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This paper aims to investigate the problem of attitude control for a horizontal takeoff and horizontal landing reusable launch vehicle.

In this paper, a predefined-time attitude tracking controller is presented for a horizontal takeoff and horizontal landing reusable launch vehicle (HTHLRLV). Firstly, the attitude tracking error dynamics model of the HTHLRLV is developed. Subsequently, a novel sliding mode surface is designed with predefined-time stability. Furthermore, by using the proposed sliding mode surface, a predefined-time controller is derived. To compensate the external disturbances or model uncertainties, a fixed-time disturbance observer is developed, and its convergence time can be defined as a prior control parameter. Finally, the stability of the proposed sliding mode surface and the controller can be proved by the Lyapunov theory.

In contrast to other fixed-time methods, this controller only requires three control parameters, and the convergence time can be predefined instead of being estimated. The simulation results also demonstrate the effectiveness of the proposed controller.

A novel predefined-time attitude tracking controller is developed based on the predefined-time sliding mode surface (SMS) and fixed-time disturbance observer (FxTDO). The convergence time of the system can be selected as a prior control parameter for SMS and FxTDO.

This paper aims to present a novel adaptive sliding mode control (ASMC) method based on the predefined performance barrier function for reusable launch vehicle under attitude constraints and mismatched disturbances.

A novel ASMC based on barrier function is adopted to deal with matched and mismatched disturbances. The upper bounds of the disturbances are not required to be known in advance. Meanwhile, a predefined performance function (PPF) with prescribed convergence time is used to adjust the boundary of the barrier function. The transient performance, including the overshoot, convergence rate and settling time, as well as the steady-state performance of the attitude tracking error are retained in the predetermined region under the barrier function and PPF. The stability of the proposed control method is analyzed via Lyapunov method.

In contrast to conventional adaptive back-stepping methods, the proposed method is comparatively simple and effective which does not need to disassemble the control system into multiple first-order systems. The proposed barrier function based on PPF can adjust not only the switching gain in an adaptive way but also the convergence time and steady-state error. And the efficiency of the proposed method is illustrated by conducting numerical simulations.

A novel barrier function based ASMC method is proposed to fit in the amplitude of the mismatched and matched disturbances. The transient and steady-state performance of attitude tracking error can be selected as prior control parameters.

To reduce the effect of parameter uncertainties and input saturation on path tracking control for autonomous combine harvester, a path tracking controller is proposed, which integrates an adaptive neural network estimator and a saturation-aided system.

First, to analyze and compensate the influence of external factors, the vehicle model is established combining a dynamic model and a kinematic model. Meanwhile, to make the model simple, a comprehensive error is used, weighting heading error and position error simultaneously. Second, an adaptive neural network estimator is presented to calculate uncertain parameters which eventually improve the dynamic model. Then, the path tracking controller based on the improved dynamic model is designed by using the backstepping method, and its stability is proved by the Lyapunov theorem. Third, to mitigate round-trip operation of the actuator due to input saturation, a saturation-aided variable is presented during the control design process.

To verify the tracking accuracy and environmental adaptability of the proposed controller, numerical simulations are carried out under three different cases, and field experiments are performed in harvesting wheat and paddy. The experimental results demonstrate the tracking errors of the proposed controller that are reduced by more than 28% with contrast to the conventional controllers.

An adaptive neural network-based path tracking control is proposed, which considers both parameter uncertainties and input saturation. As far as we know, this is the first time a path tracking controller is specifically designed for the combine harvester with full consideration of working characteristics.

The purpose of this paper is to investigate the time-varying finite-time formation tracking control problem for multiple unmanned aerial vehicle systems under switching topologies, where the states of the unmanned aerial vehicles need to form desired time-varying formations while tracking the trajectory of the virtual leader in finite time under jointly connected topologies.

A consensus-based formation control protocol is constructed to achieve the desired formation. In this paper, the time-varying formation is specified by a piecewise continuously differentiable vector, while the finite-time convergence is guaranteed by utilizing a non-linear function. Based on the graph theory, the finite-time stability of the close-loop system with the proposed control protocol under jointly connected topologies is proven by applying LaSalle’s invariance principle and the theory of homogeneity with dilation.

The effectiveness of the proposed protocol is verified by numerical simulations. Consequently, the proposed protocol can successfully achieve the predefined time-varying formation in finite time under jointly connected topologies while tracking the trajectory generated by the leader.

This paper proposes a solution to simultaneously solve the control problems of time-varying formation tracking, finite-time convergence, and switching topologies.

The feasibility of process mining combined with simulation techniques in estimating the effectiveness of COVID-19 prevention strategies on infection and mortality trends to determine best practices is assessed in this study. The quarantine event log is built from the CUSP (the COVID-19 US State Policy) database, where the dates of implemented social policies in the USA to respond to the COVID-19 pandemic are documented.

COVID-19 is a highly infectious disease leading to a very high death toll worldwide. In most countries, the governments have resorted to a series of drastic strategies to prevent the outbreak by restricting the activities and movement among their population for a predefined time. Heretofore, different approaches have been published to estimate quarantine strategies and the majority signify the positive effect on managing this pandemic. Notably, the process perspective of COVID-19 datasets is of less concern among researchers. The purpose of this paper is to exploit the process mining techniques to model and analyze the quarantine implementation processes.

The discovered process model has 51 process variants for 51 cases (states), which indicate the quarantine activities were executed in different orders and periods during the pandemic. The time interval analysis between activities reveals the states with the most extended quarantine periods. These primary process mining insights are applied to define scenarios and variables of an agent-based model. The simulation findings indicate a meaningful relation between enforcing quarantine strategies and a declining trend of infection by 90% in the case of following strict quarantine and mask mandates. It is observed that in the post-quarantine period, the disease repeats its ascending trend unless implementation of different intervention strategies likes vaccination.

This study is the first in introducing process mining techniques in analyzing the COVID-19 quarantine strategies impact. The findings provide valuable insights for policymakers to proper control strategies and the process mining research community in expanding more process-related analysis on this pandemic. Also, the results have broad implications for research in other fields like information science to estimate the impact of quarantine strategies on process patterns in library systems.

In this study, the authors investigate a different routing and scheduling problem in the field of home health care (HHC) management system. The purpose of this paper is to route and schedule the workday of health workers, assign the patients to suitable health workers, make accurate decisions to minimize costs, provide timely services and, in general, enhance the efficiency of HHC centers.

A mixed-integer linear programming model is developed to assign health workers to patients. The model considers health professionals with different skills, namely nurses and physicians. Additionally, three groups of patients are considered: patients who need a nurse, patients who need a physician and patients who need both. In the third group, the nurse must be present at the patient’s home following the physician’s visit in order to perform the required tasks.

The results of this study show a reduction in costs which results from the fewer health workers employed and dispatched in comparison with traditional approaches. With the help of our solution approach and model, HHC centers may not only successfully reduce their costs but also manage to meet their patients’ demands by assigning suitable nurses and physicians.

Previous studies have often focused on problems involving only one group of health professionals and rarely address problems involving multiple groups. The authors consider this a shortcoming, because in many cases, patients should be visited several times and by various health professionals.

The purpose of this study is to develop a novel approach to e-participation, which is based on “passive crowdsourcing” by government agencies, exploiting the extensive political content continuously created in numerous Web 2.0 social media (e.g. political blogs and microblogs, news sharing sites and online forums) by citizens without government stimulation, to understand better their needs, issues, opinions, proposals and arguments concerning a particular domain of government activity or public policy.

This approach is developed and elaborated through cooperation with potential users experienced in the design of public policies from three countries (Austria, Greece and the UK), using a combination of quantitative and qualitative techniques: co-operative development of application scenarios, questionnaire surveys, focus groups and workshops and, finally, in-depth interviews.

A process model for the application of the proposed passive crowdsourcing approach has been developed, which is quite different from the one of the usual active crowdsourcing. Based on it, the functional architecture of the required supporting information and communication technologies (ICT) infrastructure has been formulated, and then its technological architecture has been designed, addressing the conflicting requirements: low response time and, at the same time, provision of sufficiently “fresh” content for policymakers.

Taking into account that traditionally government agencies monitor what the press writes about them, our research provides a basis for extending efficiently these activities in the new electronic media world (e.g. newspapers websites, blogs and microblogs, online forums, etc.) to understand better the needs, issues, opinions, arguments and proposals raised by the society with respect to important domains of government activity and public policies.

The proposed approach provides a new channel for the “voice” of the society to be directly communicated to the government so that the latter can design its policies and activities based on the social needs and realities and not on oversimplified models and stereotypes.

Our paper proposes a novel approach to e-participation, which exploits the Web 2.0 social media – but in a quite different way from previous approaches – for conducting “passive crowdsourcing”, and elaborates it: it develops an application process model for it and also an ICT infrastructure for supporting it, which are quite different from the ones of the existing “active crowdsourcing” approaches.

The purpose of this paper is to present a methodology combining experimental and inquiry methods used for software usability evaluation. The software product of LeViS project funded by the European Commission (Socrates/Lingua II) is used as an evaluation paradigm. The aim of the paper is twofold: to present the results of the usability evaluation using this software as an example and to suggest a number of improvements for the next version of the software tool; and to portray the advantages of combining methods from different evaluation approaches and the experiences from their application.

The evaluation for this experiment combined different usability methods, both experimental and inquiry ones. More specifically, the methods employed were the Thinking Aloud Protocol and the User Logging, which were performed in a usability evaluation laboratory, as well as the inquiry methods of Interview and Focus Group.

In this study, usability problems regarding the Learning via Subtitling (LvS) educational software were revealed as well as issues regarding the use of Thinking Aloud Protocol method and involving users with a specific profile. The research findings presented in this paper constitute an innovative and effective methodology for software usability evaluation and are useful for laboratories aiming to conduct similar evaluations.

Although this methodology has been successfully applied for over 20 software products, due to practical purposes related to this paper's extent, only one software is used as an example.

Through the evaluation process, apart from discovering certain usability problems related to the software, there are a number of important conclusions drawn, regarding the methods used and the methodology followed in software usability evaluation.

The purpose of this paper is to develop a new easy-to-implement distribution-free integrated multivariate statistical process control (MSPC) approach with an ability to recognize out-of-control points, identify the key influential variable for the out-of-control state, and determine necessary changes to achieve the state of statistical control.

The proposed approach integrates the control chart technique, the Mahalanobis-Taguchi System concept, the Andrews function plot, and nonlinear optimization for multivariate process control. Mahalanobis distance, Taguchi’s orthogonal array, and the main effect plot concept are used to identify the key influential variable responsible for the out-of-control situation. The Andrews function plot and nonlinear optimization help to identify direction and necessary correction to regain the state of statistical control. Finally, two different real life case studies illustrate the suitability of the approach.

The case studies illustrate the potential of the proposed integrated multivariate process control approach for easy implementation in varied manufacturing and process industries. In addition, the case studies also reveal that the multivariate out-of-control state is primarily contributed by a single influential variable.

The approach is limited to the situation in which a single influential variable contributes to out-of-control situation. The number and type of cases used are also limited and thus generalization may not be debated. Further research is necessary with varied case situations to refine the approach and prove its extensive applicability.

The proposed approach does not require multivariate normality assumption and thus provides greater flexibility for the industry practitioners. The approach is also easy to implement and requires minimal programming effort. A simple application Microsoft Excel is suitable for online implementation of this approach.

The key steps of the MSPC approach are identifying the out-of-control point, diagnosing the out-of-control point, identifying the “influential” variable responsible for the out-of-control state, and determining the necessary direction and the amount of adjustment required to achieve the state of control. Most of the approaches reported in open literature are focused only until identifying influencing variable, with many restrictive assumptions. This paper addresses all key steps in a single integrated distribution-free approach, which is easy to implement in real time.

The main purpose of this paper is to select appropriate voltage vectors in the switching techniques and, by selecting the proper voltage vectors, be able to achieve a DC link with the same outputs and a symmetric multi-level inverter.

The proposed structure, a two-stage DC–AC symmetric multi-level inverter with modified Model Predictive Control (MMPC) method, is presented for Photovoltaic (PV) applications. The voltage of DC-link capacitors of the boost converter is controlled by MMPC control method to select appropriate switching vectors for the multi-level inverter. The proposed structure is provided for single-phase power system, which increases 65 V input voltage to 220 V/50 Hz output voltage, with 400 V DC link. Simulation results of proposed structure with MMPC method are carried out by PSCAD/EMTDC software.

Based on the proposed structure and control method, total harmonic distortion (THD) reduces, which leads to lower power losses and higher circuit reliability. In addition, reducing the number of active switches in current path causes to lower voltage stress on the switches, lower PV leakage current and higher overall efficiency.

In the proposed structure, a new control method is presented that can make a symmetric five-level voltage with lower THD by selecting proper switching for PV applications.