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As ethical leadership has advanced as a construct, the degree to which healthy relational systems explain its effect on employee outcomes has been understudied. With this manuscript we conceptualize and test a model based on a Relational Systems approach to ethical leadership and its relationship with conflict and turnover intentions.

Two studies were conducted to test our hypothesized first- and second-stage moderated mediation model. In Study 1, online surveys were completed by 168 working adults across two different time points. Study 2 extended Study 1 by surveying 115 working adults across three time points using the Mechanical Turk platform.

The indirect relationship between ethical leadership and turnover intentions via relationship conflict was conditional based on follower moral identity. The negative influence of ethical leadership on relationship conflict and, in turn, turnover intentions was stronger for followers who had higher moral identities. In addition, our findings suggest that leader holding behaviors strengthen the negative indirect effects of ethical leadership on turnover intentions.

This paper demonstrates the usefulness of a Relational Systems theoretical approach to understanding ethical leadership. Specifically, ethical leaders, through their desire and ability to help employees feel known and not alone at work, are better able to reduce relationship conflict and, in turn, reduce employees' desire to leave the organization.

The purpose of this paper is to introduce the concept of discriminatory job loss (DJL), which occurs when discrimination and job loss intersect. The paper aims to discuss the antecedents and consequences of DJL and calls for research on the topic.

Diversity and careers research from management, psychology, economics, and sociology literatures on discrimination, job loss, and unemployment are examined.

Discriminatory job loss involves discriminatory termination, discriminatory layoff, retaliatory termination, and constructive discharge and exacerbates negative outcomes of discrimination or job loss alone. Antecedents to DJL are the external and internal environments. DJL affects unemployment duration and reemployment quality and targets self‐esteem, self‐efficacy, and perceived control.

When large numbers of people experience DJL and long unemployment durations and lower re‐employment quality, this affects the individuals as well as society. In times of high employment, when jobs are scarce, individuals have fewer employment options and employers have more freedom to engage in discrimination. Having large groups of people know that their ability to maintain employment is negatively affected by their demographic group membership while others know that their demographic membership provides employment privileges can result in long‐term negative individual, organizational, and societal consequences.

This paper brings attention to, and calls for research on, DJL and its negative consequences.

The purpose of this paper is to focus on the performance of three typical nonlinear least-squares estimation algorithms in atmospheric density model calibration.

The error of Jacchia-Roberts atmospheric density model is expressed as an objective function about temperature parameters. The estimation of parameter corrections is a typical nonlinear least-squares problem. Three algorithms for nonlinear least-squares problems, Gauss–Newton (G-N), damped Gauss–Newton (damped G-N) and Levenberg–Marquardt (L-M) algorithms, are adopted to estimate temperature parameter corrections of Jacchia-Roberts for model calibration.

The results show that G-N algorithm is not convergent at some sampling points. The main reason is the nonlinear relationship between Jacchia-Roberts and its temperature parameters. Damped G-N and L-M algorithms are both convergent at all sampling points. G-N, damped G-N and L-M algorithms reduce the root mean square error of Jacchia-Roberts from 20.4% to 9.3%, 9.4% and 9.4%, respectively. The average iterations of G-N, damped G-N and L-M algorithms are 3.0, 2.8 and 2.9, respectively.

This study is expected to provide a guidance for the selection of nonlinear least-squares estimation methods in atmospheric density model calibration.

The study analyses the performance of three typical nonlinear least-squares estimation methods in the calibration of atmospheric density model. The non-convergent phenomenon of G-N algorithm is discovered and explained. Damped G-N and L-M algorithms are more suitable for the nonlinear least-squares problems in model calibration than G-N algorithm and the first two algorithms have slightly fewer iterations.

Neurodiversity can be considered a cognitive disability that marginalizes people who experience and interpret the world differently. An estimated 19% of all US college students have disclosed a disability (NCES, 2021). Typical forms of neurodiversity are attention-deficit hyperactivity disorder (ADHD), autism, and dyslexia. There is a growing belief that entrepreneurship is well suited for neurodivergent individuals because they can specifically design and control their environments resulting in a better fit and more positive outcomes (Austin & Pisano, 2017). There is also the belief that neurodivergent people’s unique perspectives and “superpowers” lead to new innovative ways of thinking and doing business. These superpowers can allow neurodivergent people to hyper focus and outperform others (Austin & Pisano, 2017).

However, real challenges counter these positive outcomes. For example, while those with ADHD are often drawn to being entrepreneurs because they can quickly initiate, improvise, and seek novelty – their ability to engage in reflection, thoroughness, and efficiency is strained. Thus, ADHD helps and hinders entrepreneurs (Hunt & Verhuel, 2017). The same holds true for other types of neurodiversity.

Entrepreneurship education becomes more nuanced as it matures and grows. An example is the “learn by doing” method of teaching entrepreneurship. Grounded in self-determination and planned behavior theories, “learn by doing” highlights the importance of autonomy, competence, and relatedness when engaging in entrepreneurship endeavors. Heutagogy (self-guided learning) and andragogy (applied learning) approaches have an effective impact on this type of entrepreneurship pedagogy. However, these open-ended approaches present barriers for neurodivergent learners who need more structure with projects broken down into small steps.

This chapter presents a case study view of how Universal Design for Learning (UDL) frameworks support “learn by doing” approaches to build a neurodivergent-friendly entrepreneurship mindset on campus. It includes a combination of approaches that support executive function (EF) mastery, assessment, and self-development, including multimodal ways of teaching (visual, audio, and kinesthetic), self-regulation, and social interactions. Here, the authors demonstrate how neurodivergent students learn to anticipate, manage, and benefit from their differences using the UDL engagement–regulation–persistence Framework. The lessons shared in this chapter can help entrepreneurship educators see ways various teaching methods can benefits all learners and how the addition of various programs can be more inclusive for neurodivergent students.

This paper aims to propose a united kinematic calibration method for a dual-machine system in automatic drilling and riveting. The method takes both absolute and relative pose accuracy into account, which will largely influence the machining accuracy of the dual-machine system and assembly quality.

A comprehensive kinematic model of the dual-machine system is established by the superposition of sub-models with pose constraints, which involves base frame parameters, kinematic parameters and tool frame parameters. Based on the kinematic model and the actual pose error data measured by a laser tracker, the parameters of coordinated machines are identified by the Levenberg–Marquardt method as a multi-objective nonlinear optimization problem. The identified parameters of the coordinated machines will be used in the control system.

A new calibration method for the dual-machine system is developed, including a comprehensive kinematic model and an efficient parameter identification method. The experiment results show that with the proposed method, the pose accuracy of the dual-machine system was remarkably improved, especially the relative position and orientation errors.

This method has been used in an aircraft assembly project. The calibrated dual-machine system shows a good performance on system coordination and machining accuracy.

This paper proposes a new method with high accuracy and efficiency for the dual-machine system calibration. The research can be extended to multi-machine and multi-robot fields to improve the system precision.

The purpose of this study is to make artificial neural network (ANN)-based prediction about thrust using the flight control parameters of aircrafts.

In today’s transportation, airplanes have an important place because of their safety, quality and speed. One of the most important parameters affecting the secure flying of aircrafts is the thrust value of aircraft engines. Determining the optimum thrust value should be investigated. If thrust value is less than optimum level, the flight safety runs a risk. Otherwise, fuel consumption goes high and some unwanted vibrations occur that cause uncomfortable flight. In this study, multi-layer perceptron ANNs, which are one of the intelligent optimization methods and frequently used in the literature, are preferred to predict the optimum thrust value during take-off, cruise and landing. The actual flight data, which is taken from the black box of an Airbus A319 aircraft, is used to train ANN models using back propagation algorithms. Velocity, altitude and ambient temperature values of the aircraft are selected as inputs and the thrust value is selected as output. During the training process of ANN, eight different training algorithms with different structures are used to figure out optimum ANN model with minimum error.

Different ANN models were trained using eight different training algorithms. The ANN model with minimum error has multi-layer perceptron structure, which is trained using Levenberg–Marquardt (LM) algorithm.

To obtain the ANN structure with minimum error training, process takes more than a day depending on the capacity of a computer for LM training algorithm. But after training process, the trained ANN model produces sufficient output in a few milliseconds.

Totally 15,670 input-output data sets are obtained from an Airbus A319 aircraft. 12,889 of them are used as training data and the rest of the data sets, selected randomly are used as test data. Test data sets are never used in training phase, and the obtained results show that the ANN model successfully predicts thrust value using unseen input data.

The ANN could be used as an alternative method to predict other flight control parameters of aircrafts.

To the best of authors’ knowledge, this study is the first example in literature to predict the thrust value of the aircraft using ANN.

The purpose of this study is to compare the prediction models for half-cell potential (HCP) of RCC slabs cathodically protected using pure magnesium anodes and subjected to chloride ingress.The models for HCP using 1,134 data set values based on experimentation are developed and compared using ANFIS, artificial neural network (ANN) and integrated ANN-GA algorithms.

In this study, RCC slabs, 1000 mm × 1000 mm × 100 mm were cast. Five slabs were cast with 3.5% NaCl by weight of cement, and five more were cast without NaCl. The distance of the point under consideration from the anode in the x- and y-axes, temperature, relative humidity and age of the slab in days were the input parameters, while the HCP values with reference to the Standard Calomel Electrode were the output. Experimental values consisting of 80 HCP values per slab per day were collected for 270 days and were averaged for both cases to generate the prediction model.

In this study, the premise and consequent parameters are trained, validated and tested using ANFIS, ANN and by using ANN as fitness function of GA. The MAPE, RMSE and MAE of the ANFIS model were 24.57, 1702.601 and 871.762, respectively. Amongst the ANN algorithms, Levenberg−Marquardt (LM) algorithm outperforms the other methods, with an overall R-value of 0.983. GA with ANN as the objective function proves to be the best means for the development of prediction model.

Based on the original experimental values, the performance of ANFIS, ANN and GA with ANN as objective function provides excellent results.