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The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine learning approach based on Latent Dirichlet Allocation we were able to identify latent topics related to human-centric aspect of Industry 5.0.

This study aims to create a scientific map of the human-centric aspect of manufacturing and thus provide a systematic framework for further research development of Industry 5.0.

In this study a 140 unique research topics were identified, 19 of which had sufficient research impact and research interest so that we could mark them as the most significant. In addition to the most significant topics, this study contains a detailed analysis of their development and points out their connections.

Industry 5.0 has three pillars – human-centric, sustainable, and resilient. The sustainable and resilient aspect of manufacturing has been the subject of many studies in the past. The human-centric aspect of such a systematic description and deep analysis of latent topics is currently just passing through.

The overall objective of this study is envisaged to provide decision makers with actionable insights and access to multi-risk maps for the most in-danger stave churches (SCs) among the existing 28 churches at high spatial resolution to better understand, reduce and mitigate single- and multi-risk. In addition, the present contribution aims to provide decision makers with some information to face the exacerbation of the risk caused by the expected climate change.

Material and data collection started with the consultation of the available literature related to: (1) SCs' conservation status, (2) available methodologies suitable in multi-hazard approach and (3) vulnerability leading indicators to consider when dealing with the impact of natural hazards specifically on immovable cultural heritage.

The paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. The results highlight the danger at which the SCs of Røldal, in case of floods, and of Ringebu, Torpo and Øye, in case of landslide, may face and stress the urgency of increasing awareness and preparedness on these potential hazards.

The contribution for the first time aims to homogeneously collect and report all together existing spread information on architectural features, conservation status and geographical attributes for the whole group of SCs by accompanying this information with as much as possible complete 2D sections collection from existing drawings and novel 3D drawn sketches created for this contribution. Then the paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. Then it highlights the danger of floods and landslides at which the 28 SCs are subjected. Finally it reports how these risks will change under the ongoing impact of climate change.

This study aims to analyse how the adoption of Industry 4.0 technologies can help different types of agri-food supply chains introduce and manage innovations in response to the challenges and opportunities that emerged following the COVID-19 pandemic.

A systematic literature review methodology was used to bring together the most relevant contributions from different disciplines and provide comprehensive results on the use of I4.0 technologies in the agri-food industry.

Four technological clusters are identified, which group together the I4.0 technologies based on the applications in the agri-food industry, the objectives and the advantages provided. In addition, three types of agri-food supply chains have been identified and their configuration and dynamics have been studied. Finally, the I4.0 technologies most suited for each type of supply chain have been identified, and suggestions on how to effectively introduce and manage innovations at different levels of the supply chain are provided.

The study highlights how the effective adoption of I4.0 technologies in the agri-food industry depends on the characteristics of the supply chains. Technologies can be used for different purposes and managers should carefully consider the objectives to be achieved and the synergies between technologies and supply chain dynamics.

Non-verbal communication (NVC) remains largely understudied despite its importance in today's fast-paced and cross-cultural management and research landscape. This article is significant because it reveals valuable insights into NVC, which represents 65–93% (Mehrabian, 1981) of communication and has the potential to considerably increase management effectiveness and efficiency by providing leaders and researchers with the knowledge they need to understand and handle diversity with competence.

This article draws on social identity theory (SIT) (Tajfel and Turner, 1979) and rapport management theory (RMT) (Brown and Levinson, 1987) to analyse illustrative interview extracts of co-occurring verbal and NVC from an interpretative phenomenological analysis (IPA) study focussed on understanding how London-based Romanian migrant entrepreneurs experience acculturation.

Romanian migrant entrepreneurs use a variety of verbal and non-verbal communication approaches in their acculturation narratives, providing depth and occasionally shifting meaning. These tactics include repeating verbal discourse with non-verbal clues, replacing verbal communication with non-verbal gestures, complementing verbal communication and juxtaposing non-verbal cues with verbal descriptions.

This study makes a valuable contribution to the fields of qualitative organisational management and entrepreneurial studies by addressing the lack of methodological tools available for analysing non-verbal language in interpretative research. This study presents a systematic technique for assessing non-verbal language symbols that has been developed through face-to-face interviews. The article utilises the first-hand interview experience of a Romanian co-researcher to demonstrate the significance of NVC in the transmission of meaning and the formation of identities amongst Romanian migrant entrepreneurs. These findings contribute to a better understanding of organisational management and research practices, particularly about this understudied entrepreneurial minority of Romanian businesses in London, by helping researchers and managers better grasp the cultural and contextual meanings communicated non-verbally. The article holds significance in the context of cross-cultural and organisational management practices.

Agile project teams are self-managing and self-organizing teams, and these two characteristics are pivotal attributes of emergent leadership. Emergent leadership is thus common in agile teams – however, how these (informal) emergent leaders can be identified in teams remains far from understood. The purpose of this research is to uncover techniques that enable top management to identify emergent agile leaders.

We approached six agile teams from four organizations. We employ social network analysis (SNA) and aggregation approaches to identify emergent agile leaders.

We approached six agile teams from four organizations. We employ SNA and aggregation approaches to identify emergent agile leaders.

Seven emergent leaders are identified using the SNA and aggregation approaches. The same leaders are also identified using the KeyPlayer algorithms. One emergent leader is identified from each of the five teams, for a total of five emergent leaders from the five teams. However, two emergent leaders are identified for the remaining sixth team.

Emergent leadership is a relatively new phenomenon where leaders emerge from within teams without having a formal leadership assigned role. A challenge remains as to how such leaders can be identified without any formal leadership status. We contribute by showing how network analysis and aggregation approaches are suitable for the identification of emergent leadership talent within teams. In addition, we help advance leadership research by describing the network behaviors of emergent leaders and offering a way forward to identify more than one emergent leader in a team. We also show some limitations of the approaches used and offer some useful insights.

To make the robot that have real autonomous ability is always the goal of mobile robot research. For mobile robots, simultaneous localization and mapping (SLAM) research is no longer satisfied with enabling robots to build maps by remote control, more needs will focus on the autonomous exploration of unknown areas, which refer to the low light, complex spatial features and a series of unstructured environment, lick underground special space (dark and multiintersection). This study aims to propose a novel robot structure with mapping and autonomous exploration algorithms. The experiment proves the detection ability of the robot.

A small bio-inspired mobile robot suitable for underground special space (dark and multiintersection) is designed, and the control system is set up based on STM32 and Jetson Nano. The robot is equipped with double laser sensor and Ackerman chassis structure, which can adapt to the practical requirements of exploration in underground special space. Based on the graph optimization SLAM method, an optimization method for map construction is proposed. The Iterative Closest Point (ICP) algorithm is used to match two frames of laser to recalculate the relative pose of the robot, which improves the sensor utilization rate of the robot in underground space and also increase the synchronous positioning accuracy. Moreover, based on boundary cells and rapidly-exploring random tree (RRT) algorithm, a new Bio-RRT method for robot autonomous exploration is proposed in addition.

According to the experimental results, it can be seen that the upgraded SLAM method proposed in this paper achieves better results in map construction. At the same time, the algorithm presents good real-time performance as well as high accuracy and strong maintainability, particularly it can update the map continuously with the passing of time and ensure the positioning accuracy in the process of map updating. The Bio-RRT method fused with the firing excitation mechanism of boundary cells has a more purposeful random tree growth. The number of random tree expansion nodes is less, and the amount of information to be processed is reduced, which leads to the path planning time shorter and the efficiency higher. In addition, the target bias makes the random tree grow directly toward the target point with a certain probability, and the obtained path nodes are basically distributed on or on both sides of the line between the initial point and the target point, which makes the path length shorter and reduces the moving cost of the mobile robot. The final experimental results demonstrate that the proposed upgraded SLAM and Bio-RRT methods can better complete the underground special space exploration task.

Based on the background of robot autonomous exploration in underground special space, a new bio-inspired mobile robot structure with mapping and autonomous exploration algorithm is proposed in this paper. The robot structure is constructed, and the perceptual unit, control unit, driving unit and communication unit are described in detail. The robot can satisfy the practical requirements of exploring the underground dark and multiintersection space. Then, the upgraded graph optimization laser SLAM algorithm and interframe matching optimization method are proposed in this paper. The Bio-RRT independent exploration method is finally proposed, which takes shorter time in equally open space and the search strategy for multiintersection space is more efficient. The experimental results demonstrate that the proposed upgrade SLAM and Bio-RRT methods can better complete the underground space exploration task.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

In the past decade, the biopolymeric properties of chitosan (CH) have been largely exploited for various applications. This paper aims to study the use of CH in its nanoform, i.e. as nanofibers blended with polyvinyl alcohol (PVA) for various antimicrobial applications in detail. In particular, their ability toward bacterial growth inhibition, in vitro drug release and their biocompatibility toward tissue growth have been investigated in detail.

Electrospinning technique was adapted for depositing CH/PVA blended nanofilms on the silver foil under optimized conditions of high voltage. Three different concentrations of blended nanofiber samples were prepared and their antimicrobial properties were studied.

The bead diameter and average diameter of blended nanofibers increase with CH concentration. Antibacterial activity increases as CH concentration increases. Increased hydrophilicity in CH-enriched samples contributes to a higher drug release profile.

To the best of the authors’ knowledge, chick chorioallantoic membrane assay analysis has been carried out for the first time for CH/PVA films which shows that CH/PVA blends are biocompatible. CH after being converted as nanoparticles exhibits higher drug release rate by in vitro method.