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To explore how motivational interviewing (MI) training might benefit the practice of COVID-19 contact tracers.

Following co-production of a MI training package, with a United Kingdom (UK) track and trace organisation, training was delivered virtually to 101 volunteer participants involved in contact tracing. Data were captured via an online survey, incorporating questions from recognised measures of occupational self-efficacy and workplace wellbeing, prior to the training. Open data fields were used to gather feedback about participants' reasons for attending, and views about the training afterwards.

Although the contact tracers reported high occupational self-efficacy and workplace wellbeing, both quantitative and qualitative data suggested participants saw practitioner value and utility in MI.

The sample was self-selecting and typically involved contact tracers from UK local authorities. The study did not measure impact on compliance with self-isolation guidance and/or providing details of contacts, and larger-scale research would be needed to establish this. This was not a pre-post-test evaluation study, and measures of occupational self-efficacy and workplace wellbeing were gathered to give insight into the sample and to test the feasibility of using this survey for a future large-scale study. The research was conducted during the height of the pandemic. While UK COVID-19 contact tracing services have since been reduced, there are potential implications for infection control more generally.

MI is potentially a useful approach for enhancing contact tracing practice. However, implementation factors should be carefully considered, to ensure effective and sustainable practice.

Improved practice in contact tracing could have potential benefits in infection control, through improving compliance with central guidance, although this requires more widespread investigation.

This is the first empirical study to investigate how MI training could benefit COVID-19 contact tracing practice.

Thermal protection of a flange is critical for preventing tower icing and collapse of wind turbines (WTs) in extremely cold weather. This study aims to develop a novel thermal protection system for the WTs flanges using an electrical heat-tracing element.

A three-dimensional model and the Poly-Hexacore mesh structure are used, and the fluid-solid coupling method was validated and then deployed to analyze the heat transfer and convection process. Intra-volumetric heat sources are applied to represent the heat generated by the heating element, and the dynamic boundary conditions are considered. The steady temperature and temperature uniformity of the flange are the assessment criteria for the thermal protection performance of the heating element.

Enlarging the heating area and increasing the heating power improved the flange's temperature and temperature uniformity. A heating power of 4.9 kW was suitable for engineering applications with the lowest temperature nonuniformity. Compared with continuous heating, the increased temperature nonuniformity was buffered, and the electrical power consumption was reduced by half using pulse heating. Pulse heating time intervals of 1, 3 and 4 h were determined for the spring, autumn and winter, respectively.

The originality of this study is to propose a novel electrical heat-tracing thermal protection system for the WTs flanges. The effect of different arrangements, heating powers and heating strategies was studied, by which the theoretical basis is provided for a stable and long-term utilization of the WT flange.

A wide number of technologies are currently in store to harness the challenges posed by pandemic situations. As such diseases transmit by way of person-to-person contact or by any other means, the World Health Organization had recommended location tracking and tracing of people either infected or contacted with the patients as one of the standard operating procedures and has also outlined protocols for incident management. Government agencies use different inputs such as smartphone signals and details from the respondent to prepare the travel log of patients. Each and every event of their trace such as stay points, revisit locations and meeting points is important. More trained staffs and tools are required under the traditional system of contact tracing. At the time of the spiralling patient count, the time-bound tracing of primary and secondary contacts may not be possible, and there are chances of human errors as well. In this context, the purpose of this paper is to propose an algorithm called SemTraClus-Tracer, an efficient approach for computing the movement of individuals and analysing the possibility of pandemic spread and vulnerability of the locations.

Pandemic situations push the world into existential crises. In this context, this paper proposes an algorithm called SemTraClus-Tracer, an efficient approach for computing the movement of individuals and analysing the possibility of pandemic spread and vulnerability of the locations. By exploring the daily mobility and activities of the general public, the system identifies multiple levels of contacts with respect to an infected person and extracts semantic information by considering vital factors that can induce virus spread. It grades different geographic locations according to a measure called weightage of participation so that vulnerable locations can be easily identified. This paper gives directions on the advantages of using spatio-temporal aggregate queries for extracting general characteristics of social mobility. The system also facilitates room for the generation of various information by combing through the medical reports of the patients.

It is identified that context of movement is important; hence, the existing SemTraClus algorithm is modified by accounting for four important factors such as stay point, contact presence, stay time of primary contacts and waypoint severity. The priority level can be reconfigured according to the interest of authority. This approach reduces the overwhelming task of contact tracing. Different functionalities provided by the system are also explained. As the real data set is not available, experiments are conducted with similar data and results are shown for different types of journeys in different geographical locations. The proposed method efficiently handles computational movement and activity analysis by incorporating various relevant semantics of trajectories. The incorporation of cluster-based aggregate queries in the model do away with the computational headache of processing the entire mobility data.

As the trajectory of patients is not available, the authors have used the standard data sets for experimentation, which serve the purpose.

This paper proposes a framework infrastructure that allows the emergency response team to grab multiple information based on the tracked mobility details of a patient and facilitates room for various activities for the mitigation of pandemics such as the prediction of hotspots, identification of stay locations and suggestion of possible locations of primary and secondary contacts, creation of clusters of hotspots and identification of nearby medical assistance. The system provides an efficient way of activity analysis by computing the mobility of people and identifying features of geographical locations where people travelled. While formulating the framework, the authors have reviewed many different implementation plans and protocols and arrived at the conclusion that the core strategy followed is more or less the same. For the sake of a reference model, the Indian scenario is adopted for defining the concepts.

Digital trace data provide new opportunities to study how individuals act and interact with others online. One advantage of this type of data is that it measures behavior in a less obtrusive way than surveys, potentially reducing measurement error. However, it is well documented that in observational studies, participants' awareness of being observed can change their behavior, especially when the behavior is considered sensitive. Very little is known regarding this effect in the online realm. Against this background, we studied whether people change their online behavior because digital trace data are being collected.

We analyzed data from a sample of 1,959 members of a German online panel who had consented to the collection of digital trace data about their online browsing and/or mobile app usage. To identify reactivity, we studied change over time in five types of sensitive online behavior.

We found that the frequency and duration with which individuals engage in sensitive behaviors online gradually increases during the first couple of days after the installation of a tracker, mainly individuals who extensively engage in sensitive behavior show this pattern of increase after installation and this change in behavior is limited to certain types of sensitive online behavior.

There is an increased interest in the use of digital trace data in the social sciences and our study is one of the first methodological contributions measuring reactivity in digital trace data measurement.

The purpose of this study is to describe a comparative study of the perceptions of users and non-users of an early contact tracing application helping to prevent the spread of the COVID-19 pandemic. The unprecedented incidence of this disease warrants investigating theoretically the use of mobile contact tracing applications as a promising approach to curtail its transmission.

A consumption value-based model of the adoption and use of a contact tracing mobile application was built and tested through a cross-sectional survey conducted with 2 samples (of 309 already users and 306 non-users) in the Province of Alberta, Canada.

Utilitarian and social values together with health information seeking and perceived critical mass drive the use of the application while perceived privacy risk is an obstacle to usage for both users and non-users.

Study participants self-assessed their risk category of potential exposure to the COVID-19 virus, and this was a subjective measure including an emotional component.

No major differences in the approaches targeting users and non-users of a mobile contact tracing application to encourage its adoption and use are necessary.

Additional efforts are required to convey to people information on the benefits and current rate of use of such an application and to mitigate privacy risk concerns.

Overall, the study offers theoretical and practical contributions that may help improve the adoption and usage of contact tracing applications addressing the COVID-19 pandemic or other possible public health crises.

This paper aims to present an emergent framework that proposes the strategic importance of supply chain (SC) traceability beyond the traditional role in supporting product safety, recalls and sustainability initiatives.

A grounded theory (GT) approach with 22 in-depth interviews with managers from 10 countries/territories and 3 different echelons of the food SC is employed to arrive at the strategic traceability framework and framework's propositions.

The framework suggests that traceability can sometimes help expose and modify firms' core and non-core SC capabilities and provide an opportunity to align them better with SC strategies. This alignment may require a complementary execution of human coordination and the adoption of technological traceability components.

The research suggests that firms may be able to extract greater value from the firms' traceability investments. While traceability continues to serve the key purposes of preventing and mitigating the risks of recalls, this can sometimes illuminate sustained business growth opportunities.

The study extends traceability beyond a means to meet the requirements of product safety, recalls and sustainability. Rather, this study establishes traceability's role in exposing and aligning firms' capabilities for business gain and not merely recall risk mitigation. This reframing of the premise for traceability can invigorate both research and practice on the subject of traceability.

The purpose of this paper is to design a suitable guard trace to reduce the electromagentic interference between two closely spaced high frequency transmission lines. A novel cross-shaped resonator combined via fence is passed down to alleviate far-end and near-end crosstalk (NEXT) in tightly coupled high-speed transmission lines. The distance between the adjacent transmission lines is increased stepwise as a function of trace width.

A rectangular-shaped resonator via fence is connected by a guard trace has been proposed to overcome the coupling between the traces that is separated by 2 W. Similarly, by creating a cross-shaped resonator via fence connected by guard trace that reduces the spacing further by 1.5 W.

A tightly coupled transmission line structure that needs separation by a designed unit cell structure. Further research needs to be conducted to improve the NEXT, far-end crosstalk (FEXT) and spacing between the transmission lines.

This study portrays a novel method that combines the resonators via fence with a minimum spacing between the tightly coupled transmission lines which reduce the NEXT and FEXT; thereby reducing the size of the routing area. The resultant test structures are characterized at high frequencies using time domain and frequency domain analysis. The following scattering parameters such as insertion loss, NEXT and FEXT of the proposed method are measured as 1.504 dB, >30 dB and >20 dB, respectively.

Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method.

The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition.

Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces.

The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods.

The purpose of this chapter is twofold: in the first part, we will provide comprehensive, state-of-the-art urban and visual studies across the domains of visibility, urban aesthetics and the legitimate use of the urban. We will show that what we see is foremost what is accessible and legitimate as a vision, while the urban provides multiple realms of invisibility that are often neglected or rendered invisible. Art, architecture, urbanism and place-making will be used as examples of these dynamics.

In the second part of the chapter, we will present a research study on the decolonial practices of re-signification of colonial urban traces. Despite the dominant representation of Italians as ‘good people’ (a local version of ‘white innocence’), in recent years, Italy has witnessed a new interest stemming from bottom-up local movements dealing with colonial legacy in the urban space. We will show a research example (‘Decolonising the City. Visual Dialogues in Padova’) based on participatory video, arts-based methods and walking methods.

This paper explores how the blockchain food traceability system (BFTS) affects consumers' affective brand commitment and subsequent willingness to pay premium prices.

From February 11 to May 23, 2023, this study collected data from 236 Chinese customers, who had purchased blockchain-traced food in Jingdong Mall within the past three months. Structural equation modelling was used to analyse the data.

The main findings were as follows: (1) BFTS information transparency, information immutability and product diagnosticity are significant predictors of consumer-perceived trustworthiness; BFTS information transparency, product diagnosticity and product safety are significant predictors of consumer-perceived informativeness, (2) Perceived trustworthiness and perceived informativeness build consumers' affective brand commitment, (3) Affective brand commitment affects willingness to pay premiums and (4) Health consciousness positively moderates the relationship between consumers' affective brand commitment and willingness to pay premiums.

This paper complements the research on consumer behaviour in the BFTS, and the research results provide important enlightenment for guiding food enterprises to formulate reasonable and perfect marketing strategies of blockchain-traced food.