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The clothes industry in the developed countries must generate sufficient value added to justify a higher price than the articles produced in great quantity and at low prices in the countries with good wages. The offer of clothing made to measure, at a reasonable price compared with mass‐produced clothing, can constitute an interesting opportunity for the clothes trade. The use of data processing can help to achieve this goal. This assumes that the manufacturer has a virtual model of the customer. This model combined with the knowledge of the behaviour of the support/clothing couple allows the data‐processing creation of clothing specific to the customer. It is necessary to have a precise and instantaneous acquisition technique. Whatever the principle of measurement, several sensors are necessary to measure the totality of a human body. The results obtained are then like a scatter plot. Moreover, this scatter plot has a non‐uniform density according to the measured zone. The surface reconstruction finds these limits in such a situation. In addition, the voluminal reconstruction allows adaptation to this situation by linking spatially the elements describing the shape of the body. Moreover, the use of tetrahedrons allows a modeling of the deformation of the human body. To adapt to the diversity of the scatter plot a method has been developed called the sculptor method. Initially a triangulation gives the convex shape of the scatter plot. It is necessary to remove the superfluous tetrahedrons. To carry out this operation, this form is produced by spheres whose size is adapted. This makes possible a convex solid, which sticks to the scatter plot. This operation shows that this method allows one to reconstruct a body clothed or not with good fidelity.

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

The purpose of this paper is to estimate probabilistic and regional importance sensitivities of fatigue life, with respect to the laser peening (LP) parameters applied to a Titanium turbine disk.

The sensitivities were calculated from Monte Carlo (MC) analysis of 21,000 simulations and probabilistic sensitivity methods.

The probabilistic sensitivity results indicate that the peak pressure and the mid‐span are the most important variables. The regional importance sensitivity results indicate that probability of failure is the most sensitive to the left tail of peak pressure and middle region of mid‐span and the fatigue life mean is the most sensitive to the left tails of the peak pressure and the mid‐span.

The sensitivity results of this research indicate that more time and energy should be focused on managing peak pressure and mid‐span, as compared to the remaining variables, to design and improve the laser peening process.

The paper presents four sensitivity analysis approaches which were formulated and employed to estimate fatigue life sensitivities with respect to the LP variables.

The aim of this study is to report on the results of an empirical investigation of the various factors which have significant impacts on the Internet user’s ability to correctly identify a phishing website.

The research participants were Internet users who have had at least some experience of financial transactions over the Internet. This study conducted a quantitative research with the help of a structured survey questionnaire along with three experimental tasks. A total of 621 valid samples were collected and the multiple regression analysis technique was used to deduce the answers to the research question.

The results show that the model is useful and has explanatory power. And adjusted R2 computed as 0.927, means that 92.7 per cent of the variations in the Internet user’s ability to identify phishing website can be explained by the predictors selected for the model.

Future research should account for the Internet user’s general security practices and behaviour, attitude towards online financial activity, risk-taking ability or risk behaviour and their potential effects on Internet users' ability to identify a phishing website.

The implications of this study provide the foundation for future research on the areas that intend to explain the Internet user’s necessity to take protection or avoid risky behaviour while performing financial transaction over the Internet.

This study provides the body of knowledge with an empirical analysis of impact of various factors on an Internet user’s ability to identify phishing websites. The results of this study can help practitioners create a more successful research model and help researchers better understand user behaviour on the Internet.

Lighting in building sectors (consumes the highest energy in commercial buildings and the second highest in residential buildings in India) has very much potential for energy conservation in buildings. With the use of daylighting system, energy consumption in lighting can be lowered up to 30 to 40 per cent.

An experimental effort has been made in this paper to explore the internal wall coloring effect on the performance of tubular light pipe. Trace-pro software has been used and validated. With the help of this software, light pipe has been designed and simulated in a ray tracing mode. Assessment of four globally used prediction models has also been conducted to compare the performances in different seasons for light pipes in the composite climate of New Delhi.

It has been conducted based on three statistical indicators as mean bias error, root mean square error and R². Using regression, an empirical model for average internal illuminance has been developed as a function of light reflectance value (LRV) and solar altitude angle. Trace-pro results confirmed that maximum internal illuminance can be obtained with wall surfaces coated with high LRV color. Finally, by using of a single light pipe system for a test room with the artificial lighting system and applying continuous dimming control, the amount of electrical energy has been saved up to 38.5 per cent per year.

After going through the literature, it has been identified that there has been no paper published which explores the effect of colors of the internal walls on the performance of the light pipe. Along with this, the comparison between existing empirical performance models and find out which model gives the best result in different seasons has been carried out for New Delhi, India.

The purpose of this paper is to construct a realistic best practice benchmark and develop a methodology for comparison of individual suppliers of marine bunker fuel. As is well‐known in this trade, unfair business behaviors in the bunker fuel market are not uncommon, resulting in financial losses for the buyers.

Establishing a best practice will naturally involve some degree of subjectivity as there is not an a priori correct answer to this problem. Using the concept of membership functions from fuzzy set theory, a score can be derived from a best practice benchmark histogram. The main advantages of this method are its relative independence both of sample size and of the underlying distribution, as well as being computationally very efficient.

The authors' methodology turns out to be more powerful than standard descriptive statistics, as it is less sensitive to outliers and is well suited for small datasets and even single numbers. When applied to data for all suppliers worldwide, it is shown that the number of good suppliers is actually much lower than might be expected.

Bunker fuel is a major expense for ship owners, and can easily reach $30 million/year for a single container ship. There is, therefore, a considerable interest in the market for benchmarking of individual fuel suppliers. The authors' methodology is also applicable to other quality‐related fuel parameters.

To the best of the authors' knowledge, this is the first attempt to benchmark actors in the marine bunker fuel industry and to quantify their behaviors.

Analytics is the science of examining raw data with the purpose of drawing conclusions about that information and using it for decision-making. Before the formal written language, there were pictures which shared ideas, plans, and history. Most of the knowledge that we have of our ancestors is from these pictures drawn on caves or monuments. In today’s world, visualizations in the form of bar charts, scatter plots, or dashboards are essential tools in business intelligence as they help managers to absorb information and take apt decisions quickly. Dashboards in particular are very helpful for managers as multiple charts and graphs giving the latest information about sales, returns, market share, etc. keep them up to date on the latest developments in the company. There are a number of visualization software in the market which are easy to learn and communicate the analyzed data in an easily understood form; the leading ones being Tableau, QlikView, etc. with each one having its positives. This chapter also looks at the pairing of visualization tools with different measurements of data.

Construction is by its nature a data‐rich domain. It is also a competitive market with historically low profit margins. One step towards improving design efficiency is to develop techniques to examine and interpret construction related data. To achieve any significant improvement it is necessary to focus on a given sub‐domain of engineering data. This paper focuses on analysis data, in particular building performance analysis data. This data, generated by performing an analysis of a building or its elements, may be used more efficiently and effectively by improving its visual representation. This is illustrated in this paper by three applications developed to improve the representation of specific building analysis datasets. Each exemplifies a structured and innovative approach to the development of applications used to enable engineers to dedicate more resources to understanding the results of their analyses.

The purpose of this paper is to report a study in which the feasibility of conducting probabilistic finite element analysis (FEA) for crane hook design has been explored.

This paper presents the results of probabilistic analysis, where in the input random variables are varied and corresponding variations in the output parameters were observed. In this study, material properties and load have been considered as random input variables and the maximum stress, maximum deflection variations were considered as output random variables.

The probability of occurrence of output variation and the sensitivity of output variables on the input variables are the important results generated from this analysis. By performing this probabilistic analysis, the random selection of factor of safety could be avoided.

The implementation study has been carried out for a single product.

The usage of the approach will indicate the importance of probabilistic analysis in product design and development process. This process will enable the organization to compete in the global market.

A case study has been reported to indicate the feasibility of performing probabilistic FEA for crane hook design. Hence, the contributions are original.