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The purpose of this paper is to address a fractional order fuzzy PID (FOFPID) control approach for solving the problem of enhancing high precision tracking performance and robustness against to different reference trajectories of a 6-DOF Stewart Platform (SP) in joint space.

For the optimal design of the proposed control approach, tuning of the controller parameters including membership functions and input-output scaling factors along with the fractional order rate of error and fractional order integral of control signal is tuned with off-line by using particle swarm optimization (PSO) algorithm. For achieving this off-line optimization in the simulation environment, very accurate dynamic model of SP which has more complicated dynamical characteristics is required. Therefore, the coupling dynamic model of multi-rigid-body system is developed by Lagrange-Euler approach. For completeness, the mathematical model of the actuators is established and integrated with the dynamic model of SP mechanical system to state electromechanical coupling dynamic model. To study the validness of the proposed FOFPID controller, using this accurate dynamic model of the SP, other published control approaches such as the PID control, FOPID control and fuzzy PID control are also optimized with PSO in simulation environment. To compare trajectory tracking performance and effectiveness of the tuned controllers, the real time validation trajectory tracking experiments are conducted using the experimental setup of the SP by applying the optimum parameters of the controllers. The credibility of the results obtained with the controllers tuned in simulation environment is examined using statistical analysis.

The experimental results clearly demonstrate that the proposed optimal FOFPID controller can improve the control performance and reduce reference trajectory tracking errors of the SP. Also, the proposed PSO optimized FOFPID control strategy outperforms other control schemes in terms of the different difficulty levels of the given trajectories.

To the best of the authors’ knowledge, such a motion controller incorporating the fractional order approach to the fuzzy is first time applied in trajectory tracking control of SP.

Most of the redundant dual-arm robots are singular free, dexterous and collision free compared to other robotic arms. This paper aims to analyse the workspace of redundant arms to study the manipulability. Furthermore, multi-layer perceptron (MLP) algorithm is used to determine the various joint parameters of both the upper body redundant arms. Trajectory planning of robotic arms is carried out with the help of inverse solutions obtained from the MLP algorithm.

In this paper, the kinematic equations are derived from screw theory approach and inverse kinematic solutions are determined using MLP algorithm. Levenberg–Marquardt (LM) and Bayesian regulation (BR) techniques are used as the backpropagation algorithms. The results from two backpropagation techniques are compared for determining the prediction accuracy. The inverse solutions obtained from the MLP algorithm are then used to optimize the cubic spline trajectories planned for avoiding collision between arms with the help of convex optimization technique. The dexterity of the redundant arms is analysed with the help of Cartesian workspace of arms.

Dexterity of redundant arms is analysed by studying the voids and singular spaces present inside the workspace of arms. MLP algorithms determine unique solutions with less computational effort using BR backpropagation. The inverse solutions obtained from MLP algorithm effectively optimize the cubic spline trajectory for the redundant dual arms using convex optimization technique.

Most of the MLP algorithms used for determining the inverse solutions are used with LM backpropagation technique. In this paper, BR technique is used as the backpropagation technique. BR technique converges fast with less computational time than LM method. The inverse solutions of arm joints for traversing optimized cubic spline trajectory using convex optimization technique are computed from the MLP algorithm.

The gender-gap reversal in education could have far-reaching consequences for marriage and family lives in the United States. This study seeks to address the following question: As women increasingly marry men with less education than they have themselves, is the traditional male breadwinner model in marriage challenged?

This study takes a life course approach to examine how educational assortative mating shapes trajectories of change in female breadwinning status over the course of marriage. It uses group-based trajectory models to analyze data from the National Longitudinal Survey of Youth 1979.

The results reveal substantial movement by wives in and out of the primary breadwinner role across marital years and great heterogeneity in female breadwinning trajectories across couples. In addition, educational assortative mating plays a role in shaping female breadwinning trajectories: Compared with wives married to men whose educational levels equal or exceed their own, wives married to men with less education than themselves are more likely to have a continuously high probability of being primary earners and are also more likely to gradually or rapidly transition into primary earners if initially they are not.

This study examines couples’ breadwinning arrangements over an extended period of time and identifies qualitatively distinct patterns of change in female breadwinning that are not readily identifiable using ad hoc, ex ante classification rules. The findings suggest that future research on the economics of marriage and couple relations in families would benefit from a life course approach to conceptualizing couples’ dynamic divisions of breadwinning.

The purpose of this paper is to explain how trajectory management in hospitals is challenged by the introduction of accelerated discharge schemes. The patient trajectory is formed by short stays within health-care organizations, which requires a substantial effort for professionals to be successful in clarifying each patient's medical situation. The patients, at the same time, often have complicated illness stories, and professionals only see a limited part of the patient's trajectory.

This paper is based on extensive ethnographic studies in a newly established cardiac day unit introducing same-day discharge schemes for patients with ischemic and arrhythmic heart disease.

The findings demonstrate that the patient trajectory becomes a “temporal patient trajectory” and encounters a short-term reality, where tensions arise between admission time and the trajectory as a whole. In managing temporal patient trajectories, formal organizing and patient experiences intersect in events that emerge from conversations and span past, present and future in relation to patient treatment. Professionals engage in articulation work to maintain coherence by allowing patients to hold different events together over time.

The paper provides new insights into the challenges of managing trajectories in same-day discharge schemes where the pressure to move quickly and ensure patient discharge is intense. The paper offers a novel theoretical perspective on trajectory management as an ongoing temporal process. The analysis displays temporal tensions between patient experiences and the accelerated discharge scheme and how professionals manage to overcome these tensions by bridging the patient's long illness story and the short trajectory within the cardiac day unit.

Collaboration and acquisition have traditionally been observed as two alternative strategies when accessing external technologies. However, real option scholars have recently argued that firms can also engage in transitional technology sourcing trajectories where collaboration and acquisition are used as complementary strategies. While these real option scholars have identified factors that influence when partners are likely to shift from collaboration to acquisition, they remain silent on how such a transition can be effectively managed. Based on a multiple case study of four transitional technology sourcing trajectories between one new entrepreneurial and one established firm, this study therefore explores how the pre-acquisition collaboration stage and the post-acquisition integration are related to each other. Findings suggest that entrepreneurial companies may use the pre-acquisition collaboration stage as a period to evaluate the goodwill of the established partner. In addition, we point to the presence of pre-acquisition integration efforts and the extent of strategic convergence during the pre-acquisition collaboration stage as factors that substantially influence the success of the post-acquisition integration process in transitional governance trajectories.

In order to deepen understanding of team processes in dynamic organizational contexts, we suggest that analyses employing techniques to identify and analyze team member interaction patterns and trajectories are necessary. After presenting a brief review of interaction data coding and reliability requirements, we first review examples of two approaches used in the identification and analysis of interaction patterns in teams: lag sequential analysis and T-pattern analysis. We then describe and discuss three statistical techniques used to analyze team interaction trajectories: random coefficient modeling, latent growth modeling, and discontinuous growth analysis. We close by suggesting several ways in which these techniques could be applied to data analysis in order to expand our knowledge of team interaction, processes, and outcomes in complex and dynamic settings.

The current air traffic management (ATM) operational approach is changing; “time” is now integrated as an additional fourth dimension on trajectories. This notion will impose on aircraft the compliance of accurate arrival times over designated checkpoints (CPs), called time windows (TWs). This paper aims to clarify the basic requirements and foundations for the practical implementation of this functional framework.

This paper reviews the operational deployment of 4D trajectories, by defining its relationship with other concepts and systems of the future ATM and communications, navigation and surveillance (CNS) context. This allows to establish the main tools that should be considered to ease the application of the 4D-trajectories approach. This paper appraises how 4D trajectories must be managed and planned (negotiation, synchronization, modification and verification processes). Then, based on the evolution of a simulated 4D trajectory, the necessary corrective measures by evaluating the degradation tolerances and conditions are described and introduced.

The proposed TWs model can control the time tolerance within less than 100 s along the passing CPs of a generic trajectory, which is in line with the expected future ATM time-performance requirements.

The main contribution of this work is the provision of a holistic vision of the systems and concepts that will be necessary to implement the new 4D-trajectory concept efficiently, thus enhancing performance. It also proposes tolerance windows for trajectory degradation, to understand both when an update is necessary and what are the conditions required for pilots and air traffic controllers to provide this update.

This paper explores how organizational capabilities and path dependence affect the implementation of supply chain (SC) sustainability initiatives. Through the lenses of contingency and evolutionary theory, the paper addresses the underexplored supply chain dynamics that enhance or inhibit sustainability trajectories.

Using in-depth multi-case studies for theory elaboration, five supply chains were studied through open-ended interviews with SC members, secondary data collection and site visit observation. The design consists of a combination of deductive and inductive approaches to elaborate theory on supply chain dynamics and enhanced sustainability trajectories.

The empirical study shows that learning is a fundamental condition for supply chains as they implement sustainability initiatives, and that exploitation capabilities are more frequently used than exploration capabilities. Path dependence plays a role in the outcomes of supply chain sustainability initiatives, which are influenced by both path dependence and contingencies of the contexts in which these systems operate.

This paper puts forward five propositions that emerge from the literature and from the field study results. Although this is an exploratory research bounded by geographical limitations and the limited number of SC cases, the goal of elaborating theory may open up several promising avenues for future large-scale and longitudinal research studies.

By enhancing our understanding of the dynamics of supply chain sustainability trajectories, decision-makers, scholars and policy-makers can better understand how supply chains learn, how they employ SC member capabilities and how they deal with stakeholder resistance.

This paper extends supply chain sustainability theory by addressing the knowledge gap that exists with regard to understanding the dynamics of evolving supply chain sustainability trajectories. This paper sheds additional light on this important topic and contributes in multiple ways to the sustainable supply chain management literature.

For autonomous vehicles, trajectory prediction of surrounding vehicles is beneficial to improving the situational awareness of dynamic and stochastic traffic environments, which is a crucial and indispensable element to realize highly automated driving.

In this paper, the overall framework consists of two parts: first, a novel driver characteristic and intention estimation (DCIE) model is built to indicate the higher-level information of the vehicle using its low-level motion variables; then, according to the estimation results of the DCIE model, a classified Gaussian process model is established for probabilistic vehicle trajectory prediction under different motion patterns.

The whole method is later applied and analyzed in the highway lane-change scenarios with the parameters of models learned from the public naturalistic driving data set. Compared with other traditional methods, the performance of this proposed approach is proved superior, demonstrated by the higher accuracy in the long prediction horizon and a more reasonable description of uncertainty.

This hierarchical approach is proposed to make trajectory prediction accurately both in the short term and long term, which can also deal with the uncertainties caused by the perception system or indeterminate vehicle behaviors.