be small after making the adjustment.What is new: We show that an alternative method can result in a larger ESS. We provide new analytical results showing why this is the case. We derive a new descriptive statistic that is based on maximising the ESS that quantifies the difficulties in adjusting for particular covariates.Potential impact for RSM readers outside the authors' field: Reweighting methods for population adjustment are becoming more commonly used and their implications for research synthesis methodology is now considerable. This paper provides important new links between the theoretical literature, and the more applied research synthesis methodology literature, relating to this topic. [ABSTRACT FROM AUTHOR]

Objective: To present strategy and script for post-stratification weights estimation of data from complex surveys. Methods: Using the 2010 national census population as the standard population, we estimated the post-stratification weights of 2011 CHARLS baseline survey sample based on a total of 224 levels stratified by three auxiliary variables including age ( 56 levels), gender (2 levels) and urban/rural areas ( 2 levels). The command script was programmed with software Stata13. 1. Results: The command script for post-stratification weight estimation and adjustment was accomplished and provided in this study. The post-stratification weights and the adjusted final weights of CHARLS baseline samples were obtained. The post-stratification weighted population structure was consistent with the standard population composition in the example. Conclusion: Compared with direct standardization, post-stratification weights adjustment could obtain both the pooled estimation of and stratified estimation of parameters in complex sampling survey. [ABSTRACT FROM AUTHOR]

More and more enterprises hope to achieve cooperation and win–win. However, many companies often have problems such as insufficient partner credit, which seriously affects the quality of cooperation. In order to effectively evaluate the credit, this paper constructs a personal credit evaluation model. The model compares the weight adjustment method with BP neural network and other methods. Compared with the BP neural network weight adjustment algorithm, the improved algorithm has obvious advantages in accuracy and convergence speed. The simulation results show that the green supply chain cooperation credit evaluation model can better evaluate the environmental behavior of enterprises. The BP neural network can better solve the problem of slow convergence and premature convergence, and can search data more accurately. The algorithm has good robustness. The evaluation model has high optimization accuracy, which shows that BP neural network can better learn and evaluate the credit of green supply chain at different levels. [ABSTRACT FROM AUTHOR]

Remote sensing scene classification is still a challenging task. Extracting features effectively from restricted existing labeled data is key to scene classification. Convolutional neural networks (CNNs) are an effective method of constructing discriminating feature representation. However, CNNs usually utilize the feature map from the last layer and ignore additional layers with valuable feature information. In addition, the direct integration of multiple layers brings only a small improvement due to feature redundancy and destruction. To explore the potential information from additional layers and improve the effect of feature fusion, we propose multilayer feature fusion accesses with weight adjustment based on a CNN. We construct access to deliver additional features to one layer to achieve feature fusion and set weight factors to adjust the fusion degree to reduce feature redundancy and destruction. We perform experiments on two common data sets, which indicate improved accuracies and advantages of the extraction capability of our method. [ABSTRACT FROM AUTHOR]

In this paper, a particle filter design scheme for a robust nonlinear control system of uncertain heat exchange process against noise and communication time delay is presented. The particle filter employs a cluster of particles and associated weights to approximate the posterior distribution of states and is capable of handling nonlinear and non-Gaussian issues. However, when the realistic given noise is much larger than that of the one modeled by the particle filter, the estimated posterior distribution is no longer reliable. Considering that, the exponential weights take the place of the original absolute particle weights in this paper, which act as an adjustment to the particle filter weights for a better state estimation. This adjustment for the weight of the particle filter takes into account the practical significance and can ensure the stability, tracking performance, and continuous operation of the control process incorporated with the particle filter. The simulation verifies the feasibility and usefulness of the method.

• Deep reinforcement learning framework called Safety AARL for asset allocation. • Proposes a Safety AARL framework that integrates PDAS to mitigate investment risks. • Safety AARL framework combines and adapts investment strategies to market conditions. • New framework has excellent stability and guarantees steady returns. • Three experiments showed that the returns are stable on an upward slope. Dynamic asset allocation involves adjusting asset weights based on performance to reduce risks according to market conditions. This paper proposes a novel framework called safety asset allocation reinforcement learning (AARL), which combines multiple protective dynamic asset allocation strategies (PDAS) to minimize risks through reinforcement learning (RL). The framework integrates eight validated PDAS that maintain their unique characteristics. To implement the proposed framework, a novel RL environment and agent were developed to invest in PDAS using three high-probability actions. The agent receives state and reward information from the environment and is trained using the proximal policy optimization algorithm to output weights for investing. The actions are PDAS, which facilitates for diversified investments and the learning of various combinations that are suitable for market conditions. Three experiments were conducted to verify the effectiveness of the proposed Safety AARL framework. The first experiment demonstrates the validity of adjusting weights using the framework. The second experiment demonstrates that the framework outperforms existing techniques during global market decline. The third experiment demonstrates that risk is minimized, outperforming the latest methods for asset allocation. Overall, the results demonstrate that the Safety AARL framework outperforms existing techniques by minimizing risks and maintaining stable returns by combining the advantages of multiple PDAS. [ABSTRACT FROM AUTHOR]

For an uncontrollable system, adding leaders and adjusting edge weights are two methods to improve controllability. In this paper, the controllability of multi-agent systems under directed topologies is studied, especially on the leader selection problem and the weight adjustment problem. For a multi-agent system, necessary and sufficient algebraic conditions for controllability with fewest leaders are proposed. To improve controllability by adjusting edge weights, the system is supposed to be structurally controllable, which holds if and only if the communication topology contains a spanning tree. It is also proved that the number of the fewest edges needed to be assigned on new weights equals the rank deficiency of controllability matrix. In addition, a leader selection algorithm and a weight adjustment algorithm are presented. Simulation examples are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Weighting methods that adjust for observed covariates, such as inverse probability weighting, are widely used for causal inference and estimation with incomplete outcome data. Part of the appeal of such methods is that one set of weights can be used to estimate a range of treatment effects based on different outcomes, or a variety of population means for several variables. However, this appeal can be diminished in practice by the instability of the estimated weights and by the difficulty of adequately adjusting for observed covariates in some settings. To address these limitations, this article presents a new weighting method that finds the weights of minimum variance that adjust or balance the empirical distribution of the observed covariates up to levels prespecified by the researcher. This method allows the researcher to balance very precisely the means of the observed covariates and other features of their marginal and joint distributions, such as variances and correlations and also, for example, the quantiles of interactions of pairs and triples of observed covariates, thus, balancing entire two- and three-way marginals. Since the weighting method is based on a well-defined convex optimization problem, duality theory provides insight into the behavior of the variance of the optimal weights in relation to the level of covariate balance adjustment, answering the question, how much does tightening a balance constraint increases the variance of the weights? Also, the weighting method runs in polynomial time so relatively large datasets can be handled quickly. An implementation of the method is provided in the new package sbw for R. This article shows some theoretical properties of the resulting weights and illustrates their use by analyzing both a dataset from the 2010 Chilean earthquake and a simulated example. [ABSTRACT FROM PUBLISHER]

A large number of web data repositories are hidden behind restrictive web interfaces, making it an important challenge to enable data analytics over these hidden web databases. Most existing techniques assume a form-like web interface which consists solely of categorical attributes (or numeric ones that can be discretized). Nonetheless, many real-world web interfaces (of hidden databases) also feature checkbox interfaces—e.g., the specification of a set of desired features, such as A/C, navigation, etc., for a car-search website like Yahoo! Autos. We find that, for the purpose of data analytics, such checkbox-represented attributes differ fundamentally from the categorical/numerical ones that were traditionally studied. In this paper, we address the problem of data analytics over hidden databases with checkbox interfaces. Extensive experiments on both synthetic and real datasets demonstrate the accuracy and efficiency of our proposed algorithms. [ABSTRACT FROM AUTHOR]