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Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.

In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.

Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

The purpose of this study is to offer a better and more effective hexacopter design for a 3 kg payload using finite element analysis (FEA), facilitating the use of different materials for different components that too without compromising strength.

A 3D computer-aided design (CAD) model of a hexacopter with a regular hexagonal frame is presented. Furthermore, a finite element model is developed to perform a structural analysis and determine Von Mises stress and strain values along with deformations of different components of the proposed hexacopter design.

The results establish that carbon fibre outperforms acrylonitrile butadiene (ABS) with respect to deformations. Within the permissible limits of the stress and strain values, both carbon fiber and ABS are suggested for different components. Thus, a proposed hexacopter offers lighter weight, high strength and low cost.

The use of different materials for different components is suggested by making use of static structural analysis. This encourages new research work and helps in developing new applications of hexacopter, and it has never been reported in literature. The suggested materials for the components of the hexacopter will prove to be suitable considering weight, strength and cost.

The purpose of this paper is to investigate the process of fused filament fabrication (FFF) of a compliant gripper (CG) using thermoplastic polyurethane (TPU) material. The paper studies the applicability of different CG designs and the efficiency of some design parameters.

After reviewing a number of different papers, two designs were selected for a number of exploratory experiments. Using design of experiments (DOE) techniques to identify important design parameters. Finally, the efficiency of the parts was investigated.

The research finds that a simpler design sacrifices some effectiveness in exchange for a remarkable decrease in production cost. Decreasing infill percentage of previous designs and 3D printing them, out of TPU, experimenting with different parameters yields functional products. Moreover, the paper identified some key parameters for further optimization attempts of such prototypes.

The cost of conducting FFF experiments for TPU increases dramatically with product size, number of parameters studied and the number of experiments. Therefore, all three of these factors had to be kept at a minimum. Further confirmatory experiments encouraged.

This paper addresses an identified need to investigate applications of FFF and TPU in manufacturing functional efficient flexible mechanisms, grippers specifically. While most research focused on designing for increased performance, some research lacks discussion on design philosophy, as well as manufacturing issues. As the needs for flexible grippers vary from high-performance grippers to lower performance grippers created for specific functions/conditions, some effectiveness can be sacrificed to reduce cost, reduce complexity and improve applicability in different robotic assemblies and environments.

The study aims to examine the impact of three types of supply chain integration (SCI) on supply chain flexibility (SCF), investigate the impact of SCF on supply chain performance (SCP) and analyse the indirect impact of SCI on SCP by considering the mediating role of SCF within the manufacturing sector of Jordan.

This study used a quantitative approach to validate the study model. An online self-completed questionnaire was used to gather data from 219 participants from managers in various Jordanian manufacturing firms. SmartPLS software was used to perform structural equation modelling to test the formulated hypotheses.

Based on the findings of the study, firms in Jordan's manufacturing sector would benefit from developing an integrative and flexible supply chain to boost SCP in the present volatile, uncertain, complex and speculative market. In addition, SCP was significantly influenced by investments in supply chain management practices related to SCI and SCF. Moreover, SCF significantly moderated the relationship between SCI and SCP. Thus, SCI and SCF assisted firms in reaching their highest potential performance through increased productivity, decreased expenses and increased satisfaction of their customers.

The study employed a cross-sectional design using SCF as a single construct. Future research should look into the specific type of SCFs that have an immense effect on SCP and how these types are affected by the three types of SCI. Furthermore, future research ought to employ probability sampling techniques to improve the generalizability of results or using a longitudinal data-collection design. Finally, additional research should be conducted to validate the findings of this study by replicating it in other specific industries or countries.

The study fills an identified gap based on previous studies by exploring the linkages between SCI, SCF and SCP in the context of manufacturing sector. Moreover, based on the relational view theory, the study proposed an assessment mechanism for SCP for firms based on the link between three types of SCI and SCF.

The purpose of this paper is to develop a framework to facilitate early supplier involvement (ESI) as an approach for reducing construction waste (CW) generated during the design process.

A research methodology consists of literature review, case studies and survey questionnaire was designed to achieve the abovementioned aim. First, the literature review was used to investigate CW, CW generated during the design process, the design process and ESI. Second, two construction projects were analysed to investigate the role of ESI towards reducing CW generated during the design waste. Third, a survey questionnaire was carried out with a representative sample of architectural design firms (ADFs) in Egypt to examine their perception and application of ESI towards reducing CW generated during the design process. Based on the results of the above, the research developed a framework to facilitate ESI during the design process to reduce CW.

Through literature review, the research highlighted the causes of CW generated during the design process and identified the benefits and challenges that encounter ESI in ADFs. Results of data analysis showed that “last-minute changes due to client requirements” was ranked the highest challenges of ESI in ADFs in Egypt followed by “lack of design experience”. Moreover, “providing technical information about materials and equipment and their capabilities” was ranked by respondents as the highest contributions of suppliers during the design process which leads to waste reduction followed by “better estimation of materials and costs”. Finally, respondents stated that “resistance of ADFs to include suppliers in the design phase” was ranked the highest challenge of ESI in the design process followed by “no clear guidelines or policies that organise ESI”.

Because of the conceptual nature of the proposed framework, it has to be tested and validated to ensure its capability to overcome the challenges of ESI as an approach for reducing CW generated during the design process.

This research presents a practical solution to the problem of CW generated during the design process through ESI.

The research discussed the causes of CW originated from the design process and the benefits and challenges of ESI in ADFs. It investigated the perception and application of ESI in ADFs in Egypt. The proposed framework which was designed to facilitate the integration of suppliers in the early stages of the project life cycle represents a synthesis that is novel and creative in thought and adds value to the knowledge in a manner that has not previously occurred.

The purpose of this paper is to analyze to what extent and in what ways signaling theory has been explored within the field of international marketing. This paper systematically reviews the use of signaling theory in the field of international marketing. Communication is a core aspect of the international marketing process. Research in this field has explored effective and unique ways of improving the communication flow to reduce the asymmetry of information between international consumers and the firm. This notion is adopted, enhanced and strengthened by signaling theory. Signaling theory has recently received the attention of international marketing scholars.

The systematic review methodology was applied for the purpose of identifying the relevant studies. We extracted academic articles over the last 23 years from the domain of international marketing that directly contribute to signaling theory based on 57 journal articles extracted through the systematic review process.

Based on systematic research the results reveal that the topic has grown and continues to expand within the broader international marketing field. We offer a theoretical conceptual framework to better understand signaling theory in the context of international marketing.

The authors map and critically evaluate the use of signaling theory in international marketing. Relevance of signaling theory in international marketing is growing and authors present an integrative framework that organizes the existing literature, and provides scholars to further expand on emerging themes of the domain. The paper offers some useful future research directions.

Data from different countries suggest a higher prevalence of anxiety, depression and suicides among manual and trade workers in the construction industry than in the general population. The present review examines the causes and effects of poor mental health and the effectiveness of interventions to improve manual and trade workers' mental health in the construction industry. It also identifies gaps in research and makes several suggestions for practice and future research.

A systematic literature review was conducted to examine and consolidate evidence reported in 54 relevant journal articles published between 2010 and 2021 on the mental health of manual and trade workers.

Three major themes emerged in the review of the 54 journal articles: causes of poor mental health, effects of poor mental health and interventions to improve mental health. The leading causes of poor mental health among construction manual and trade workers are poor work-life balance, high job demand, poor cultural norms and mental health stigma, chronic bodily pain, lack of social support, workplace injustice and job insecurity. The prominent effects of poor mental health are suicidality, drug and alcohol addiction, poor workplace safety and poor work performance. Moreover, the study found that some of the strategies recently implemented in the construction industry to improve mental health are deemed ineffective, or their effectiveness remains inconclusive.

The review's scope is limited to research on manual and trade workers, and it did not investigate the mental health of construction professionals and construction management students.

The review provides valuable insights into the causes and effects of poor mental health among manual and trade workers and the effectiveness of mental health interventions in the construction industry.

Modern subway transportation systems need to satisfy increasing safety demands to rapidly evacuate passengers under hazardous emergency circumstances, such as fires, accidents or terrorist attacks, to reduce passenger injuries or life losses. The emergency evacuation capacity (EEC) of a subway station needs to be revised timely, in case passenger demand increases or the evacuation route changes in the future. However, traditional ways of estimating EEC, e.g. fire drills are time- and resource-consuming and are difficult to revise from time to time. The purpose of this study is to establish an intuitive modelling approach to increase the EEC of subway stations in a stepwised manner.

This study develops an approach to combine agent-based evacuation modelling and building information modelling (BIM) technology to estimate the total evacuation time of a subway station.

Evacuation time can be saved (33% in the studied case) from iterative improvements including stopping escalators running against the evacuation flow and modifying the geometry around escalator exits. Such iterative improvements rely on integrating agent-based modelling and BIM.

The agent-based model can provide a more realistic simulation of intelligent individual movements under emergency circumstances and provides precise feedback on locations of evacuation bottlenecks. This study also examined the effectiveness of two rounds of stepwise improvements in terms of operation or design to increase the EEC of the station.

The Government’s investment in infrastructure projects is considerably high, especially in bridge construction projects. Government authorities must establish an initial forecasted budget to have transparency in transactions. Early cost estimating is challenging for Quantity Surveyors due to incomplete project details at the initial stage and the unavailability of standard cost estimating techniques for bridge projects. To mitigate the difficulties in the traditional preliminary cost estimating methods, there is a requirement to develop a new initial cost estimating model which is accurate, user friendly and straightforward. The research was carried out in Sri Lanka, and this paper aims to develop the artificial neural network (ANN) model for an early cost estimate of concrete bridge systems.

The construction cost data of 30 concrete bridge projects which are in Sri Lanka constructed within the past ten years were trained and tested to develop an ANN cost model. Backpropagation technique was used to identify the number of hidden layers, iteration and momentum for optimum neural network architectures.

An ANN cost model was developed, furnishing the best result since it succeeded with around 90% validation accuracy. It created a cost estimation model for the public sector as an accurate, heuristic, flexible and efficient technique.

The research contributes to the current body of knowledge by providing the most accurate early-stage cost estimate for the concrete bridge systems in Sri Lanka. In addition, the research findings would be helpful for stakeholders and policymakers to propose policy recommendations that positively influence the prediction of the most accurate cost estimate for concrete bridge construction projects in Sri Lanka and other developing countries.