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The use of additive manufacturing in many branches of industry is increasing significantly because of its many advantages, such as being able to produce complex parts that cannot be produced by classical methods, using fewer materials, easing the supply chain with on-site production, being able to produce with all kinds of materials and producing lighter parts. The binder jetting technique, one of the additive manufacturing methods researched within the scope of this work, is predicted to be the additive manufacturing method that will grow the most in the next decade, according to many economic reports. Although additive manufacturing methods have many advantages, they can be slower than classical manufacturing methods regarding production speed. For this reason, this study aims to increase the manufacturing speed in the binder jetting method.

Adaptive slicing and variable binder amount algorithm (VBAA) were used to increase manufacturing speed in binder jetting. Taguchi method was used to optimize the layer thickness and saturation ratio in VBAA. According to the Taguchi experimental design, 27 samples were produced in nine different conditions, three replicates each. The width of the samples in their raw form was measured. Afterward, the samples were sintered at 1,500 °C for 2 h. After sintering, surface roughness and density tests were performed. Therefore, the methods used have been proven to be successful. In addition, measurement possibilities with image processing were investigated to make surface roughness measurements more accessible and more economical.

As a result of the tests, the optimum printing condition was decided to be 180–250 µm for layer thickness and 50% for saturation. A separate test sample was then designed to implement adaptive slicing. This test sample was produced in three pieces: adaptive (180–250 µm), thin layer (180 µm) and thick layer (250 µm) with the determined parameters. The roughness values of the adaptive sliced sample and the thin layer sample were similar and better than the thick layer sample. A similar result was obtained using 12.31% fewer layers in the adaptive sample than in the thin layer sample.

The use of adaptive slicing in binder jetting has become more efficient. In this way, it will increase the use of adaptive slicing in binder jetting. In addition, a cheap and straightforward image processing method has been developed to calculate the surface roughness of the parts.

Parameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper aims to introduce an adaptive multi-output Gaussian process (MOGP) surrogate model for parameter estimation in time-consuming models.

The MOGP surrogate model is established to replace the computationally expensive finite element method (FEM) analysis during the estimation process. We propose a novel adaptive sampling method for MOGP inspired by the traditional expected improvement (EI) method, aiming to reduce the number of required sample points for building the surrogate model. Two mathematical examples and an application in the back analysis of a concrete arch dam are tested to demonstrate the effectiveness of the proposed method.

The numerical results show that the proposed method requires a relatively small number of sample points to achieve accurate estimates. The proposed adaptive sampling method combined with the MOGP surrogate model shows an obvious advantage in parameter estimation problems involving expensive-to-evaluate models, particularly those with high-dimensional output.

A novel adaptive sampling method for establishing the MOGP surrogate model is proposed to accelerate the procedure of solving large-scale parameter estimation problems. This modified adaptive sampling method, based on the traditional EI method, is better suited for multi-output problems, making it highly valuable for numerous practical engineering applications.

Drawing on the paradox perspective and the Job Demands-Resources Model (JD-R), we investigate how paradoxical leadership behavior (PLB) affects employee adaptive performance.

Conducted a multi-wave field study with a sample of 64 leaders and 224 subordinates (study 1), and a quasi-experiment involving 156 participants (study 2). The collected data were analyzed using ANOVA and HLM techniques.

Both studies reveal that (1) PLB exhibits a positive association with employee adaptive performance, (2) employee paradox mindset and role stress partly mediated the relationship between PLB and employee adaptive performance, (3) the indirect effects of PLB on employee adaptive performance through employee paradox mindset and role stress are moderated by employee gender-role identity (GRI).

This research developed a moderated mediation model to examine the potential impact of PLB on employee adaptive performance, which contributes to the literature by integrating the paradox perspective and emphasizing the effectiveness of combining “top-down” and “bottom-up” leadership approaches. Moreover, we elucidate the underlying mechanisms through which PLB facilitates employees in effectively managing work role tensions to enhance adaptive performance Finally, this study also extends the investigation of the contextual efficacy of PLB by incorporating GRI as a moderator.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

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.

Drawing on social learning theory, the purpose of this study is to examine the relationship between team cohesion and employee adaptive performance directly and through employee ambidexterity. The study also investigates the moderating role of team empowerment climate in the relationship between team cohesion and employee adaptive performance.

Time-lagged data were collected in two waves from a sample of 212 employees from 43 teams working in software houses in Pakistan. R (lavaan package) was used to analyze the multilevel framework.

The findings reveal that both team cohesion and employee ambidexterity positively relate to employee adaptive performance. Moreover, employee ambidexterity mediates the cross-level relationship between team cohesion and employee adaptive performance. The results also support that team empowerment climate (a contextual boundary condition) moderates the direct relationship between team cohesion and employee adaptive performance.

The primary novelty of this study lies in developing and examining a holistic conceptual framework for a multilevel model in the software industry that incorporates team cohesion (level 2) as an antecedent, employee ambidexterity (level 1) as an underlying mechanism, employee adaptive performance (level 1) as an outcome and team empowerment climate as a boundary condition (level 2).

The purpose of this paper is to address two major challenges faced by robotic fish when operating in underwater environments: insufficient path planning capabilities and difficulties in avoiding dynamic obstacles. To achieve this, a method is proposed that combines the Improved Rapid Randomized Tree Star (IRRT*) with the dynamic window approach (DWA).

The RRT-connect algorithm is used to determine an initial feasible path quickly. The quality of sampling points is then improved by dividing the regions and selecting each region’s probability based on its fitness value. The fitness function and roulette wheel method are introduced for region selection. Subtarget points of the DWA algorithm are extracted from the IRRT* algorithm to achieve real-time dynamic path planning.

In various maps, the iteration count for the IRRT* algorithm decreased by 61%, 35% and 51% respectively, compared to the RRT* algorithm, whereas the iteration time was reduced by 75%, 34% and 57%, respectively. In addition, the IRRT*-DWA algorithm can successfully navigate through multiple dynamic obstacles, and the average time, path length, etc. do not change much when parameters change, and the stability is high.

A novel IRRT*-DWA algorithm is proposed, which, by refining the sampling strategy and updating sub-target points in real time, not only addresses the limitations of existing algorithms in terms of path planning efficiency in complex environments but also enhances their capability to avoid dynamic obstacles. Ultimately, experimental results indicate a high level of similarity between the actual and ideal paths.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

The purpose of this study is to address the welding demands within large steel structures by presenting a global spatial motion planning algorithm for a mobile manipulator. This algorithm is based on an independently developed wall-climbing robot, which comprises a four-wheeled climbing mobile platform and a six-degree-of-freedom robotic manipulator, ensuring high mobility and operational flexibility.

A convex hull feasible domain constraint is developed for motion planning in the mobile manipulator. For extensive spatial movements, connected sequences of convex polyhedra are established between the composite robot’s initial and target states. The composite robot’s path and obstacle avoidance optimization problem are solved by constraining the control points on B-spline curves. A dynamic spatial constraint rapidlye-xploring random trees-connect (RRTC) motion planning algorithm is proposed for the manipulator, which quickly generates reference paths using spherical spatial constraints at the manipulator’s end, eliminating the need for complex nonconvex constraint modeling.

Experimental results show that the proposed motion planning algorithm achieves optimal paths that meet task constraints, significantly reducing computation times in task conditions and shortening operation times in non-task conditions.

The algorithm proposed in this paper holds certain application value for the realization of automated welding operations within large steel structures using mobile manipulator.

The purpose of this research is to conceptualize, define and measure resource orchestration capabilities of R&D teams pursuing advanced scientific research and technological innovation at public-funded R&D organizations in India.

A series of five mutually exclusive studies were designed over two years to develop and validate the ROCI scale within public research and development (R&D) organizations pursuing advanced scientific research and technological development in India. The first three studies address the refinement, reduction and rationalization of items for measuring the ROCI construct. The next study explores the factor structure underlying the ROCI construct whereas the subsequent one confirms the three-factor structure within empirical settings.

The resource orchestration capability towards innovation (ROCI) construct reflected through three sub-dimensions namely – adaptive structuring capability (ASC), synergistic leveraging capability (SLC) and decentralized decision-making capability (DDC), each loaded with their respective items can be used for capability measurement in public-funded R&D organizations.

R&D managers can use this ROCI scale to measure, monitor and improve the innovation-oriented resource orchestration capabilities of their R&D teams and help them improve their innovation performance.

This research contributes to the extant literature on resource orchestration for innovation management in three unique and original ways – theoretically-grounded conceptualization, empirical measurement and rigorous validation through multiple studies conducted in public-funded R&D organizations in India.