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The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently.

In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices.

Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level.

This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.

The belt and road initiative (BRI) emanates from China and seeks to connect Europe, Asia and Africa through transport and telecommunications infrastructure. Despite the importance of Africa in the BRI network, very little research has been done on the BRI in Africa, and even less of this emanates from Africa itself. In particular, considering that the BRI investments in Africa are largely transport related, there is almost no research covering the area of logistics, which should be greatly affected by the infrastructure investments. This paper sought to establish the current state of logistics research related to the BRI in Africa.

A bibliometric analysis was conducted on documents extracted from the SCOPUS database.

The findings indicate that there is a lack of research in critical areas such as environmental, social and economic impact of BRI transport investments, governance, logistics performance and international cooperation. In particular, there is a massive gap in local knowledge regarding the BRI.

The study is limited to published research indexed in the SCOPUS database. Future research directions include empirical studies into BRI project initiation investigation, economic and environmental impacts, governance structures and policy intervention requirements and macro-level logistics impacts.

The study emphasises the importance publishing all the relevant information regarding BRI related projects in Africa to create transparency.

The study investigates the current research on the effect of China's BRI on transport and logistics in Africa through a bibliometric analysis. The investigation reveals that while there are huge investments in infrastructure, the actual effect on logistics of participating countries in Africa has not been interrogated.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

This study aims to present a comprehensive knowledge mapping and an in-depth analysis of plastic and sustainability research to understand better global trends and directions in this field that emerged between 1995 and 2022.

This study presents a visual analysis of 1933 research articles listed in the Web of Science (WoS) databases between the years 1995 and 2022 related to plastic and sustainability. The knowledge mapping based on CiteSpace and VOSviewer presents the current research status, which contains the analysis of the collaboration network, co-citation network, references with citation bursts and keyword analysis.

The results reveal that China and the USA are the most prominent countries in exploring the notion of sustainability and plastic. The Chinese Academy of Science is the most prominent institution. Chai Qiang, Friedrich Daniel, Sahajwalla Veena and Ok Yong Sik are the most prolific authors in this field. Furthermore, circular economy, bioplastic, sustainable development, polyester and bioplastics are the highly discussed issues in recent years. Not surprisingly, COVID-19 is the latest topic of discussion started in 2021 due to its negative impact on plastic pollution and the challenges it posed to sustainability.

This study is among the pioneers to shed light on the current research status of plastic and sustainability using the bibliometric method and the newest data. This study also suggests that collaborations between scholars and institutions require to be enhanced for better management of plastic pollution and to contribute to sustainable development.

Accurate values for infiltration rate are important to reliably estimate heat losses from buildings. Infiltration rate is rarely measured directly, and instead is usually estimated using algorithms or data from fan pressurisation tests. However, there is growing evidence that the commonly used methods for estimating infiltration rate are inaccurate in UK dwellings. Furthermore, most prior research was conducted during the winter season or relies on single measurements in each dwelling. Infiltration rates also affect the likelihood and severity of summertime overheating. The purpose of this work is to measure infiltration rates in summer, to compare this to different infiltration estimation methods, and to quantify the differences.

Fifteen whole house tracer gas tests were undertaken in the same test house during spring and summer to measure the whole building infiltration rate. Eleven infiltration estimation methods were used to predict infiltration rate, and these were compared to the measured values. Most, but not all, infiltration estimation methods relied on data from fan pressurisation (blower door) tests. A further four tracer gas tests were also done with trickle vents open to allow for comment on indoor air quality, but not compared to infiltration estimation methods.

The eleven estimation methods predicted infiltration rates between 64 and 208% higher than measured. The ASHRAE Enhanced derived infiltration rate (0.41 ach) was closest to the measured value of 0.25 ach, but still significantly different. The infiltration rate predicted by the “divide-by-20” rule of thumb, which is commonly used in the UK, was second furthest from the measured value at 0.73 ach. Indoor air quality is likely to be unsatisfactory in summer when windows are closed, even if trickle vents are open.

The findings have implications for those using dynamic thermal modelling to predict summertime overheating who, in the absence of a directly measured value for infiltration rate (i.e. by tracer gas), currently commonly use infiltration estimation methods such as the “divide-by-20” rule. Therefore, infiltration may be overestimated resulting in overheating risk and indoor air quality being incorrectly predicted.

Direct measurement of air infiltration rate is rare, especially multiple tests in a single home. Past measurements have invariably focused on the winter heating season. This work is original in that the tracer gas technique used to measure infiltration rate many times in a single dwelling during the summer. This work is also original in that it quantifies both the infiltration rate and its variability, and compares these to values produced by eleven infiltration estimation methods.

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations.

After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations).

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).

The era of crowd network is coming and the research of its steady-state is of great importance. This paper aims to establish a crowd network simulation platform and maintaining the relative stability of multi-source dissemination systems.

With this simulation platform, this paper studies the characteristics of “emergence,” monitors the state of the system and according to the fixed point judges the system of steady-state conditions, then uses three control conditions and control methods to control the system status to acquire general rules for maintain the stability of multi-source information dissemination systems.

This paper establishes a novel steady-state maintenance simulation framework. It will be useful for achieving controllability to the evolution of information dissemination and simulating the effectiveness of control conditions for multi-source information dissemination systems.

This paper will help researchers to solve problems of public opinion control in multi-source information dissemination in crowd network.

This paper aims to outline a map of all the research that exists on mobile technology and tourism archived in the two main databases worldwide (Web of Science and Scopus). Accordingly, with the identification of all the scientific articles that deal with both mobile technology and tourism, the authors seek to ascertain the evolution of mobile technology in the tourism sector through the years, countries, universities and authors and determine the various collaborations brought about between authors, universities, institutions and/or companies in various research projects. Finally, it also allows the authors to distinguish the main topics under study within the scope of ‘mobile tourism’.

A mixed methodology has been carried out. The search focused on the principal databases of bibliographic references and citations of periodical publications, such as articles from scientific journals, books and other types of printed material. Once the results were obtained in the respective databases, it was necessary to be able to work with them. In this respect, the authors had to extract the relevant data and dump it in a bibliographic reference manager, for which they chose Mendeley. After this, the tabulation of data was performed in Excel and tables and graphs were created from all the data collected.

The main results obtained and analyzed are the number of articles per year, countries and universities. In the same way, it is interesting to highlight the number of countries and universities that participate in each article under study. On the other hand, an analysis has been carried out regarding the number of articles per author, as well as the topics dealt with in the different articles.

This analysis reveals the role that has been played by mobile phones in tourism since the first scientific article was recorded in 2002. In this regard, in recent years there has been a significant increase in the number of articles, finally resulting in moderate figures in relation to countries (40) and universities (233) that have formed part of the subject matter under study. In contrast to other areas of research in tourism, the relevance of this subject is therefore evident, as is the need for greater background knowledge to establish research models adapted to the new reality of tourism in a world of ever-increasing mobility.

Background: This paper examines the impact of coronavirus COVID-19 outbreak on employees’ mental health, specifically psychological distress and depression. It aims at identifying the main stressors during and post COVID-19, examining the main moderating factors which may mitigate or aggravate the impact of COVID-19 on employees’ mental health and finally to suggest recommendations from a human resource management perspective to mitigate COVID-19’s impact on employees’ mental health.

Methods: This paper is a literature review. The search for articles was made in Google scholar, Web of Science and Semantic scholar. We used a combination of terms related to coronavirus OR COVID-19, workplace and mental health. Due to the paucity of studies on the COVID-19 impact on employees’ mental health, we had to draw on studies on recent epidemics.

Results: The identified literature reports a negative impact of COVID-19 on individual’s mental health. Stressors include perception of safety, threat and risk of contagion, infobesity versus the unknown, quarantine and confinement, stigma and social exclusion as well as financial loss and job insecurity. Furthermore, three dimensions of moderating factors have been identified: organizational, institutional and individual factors. In addition, a list of recommendations has been presented to mitigate the impact of COVID-19 on the employee’s mental health, during and after the outbreak, from a human resource management perspective.

Conclusions: Coronavirus is new and is in a rapid progress while writing this paper. Most of current research are biomedical focusing on individuals’ physical health. In this context, mental health issues seem overlooked. This paper helps to broaden the scope of research on workplace mental health, by examining the impact of a complex new pandemic: COVID-19 on employees’ mental health, from social sciences perceptive, mobilizing psychology and human resource management.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.