Monmouth University

To solve the problem of scarcity of bolt defect samples in transmission lines, we propose a bolt defect image generation method based on dual discriminator architecture and pseudo-enhancement strategy (DP-GAN). First, we propose a residual discriminator network structure, coupled with a dual discriminator GAN architecture, to enhance the diversity of generation while preserving image feature information. Then, a generated image fidelity assessment method is designed to evaluate the fidelity of generated images by fitting the real dataset and screening out high-quality fake samples. Finally, a new pseudo-enhanced training strategy is proposed, which uses pseudo-samples to augment the few-shot dataset, which solves the problem of poor generation quality due to too few images of bolt defects. We construct a few-shot bolt defect dataset and conduct experiments on this dataset. Experimental results demonstrate that the bolt defect images generated by our proposed method have better quality and richer diversity than other image generation methods. Additionally, the proposed method significantly improves the performance of bolt defect classification. The classification accuracy shows a significant improvement over the CNN-only baseline.

Introduction: Hypopressive exercise (HE) can be viewed as a mind-body activity, characterized by the integration of breath control and stretching postures. Proponents of HE claim that this type of training can offer potential therapeutic or health benefits. To date, there is no existing comprehensive published overview on HE. This scoping review aims to map and summarize the current literature reporting data on HE and identify key knowledge gaps and future research directions. Methods: This review considered studies that report on the immediate, short-, or long-term practice of HE regardless of condition, sex, age, and/or level of practice or physical condition. Any context or setting was considered for inclusion. This review was performed in accordance with the methodological framework proposed by the Joanna Briggs Institute and by Arksey and O'Malley. MEDLINE, CINAHL, SPORTDiscus, Scopus, and Web of Science were searched from inception up to July 2023. Literature was mapped following the Patterns-Advances-Gaps-Evidence for Practice Recommendations framework to identify patterns and inform practice. Results: In total, 87 studies were identified that reported on the following themes: (1) therapeutic application of a short- or long-term HE programs (n = 56); (2) physiologic and physical responsiveness to a short- or long-term HE programs (n = 22); (3) psychologic and behavioral response to a short-term HE program (n = 14); and (4) acute or immediate physiological responses (n = 21). Literature gaps included poor methodological design, incomplete reporting of intervention, lack of male participants, and exploration of muscle groups distinct from the pelvic floor and abdominal muscles. Discussion: There is a need for high-quality randomized controlled trials, adherence to reporting guidelines on exercise, and the use of active control groups to verify clinical significance, the dose response, and health applications of HE.

Selective optimal disassembly sequencing (SODS) is a methodology for the disassembly of waste products. Mathematically, it is an optimization problem. However, in the existing research, the connection between the optimization algorithms and the established model is limited to some specific processes, and their generality is poor. Due to the unique characteristics of each disassembly product, most disassembly sequences require modification and even reconstruction of the mathematical model. In this article, reinforcement learning (RL) is used to produce a single-item selective disassembly sequence based on the AND/OR graph. First, the AND/OR graph is mapped to a value matrix and represents the precedence relationship between the component and the values of the component itself. Second, on the basis of the established mathematical model and graph, value-based RL is used to solve the selective disassembly sequencing problem. Finally, the experimental results of the genetic algorithm (GA), Sarsa, Deep Q-learning (DQN), and CPLEX are compared to verify the correctness of the proposed model and the effectiveness of the RL algorithm.

With the improvement of modern human living standards, the speed of product renewal is accelerating, and the classification and recycling of waste products and the reuse of resources have received great attention from scholars and industry. The disassembly of used products plays an important role in the implementation of the work. Considering the difference between the component values of a product and the disassembling complexity of each component, this article studies human–robot collaborative disassembly and investigates multiobjective disassembly sequence planning (DSP). Mathematical models are established with the optimization goals of maximizing profit and minimizing working time. The well-known optimizer CPLEX is used to verify the correctness of the mathematical model, and the gray wolf optimization algorithm (GWOA) is adopted to find the optimal solution. By comparing it with non-dominated sorting genetic algorithm III (NSGAIII) and multi-objective evolutionary algorithms based on decomposition with a collaborative resource allocation strategy (MOEAD-CRA), the adaptability of the algorithm to the proposed mathematical model is proved.

Safety skills are recognized as essential lab skills for students, necessitating the incorporation of active learning of chemical safety education into undergraduate curricula. Meaningful engagement of students in training and education on principle-based lab safety skills, chemical information sources, and general safety instructions is crucial for the development and enhancement of their safety awareness, knowledge, and ability to apply safe practices. However, limited research has comprehensively examined students’ lab safety behavior, mindset, and culture. In this study, we investigate the cognitive engagement and behavior exhibited by students as well as their questions and concerns regarding lab safety. The findings suggest a misalignment between students’ behavior and their safety mindset, highlighting the importance of further research and interventions to bridge this gap.

This paper investigates the growth and clustering of craft breweries in New Jersey. We compiled a historical dataset from 1995 to 2020 that allows us to measure the degree of geographic clustering among craft breweries in New Jersey. The number of craft breweries in New Jersey grew 491% from 2012 to 2020 (from 22 to 130 craft breweries). An impetus for this growth was that New Jersey enacted legislation in 2012 that made opening and operating a craft brewery in the state more economically viable. Our analysis finds that craft breweries in New Jersey are clustering in specific parts of the state and that this is likely due to co-location benefits such as building a culture of craft beer that drives innovation, knowledge sharing, customer sharing, and a thicker labor market. While distinct craft beer clusters have formed in New Jersey, we find there is still significant opportunity for growth. Our analysis confirms this using data on planned craft brewery openings to measure changes in the size and density of clusters and where, in New Jersey, new clusters are likely to form.

This chapter examines the interplay between humor and culture in a time of political polarization and division in the United States. Incongruity is a primary way that humor is enacted in general and all the more so for political humor in America. Rubin’s concept of “The Great American Joke” is offered as a lens through which to view comedy in America with a focus on the incongruity between the stated ideals of the country’s founding and the lived reality of the people. Americans frequently fail to see that the gap between the ideal and the real in America involves willfully ignoring reality or redefining it to suit a different view. Those seeking to narrow the gap as well as those who want to maintain it can use humor to explore and express this condition. Humor that exploits this gap between the ideal and the real is found in diverse texts of American humor. Examples: The fictional politics of Parks and Recreation depicts an idealized and exaggerated look at local government; the rhetorical use of humor by politicians, particularly Donald Trump, shows how rhetorical humor functions. Finally, an examination of “cancel culture” demonstrates how American comedy wrestles with free speech and accountability.

The effects of hypopressive exercise (HE) on dynamic balance have never been studied. We aimed to study the effects of a HE program on dynamic balance, posterior chain kinematics and expiratory peak flow on female competitive roller skaters over a 6-week training period. Twenty competitive female roller-skaters (13–22 years of age, SD 2.25) performed a 30-minute HE session once weekly before the regular roller-skating practice for 6 weeks. The HE program consisted of breathing and postural awareness exercises in addition to 5 basic HE poses performed three times each. Dynamic neuromuscular control was assessed with the Y-Balance Test (YBT), posterior back chain kinematics with the sit and reach test and peak expiratory flow rate with a digital spirometer. Paired t-test revealed significant differences between the measurement periods for all YBT leg directions and composite score (p ≤ 0.01). Significant differences were also revealed between baseline and after the intervention for the sit and reach test (p ≤ 0.01) and peak expiratory flow (p = 0.01). No differences in forced expiratory volume in the first second were found (p = 0.04). These preliminary findings suggest that a 6-week HE program could be a feasible neuromuscular option for training dynamic balance, posterior back chain kinematics and peak expiratory flow in female roller-skaters.