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The paper examines interviewee insights into accountability for clinical governance in high-consequence, life-and-death hospital settings. The analysis draws on the distinction between formal “imposed accountability” and front-line “felt accountability”. From these insights, the paper introduces an emergent concept, “grounded accountability”.

Interviews are conducted with 41 clinicians, managers and governors in two large academic hospitals. The authors ask interviewees to recall a critical clinical incident as a focus for elucidating their experiences of and observation on the practice of accountability.

Accountability emerges from the front-line, on-the-ground. Together, clinicians, managers and governors co-construct accountability. Less attention is paid to cost, blame, legal processes or personal reputation. Money and other accountability assumptions in business do not always apply in a hospital setting.

The authors propose the concept of co-constructed “grounded accountability” comprising interrelationships between the concept’s three constituent themes of front-line staff’s felt accountability, along with grounded engagement by managers/governors, supported by a culture of openness.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

Modern meat processing requires automation and robotisation to remain sustainable and adapt to future challenges, including those brought by global infection events. Automation of all or many processes is seen as the way forward, with robots performing various tasks instead of people. Meat cutting is one of these tasks. Smart novel solutions, including smart knives, are required, with the smart knife being able to analyse and predict the meat it cuts. This paper aims to review technologies with the potential to be used as a so-called “smart knife” The criteria for a smart knife are also defined.

This paper reviews various technologies that can be used, either alone or in combination, for developing a future smart knife for robotic meat cutting, with possibilities for their integration into automatic meat processing. Optical methods, Near Infra-Red spectroscopy, electrical impedance spectroscopy, force sensing and electromagnetic wave-based sensing approaches are assessed against the defined criteria for a smart knife.

Optical methods are well established for meat quality and composition characterisation but lack speed and robustness for real-time use as part of a cutting tool. Combining these methods with artificial intelligence (AI) could improve the performance. Methods, such as electrical impedance measurements and rapid evaporative ionisation mass spectrometry, are invasive and not suitable in meat processing since they damage the meat. One attractive option is using athermal electromagnetic waves, although no commercially developed solutions exist that are readily adaptable to produce a smart knife with proven functionality, robustness or reliability.

This paper critically reviews and assesses a range of sensing technologies with very specific requirements: to be compatible with robotic assisted cutting in the meat industry. The concept of a smart knife that can benefit from these technologies to provide a real-time “feeling feedback” to the robot is at the centre of the discussion.

This study aims to present a theoretical analysis on the capabilities (at the organizational) and skills (at the individual level) of the hub organization (orchestrator) in an innovation network.

The authors conducted literature reviews on the orchestration of innovation networks; and networking capabilities.

This study presents a theoretical model and a research agenda.

In interorganizational relations, a central actor can stand out the role of intentionally creating, extracting and distributing value in the network, generating gains for all members. Literature recognizes this set of intentional and deliberate actions as the “orchestration” of resources in the network. Despite the increasing interest regarding the theme, the phases and specific capabilities for orchestration still lack further investigation.

The migration-tourism discourse has gained attention in global tourism and mobility among scholars because of the vast contributions of migrant visitors to various sectors of the host country's economy, including the tourism and hospitality industry. However, few studies appear to have been undertaken on the subject matter, particularly within the developing country tourism context. The purpose of this research was to assess and understand migrant visitors' sense of safety among five different nationalities (United Kingdom, USA, Germany, China and India) and their perceptions on trustworthiness of tourism information.

Anchored on the national cultural dimension of Hofstede model, this study sampled 306 migrant visitors, with the use of one-way analysis of variance (ANOVA) and the Games–Howell post-hoc test to examine mean differences of nationalities, with regard to trust of tourism information and sense of safety. Finally, the multiple regression analysis was conducted to establish the significant effects of national culture characteristics and trustworthiness of tourism information on sense of safety of migrant visitors.

The study finds that there were no significant differences in the sense of safety by the five nationalities. However, national culture characteristics (power distance and uncertainty avoidance) and trustworthiness of tourism information were significant predictors of sense of safety of migrant visitors.

This research adopted only two cultural dimensions of the Hofstede's model, so future studies within the migration-tourism literature should consider adopting other cultural dimensions of the model.

The research provides, first, insight into sense of safety, trustworthiness of tourism information and national culture characteristics which are relevant for destination marketers, the ministry of tourism and local tourism officials to promote safety tourism.

The study expands the application of the cultural dimension of Hofstede model within the migration-tourism literature and establishes that national cultural characteristics and trustworthiness of tourism information are significant predictors of sense of safety of migrant visitors.

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.

Given declining tuition funds and government grants, Kenyan universities need to develop strategies, including increased research grants and collaborations, to diversify their income sources. Well-managed doctoral students can boost a university’s teaching and research outputs. However, numbers of students enrolled in doctoral programmes at Kenyan universities are low, and graduation rates and time-to-graduate statistics are disturbing. Research undertaken elsewhere underline the important role played by supervisors and peers in facilitating students’ sense of belonging and their success. Therefore, this study aims to investigate the influence of supervisory and peer support on PhD students’ sense of belonging and their success at Kenyan universities.

In this cross-sectional study, data were gathered through an online questionnaire from 614 students admitted to doctoral programmes at Kenyan universities between 2010 and 2018. We used multi-item scales to collect data on PhD students’ self-efficacy, supervisory and peer support and a sense of belonging.

Structural equation modelling results revealed that PhD students’ modes of study and self-efficacy were significantly associated with the quality of supervision, peer support and a sense of belonging. However, only age, a sense of belonging and the quality of supervision were directly linked to their success.

This study contributes to the literature on doctoral-level education, responding to the need for research on the influence of relationships with supervisors and peers on PhD students’ sense of belonging and their success, especially in developing countries.

The purpose of this paper is aimed to examine natives' Fervid Attachment to religious rites, as a part of cultural heritage, in its extrinsic (sense of belonging, rituality) and intrinsic (intimate bond, emotionality) characteristics, by shedding light on how leveraging on these characteristics could be emphasized to promote sustainable local development.

Based on the principles of an ethnographic research approach based on observational methods, this paper analyzes the rites of Holy Week in Taranto, a city located in the Southern Italy, by capturing individuals' behavior according the concept of Fervid Attachment.

Results show that tourism destinations preserving their traditions and religious rites as part of their cultural heritage can satisfy tourists' spirituality needs and, by promoting the interaction with the local population (natives) in terms of relationship between them and tourists, supporting local communities' development. Moreover their Fervid Attachment in terms of sense of belonging, rituality, intimate bond and emotionality could be empathized to promote sustainable local development.

Our results provide suggestions on how local policymakers and tourism marketers could leverage natives' attachment to religious rites to boost religious tourism.

This paper shows from a new perspective based on the concept of natives' Fervid Attachment how local people are relevant in promoting a tourism destination.

The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for completing missing traffic data, plays a significant role in the intelligent transportation system (ITS). However, existing methods of tensor decomposition focus on the global data structure, resulting in relatively low accuracy in fibrosis missing scenarios. Therefore, this paper aims to propose a novel tensor decomposition model which further considers the local spatiotemporal similarity for fibrosis missing to improve travel time completion accuracy.

The proposed model can aggregate road sections with similar physical attributes by spatial clustering, and then it calculates the temporal association of road sections by the dynamic longest common subsequence. A similarity relationship matrix in the temporal dimension is constructed and incorporated into the tensor completion model, which can enhance the local spatiotemporal relationship of the missing parts of the fibrosis type.

The experiment shows that this method is superior and robust. Compared with other baseline models, this method has the smallest error and maintains good completion results despite high missing rates.

This model has higher accuracy for the fibrosis missing and performs good convergence effects in the case of the high missing rate.

This paper aims to identify and analyze the agtech classification and categorization systems in the Brazilian context.

The systematic literature review (SLR) was carried out according to the protocol of Kitchenham and Charters (2007). The classification systems found in literature were evaluated using the thinking aloud protocol, as proposed by Ericsson and Simon (1993). The responses obtained were evaluated through lexicographic analysis, described by Bécue-Bertaut (2019) and content analysis, described by Bardin (2011).

SLR identified four agtech classification systems. The model proposed by Dias, Jardim, and Sakuda (2019) was the one with the highest adherence to classify Brazilian agtechs. From the analysis of the systems found in literature, the authors proposed a new categorization model of agricultural startups (agtechs).

The study has limitations in relation to the theoretical and empirical validation of the model proposed by the authors. This limitation can be the subject of subsequent research.

The SLR study considers the evolution of the classification systems of a new agribusiness reality, the agtechs. In addition, there is a practical contribution in proposing a new classification system that attempts to address some of the limitations found in previous studies.

Agtechs are startups focused on developing solutions for agriculture and have shown a significant increase in recent years. However, there are few studies focused on this type of company. Even rarer are the studies that seek to classify and categorize them. The present work opens the horizon for future studies focused on this new reality.