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This paper aims to devise a case-controlled method combined with Bradford Hill criteria for causal inference of contractual disputes in construction projects. It is a genuine attempt in a systematic method from research design to execution for causal issues where only observational data is available.

The authors located insufficient top management support as the putative pathogen of construction disputes based on a literature review, an interview and Delphi surveys. A questionnaire survey was then conducted to collect case-controlled data to ensure comparability, in which for each disputed project put in the experimental group, the authors sought for a dispute-free project of similar characteristics. The incidence rates of insufficient top management support in the experimental and control groups were then examined by Bradford Hill criteria as an alternative to the test of intervention effect.

The association of insufficient top management support and construction disputes was tested to conform with the Bradford Hill criteria with case-controlled data where applicable and logical deduction where statistical tests were not applicable. With a clear, positive, reasonable and statistically significant association, while excluding methodological biases, confounding and chance, the authors reached a causal verdict of insufficient top management support causing contractual disputes.

This paper supports the validity of applying a case-controlled method combined with Bradford Hill criteria in investigating causal issues in project management, especially the verdict of causal inference based on empirical data. In addition, the located root cause of contractual disputes could inform project management personnel with reasoned strategies for dispute avoidance.

In complex environments, a spherical robot has great application value. When the pendulum spherical robot is stopped or disturbed, there will be a periodic oscillation. This situation will seriously affect the stability of the spherical robot. Therefore, this paper aims to propose a control method based on backstepping and disturbance observers for oscillation suppression.

This paper analyzes the mechanism of oscillation. The oscillation model of the spherical robot is constructed and the relationship between the oscillation and the internal structure of the sphere is analyzed. Based on the oscillation model, the authors design the oscillation suppression control of the spherical robot using the backstepping method. At the same time, a disturbance observer is added to suppress the disturbance.

It is found that the control system based on backstepping and disturbance observer is simple and efficient for nonlinear models. Compared with the PID controller commonly used in engineering, this control method has a better control effect.

The proposed method can provide a reliable and effective stability scheme for spherical robots. The problem of instability in real motion is solved.

In this paper, the oscillation model of a spherical robot is innovatively constructed. Second, a new backstepping control method combined with a disturbance observer for the spherical robot is proposed to suppress the oscillation.

This study aims to introduce a novel technique for nonlinear sensor time constant estimation and sensor dynamic compensation in hot-bar soldering using an extended Kalman filter (EKF) to estimate the temperature of the thermocouple.

Temperature optimal control is combined with a closed-loop proportional integral differential (PID) control method based on an EKF. Different control methods for measuring the temperature of the thermode in terms of temperature control, error and antidisturbance are studied. A soldering process in a semi-industrial environment is performed. The proposed control method was applied to the soldering of flexible printed circuits and circuit boards. An infrared camera was used to measure the top-surface temperature.

The proposed method can not only estimate the soldering temperature but also eliminate the noise of the system. The performance of this methodology was exemplary, characterized by rapid convergence and negligible error margins. Compared with the conventional control, the temperature variability of the proposed control is significantly attenuated.

An EKF was designed to estimate the temperature of the thermocouple during hot-bar soldering. Using the EKF and PID controller, the nonlinear properties of the system could be effectively overcome and the effects of disturbances and system noise could be decreased. The proposed method significantly enhanced the temperature control performance of hot-bar soldering, effectively suppressing overshoot and shortening the adjustment time, thereby achieving precise temperature control of the controlled object.

This paper aims to solve the problem of the inability to apply learning methods for robot motion skills based on dynamic movement primitives (DMPs) in tasks with explicit environmental constraints, while ensuring the reliability of the robot system.

The authors propose a novel DMP that takes into account environmental constraints to enhance the generality of the robot motion skill learning method. First, based on the real-time state of the robot and environmental constraints, the task space is divided into different regions and different control strategies are used in each region. Second, to ensure the effectiveness of the generalized skills (trajectories), the control barrier function is extended to DMP to enforce constraint conditions. Finally, a skill modeling and learning algorithm flow is proposed that takes into account environmental constraints within DMPs.

By designing numerical simulation and prototype demonstration experiments to study skill learning and generalization under constrained environments. The experimental results demonstrate that the proposed method is capable of generating motion skills that satisfy environmental constraints. It ensures that robots remain in a safe position throughout the execution of generation skills, thereby avoiding any adverse impact on the surrounding environment.

This paper explores further applications of generalized motion skill learning methods on robots, enhancing the efficiency of robot operations in constrained environments, particularly in non-point-constrained environments. The improved methods are applicable to different types of robots.

This study aims to examine the perceptions of students about learning science and physics using the engineering design process (EDP).

The study employed a mixed-methods research design: The quantitative session features a pre–post-test control group study. In the qualitative aspect, the study conducted semistructured interviews for data collection. In the experimental group, the flipped classroom (FC) model and an instructional design are combined to design, develop and implement a physics course using the steps of the EDP, while the conventional method was applied to the control group. The respondents are students of the Department of Mechanical Engineering at Cao Thang Technical College in Vietnam for the academic year 2022–2023. The control and experimental groups are composed of 80 students each. An independent sample Mann–Whitney U test is applied to the quantitative data, while thematic analysis is employed for the qualitative data.

The results demonstrate a statistically significant difference between the experimental and control groups in terms of perceptions about learning science and physics using the EDP, which, when combined with a FC, enhances physics learning for engineering students.

This study implemented the EDP in teaching physics to first-year engineering students in the Department of Mechanical Engineering using the combined FC and instructional design models. The results revealed that a difference exists in the perception of the students in terms of integrating the EDP into learning physics between the experimental and control groups. The experimental group, which underwent the EDP, obtained better results than did the control group, which used the conventional method. The results demonstrated that the EDP encouraged the students to explore and learn new content knowledge by selecting the appropriate solution to the problem. The EDP also helped them integrate new knowledge and engineering skills into mechanical engineering. This research also introduced a new perspective on physics teaching and learning using the EDP for engineering college students.

The research findings are important for teaching and learning physics using EDP in the context of engineering education. Thus, educators can integrate the teaching and learning of physics into the EDP to motivate and engage student learning.

Using the EDP combined with a FC designed under stages of the analyze, design, develop, implement and evaluate (ADDIE) model has enhanced the learning of physics for engineering college students.

Effective information security management (ISM) contributes to building a healthy organizational digital ecology. However, few studies have built an analysis framework for critical influencing factors to discuss the combined influence mechanism of multiple factors on ISM performance (ISMP). This study aims to explore the critical success factors and understand how these factors contribute to ISMP.

This study used a mixed-method approach to achieve this study’s research goals. In Study 1, the authors conducted a qualitative analysis to take a series of International Organization for Standardization/International Electrotechnical Commission standard documents as the basis to refine the critical factors that may influence organizations’ ISMP. In Study 2, the authors built a research model based on the organizational control perspective and used the survey-based partial least squares-based structural equation modeling (PLS-SEM) approach to understand the relationships between these factors in promoting ISMP. In Study 3, the authors used the fuzzy set qualitative comparative analysis (fsQCA) method to empirically analyze the complex mechanisms of how the combinations of the factors affect ISMP.

The following three research findings are obtained. First, based on the text-based qualitative analysis, the authors refined the critical success factors that may increase ISMP, including information security policies (ISP), top management support (TMS), alignment (ALI), information security risk assessment (IRA), information security awareness (ISA) and information security culture (ISC). Second, the PLS-SEM testing results confirmed TMS is the antecedent variable motivating organization’s formation (ISP) and information control (ISC) approaches; these two types of organization control approaches increase IRA, ISA and ALI and then promote ISMP directly and indirectly. Third, the fsQCA testing results found two configurations that can achieve high ISMP and one driving path that leads to non-high ISMP.

This study extends knowledge by exploring configuration factors to improve or impede the performances of organizations’ ISM. To the best of the authors’ knowledge, this study is one of the first to explore the use of the fsQCA approach in information security studies, and the results not only revealed causal associations between single factors but also highlighted the critical role of configuration factors in developing organizational ISMP. This study calls attention to information security managers of an organization should highlight the combined effect between the factors and reasonably allocate organizational resources to achieve high ISMP.

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

The purpose of this manuscript, a state feedback gain depends on the optimal design of fractional order PID controller to time-delay system is established. In established optimal design known as advanced cuttlefish optimizer and random decision forest that is combined performance of random decision forest algorithm (RDFA) and advanced cuttlefish optimizer (ACFO).

The proposed ACFO uses the concept of crossover and mutation operator depend on position upgrading to enhance its search behavior, calculational speed as well as convergence profile at basic cuttlefish optimizer.

Fractional order proportional-integrator-derivative (FOPID) controller, apart from as tuning parameters (kp, ki and kd) it consists of two extra tuning parameters λ and µ. In established technology, the increase of FOPID controller is adjusted to reach needed responses that demonstrated using RDFA theory as well as RDF weight matrices is probable to the help of the ACFO method. The uniqueness of the established method is to decrease the failure of the FOPID controller at greater order time delay method with the help of controller maximize restrictions. The objective of the established method is selected to consider parameters set point as well as achieved parameters of time-delay system.

In the established technique used to evade large order delays as well as reliability restrictions such as small excesses, time resolution, as well as fixed condition defect. These methods is implemented at MATLAB/Simulink platform as well as outcomes compared to various existing methods such as Ziegler-Nichols fit, curve fit, Wang method, regression and invasive weed optimization and linear-quadratic regression method.