Consumptive Behavior, Promotional Preferences, And Shopping Patterns Of Hispanic Americans: An Empirical Perspective

Hispanic Americans are becoming a substantial purchasing force in the United States, thus creating as many opportunities as challenges for marketers in many companies. Although the literature is rich with studies of this sub-culture, new information is always welcome due to the extensive changes that this segment of society is experiencing, from population growth, increasing purchasing power and income, to shifting demographics, diversity, and acculturation. This study is designed to enhance understanding about Hispanic Americans via a survey of a random sample of 120 Hispanic American adult individuals in the Inland Empire area of Southern California. The data collected was analyzed and in this paper, the findings are reported and discussed as well as some marketing tactics recommended. At the strategic marketing level, the authors recommend the application of a theoretical marketing framework, one that matches the nature of the Hispanics’ market and its dynamics, to develop winning strategies and guidelines for marketers who are interested in this vital market segment

Enhancement of reliability in condition monitoring techniques in wind turbines

The majority of electrical failures in wind turbines occur in the semiconductor components (IGBTs) of converters. To increase reliability and decrease the maintenance costs associated with this component, several health-monitoring methods have been proposed in the literature. Many laboratory-based tests have been conducted to detect the failure mechanisms of the IGBT in their early stages through monitoring the variations of thermo-sensitive electrical parameters. The methods are generally proposed and validated with a single-phase converter with an air-cored inductive or resistive load. However, limited work has been carried out considering limitations associated with measurement and processing of these parameters in a three-phase converter. Furthermore, looking at just variations of the module junction temperature will most likely lead to unreliable health monitoring as different failure mechanisms have their own individual effects on temperature variations of some, or all, of the electrical parameters. A reliable health monitoring system is necessary to determine whether the temperature variations are due to the presence of a premature failure or from normal converter operation. To address this issue, a temperature measurement approach should be independent from the failure mechanisms. In this paper, temperature is estimated by monitoring an electrical parameter particularly affected by different failure types. Early bond wire lift-off is detected by another electrical parameter that is sensitive to the progress of the failure. Considering two separate electrical parameters, one for estimation of temperature (switching off time) and another to detect the premature bond wire lift-off (collector emitter on-state voltage) enhance the reliability of an IGBT could increase the accuracy of the temperature estimation as well as premature failure detection

Contemporary remotely sensed data products refine invasive plants risk mapping in data poor regions

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam’s lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species

A method to predict propulsion architecture for future jetliners

The electrification of propulsion technologies in aerospace engineering has been considered as the future-vision for aviation industries. The Selection of electrified propulsion architecture for a particular mission-flight has become a new challenge. In this paper, a method to study different propulsion architectures and battery sizing for jetliners using multi-physics modeling is presented. The designed approach is then carried out to investigate conventional and hybrid/electric propulsion architectures of a commercial jetliner (Avro RJ-85). Based on the comparative study, an effective propulsion architecture is also suggested. The designed method is expected to help predict effective propulsion architecture for future aviation

Conjunctival epithelial flap in continuous contact lens wear.

PurposeComposed of sheets of cells detached from the underlying conjunctiva, conjunctival epithelial flap (CEF) is a recently reported phenomenon associated with contact lens wear with potential consequences for ocular health. Although CEF is generally asymptomatic, it is not known to what extent it might increase the longer-term risk of discomfort, inflammatory response, or infection. In this study, we use survival analysis methods to obtain unbiased estimates of the probability of developing CEF, the mean survival time free of CEF, and the effects of age, gender, ethnicity, and contact lens type.MethodsTwo hundred four subjects were recruited for a continuous wear (CW) study of silicone hydrogel (SiH) and gas permeable (GP) contact lenses. Subjects were examined by optometrists throughout contact lens adaptation and CW periods. Statistical methods included the Kaplan-Meier nonparametric estimator of the survival function and the Cox proportional hazards model for estimating the relative effects of covariates.ResultsOf the 204 subjects, 72 (35%) developed CEF. In 64% of cases, CEFs were observed bilaterally. The majority of cases (90.3%) presented with CEF in the superior conjunctiva. Mean survival time free of CEF was longer for GP lenses (94.3 days) than for SiH lenses (76.5 days), and the probability of developing CEF was significantly greater for SiH lenses (p = 0.002). Although there was some evidence that women and non-Asians remain free of CEF longer, the effects of age, gender, and ethnicity were not statistically significant.ConclusionsThere was a significantly increased risk of CEF in subjects wearing SiH lenses, compared with GP lenses. Subjects wearing SiH lenses remained free of CEF for a shorter time on average. Further study is needed to determine whether the increased incidence of CEF in CW with SiH lenses poses an increased risk of adverse ocular response or infection

CAPIR: Collaborative Action Planning with Intention Recognition

We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments show that the method can be effective, giving near-human level performance in helping a human in a collaborative game.Comment: 6 pages, accepted for presentation at AIIDE'1

Evaluation of a modified equivalent fuel-consumption minimization strategy considering engine start frequency and battery parameters for a plugin hybrid two-wheeler

An appropriate energy management strategy is essential to enhance the performance of hybrid electric vehicles. A novel modified equivalent fuel-consumption minimization strategy (ECMS) is developed considering the engine operating point deviation from the optimum operating line. This paper focuses on an all-inclusive evaluation of this modified ECMS with other state-of-art energy management strategies concerning battery ageing, engine switching along with fuel economy and charge sustenance. The simulation-based results of a hybrid two-wheeler concept are analysed, which shows that the modified ECMS offers the highest benefit compared to rule-based controllers concerning fuel economy and reduction in engine switching events. However, the improvement in fuel economy using modified ECMS has significant negative potential effects on critical battery parameters influencing battery ageing. The results are analysed and found consistent for two different drive cycles and three different powertrain component configurations. The results show a significant reduction in fuel consumption of up to 21.18% and a reduction in engine switching events of up to 55% with modified ECMS when compared with rule-based strategies. However, there is a significant increase in battery temperature by 31% and battery throughput by 378%, which plays a major role in accelerating battery ageing. This paper emphasizes the need to consider battery-ageing parameters along with other control objectives for a robust assessment of energy management strategies. This study helps in laying down a foundation for future improvements in energy management development and it also aids in establishing a basis for comparing energy management controllers

Optimisation of direct battery thermal management for EVs operating in low-temperature climates

Electric vehicles (EVs) experience a range reduction at low temperatures caused by the impact of cabin heating and a reduction in lithium ion performance. Heat pump equipped vehicles have been shown to reduce heating ventilation and air conditioning (HVAC) consumption and improve low ambient temperature range. Heating the electric battery, to improve its low temperature performance, leads to a reduction in heat availability for the cabin. In this paper, dynamic programming is used to find the optimal battery heating trajectory which can optimise the vehicle’s control for either cabin comfort or battery performance and, therefore, range. Using the strategy proposed in this research, a 6.2% increase in range compared to no battery heating and 5.5% increase in thermal comfort compared to full battery heating was achieved at an ambient temperature at −7 ∘C

Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection.

Fracture detection has been a long-standingparadigm on the medical imaging community. Many algo-rithms and systems have been presented to accurately detectand classify images in terms of the presence and absence offractures in different parts of the body. While these solutionsare capable of obtaining results which even surpass humanscores, few efforts have been dedicated to evaluate how thesesystems can be embedded in the clinicians and radiologistsworking pipeline. Moreover, the reports that are included withthe radiography could also provide key information regardingthe nature and the severity of the fracture. In this paper, wepresent our first findings towards assessing how computer vi-sion, natural language processing and other systems could becorrectly embedded in the clinicians’ pathway to better aidon the fracture detection task. We present some initial exper-imental results using publicly available fracture datasets alongwith a handful of data provided by the National HealthcareSystem from the United Kingdom in a research initiative call.Results show that there is a high likelihood of applying trans-fer learning from different existing and pre-trained models tothe new records provided in the challenge, and that thereare various ways in which these techniques can be embeddedalong the clinicians’ pathwa