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Whilst motorcycling is an activity of pleasure in most parts of the world, in India, it is a regular mode of commuting. The number of registered motorized two wheelers increased at the rate of 14.7 percent during the year 2016-2017 to reach the figure of 20.19m in 2018. But, with this increase, the number of motorcycle road accidents is also increasing. Uncomfortable riding clothing is one of the major factors for motorcycle rider’s muscular fatigue, which might at times lead to serious accidents. No kinematic human models have been, so far, used for the design of protective, functional and aesthetic looking products, and the result is, hence, a compromised fit that is not protective or comfortable. The purpose of this paper is to develop virtual 3D human body models for specific postures of a motorcycle rider.

Kinematic analysis of a motorcycle rider was conducted to identify typical body postures obtained by the motorcycle rider while mounting and riding a motorcycle. The identified body postures were mapped on a virtual parametric human model to obtain digital model of a motorcycle rider. 3D garment patterns for jacket and trouser were developed on all the four body postures. 3D patterns were flattened out to get 2D flat patterns that were compared and analyzed, and appropriate pattern shapes from each of the four postures were selected. Virtual fit analysis was conducted for the finally garment.

It is well established that a static 2D anthropometry fails to accurately capture the dimensions of complex 3D human form, yielding poor garment fit. Therefore, in this study, virtual, 3D human body models were developed in selected dynamic poses. Garment patterns developed in 3D have the typical movement inbuilt in them; hence, they offer more comfort and ease of motion to the wearer.

The identification of typical body postures of motorcycle rider has not been done before. The CAD models developed in the study can be used for the generation of ergonomic garment patterns for the motorcycle riders.

Sharing patient health information (PHI) among hospitals has been much slower than the adoption of health record systems. This paper aims to investigate if privacy regulation (PR) or security measures (SMs) influence hospitals’ use of health information exchange (HIE) to share PHI with other providers (e.g. physicians, labs, hospitals). The study specifically focuses on how multiple PRs can impede and a strong national security infrastructure (NSI) can support HIE.

The study uses secondary data from a multi-national and multi-hospital survey administered by the European Union. The multi-level structure of the cross-sectional panel data is used to test the influence of both hospital-level (e.g. PR) and national-level variables (e.g. NSI) on HIE. A total of nine types of HIE, three types of PRs, nine SMs and other relevant control variables are considered. This study uses a two-level random intercept generalized linear model to test the hypothesis proposed in the study.

The study finds that national-level PRs (NLPR) have the strongest positive influence on HIE in comparison to regional (RLPR) and hospital-level (HLPR) PRs. Moreover, the study finds evidence that the presence of RLPR and HLPR, on average, decreases the positive impact of NLPR by 264%. The SMs also have a significant and positive impact on HIE. Adoption of an additional SM can increase the odds of engaging in a certain type of HIE between 21% and 61%. On the other hand, a strong NSI can also amplify the positive impact of SM on certain types of HIE.

This study extends prior research on the role of PRs in enabling HIE by considering the complexities brought up by adopting multiple PRs. NLPRs have the strongest impact on HIE in comparison to RLPRs or HLPRs. Moreover, public infrastructure initiatives such as those related to secure communications can also complement SMs adopted by the providers by encouraging HIE.

Driving needs high levels of alertness. Increased somnolence is the most important negative influence to maintain proper watchfulness and vigilance. Drowsiness, working conditions and their affective impact must be taken into account to determine driving safety. The purpose of this paper is to assess excessive daytime sleepiness, sleep habits, quality of sleep, stress-related symptoms, and working conditions in a large sample of short-distance bus drivers in the city of Buenos Aires.

This was a cross-sectional study performed to evaluate sleep habits and obstructive apnea risk in short-distance bus drivers of the Metropolitan Area of Buenos Aires, Argentina. Questionnaires regarding anthropometric data, sleep habits, snoring, daytime sleepiness (Epworth Sleepiness Scale), quality of sleep (Pittsburgh Quality of Sleep Index, PQSI), working conditions and fatigue and anxiety related to work were administered to professional short-distance bus drivers (n=1023).

A prevalence of 34.6 percent of obesity and 80 percent of snoring was observed. Mean sleep time during workdays was 6.5±0.1 h and bad sleep quality was reported by 54.6 percent of the subjects. Excessive daytime sleepiness had a prevalence of 48.8 percent and was independently associated with reduced sleep time, increased sleep debt, long time to wake up, snoring, and short resting time along the working day (p<0.05).

Short-distance drivers in Buenos Aires, the largest urban area of Argentina, are a partially sleep-deprived, overweighted population, showing a high daytime somnolence, poor work-rest conditions and high levels of anxiety and fatigue. This association can be very harmful in view of the demanding working conditions considered.

Interdisciplinary PhD programs can be challenging as the vital information needed by students may not be readily available; it is scattered across the university’s websites, while tacit knowledge can be obtained only by interacting with people. Hence, there is a need to develop a knowledge management model to create, query and maintain a knowledge repository for interdisciplinary students. This study aims to propose a knowledge graph containing information on critical categories (faculty, classes, dissertations, etc.) and their relationships, extracted from multiple sources, essential for interdisciplinary PhD students. This study evaluates the usability of a participatory-designed knowledge graph intended to facilitate information exchange and decision-making.

The authors used data from multiple sources (such as university websites, faculty profiles, publication and dissertation metadata and crowdsourced data) to generate a knowledge graph in the Neo4J Bloom platform. The authors recruited 15 interdisciplinary PhD students using convenience sampling from the University of Hawaiʻi at Mānoa at various PhD stages to design and populate the knowledge graph. Next, the authors conducted a mixed methods study to perform its usability evaluation. First, the authors engaged the students in a participatory design workshop to identify relevant graph queries. Second, the authors conducted semi-structured interviews to determine the usability of the knowledge graph and rate the queries. Each interview was coded with structural and thematic codes and was further analyzed using sentiment analysis in R programming language.

The usability findings demonstrate that interaction with this knowledge graph benefits PhD students by notably reducing uncertainty and academic stress, particularly among newcomers. Knowledge graph supported them in decision-making, especially when choosing collaborators (e.g. supervisor or dissertation committee members) in an interdisciplinary setting. Key helpful features are related to exploring student–faculty networks, milestones tracking, rapid access to aggregated data and insights into crowdsourced fellow students’ activities. However, they showed concerns about crowdsourced data privacy and accessibility. Although participants expressed the need for more qualitative data in the graph, they noted it helped identify people to talk to about the topics of their interest.

The knowledge graph provides a solution to meet the personalized needs of doctoral researchers and has the potential to improve the information discovery and decision-making process substantially. It also includes the tacit knowledge exchange support missing from most current approaches, which is critical for this population and establishing interdisciplinary collaborations. This approach can be applied to other interdisciplinary programs and domains globally.

IN presenting our readers with an illustration of the bust of this distinguished philanthropist, we may recall a few particulars of his life.

The management and control of random process variations is often neglected in Europe. The Japanese emphasise the control and reduction of these variations and some multinationals have realised that this is an area of great potential for quality improvements. There are essentially two approaches. The first is to design the effects out of the product and therefore make it robust against both the random and the non‐random process variations. The second approach is to improve the process capability. It is stressed that if industry is to stay competitive then it must tackle product quality at all levels of design and manufacture. Some useful insights into process variation reduction are given.