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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.

Bayesian cubature (BC) has emerged to be one of most competitive approach for estimating the multi-dimensional integral especially when the integrand is expensive to evaluate, and alternative acquisition functions, such as the Posterior Variance Contribution (PVC) function, have been developed for adaptive experiment design of the integration points. However, those sequential design strategies also prevent BC from being implemented in a parallel scheme. Therefore, this paper aims at developing a parallelized adaptive BC method to further improve the computational efficiency.

By theoretically examining the multimodal behavior of the PVC function, it is concluded that the multiple local maxima all have important contribution to the integration accuracy as can be selected as design points, providing a practical way for parallelization of the adaptive BC. Inspired by the above finding, four multimodal optimization algorithms, including one newly developed in this work, are then introduced for finding multiple local maxima of the PVC function in one run, and further for parallel implementation of the adaptive BC.

The superiority of the parallel schemes and the performance of the four multimodal optimization algorithms are then demonstrated and compared with the k-means clustering method by using two numerical benchmarks and two engineering examples.

Multimodal behavior of acquisition function for BC is comprehensively investigated. All the local maxima of the acquisition function contribute to adaptive BC accuracy. Parallelization of adaptive BC is realized with four multimodal optimization methods.

To present the detailed implementation processes of the IDEAL algorithm for two-dimensional compressible flows based on Delaunay triangular mesh, and compare the performance of the SIMPLE and IDEAL algorithms for solving compressible problems. What’s more, the implementation processes of Delaunay mesh generation and derivation of the pressure correction equation are also introduced.

Programming completely in C++.

Five compressible examples are used to test the SIMPLE and IDEAL algorithms, and the comparison with measurement data shows good agreement. The IDEAL algorithm has much better performance in both convergence rate and stability over the SIMPLE algorithm.

The detail solution procedure of implementing the IDEAL algorithm for compressible flows based on Delaunay triangular mesh is presented in this work, seemingly first in the literature.

To improve the position tracking efficiency of the upper-limb rehabilitation robot for stroke hemiplegia patients, the optimization Learning rate of the membership function based on the fuzzy impedance controller of the rehabilitation robot is propose.

First, the impaired limb’s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using weighted least squares method based on recursive algorithm. Second, the fuzzy impedance control with the rule has been designed with the optimal impedance parameters. Finally, the membership function learning rate online optimization strategy based on Takagi-Sugeno (TS) fuzzy impedance model was proposed to improve the position tracking speed of fuzzy impedance control.

This method provides a solution for improving the membership function learning rate of the fuzzy impedance controller of the upper limb rehabilitation robot. Compared with traditional TS fuzzy impedance controller in position control, the improved TS fuzzy impedance controller has reduced the overshoot stability time by 0.025 s, and the position error caused by simulating the thrust interference of the impaired limb has been reduced by 8.4%. This fact is verified by simulation and test.

The TS fuzzy impedance controller based on membership function online optimization learning strategy can effectively optimize control parameters and improve the position tracking speed of upper limb rehabilitation robots. This controller improves the auxiliary rehabilitation efficiency of the upper limb rehabilitation robot and ensures the stability of auxiliary rehabilitation training.

The study aims to equip robots with the ability to precisely maintain interaction forces, which is crucial for tasks such as polishing in highly dynamic environments with unknown and varying stiffness and geometry, including those found in airplane wings or thin, soft materials. The purpose of this study is to develop a novel adaptive force-tracking admittance control scheme aimed at achieving a faster response rate with higher tracking accuracy for robot force control.

In the proposed method, the traditional admittance model is improved by introducing a pre-proportional-derivative controller to accelerate parameter convergence. Subsequently, the authors design an adaptive law based on fuzzy logic systems (FLS) to compensate for uncertainties in the unknown environment. Stability conditions are established for the proposed method through Lyapunov analysis, which ensures the force tracking accuracy and the stability of the coupled system consisting of the robot and the interaction environment. Furthermore, the effectiveness and robustness of the proposed control algorithm are demonstrated by simulation and experiment.

A variety of unstructured simulations and experimental scenarios are designed to validate the effectiveness of the proposed algorithm in force control. The outcomes demonstrate that this control strategy excels in providing fast response, precise tracking accuracy and robust performance.

In real-world applications spanning industrial, service and medical fields where accurate force control by robots is essential, the proposed method stands out as both practical and straightforward, delivering consistently satisfactory performance across various scenarios.

This research introduces a novel adaptive force-tracking admittance controller based on FLS and validated through both simulations and experiments. The proposed controller demonstrates exceptional performance in force control within environments characterized by unknown and varying.

The purpose of this study is to establish the relationship between digital leadership and academic performance. It models the digitalization process, outlining why and how digital leadership is important for better academic performance. At the same time, this study examines the role of digital culture as a moderating variable in the direct relationship between main variables of the study. The study aims to expand the domain of academic performance at the university by including a much recent leadership-related aspect and organizational context of the digital culture.

The study opted for a descriptive study, using the survey instruments to collect the data. The sample population consisted of students currently enrolled at the Faculty of Business and Management, Universiti Teknologi MARA, Melaka, Malaysia. Based on the convenience sampling, 383 samples were drawn from the sample population. All items were adopted from previous literature, and expert feedback was obtained to examine the validity of the instruments. The data were analysed using SPSS and SmartPLS version 3.0.

This study provides empirical insights about how digital leadership is important for academic performance for the new millennials. Also, digital culture is found to provide significant moderation effect into the relationship. It suggests that universities must promote digitalization culture and embed the use of technology and digitalization into teaching and learning to cultivate a more effective learning process among university students. This is important as elements of digital leadership, including adaptive role, attitude, digital competency, digital skill and inspirational role, are found to significantly contribute to academic performance.

This study only focuses on samples taken from one of the faculties in one campus, thus limiting its scope. Future research is encouraged to replicate the same study setting to include larger sample size from different faculties, or perhaps from different universities. These propositions could help to better generalize the research findings on the practice of digital leadership on academic performance in the country. However, this study established a digital leadership model that can be applied to undergraduate students at the universities. Also, the inclusion of digital culture can strengthen the learning process.

This study includes implications for the development of digital leadership attributes and promoting digital culture within the university students and environment for engaging in a better academic performance. Digital leadership is found to be an important criterion of academic performance in this digital age society, and cultivating digital culture enhances students’ academic performance. These findings shall prompt the university to actively engage in fostering digitalization culture within the university. Also, the top management of the university should inform the students to be adaptive and cultivate the attributes of digital leaders, as their readiness to cope with the technological change has significant positive impact on their academic performance.

It is important to ensure that the future graduates that are being produced are ready to take on more challenges as digital leaders in the digital society. This might accelerate the country’s initiatives and efforts towards becoming a developed nation. Thus, investing in oneself to become digitally literate and competent might not only influence their academic performance, but they will also be equipped to fulfil one of the expectations of future employers of potential graduates, which is possessing digital leadership.

Digitalization is not only about the technology. It is about the people too. As the study on digital leadership is still in its infant stage, this study is unique as it is among the earliest to establish digital leadership constructs within the context of Malaysia. It informs the university that digital leadership provides significant contribution to academic performance. Thus, the university is encouraged to nurture digitalization, not only in the teaching and learning but also with the people within the university environment. Determining the right programs and plans for the curricular will help students to develop digital leadership attributes more effectively. Finally, improving digitalization among its students and culture is important, as these elements provide significant effect towards academic performance.

Grounded in dynamic capabilities theory, this study aims to examine how dynamic capabilities and a transactive memory system (TMS) can build the resilience of service organizations and improve their financial performance. Limited studies examine the link between a TMS and organizational resilience.

The authors test a theoretical model on a sample of 350 UK service firms that were impacted by the COVID-19 pandemic and analyze the data using partial least square structural equation modeling.

Results highlight the positive effects of a TMS and dynamic capabilities on organizational resilience. Only a TMS and organizational resilience have direct positive effects on financial performance.

To the best of the authors’ knowledge, this is the first study to ascertain the influence of a TMS on organizational resilience in service firms following adversity.

Organizations will be key to realizing the “transformative change for humanity” now being called for. However, the complexity calls for new ways of facilitating change and organizational learning; it also calls for moving beyond sustainability to develop practices that restore and regenerate the world in which we live. Above all, it calls for the development of new frameworks, practices, mindset and capabilities to hold space for and facilitate such transformation, to dance with the “Logic of Life.” The purpose of this study is to contribute to advancing the current leading frameworks and practices of facilitating learning and development towards the enabling of regenerative transformative change in organizations and society.

This study is based on an exploratory qualitative analysis of a facilitation prototype that expands the current framework and practice of Appreciative Inquiry Summits toward regeneration.

This study presents four paradoxes of regenerative facilitation to guide the dance for life in complex ecosystems. It also identifies that the dance needs to be widened, towards inviting more frequently the ends of the four paradoxes noted as regenerative, negative emotions, inner and more-than-human.

This study explores the intersection of practices and frameworks for facilitating complexity with principles from regenerative leadership and complexity theory, potentially making an important contribution to the urgent and widespread need to facilitate a regenerative transformative change for humanity, society and our organizations.

In this study, we applied the strategy-as-practice (SAP) framework to analyse strategic communication practices. SAP implies approaching strategy as something that organisational members do and is useful for understanding the tensions between emergence and formalisation and between planning and improvisation that characterise the everyday communication work of communication practitioners.

The paper is based on an ethnographic study of a record company and on qualitative interviews with various actors from the music industry.

Tensions exist between the emergence of inputs from active consumers that require flexibility and attempts to strategically formalise and continuously adapt plans and encourage consumers to act in anticipated ways. The findings revealed five strategic communication practices—meetings, working in the office, gathering and analysing consumer engagement and related data, collaboration and storytelling—that practitioners used to conduct strategic communication and navigate the tensions.

The study contributes to understanding the role of strategic communication practices in contemporary organisations and how practitioners manage the tensions within them. The study shows that an SAP approach can account for improvisation and emergence, as well as planning and formalisation. It also shows how SAP resonates with emergent and agile strategic communication frameworks.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.