Customer Specific Transaction Risk Management in eCommerce

Increasing potential for turnover in e-commerce is inextricably linked with an increase in risk. Online retailers (e-tailers), aiming for a company-wide value orientation should manage this risk. However, current approaches to risk management either use average retail prices elevated by an overall risk premium or restrict the payment methods offered to customers. Thus, they neglect customer-specific value and risk attributes and leave turnover potentials unconsidered. To close this gap, an innovative valuation model is proposed in this contribution that integrates customer-specific risk and potential turnover. The approach presented evaluates different payment methods using their risk-turnover characteristic, provides a risk-adjusted decision basis for selecting payment methods and allows e-tailers to derive automated risk management decisions per customer and transaction without reducing turnover potential

Imprinting, honeymooning, or maturing: Testing three theories of how interfirm social bonding impacts suppliers’ allocations of resources to business customers

In business markets, does strength of social bonds that a supplier perceives with a specific customer influence the supplier’s allocations of resources relative to other customers? If social bonding does uniquely impact supplier allocation of resources to customers, does the impact vary by relationship duration? Relationship marketing and Homans’ framework for social behavior are the theoretical bases for the study, which uses survey data to examine three alternative models that indicate how suppliers’ perceptions of social bonds with customers influence the suppliers’ allocations of resources over time. Analysis of data from sales and marketing managers confirms that two of these models, the imprinting theory and the maturity theory, are relevant. The findings indicate that relationship managers need to take into account the clear effect that creation of strong social bonds in buyer–seller relationships, as distinct from financial bonds, has on the way in which suppliers allocate resources to those relationships and how relationship duration affects the way in which they do so. The study strengthens the argument, on a strong theoretical base, to adopt a collaborative, as opposed to a transactional, approach to buyer–seller relationships

β-Klotho deficiency protects against obesity through a crosstalk between liver, microbiota, and brown adipose tissue.

β-Klotho (encoded by Klb) is the obligate coreceptor mediating FGF21 and FGF15/19 signaling. Klb-/- mice are refractory to beneficial action of pharmacological FGF21 treatment including stimulation of glucose utilization and thermogenesis. Here, we investigated the energy homeostasis in Klb-/- mice on high-fat diet in order to better understand the consequences of abrogating both endogenous FGF15/19 and FGF21 signaling during caloric overload. Surprisingly, Klb-/- mice are resistant to diet-induced obesity (DIO) owing to enhanced energy expenditure and BAT activity. Klb-/- mice exhibited not only an increase but also a shift in bile acid (BA) composition featured by activation of the classical (neutral) BA synthesis pathway at the expense of the alternative (acidic) pathway. High hepatic production of cholic acid (CA) results in a large excess of microbiota-derived deoxycholic acid (DCA). DCA is specifically responsible for activating the TGR5 receptor that stimulates BAT thermogenic activity. In fact, combined gene deletion of Klb and Tgr5 or antibiotic treatment abrogating bacterial conversion of CA into DCA both abolish DIO resistance in Klb-/- mice. These results suggested that DIO resistance in Klb-/- mice is caused by high levels of DCA, signaling through the TGR5 receptor. These data also demonstrated that gut microbiota can regulate host thermogenesis via conversion of primary into secondary BA. Pharmacologic or nutritional approaches to selectively modulate BA composition may be a promising target for treating metabolic disorders

Mapping child growth failure across low- and middle-income countries

Childhood malnutrition is associated with high morbidity and mortality globally1. Undernourished children are more likely to experience cognitive, physical, and metabolic developmental impairments that can lead to later cardiovascular disease, reduced intellectual ability and school attainment, and reduced economic productivity in adulthood2. Child growth failure (CGF), expressed as stunting, wasting, and underweight in children under five years of age (0�59 months), is a specific subset of undernutrition characterized by insufficient height or weight against age-specific growth reference standards3�5. The prevalence of stunting, wasting, or underweight in children under five is the proportion of children with a height-for-age, weight-for-height, or weight-for-age z-score, respectively, that is more than two standard deviations below the World Health Organization�s median growth reference standards for a healthy population6. Subnational estimates of CGF report substantial heterogeneity within countries, but are available primarily at the first administrative level (for example, states or provinces)7; the uneven geographical distribution of CGF has motivated further calls for assessments that can match the local scale of many public health programmes8. Building from our previous work mapping CGF in Africa9, here we provide the first, to our knowledge, mapped high-spatial-resolution estimates of CGF indicators from 2000 to 2017 across 105 low- and middle-income countries (LMICs), where 99 of affected children live1, aggregated to policy-relevant first and second (for example, districts or counties) administrative-level units and national levels. Despite remarkable declines over the study period, many LMICs remain far from the ambitious World Health Organization Global Nutrition Targets to reduce stunting by 40 and wasting to less than 5 by 2025. Large disparities in prevalence and progress exist across and within countries; our maps identify high-prevalence areas even within nations otherwise succeeding in reducing overall CGF prevalence. By highlighting where the highest-need populations reside, these geospatial estimates can support policy-makers in planning interventions that are adapted locally and in efficiently directing resources towards reducing CGF and its health implications. © 2020, The Author(s)

Mapping disparities in education across low- and middle-income countries

Analyses of the proportions of individuals who have completed key levels of schooling across all low- and middle-income countries from 2000 to 2017 reveal inequalities across countries as well as within populations. Educational attainment is an important social determinant of maternal, newborn, and child health(1-3). As a tool for promoting gender equity, it has gained increasing traction in popular media, international aid strategies, and global agenda-setting(4-6). The global health agenda is increasingly focused on evidence of precision public health, which illustrates the subnational distribution of disease and illness(7,8); however, an agenda focused on future equity must integrate comparable evidence on the distribution of social determinants of health(9-11). Here we expand on the available precision SDG evidence by estimating the subnational distribution of educational attainment, including the proportions of individuals who have completed key levels of schooling, across all low- and middle-income countries from 2000 to 2017. Previous analyses have focused on geographical disparities in average attainment across Africa or for specific countries, but-to our knowledge-no analysis has examined the subnational proportions of individuals who completed specific levels of education across all low- and middle-income countries(12-14). By geolocating subnational data for more than 184 million person-years across 528 data sources, we precisely identify inequalities across geography as well as within populations.Peer reviewe

IMG 305 - PEMBUNGKUSAN MAKANAN NOV.05.

We discuss the use of Agent-based Modelling for the development and testing of theories about emergent social phenomena in marketing and the social sciences in general. We address both theoretical aspects about the types of phenomena that are suitably addressed with this approach and practical guidelines to help plan and structure the development of a theory about the causes of such a phenomenon in conjunction with a matching ABM. We argue that research about complex social phenomena is still largely fundamental research and therefore an iterative and cyclical development process of both theory and model is to be expected. To better anticipate and manage this process, we provide theoretical and practical guidelines. These may help to identify and structure the domain of candidate explanations for a social phenomenon, and furthermore assist the process of model implementation and subsequent development. The main goal of this paper was to make research on complex social systems more accessible and help anticipate and structure the research process

Long-Baseline Neutrino Facility (LBNF) and Deep Underground Neutrino Experiment (DUNE) Conceptual Design Report Volume 2: The Physics Program for DUNE at LBNF

The Physics Program for the Deep Underground Neutrino Experiment (DUNE) at the Fermilab Long-Baseline Neutrino Facility (LBNF) is described

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype