Scheduling in parallel machines with two objectives: analysis of factors that influence the Pareto frontier

This paper explores the characteristics of solutions when scheduling jobs in a shop with parallel machines. Three classical objective functions were considered: makespan, total completion time, and total tardiness. These three criteria were combined in pairs, resulting in three bi-objective formulations. These formulations were solved using the ε-constraint method to obtain a Pareto frontier for each pair. The objective of the research is to evaluate the Pareto set of efficient schedules to characterize the solution sets. The characterization of the solutions sets is based on two performance metrics: the span of the objective functions' values for the points in the frontier and their closeness to the ideal point. Results that consider four experimental factors indicate that when the makespan is one of the objective functions, the range of the processing times among jobs has a significant influence on the characteristics of the Pareto frontier. Simultaneously, the slack of due dates is the most relevant factor when total tardiness is considered.

Allied but Deviating NATO in the Multipolar World

“How long can NATO last in a post-US hegemonic, multipolar world?” has become an important question in contemporary world politics. By statistically analyzing NATO alliance cohesion since its inception, this analysis contributes to the literature by developing an original set of indicators that rely on the ideal point estimates from a recent UN General Assembly voting dataset. It empirically verifies that NATO members have higher cohesion than other UN members, although the United States has been the most significant deviating member since 1980. The findings support some earlier proposals such as the external threat hypothesis. They also contradict some others, notably the literature on the Donald Trump administration’s withdrawal doctrine, and the decline of US hegemony and its policy implications. The article concludes that the future challenge for NATO cohesion not only would be the possibility of US abdication or abandonment, but also other members’ balancing the United States as the hegemon.

Mediated Deliberation in Deep Conflicts: How Might Deliberative Media Content Contribute to Social Integration Across Deep Divides?

Conflicts perceived by the media, either within or across national borders, are a staple of modern societies. These conflicts become especially challenging for societies that are divided along religious, ethnic, cultural or political lines. In the light of such deep conflicts, the contribution of mediated deliberation to social integration moves center stage. In this paper we discuss normative standards for mediated public communication deemed conducive to social integration in divided societies by deliberative theorists. We identify inclusiveness, responsiveness, mutual respect, and the display of group-bridging identities as the essential criteria. These criteria can be applied as yardsticks to assess the production, the content as well as the reception of media material in both mass media and social media. They therefore serve as an ideal point of departure for empirical work on the media’s role in social integration.

A New Multiprocess IRT Model With Ideal Points for Likert-Type Items

For surveys of complex issues that entail multiple steps, multiple reference points, and nongradient attributes (e.g., social inequality), this study proposes a new multiprocess model that integrates ideal-point and dominance approaches into a treelike structure (IDtree). In the IDtree, an ideal-point approach describes an individual’s attitude and then a dominance approach describes their tendency for using extreme response categories. Evaluation of IDtree performance via two empirical data sets showed that the IDtree fit these data better than other models. Furthermore, simulation studies showed a satisfactory parameter recovery of the IDtree. Thus, the IDtree model sheds light on the response processes of a multistage structure.

Risk assessment method of coal and gas outburst based on improved comprehensive weighting and cloud theory

Risk assessment is an effective method of accident prevention and is vital to actual production. To reduce the risk of mining accidents and realize green and sustainable coal mining, a coal and gas outburst risk assessment method based on the improved comprehensive weight and cloud theory is proposed. The proposed method can effectively solve problems of fuzziness and randomness, index weight deviation, and correlation between indexes in risk assessment, as well as improve the accuracy and rationality of assessment. Nine influencing factors that correspond to coal seam occurrence and geological characteristics, coal seam physical characteristics, and gas occurrence characteristics are selected to establish the risk assessment index system of coal and gas outburst. Using the improved group G1 method and improved CRITIC method to obtain the subjective and objective weights, the ideal point method is used to obtain the comprehensive weight. Using the normal cloud model of cloud theory and the comprehensive weight to assess engineering examples 1–2, the No. 3 coal seam of a mine in Shanxi, and the 21 coal seam of a mine in Henan, the risk grade of coal and gas outburst is determined and then compared with the assessment results obtained from the engineering examples and the actual situations of the above mentioned coal seams. The results show that the coal and gas outburst risks of engineering examples 1–2, No. 3 coal seam, and 21 coal seam are of grades IV, IV, II, and IV, respectively. The No. 3 coal seam and 21 coal seam belong to lower and higher risk categories, respectively. The assessment results are consistent with the actual situation of the coal seams, thereby confirming the rationality and accuracy of the proposed method. This study expands the methods of coal and gas outburst risk assessment and facilitates the formulation of effective preventive measures.

MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template

With advancements in photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually being applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain the final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.

Dielectric Film Thickness Measurement Via a Convolutional Neural Network for Integrated Circuit Delayering End Point Detection

The integrated circuit (IC) delayering workflow is heavily reliant on operator experience to determine the processing end point, which is the ideal point on an IC where processing should be terminated, to optimize region of interest imaging. The current method of end point detection during IC delayering utilizes qualitative correlation between dielectric film color and dielectric thickness observed via optical microscopy to guide decision making. The goal of this work is to quantify this relationship using computer vision. In the field of computer vision, convolutional neural networks (CNNs) have been successfully applied to capture spatial relationships within images. Given this success, a CNN was trained for thickness estimates of dielectric films using optical images captured during processing for eventual automated end point detection. The trained model explained 39% of the variance in dielectric film thickness with a mean absolute error of approximately 47 nm.

Assessing Dimensionality of the Ideal Point Item Response Theory Model Using Posterior Predictive Model Checking

Although the use of ideal point item response theory (IRT) models for organizational research has increased over the last decade, the assessment of construct dimensionality of ideal point scales has been overlooked in previous research. In this study, we developed and evaluated dimensionality assessment methods for an ideal point IRT model under the Bayesian framework. We applied the posterior predictive model checking (PPMC) approach to the most widely used ideal point IRT model, the generalized graded unfolding model (GGUM). We conducted a Monte Carlo simulation to compare the performance of item pair discrepancy statistics and to evaluate the Type I error and power rates of the methods. The simulation results indicated that the Bayesian dimensionality detection method controlled Type I errors reasonably well across the conditions. In addition, the proposed method showed better performance than existing methods, yielding acceptable power when 20% of the items were generated from the secondary dimension. Organizational implications and limitations of the study are further discussed.

Elites and Brexit: Ideological Realignment in the House of Lords, 2000-2020

The UK House of Lords has increasingly attracted public attention due to several government defeats of Brexit bills. Despite this growing attention, there is little research on how coalitions' and individuals' voting behavior in the upper house of the UK Parliament has transformed since its major reform in 1999. This paper addresses this gap by shedding light on the transformation of Lords' voting behavior between 2000 and 2020. We argue that the British party system's growing bipolarity along the UK's future relationship with the EU had substantive repercussions for decision-making processes in the Lords. Analyzing about 2,400 roll call votes with Optimal Classification (OC) ideal point estimation models, we show that, in the post-Brexit period, partisan clusters among peers dissolved and new voting coalitions emerged, leading to an increasingly unidimensional voting space in the Lords. Our results suggest that the UK's second chamber has undergone a process of ideological realignment after the 2016 Brexit referendum.