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The purpose of this paper is to propose a new acceleration/deceleration (acc/dec) algorithm for motion profiles. The motion efficiency, flexibility of the motion profiles and the residual vibration of the movement are discussed in this paper.

A dynamics model is developed to assess the residual vibration of these two kinds of motion profile. And a Simulink model is created to assess the motion efficiency and flexibility of the motion profiles with the proposed acc/dec algorithm.

Considering the flexibility of trigonometric motion profiles and the higher motion efficiency of S-curve motion profiles, the authors add the polynomial parts into the jerk profile of the cosine function acc/dec algorithm to hold the jerk when it reaches the maximum so that the motion efficiency can increase and decrease residual vibration at the same time. And the cyclical parameter k shows the decisive factor for the flexibility of trigonometric motion profiles.

Comparing with the traditional motion profiles, the proposed motion profiles have higher motion efficiency and excite less residual vibration. The acc/dec algorithm proposed in this paper is useful for the present motion control and servo system.

The purpose of this study is to examine the impact of social media affordances, specifically social engagement and social endorsement, on knowledge contribution in online Q&A communities. Building on self-determination theory, this research seeks to tackle the issue of under-provision of knowledge in these communities.

The study employs a sample collected from a popular social Q&A community in China and uses linear panel data models along with multiple robustness checks to test the research model.

The findings reveal that both social engagement and social endorsement have a positive effect on users' knowledge contribution to the online Q&A community. However, the impact of social engagement is mitigated by social endorsement.

This paper makes a valuable contribution to the field by filling the research gap on the role of social engagement behaviors and their interaction with social endorsement in online Q&A communities. The results provide insights into how social media affordances can be leveraged to enhance knowledge contribution in these communities.

The digital revolution and the use of big data (BD) in particular has important applications in the construction industry. In construction, massive amounts of heterogeneous data need to be analyzed to improve onsite efficiency. This article presents a systematic review and identifies future research directions, presenting valuable conclusions derived from rigorous bibliometric tools. The results of this study may provide guidelines for construction engineering and global policymaking to change the current low-efficiency of construction sites.

This study identifies research trends from 1,253 peer-reviewed papers, using general statistics, keyword co-occurrence analysis, critical review, and qualitative-bibliometric techniques in two rounds of search.

The number of studies in this area rapidly increased from 2012 to 2020. A significant number of publications originated in the UK, China, the US, and Australia, and the smallest number from one of these countries is more than twice the largest number in the remaining countries. Keyword co-occurrence is divided into three clusters: BD application scenarios, emerging technology in BD, and BD management. Currently developing approaches in BD analytics include machine learning, data mining, and heuristic-optimization algorithms such as graph convolutional, recurrent neural networks and natural language processes (NLP). Studies have focused on safety management, energy reduction, and cost prediction. Blockchain integrated with BD is a promising means of managing construction contracts.

The study of BD is in a stage of rapid development, and this bibliometric analysis is only a part of the necessary practical analysis.

National policies, temporal and spatial distribution, BD flow are interpreted, and the results of this may provide guidelines for policymakers. Overall, this work may develop the body of knowledge, producing a reference point and identifying future development.

To our knowledge, this is the first bibliometric review of BD in the construction industry. This study can also benefit construction practitioners by providing them a focused perspective of BD for emerging practices in the construction industry.

This study aims to identify the impact of affective components on behavioral intentions applying electronic word of mouth (eWOM) and is based on the “cognitive–affective–conative” model. EWOM allows researchers to get new insights about consumers’ behavior and explores new patterns of consumers’ decision-making processes.

This study is based on the theory of planned behavior (Ajzen, 1991), doubled with “cognitive–affective–conative” model (Oliver, 2014). This study applies structural topic modeling to examine the impact of satisfaction dimensions from all the Tripadvisor reviews on consumer behavioral intentions. The research sample covers all restaurants located in St. Petersburg (n = 10,424) and all consumers’ reviews (n = 286,642).

In this study, the dimensions of the affective component were identified. The results demonstrate that dimensions of the affective component (food quality, service quality, atmosphere and cost fairness) affect behavioral intention (willingness to share positive emotions). In total, 20 topics, forming these dimensions, have been indicated. Consumers tend to pay more attention toward food quality and restaurant staffs’ work when they are willing to share positive emotions and tend to point out auxiliary service when they have less willingness to share positive emotions. Random restaurant visits tend to increase the willingness to share positive emotions.

Research originality lies in a new methodological approach which is based on text mining techniques. To the best of the authors’ knowledge, this study is the first attempt to examine consumer behavior through the lens of the “cognitive–affective–conative” model based on eWOM and covers all businesses in the specific economic sphere. This has allowed the researchers to reveal new dimensions of consumer behavior and brought more insights into the consumers’ decision-making process.

The purpose of this study is to examine the effects of banking innovations (INNs) on customer experience (EXP), satisfaction (SAT) and loyalty (LOY).

The author evaluated the data using a structural equation method-artificial neural network (SEM-ANN) method. The author’s results show the presence of relationship between INN, EXP, SAT and LOY. In this study, the node layers of ANNs add an input layer, hidden layers and an output layer. Each “node” acts as an artificial neuron that communicates with others. The ANN model takes the variables from the SEM analysis as input neurons.

The author observed the significant effects between INN, EXP, SAT and LOY using the normalised importance generated by the multilayer perceptron used in the feed-forward back propagation of the ANN methodology. In this study, the ANN model can predict LOY through service innovation, with a forecast accuracy of 77.6%.

By applying neural network modelling, this research helps us understand how service innovation affects customer behaviour. For the first time, the author examined service innovations' direct and indirect impact on loyalty through EXP and SAT. The author made a significant conceptual contribution by using a non-compensatory model of ANNs to circumvent the limitations of linear models.

This paper aims to review recent research in design for additive manufacturing (DfAM), including additive manufacturing (AM) terminology, trends, methods, classification of DfAM methods and software. The focus is on the design engineer’s role in the DfAM process and includes which design methods and tools exist to aid the design process. This includes methods, guidelines and software to achieve design optimization and in further steps to increase the level of design automation for metal AM techniques. The research has a special interest in structural optimization and the coupling between topology optimization and AM.

The method used in the review consists of six rounds in which literature was sequentially collected, sorted and removed. Full presentation of the method used could be found in the paper.

Existing DfAM research has been divided into three main groups – component, part and process design – and based on the review of existing DfAM methods, a proposal for a DfAM process has been compiled. Design support suitable for use by design engineers is linked to each step in the compiled DfAM process. Finally, the review suggests a possible new DfAM process that allows a higher degree of design automation than today’s process. Furthermore, research areas that need to be further developed to achieve this framework are pointed out.

The review maps existing research in design for additive manufacturing and compiles a proposed design method. For each step in the proposed method, existing methods and software are coupled. This type of overall methodology with connecting methods and software did not exist before. The work also contributes with a discussion regarding future design process and automation.

This paper aims to estimate the relationship between defect structure with gas concentration for use as a gas sensor. The change in defect concentration caused a shift in the Fermi level, which in turn changed the surface potential, which is manifested as the potentiometric response of the sensing element.

A new theoretical concept based on defect chemistry and band structure was used to explain the experimental gas response of a sensor. The theoretically simulated response was compared with experimental results.

Understanding the origin of potentiometric response, through the generation of defects and a corresponding shift in Fermi level of sensing surface, by the adsorption of gas. Through this understanding, the design of a sensor with improved selectivity and stability to a gas can be achieved by the study of defect structure and subsequent band analysis.

This paper provides information about various types of surface defects and numerical simulation of material with defect structure. The Fermi energy of the simulated value is correlated with the potentiometric sensor response.

Gas sensors are an integral part of vehicular and industrial pollution control. The theory developed shows the origin of response which can help in identifying the best sensing material and its optimum temperature of operation.

Low-cost, reliable and highly sensitive gas sensors are highly demanded which is fulfilled by potentiometric sensors.

The operating principle of potentiometric sensors is analyzed through electron band structure analysis. With the change in measured gas concentration, the oxygen partial pressure changes. This results in a change in defect concentration in the sensing surface. Band structure analysis shows that change in defect concentration is associated with a shift in Fermi level. This is the origin of the potentiometric response.

A review on additive manufacturing (AM) of shape memory polymer composites (SMPCs) is put forward to highlight the progress made up to date, conduct a critical review and show the limitations and possible improvements in the different research areas within the different AM techniques. The purpose of this study is to identify academic and industrial opportunities.

This paper introduces the reader to three-dimensional (3 D) and four-dimensional printing of shape memory polymers (SMPs). Specifically, this review centres on manufacturing technologies based on material extrusion, photopolymerization, powder-based and lamination manufacturing processes. AM of SMPC was classified according to the nature of the filler material: particle dispersed, i.e. carbon, metallic and ceramic and long fibre reinforced materials, i.e. carbon fibres. This paper makes a distinction for multi-material printing with SMPs, as multi-functionality and exciting applications can be proposed through this method. Manufacturing strategies and technologies for SMPC are addressed in this review and opportunities in the research are highlighted.

This paper denotes the existing limitations in the current AM technologies and proposes several directions that will contribute to better use and improvements in the production of additive manufactured SMPC. With advances in AM technologies, gradient changes in material properties can open diverse applications of SMPC. Because of multi-material printing, co-manufacturing sensors to 3D printed smart structures can bring this technology a step closer to obtain full control of the shape memory effect and its characteristics. This paper discusses the novel developments in device and functional part design using SMPC, which should be aided with simple first stage design models followed by complex simulations for iterative and optimized design. A change in paradigm for designing complex structures is still to be made from engineers to exploit the full potential of additive manufactured SMPC structures.

Advances in AM have opened the gateway to the potential design and fabrication of functional parts with SMPs and their composites. There have been many publications and reviews conducted in this area; yet, many mainly focus on SMPs and reserve a small section to SMPC. This paper presents a comprehensive review directed solely on the AM of SMPC while highlighting the research opportunities.