The effects of heterogeneity of cooperative membership on cooperative and collective action sustainability has been previously discussed. However, despite the importance of membership heterogeneity in recent theoretical frameworks, empirical examinations have been limited. We determine the effect of changes to cooperative member heterogeneity on cooperative sustainability and discuss changes to heterogeneity overtime that can advance our understanding to cooperative sustainability long-term. This study uses USDA Agricultural Management Resource Survey data, coupled with USDA-Rural Development cooperative financial data at the state level, to quantify effects of cooperative member heterogeneity to sustainability of U.S. farmer cooperatives. We use random forest regression to interpret the significance of heterogeneity with cooperative sustainability at an aggregate level. The findings of this empirical study narrowly reconciles the theoretical understanding of the emergence of intra-cooperative issues while providing consistent empirical evidence and expectations for the sustainability of cooperatives in the near term.

In this research paper an algorithm for the derivation of methods based on theorems and axioms of fuzzy logic is presented, analyzed and applied. This new proposed procedure generates fuzzy methods and evaluates the value of fuzzy propositions through fuzzy implication. The new implications of the family should satisfy a number of axioms. Also, we denote the conditions in order to satisfy the maximum number of axioms. Moreover, authors state and prove theorems focusing on fuzzy implication, observing the rule: the fuzzy function of fuzzy implication is strong and that lead to fuzzy negation. In addition, the optimal number of repetitions is calculated according to the expected truth value. The formulas produced are verified through the use of temperature and humidity values over a certain period of time and in a certain geographical area in Greece. The basic steps of the methodology are the fuzzification of the data given ​​using four membership degree functions and the application of the membership degrees of temperature and humidity values on a new type of fuzzy implication. Finally, we observed that the isosceles trapezium gave the best results and the type of fuzzy implication can be effectively applied.

The main ideas of F-transform came from representing expert rules. It would be therefore re reasonable to expect that the more accurately the membership functions describe human reasoning, the more efficient will be the corresponding F-transform formulas. We know that an adequate description of our reasoning corresponds to complicated membership functions -- however, somewhat surprisingly, most efficient applications of F-transform use the simplest possible triangular membership functions. There exist some explanations for this phenomenon which are based on local behavior of the signal. In this paper, we supplement this local explanation by a global one: namely, we prove that triangular membership functions are the only one that provide the accurate description of appropriate global characteristics of the signal.

The COVID-19 pandemic globally had worrisome effects on health-promoting behaviors of children. Currently, the impact on primary school children's membership in sports clubs, especially considering gender differences, is poorly studied. The main objective of this study was to analyze the status of sports club memberships among children in Austria after the stringent COVID-19 restrictions in June 2021 and to identify gender differences. 755 Austrian school children (mean age 9.9 years, 49.9% girls) were interviewed about their sports club memberships. Our results show significantly lower self-reported sports club membership in June 2021 compared to pre-pandemic studies. 50% of boys reported being a member of a sports club, while the percentage for girls was only at 29.7% (p<0.001). Besides, a significant gender difference in the type of sports club membership was observed with most boys being members of ball sports clubs, while girls were more likely to be members of other sports clubs (p<0.001). Reduced rates of sports club membership after the COVID-19 pandemic underline the need of intervention programs. Health authorities, schools, sports clubs and families should form an alliance to promote an overall increase of physical activity and thus to support the long-term healthy development of children.

Multi-Object Tracking (MOT) is a key technology for Unmanned Aerial Vehicles (UAVs). Traditional tracking-by-detection methods firstly employ an object detector to retrieve targets in each image and then track them based on a matching algorithm. Recently, the popular multi-task learning methods has been dominating this area since they can detect targets and extract Re-Identification (Re-ID) features in a computationally efficient way. However, the detection task and the tracking task have conflicting requirements on image features, leading to the poor performance of the joint learning model compared to separate detection and tracking methods. The problem is more severe when it comes to UAV images due to the presence of irregular motion of large number of small targets. In this paper, we propose a balanced Joint Detection and Re-ID learning (JDR) network to address the MOT problem in UAV vision. To better handle the non-uniform motion of objects in UAV videos, the Set-Member Filter is applied which describes object state as a bounded set. An appearance matching cascade is then proposed based on target state set. Furthermore, a Motion-Mutation module is designed to address the challenges posed by the abrupt motion of UAV. Extensive experiments on the VisDrone-MOT2019 dataset demonstrate that our proposed model, termed as SMFMOT, outperforms state-of-the-arts by a large margin and achieves superior performance on the MOT tasks in UAV videos.

Background: There is a lack of literature specifically examining the workplace bullying of apprentices and trainees in traditional, male-dominated sectors such as the Australian building and construction industry. Using social identity theory (SIT), the aim of this study was to gather the attitudes, thoughts, and feelings of construction industry leaders to better understand how social identification (i.e., group membership) impacts bullying on targets and perpetrators, and the willingness to report bullying for targets and bystanders. Method: One-on-one, semi-structured interviews using a purposive sample of eight leaders from construction and blue-collar industries. Qualitative data were analysed using reflexive thematic analysis. Results: Four overarching themes were identified: difficulties for apprentices transitioning into industry, the need for continued improvement to industry culture, reluctance to report bullying, and rethinking apprenticeships to empower. Each theme provides insight into the psychosocial phenomenon of the bullying of trade apprentices and suggests that an apprentices’ level of social identification with work groups shapes how bullying is identified, interpreted, and prevented. Conclusion: Findings from this study will be important for tailoring evidence-based interventions, human resource policies and initiatives for education and awareness training. Themes also highlight systemic inadequacies impacting apprentices’ mental health and skill development, with implications for the future sustainability of apprenticeship training agreements.

In this paper, the set-membership affine projection (SM-AP) algorithm is utilized to censor non-informative data in big data applications. To this end, the probability distribution of the additive noise signal and the excess of mean-squared error (EMSE) in steady-state are employed in order to estimate the threshold parameter of the single threshold SM-AP (ST-SM-AP) algorithm aiming at attaining the desired update rate. Furthermore, by defining an acceptable range for the error signal, the double threshold SM-AP (DT-SM-AP) algorithm is proposed to detect very large errors due to the irrelevant data such as outliers. The DT-SM-AP algorithm can censor non-informative and irrelevant data in big data applications, and it can improve misalignment and convergence rate of the learning process with high computational efficiency. The simulation and numerical results corroborate the superiority of the proposed algorithms over traditional algorithms.

The airport ground handling services (AGHS) equipment supplier selection problem involves a safety guarantee on the part of the AGHS company that carries out the daily work. AGHS company can prevent aircraft damage and delays in airlines schedules, and ensure reliable and high-quality ground handling service. In our research, we developed an AGHS equipment supplier selection model based on the analytic hierarchy process and an AHP weighted fuzzy linear programming approach, and we solved the AGHS equipment supplier’s selection problem. The main objective of this article is to create an AHP and AHP-FLP decision model in order to help the AGHS company authorities select the best AGHS equipment supplier. The practical application in AGHS equipment supplier selection decisions can be interpreted as demonstrating that the proposed model provides knowledge and practical value for the AGHS industry.

In this paper, we propose the set-membership quaternion normalized least-mean-square (SM-QNLMS) algorithm. For this purpose, first, we review the quaternion least-mean-square (QLMS) algorithm, then go into the quaternion normalized least-mean-square (QNLMS) algorithm. By having the QNLMS algorithm, we propose the SM-QNLMS algorithm in order to reduce the update rate of the QNLMS algorithm and avoid updating the system parameters when there is not enough innovation in upcoming data. Moreover, the SM-QNLMS algorithm, thanks to the time-varying step-size, has higher convergence rate as compared to the QNLMS algorithm. Finally, the proposed algorithm is utilized in wind profile prediction and quaternionic adaptive beamforming. The simulation results demonstrate that the SM-QNLMS algorithm outperforms the QNLMS algorithm and it has higher convergence speed and lower update rate.

Gas Path Analysis and matching turbine engine models to experimental data are inverse problems of mathematical modelling which are characterized by parametric uncertainty. This results from the fact that the number of measured parameters is significantly lower than the number of components’ performance parameters needed to describe the real engine. In these conditions, even small measurement errors can result in a high variation of results, and obtained efficiency, loss factors etc. can appear out of the physical range. The current methods of engine model identification have developed considerably to provide stable, precise and physically adequate solutions. Presented in this work is an estimation method of engine components’ parameters based on multi-criteria identification which provides stable estimations of parameters and their confidence intervals with the known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularised identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Forming objective functions and scalar convolutions synthesis of these functions is used to estimate gas-path components’ parameters. A comparison of the proposed approach with traditional methods showed that its main advantage is high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions which do not correspond to a priori assumptions, and also helps to implement the Gas Path Analysis at the limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, Gas Path Analysis and also adapting dynamic models for the needs of the engine control system.