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This study aims to propose an adaptive fractional-order sliding mode controller to solve the problem of train speed tracking control and position interval control under disturbance environment in moving block system, so as to improve the tracking efficiency and collision avoidance performance.

The mathematical model of information interaction between trains is established based on algebraic graph theory, so that the train can obtain the state information of adjacent trains, and then realize the distributed cooperative control of each train. In the controller design, the sliding mode control and fractional calculus are combined to avoid the discontinuous switching phenomenon, so as to suppress the chattering of sliding mode control, and a parameter adaptive law is constructed to approximate the time-varying operating resistance coefficient.

The simulation results show that compared with proportional integral derivative (PID) control and ordinary sliding mode control, the control accuracy of the proposed algorithm in terms of speed is, respectively, improved by 25% and 75%. The error frequency and fluctuation range of the proposed algorithm are reduced in the position error control, the error value tends to 0, and the operation trend tends to be consistent. Therefore, the control method can improve the control accuracy of the system and prove that it has strong immunity.

The algorithm can reduce the influence of external interference in the actual operating environment, realize efficient and stable tracking of trains, and ensure the safety of train control.

Testing business processes is crucial to assess the compliance of business process models with requirements. Automating this task optimizes testing efforts and reduces human error while also providing improvement insights for the business process modeling activity. The primary purposes of this paper are to conduct a literature review of Business Process Model and Notation (BPMN) testing and formal verification and to propose the Business Process Evaluation and Research Framework for Enhancement and Continuous Testing (bPERFECT) framework, which aims to guide business process testing (BPT) research and implementation. Secondary objectives include (1) eliciting the existing types of testing, (2) evaluating their impact on efficiency and (3) assessing the formal verification techniques that complement testing.

The methodology used is based on Kitchenham's (2004) original procedures for conducting systematic literature reviews.

Results of this study indicate that three distinct business process model testing types can be found in the literature: black/gray-box, regression and integration. Testing and verification approaches differ in aspects such as awareness of test data, coverage criteria and auxiliary representations used. However, most solutions pose notable hindrances, such as BPMN element limitations, that lead to limited practicality.

The databases selected in the review protocol may have excluded relevant studies on this topic. More databases and gray literature could also be considered for inclusion in this review.

Three main originality aspects are identified in this study as follows: (1) the classification of process model testing types, (2) the future trends foreseen for BPMN model testing and verification and (3) the bPERFECT framework for testing business processes.

The goal of this work is to create a computational finite element model to perform thermoelastic stress analysis (TSA) with the usage of a non-ideal load frequency, containing the effects of the material thermal properties.

Throughout this document, the methodology of the model is presented first, followed by the procedure and results. The last part is reserved to results, discussion and conclusions.

This work had the main goal to create a model to perform TSA with the usage of non-ideal loading frequencies, considering the materials’ thermal properties. Loading frequencies out of the ideal range were applied and the model showed capable of good results. The created model reproduced acceptably the TSA, with the desired conditions.

This work creates a model to perform TSA with the usage of non-ideal loading frequencies, considering the materials’ thermal properties.

A flexible decision technology called the Graph Model for Conflict Resolution (GMCR) is applied to a generic aquaculture conflict to illustrate how GMCR can be used to systematically investigate a wide range of conflicts arising in aquaculture in order to obtain meaningful strategic insights and thereby assist in making informed decisions in aquaculture development. To emphasize the importance of being able to resolve aquaculture controversies, a review of the global economic impacts of the aquaculture industry is provided and the key stakeholders who may be involved in aquaculture disputes along with their legitimate interests are identified. The paper aims to discuss these issues.

The GMCR methodology comprises two main stages: modeling and analysis. During the modeling stage, key decision makers (DMs), the options under each DM’s control and each DM’s relative preferences over feasible states are identified based on a thorough background investigation to a given dispute. Within the analysis stage, solution concepts that describe key characteristics of human behavior under conflict are utilized to determine resolutions that could occur when DMs interact under pure competition and cooperatively. Interpretation of the equilibrium results provides meaningful strategic insights for better understanding which strategies a given DM could select as the conflict evolves over time.

The results demonstrate how difficult it can be to balance the interests of different key stakeholders in aquaculture development. In all possible resolutions identified in the generic aquaculture conflict, at least two DMs among First Nations, environmental group and residents (Res) would object to the expansion of aquaculture activities due to the assumption that the government would choose to appease one stakeholder at a time. They also reflect the need for a useful tool box of decision technologies for addressing the vast range of challenges that could arise in the important area of marine economics and management.

The GMCR methodology possesses several unique and key original capabilities in comparison to other conflict analysis models. First, it only requires limited information to calibrate a conflict model. Second, it contains a number of solution concepts that describe how a DM could think and behave under conflict. Third, it furnishes a range of informative output, follow-up analyses and advice for use in real-life decision support. Finally, all of the foregoing advantages of GMCR can be contained within decision support systems that permit practitioners and researchers to readily apply the GMCR methodology to real-life conflicts.

This paper aims to propose a technique based on cognitive assessments to quantify identified operational risks from the perspective of container shipping or logistics system administrators. The results derived from the risk quantification could be used to prioritize risks as well as support the decision-making process in risk prevention and mitigation.

This paper identified container shipping operational risks (CSORs) from a logistics perspective. A multivariate risk evaluation mechanism by fuzzy rules Bayesian network (FRBN) was established. An improved two-level parameter set based on the failure mode and effects analysis (FMEA) was used to support the input extraction process. By feeding cognitive assessments into the model, the identified risks are evaluated based on their utility values. An illustration example and a sensitivity analysis were carried out to justify and validate the proposed model.

The highest positions in the prioritized list of CSORs in the case study are dominated by risks in the physical flow with the first three are piracy and terrorism, force majeure and port congestion. The results derived from the case study with the satisfaction of all pre-defined axioms proved the feasibility and illustrated the functionality of the proposed risk assessment and prioritization technique.

Controlling risk is irrefutably a significant issue of container shipping and logistics management because of the inconsistency of risk definitions and the involvement of uncertainties. The proposed risk evaluation mechanism and the identified list of CSORs could be beneficial in system management, decision-making and reliability performance.