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Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments.

Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments.

The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation.

Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.

This paper aims to optimize the stiffness coefficient of the elastic element for a passive waist assistive exoskeleton (WAE). There is a tradeoff between stiffness coefficient of elastic element of the exoskeleton and work efficiency of the wearer, because elastic element affects original bending motion of the wearer and the force requirement of erector spinae is compensated by the other muscles. However, there is no accepted conclusion on how to determine the proper stiffness coefficient, especially with respected to the effort of groups of muscles, not only erector spinae.

In this study, a consumption indicator based on muscle fatigue of seven muscles is proposed and a Bayesian-based human-in-the-loop optimization approach is adopted to optimize the stiffness coefficient. Pneumatic artificial muscles are used to replace the mechanical elastic part to adjust the assistive force automatically. The proposed optimization method is verified by the way of load-lifting experiments with three different conditions: without exoskeleton, with fixed air pressure and with optimized air pressure. Six subjects participated in the experiment and each experiment is performed in different day.

Compared with No-Exo condition and static assistance condition, the parameter-optimized waist exoskeleton averagely reduces muscle fatigue of the six subjects by 45.30 ± 29.14% and 30.94 ± 30.29%, respectively. The experimental results indicate that the proposed method is effective to reduce muscle fatigue during stoop lifting task.

This paper provides a novel cost function construction method based on muscle fatigue and muscle synergy for passive WAE stiffness optimization.

Plantar force is the interface pressure existing between the foot plantar surface and the shoe sole during static or dynamic gait. Plantar force derived from gait and posture plays a critical role for rehabilitation, footwear design, clinical diagnostics and sports activities, and so on. This paper aims to review plantar force measurement technologies based on piezoelectric materials, which can make the reader understand preliminary works systematically and provide convenience for researchers to further study.

The review introduces working principle of piezoelectric sensor, structures and hardware design of plantar force measurement systems based on piezoelectric materials. The structures of sensors in plantar force measurement systems can be divided into four kinds, including monolayered sensor, multilayered sensor, tri-axial sensor and other sensor. The previous studies about plantar force measurement system based on piezoelectric technology are reviewed in detail, and their characteristics and performances are compared.

A good deal of measurement technologies have been studied by researchers to detect and analyze the plantar force. Among these measurement technologies, taking advantage of easy fabrication and high sensitivity, piezoelectric sensor is an ideal candidate sensing element. However, the number and arrangement of the sensors will influence the characteristics and performances of plantar force measurement systems. Therefore, it is necessary to further study plantar force measurement system for better performances.

So far, many plantar force measurement systems have been proposed, and several reviews already introduced plantar force measurement systems in the aspect of types of pressure sensors, experimental setups for foot pressure measurement analysis and the technologies used in plantar shear stress measurements. However, this paper reviews plantar force measurement systems based on piezoelectric materials. The structures of piezoelectric sensors in the measurement systems are discussed. Hardware design applied to measurement system is summarized. Moreover, the main point of further study is presented in this paper.

In the past, with the development of the internet of things (IoT), this paper aims to consider fog computing (FC) as an efficient accompaniment to the cloud to control the IoT’s information and relation requirements. Wholly, FC is placed carefully around the IoT systems/sensors and develops cloud-based computing, memory and networking devices. Fog shares many similarities with the cloud, but the only difference between them is its location, in which fog devices are very close to end-users to process and respond to the client in less time. On the other hand, this system is useful for real-time flowing programs, sensor systems, and IoT that need high speed and reliable internet connectivity. However, there are many applications such as remote healthcare and medical cyber-physical systems, where low latency is needed. To reduce the latency of FC, the task scheduler plays a vital role. The task scheduling means to devote the task to fog resources in an efficient way. Yet, according to the findings, in spite of the preference of task scheduling techniques in the FC, there is not any review and research in this case. So, this paper offers systematic literature research about the available task scheduling techniques. In addition, the advantages and disadvantages associated with different task scheduling processes are considered, and the main challenges of them are addressed to design a more efficient task scheduler in the future. Additionally, according to the seen facts, future instructions are provided for these studies.

The paper complies with the methodological requirements of systematic literature reviews (SLR). The present paper investigates the newest systems and studies their practical techniques in detail. The applications of task scheduling mechanisms in FC have been categorized into two major groups, including heuristic and meta-heuristic.

Particularly, the replies to the project problem analyzed task scheduling are principal aim, present problems, project terminologies, methods and approaches in the fog settings. The authors tried to design his systematic discussion as precisely as possible. However, it might have still endured various confidence risks.

This study aimed to be comprehensive but there were some limitations. First, the usage of affair scheduling in fog settings are contained in many places such as editorial notes, academic publications, technical writings, Web pages and so on. The published papers in national magazines were omitted. Also, the papers with the purpose of a special task scheduling issue, which probably consider other subjects rather than affair planning issue are omitted. So, in the competence of this study, this systematic analysis must be considered as the studies published in the central international FC journals. Second, the given issues might not have considered the general task scheduling area, which points to the possibility of describing more related questions that could be described. Third, research and publication bias: five confident electronic databases were chosen based on past study experiments. Finally, the numbers show that these five electronic databases must suggest the most related and reliable projects. Yet, selecting all main performing projects has not been confirmed. Probably some effective projects were omitted throughout the processes in Section 3. Different from the conclusion, changing from the search string to the information extraction exists, and the authors tried to exclude this by satisfying the source in central projects.

The results of this survey will be valuable for academicians, and it can provide visions into future research areas in this domain. Moreover, the disadvantages and advantages of the above systems have been studied, and their key issues have been emphasized to develop a more effective task scheduling mechanisms in the FC mechanisms.

It is useful to show the authors the state-of-the-art in the fog task scheduling area. The consequences of this project make researchers provide a more effective task planning approach in fog settings.

This rejoinder aims to critique some of the perspectives in Chen’s (2018) study on hospitality and host–guest paradigm in China.

In this preliminary probe into Chen’s study, critical analysis is performed on Chen’s approaches, whereas document analysis is applied to the relevant ancient Chinese writings. Translations are the author’s own unless indicated otherwise.

Chen’s views are problematic and supported by patchy evidence. “Hierarchy principle” and “host-centric foundation” do not hold up. In a series of asymmetrical comparisons, Chen goes a bit too far in arguing for uniqueness.

This rejoinder presents valid critiques of Chen’s study, thus directing future research in the right direction. It is of some significance to clarify Chen’s misrepresentations and offer a truer and fuller account of Chinese hospitality, given the difficulty for international scholars to validate Chen’s views because of language barrier and cultural unfamiliarity.

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.

This study aims to outline the transformative impacts of technological developments (TD) on architectural education (AE). The focus is on studying the dynamics of convergence and erosion, aiming to understand the impact of current digital transformations such as the COVID-19 pandemic, sustainability considerations and technology integration on AE.

A scientometric analysis and bibliometric search were performed to understand the current knowledge in the field. The Web of Science (WoS) was selected for its comprehensive collection of significant research articles and integrated analytical tools for generating representative data. The study involved an extensive bibliometric analysis of 131 journal articles on TD in AE from 2000 to 2023. Subsequently, the VOSviewer software was employed to illuminate the transformative impacts of technological advancements on AE. Robust methodologies, including citation analysis and co-citation networks, unravel quantitative dimensions such as publication trends, influential authors, prolific journals, geographic distribution and prevalent themes or technological domains within the discourse.

The findings reveal significant evolution in AE due to TD, with notable shifts influenced by factors such as the COVID-19 pandemic, sustainability concerns and the integration of modern technologies. Key findings include the increasing adoption of online platforms and technologies like Building Information Modeling (BIM), the crucial role of design thinking methodologies and the recognition of innovative modules such as architectural photogrammetry and augmented reality (AR) applications. Furthermore, keyword clusters were classified into six groups: “AE pedagogy and curriculum development,” “Development of architectural design process and studio,” “Educational approaches and digitalization in architectural design,” “Transition to online AE,” “Development/emergence of photogrammetry at architectural education” and “use of AR.”

Although several studies have addressed TD in AE from various perspectives and methods, they have yet to investigate the subject using scientometric analysis from a holistic perspective. A holistic exploration of TD in AE still needs to be improved in the existing literature. In contrast to previous investigations, this study is the first to leverage the quantitative analytical capabilities of VOSviewer. The originality lies in uncovering quantitative dimensions such as publication trends, influential authors and prevalent themes, providing a comprehensive understanding of the nuanced dimensions of this evolving intersection.

Text mining has been used for various knowledge discovery based applications, and thus, a lot of research has been contributed towards it. Latest trending research in the text mining is adopting the incremental learning data, as it is economical while dealing with large volume of information.

The primary intention of this research is to design and develop a technique for incremental text categorization using optimized Support Vector Neural Network (SVNN). The proposed technique involves four major steps, such as pre-processing, feature selection, classification and feature extraction. Initially, the data is pre-processed based on stop word removal and stemming. Then, the feature extraction is done by extracting semantic word-based features and Term Frequency and Inverse Document Frequency (TF-IDF). From the extracted features, the important features are selected using Bhattacharya distance measure and the features are subjected as the input to the proposed classifier. The proposed classifier performs incremental learning using SVNN, wherein the weights are bounded in a limit using rough set theory. Moreover, for the optimal selection of weights in SVNN, Moth Search (MS) algorithm is used. Thus, the proposed classifier, named Rough set MS-SVNN, performs the text categorization for the incremental data, given as the input.

For the experimentation, the 20 News group dataset, and the Reuters dataset are used. Simulation results indicate that the proposed Rough set based MS-SVNN has achieved 0.7743, 0.7774 and 0.7745 for the precision, recall and F-measure, respectively.

In this paper, an online incremental learner is developed for the text categorization. The text categorization is done by developing the Rough set MS-SVNN classifier, which classifies the incoming texts based on the boundary condition evaluated by the Rough set theory, and the optimal weights from the MS. The proposed online text categorization scheme has the basic steps, like pre-processing, feature extraction, feature selection and classification. The pre-processing is carried out to identify the unique words from the dataset, and the features like semantic word-based features and TF-IDF are obtained from the keyword set. Feature selection is done by setting a minimum Bhattacharya distance measure, and the selected features are provided to the proposed Rough set MS-SVNN for the classification.

By using food consumption as a kaleidoscope, the aim of this paper is to illustrate the bidirectional and dialectical interactions among caregivers and between single children and their multiple caregivers in Xiamen.

This paper reports findings from in‐depth interviews with grandparents and parents (n=33) from ten three‐generational families and parents (n=20) from ten nuclear families with single children between six and nine years old.

Grandparents unequivocally accorded supreme importance to ensuring a child finishes his/her meals while parents tended to hold slightly more liberal views. These differences created higher conflict and tensions between the caregivers in three‐generational families as compared to nuclear households. Children in multigenerational families were more frequently force‐fed by adult‐caregivers whereas their counterparts were more capable of feeding themselves during meal times.

This exploratory study provides in‐depth insights but carries the limitation of generalisability. Future research can apply similar methodology on bigger samples so as to ascertain a better estimation of the extent of stress and tension across generations over meal times in urban China.

The initial finding reported in this paper is useful for public health workers and family life educators in China to help families with children of middle childhood to manage tension arising from meal times.

Diverging from most parenting research which tends to focus on parent‐child dynamics, this paper included three generations – grandparents, parents, grandchildren – in the research conceptualization. This is important as grandparents are integral parts of childrearing in China.

Technological disruptions such as the Internet of Things and autonomous devices, enhanced analytical capabilities (artificial intelligence) and rich media (virtual and augmented reality) are creating smart environments that are transforming industry structures, processes and practices. The purpose of this paper is to explore critical technological advancements using a value co-creation lens to provide insights into service innovations that impact ecosystems. The paper provides examples from tourism and hospitality industries as an information dependent service management context.

The research synthesizes prevailing theories of co-creation, service ecosystems, networks and technology disruption with emerging technological developments.

Findings highlight the need for research into service innovations in the tourism and hospitality sector at both macro-market and micro-firm levels, emanating from the rapid and radical nature of technological advancements. Specifically, the paper identifies three areas of likely future disruption in service experiences that may benefit from immediate attention: extra-sensory experiences, hyper-personalized experiences and beyond-automation experiences.

Tourism and hospitality services prevail under varying levels of infrastructure, organization and cultural constraints. This paper provides an overview of potential disruptions and developments and does not delve into individual destination types and settings. This will require future work that conceptualizes and examines how stakeholders may adapt within specific contexts.

Technological disruptions impact all facets of life. A comprehensive picture of developments here provides policymakers with nuanced perspectives to better prepare for impending change.

Guest experiences in tourism and hospitality by definition take place in hostile environments that are outside the safety and familiarity of one’s own surroundings. The emergence of smart environments will redefine how customers navigate their experiences. At a conceptual level, this requires a complete rethink of how stakeholders should leverage technologies, engage and reengineer services to remain competitive. The paper illustrates how technology disrupts industry structures and stimulates value co-creation at the micro and macro-societal level.