Application of digital human modeling and simulation for vision analysis of pilots in a jet aircraft: a case study

Ergonomic evaluation of visual demands becomes crucial for the operators/users when rapid decision making is needed under extreme time constraint like navigation task of jet aircraft. Research reported here comprises ergonomic evaluation of pilot's vision in a jet aircraft in virtual environment to demonstrate how vision analysis tools of digital human modeling software can be used effectively for such study. Three (03) dynamic digital pilot models, representative of smallest, average and largest Indian pilot population were generated from anthropometric database and interfaced with digital prototype of the cockpit in Jack software for analysis of vision within and outside the cockpit. Vision analysis tools like view cones, eye view windows, blind spot area, obscuration zone, reflection zone etc. were employed during evaluation of visual fields. Vision analysis tool was also used for studying kinematic changes of pilot's body joints during simulated gazing activity. From present study, it can be concluded that vision analysis tool of digital human modeling software was found very effective in evaluation of position and alignment of different displays and controls in the workstation based upon their priorities within the visual fields and anthropometry of the targeted users, long before the development of its physical prototype.     

Emotion and interior space design: an ergonomic perspective

Interaction between the users and their environment is spontaneous and unavoidable. This interaction can be positive or negative. A good interior space is about considering all the physical, environmental and cognitive elements and harmonizing them to make it a space that feels right, functionally and emotionally. The important element that has to be considered the most in an interior space is the "user". Balancing all these elements is a challenging job and results in a perfect interior space design. This paper intends to bring to light the necessity of designing an optimum interior space, which is a balance of the user's choice and the mandatory standards that ought to be followed for user safety and convenience. There has to be an intervening domain of ergonomics which will guide to bring out a balance between the personal choice of the user and the usual standards followed. It should also provide a step-by-step information, guidance and direction to act to the specifications and standards systematically to adapt an integrated approach of handling all the elements holistically which will indeed result in a good interior space.     

The Competitive Impact of Targeted Television Advertisements Using DVR Technology

Digital video recorders (DVRs) are an emerging technology that is fundamentally changing the competitive landscape in industries that advertise on television. Perhaps the most familiar impact of DVR technology is that it enables consumers to avoid advertisements (ads) by fast forwarding through them. However, this “zipping” of ads is only one aspect of the impact of DVR technology. DVRs also collect a wealth of information at the consumer level that can be used by firms to target their ads more effectively. We examine how this targeting capability moderates the impact of ad avoidance in a competitive setting. Insights are provided on how best to manage this emerging technology in terms of the key managerial decisions of product pricing and advertising efforts, as well as its impact on profits in a competitive (duopoly) marketplace.     

Optimal Retail Return Policies with Wardrobing

Many retailers offer refunds to consumers who, after a trial period, return a product that they find does not fit their needs. Some consumers are willing to use this return option opportunistically for short‐term consumption rather than its intended purpose of resolving fit uncertainty. Such behavior has been termed “wardrobing.” Restocking fees (partial refunds) can be used to combat wardrobing. However, there is a trade‐off involved, since partial refunds will be viewed negatively by consumers who return an item due to a true lack of fit. In this study, we consider how the extent of wardrobing (how many consumers consider such behavior) and the benefit of wardrobing (how much value can be extracted during the trial) impact firm pricing decisions and profits in this retail context. Our results imply that an increase in the extent of wardrobing is most detrimental to profits when the current extent of wardrobing is low. On the contrary, if the extent of wardrobing is already very high, and the benefit of wardrobing to consumers is also high, the retailer can set prices and refunds such that additional wardrobing actually increases firm profits. In a model extension, we show how a retailer can effectively screen wardrobers from ordinary consumers by offering a menu of price/refund pairs, and that such an approach can lead to increased profits if the extent of wardrobing is sufficiently high. Overall, our findings provide new insights into how retailers can set prices and refund policies to effectively manage opportunistic behavior by consumers.     

Competition and Sustainability: The Impact of Consumer Awareness

Sustainability, a broad concept that includes numerous environmental and social dimensions, has emerged as an important product evaluation criterion for consumers. We suggest the impact of sustainability on consumer behavior depends on two factors—each individual consumer's unique level of concern about sustainability, and the general level of awareness regarding the sustainability of competing products—that together determine the level of heterogeneity among consumer attitudes toward sustainability. We incorporate sustainability concern and awareness into a model of horizontal competition in a duopoly, where one firm's product is more sustainable than the other's. Our results suggest that marginal increases in awareness can benefit all firms, including the less sustainable one, when awareness is sufficiently high (the explicit goal of recent sustainability labeling initiatives). In several model extensions, we provide additional insights for the following cases: the sustainable firm controls the extent of its sustainability advantage, the sustainable firm can directly influence the general level of awareness, and the distribution of sustainability concern across consumers is nonuniform. Our results enable us to suggest several new insights for managers, both those whose products enjoy a sustainability advantage and those whose products do not.     

Some variations on constrained minimum enclosing circle problem

Given a set P of n points and a straight line L, we study three important variations of minimum enclosing circle problem as follows:

1. Computing k identical circles of minimum radius with centers on L, whose union covers all the points in P.
2. Computing the minimum radius circle centered on L that can enclose at least k points of P.
3. If each point in P is associated with one of the k given colors, then compute a minimum radius circle with center on L such that at least one point of each color lies inside it.

We propose three algorithms for Problem (i). The first one runs in O(nklogn) time and O(n) space. The second one is efficient where k?n; it runs in O(nlogn+nk+k ²log³ n) time and O(nlogn) space. The third one is based on parametric search and it runs in O(nlogn+klog⁴ n) time. For Problem (ii), the time and space complexities of the proposed algorithm are O(nk) and O(n) respectively. For Problem (iii), our proposed algorithm runs in O(nlogn) time and O(n) space.     

Risk efficient estimation of fully dependent random coefficient autoregressive models of general order

We consider a stochastic dynamic model with autoregressive progression. The drift coefficients of the autoregressive model are random where the randomness in the coefficients can have any dependence structure. We propose a two-step sequential estimator and study the asymptotic behavior of few important properties. Paradigm of sequential estimation has its own advantage in reducing sample size and plugging estimates of nuisance parameters while inferring about the main parameters. Our proposed estimator is asymptotically optimal as the predictive risk of the proposed estimator attains the risk of the oracle that assumes known nuisance parameters. Extensive simulation confirms our results.     

A Framework for Assessing Uncertainty Associated with Human Health Risks from MSW Landfill Leachate Contamination

Landfilling is a cost‐effective method, which makes it a widely used practice around the world, especially in developing countries. However, because of the improper management of landfills, high leachate leakage can have adverse impacts on soils, plants, groundwater, aquatic organisms, and, subsequently, human health. A comprehensive survey of the literature finds that the probabilistic quantification of uncertainty based on estimations of the human health risks due to landfill leachate contamination has rarely been reported. Hence, in the present study, the uncertainty about the human health risks from municipal solid waste landfill leachate contamination to children and adults was quantified to investigate its long‐term risks by using a Monte Carlo simulation framework for selected heavy metals. The Turbhe sanitary landfill of Navi Mumbai, India, which was commissioned in the recent past, was selected to understand the fate and transport of heavy metals in leachate. A large residential area is located near the site, which makes the risk assessment problem both crucial and challenging. In this article, an integral approach in the form of a framework has been proposed to quantify the uncertainty that is intrinsic to human health risk estimation. A set of nonparametric cubic splines was fitted to identify the nonlinear seasonal trend in leachate quality parameters. LandSim 2.5, a landfill simulator, was used to simulate the landfill activities for various time slices, and further uncertainty in noncarcinogenic human health risk was estimated using a Monte Carlo simulation followed by univariate and multivariate sensitivity analyses.     

Tests for statistical significance of a treatment effect in the presence of hidden sub-populations

For testing the statistical significance of a treatment effect, we often compare between two parts of a population; one is exposed to the treatment, and the other is not exposed to it. Standard parametric or nonparametric two-sample tests are commonly used for this comparison. But direct applications of these tests can yield misleading results, especially when the population has some hidden sub-populations, and the effect of this sub-population difference on the response dominates the treatment effect. This problem becomes more evident if these sub-populations have widely different proportions of representatives in the samples obtained from these two parts. In this article, we propose some simple methods to overcome these limitations. These proposed methods first use a suitable clustering algorithm to find the hidden sub-populations, and then they eliminate the sub-population effect by using a suitable transformation of the data. Standard two-sample tests, when they are applied on the transformed data, usually yield better results. We analyze some simulated and real data sets to demonstrate the utility of these proposed methods.