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Reliable reservoir characterization of the strata, fractures, and hydraulic properties is needed to determine the energy storage capacity of geothermal systems. We apply the state-of-the-art Physics-Informed Neural Networks (PINN) to model subsurface flow in a geothermal reservoir. A PINN can incorporate any physical laws that can be described by partial differential equations. We obtain a ground truth dataset by running a virtual pumping-well test in the numerical code “Code_Bright”. This model consists of a low-permeability rock matrix, intersected by high-permeability fractures. We approximate the reservoir permeability with an Artificial Neural Network (ANN) denoted by . Secondly, we model the fluid pressure evolution with the PINN by informing it about the experimental well-testing data. Since observation wells are sparse in space (only the injection well in our case), we feed into a hydraulic mass balance equation. The residual of this equation enforces the loss function of for random collocation points inside the domain. Our results indicate that the ANN is able to approximate even for a high permeability contrast. In addition, the successful interpolation of proves the PINN is a promising method for matching field data with physical laws. In contrast to numerical models PINNs shift the computational efforts toward the training, while reducing the resources needed for the forward evaluation. Nevertheless, training a 3D reservoir model can hardly be achieved on an ordinary workstation since the training data may include several millions of entries. In addition, computational costs increase due to the inclusion of multiphysics processes in the PINN. We plan to prepare the PINN model for training using parallelized GPUs to significantly increase the training speed of the ANNs.

Consumer-to-consumer (C2C) secondhand exchange activity has exploded due to increased user connectivity stemming from widespread digital platform development and adoption. This research foc...

The shift from analog to digital point-of-sale systems (e.g. Square) and app-based service platforms (e.g. Uber) disrupted frontline services by creating new tipping processes that occur in an ever-expanding range of service contexts and involve new stakeholders. The increasing importance of tipping in the global economy and the uncertainty regarding tipping practices suggest the need for a comprehensive framework that accounts for evolving tipped service networks. We introduce the multi-stakeholder service journey lens to build a conceptual framework that accounts for the competing interests of customers, employees, frontline service managers, technology providers, and other stakeholders in emergent tipped services. This framework examines interactions between stakeholders at different points along the tipped service journey, while accounting for the technologies and contexts that shape stakeholder interactions and the sometimes divergent outcomes that result. Stakeholder interactions at each stage of the tipped service journey suggest theoretically rich research questions, such as “How do digital tipping technologies diffuse into and realign cultural practices?”, and important practical questions, such as “Which tip request framing and formatting choices result in the highest tips, most customer satisfaction, and optimum employee outcomes?” Our conclusion emphasizes the importance of multi-stakeholder service journey perspectives for examining digitally disrupted services.

This chapter describes secondhand exchange in the context of the platform economy. Consumers have long engaged in reselling and buying used items as an alternative to purchasing firsthand items, but researchers have little understanding of how these exchanges are different theoretically from traditional consumption patterns. This chapter presents a clear definition of secondhand exchange and separates it from related concepts, including lateral exchange markets, the sharing economy, access-based consumption, and collaborative consumption. It is suggested that secondhand exchange and related consumer behavior in the platform economy can be understood by considering platform differences related to 1) when and how product ownership is transferred (i.e., direct and indirect), 2) the level of platform intermediation (i.e., low, moderate, or high), and 3) buyers' knowledge of reseller identity (i.e., unknown, obscured, and known). Research propositions are presented for these dimensions for each facet of the consumption process (i.e., buying, owning, and disposal).

Technology is changing frontline service scripts. Businesses are now using mobile point-of-sale applications (e.g., Square) and mobile technology (e.g., iPad) to prompt customers for tips. Tip requests are occurring more frequently at the start of service transactions, before any service has been provided. This research examines how requesting a tip either before or after service completion affects customers and service providers. We test the effects of preservice versus postservice tip sequence in four studies (a natural experiment in the field and three controlled experiments) across food and beauty service contexts. Findings reveal that requesting a tip before (vs. after) completing a service leads to smaller tips, reduced return intentions, diminished word-of-mouth intentions, and lower online ratings. Inferred manipulative intent is revealed as the psychological mechanism underlying the harmful effects of requesting a tip before service. Findings suggest that emphasizing the benefits of automated point-of-sale systems can reduce, but not eliminate, the negative effects of preservice tip requests. Contrary to norms within the service industry, we find that service providers should avoid requesting tips before serving customers.

Generating and maintaining consumers’ engagement in online brand communities is critical for marketing managers to enhance relationships and gain customer loyalty. In this research, we investigate how the type of signal used to indicate user reputation can enhance (or diminish) consumers’ community engagement. Specifically, we explore differences in perceptions of points (i.e., point accrual systems), labels (i.e., descriptive, hierarchical identification systems), and badges (i.e., descriptive, horizontally-ordered identification systems). We argue that reputation signals vary in the degree to which they can provide role clarity—the presence of user roles that deliver information about expected behaviors within a group. Across several studies, including a natural experiment using panel data, a survey of community members, and two controlled experiments, we show that signals that evoke a positive social role have the ability to drive greater engagement (i.e., creating discussions, posting comments, and future engagement intentions) than signals that do not provide role clarity. The effect is moderated by user tenure, such that new consumers’ engagement is particularly influenced by signal type. These findings have important implications for marketers as they use reputation signals as a strategic tool when managing online communities.

Branded buy/sell/trade (BBST) is a consumer-to-consumer (C2C) selling phenomenon that is both massive in scale and meaningful in its impact on consumer behavior and the traditional retailin...

This research examines how and why consumers evaluate brand messages about corporate social responsibility (CSR) activities differently. Insights from secondary data suggest that brands may prioritize environmental activities over social activities, and vice versa, depending on the type of company. Using a field experiment and surveys, we explore whether consumers’ attitudes toward these brand decisions follow company priorities. We find that consumers perceive brands that sell goods and communicate messages about environmental sustainability activities more positively than services companies, while consumers perceive brands that provide services and communicate messages about social sustainability activities more positively than goods companies. We show that the tangibility of the brand’s offering also impacts brand attitudes in a similar way. These findings have important implications for brand managers as they communicate CSR activities and attempt to maximize sustainability investments across various causes.

Businesses often seek to leverage customers’ social networks to acquire new customers and stimulate word-of-mouth recommendations. While customers make brand recommendations for various reasons (e.g., incentives, reputation enhancement), they are also motivated by a desire for social empowerment—to feel an impact on others. In several multi-method studies, we show that facilitating sharing of social coupons (i.e., coupon sets that include one for self-use and one to be shared) is a unique marketing strategy that facilitates social empowerment. Firms benefit from social coupons because customers who share spend more and report greater purchase intentions than those who do not. Furthermore, we demonstrate that social coupons are most effective when the sharer’s brand relationship is new versus established. For customers with an established relationship, sharing with a receiver who also has an established relationship maximizes potential impact. Together, these studies connect social empowerment to relationship marketing and provide guidance to managers targeting social coupons.

Walking in place is a standard method for moving through large virtual environments when physical space or positional tracking is limited. This technique has become increasingly prominent with the advent of mobile virtual reality in which external tracking may not be present. In this paper, we revisit walking in place algorithms to address some of their technical challenges. Namely, our solutions attend to improving starting, stopping, and speed control for individual users. From a hand-tuned threshold based algorithm, we provide a new, fast method for individualizing the walking in place algorithm based on biomechanic measures of step rate. In addition, we introduce a new walking in place model based on a convolutional neural network trained to differentiate walking and standing. Over two experiments we assess these methods against a traditional threshold based algorithm on two mobile virtual reality platforms. The assessments are based on controllability, scale, and presence. Our results suggest that an adequately trained convolutional neural network can be an effective way of implementing walking in place.

Given an increasing role of sharing and social networks in shaping retail behavior, firms have introduced an innovative promotional strategy based on social sharing. Social coupons, also referred to as referral coupons, involve a coupon set in which one coupon is meant for the primary recipient (e.g., an existing customer), while the second coupon is meant to be shared with the recipient’s social network (secondary recipient). Little is known about consumer motivations to share a social coupon or the barriers to sharing. Existing research suggests that consumers who share social coupons exhibit greater purchase intentions and higher purchase amounts (Hanson and Yuan 2017), but research is needed to understand what motivates the sharing act itself.

Despite firms’ best efforts, at some point, most customers will experience difficulty using the products they have purchased. Traditionally, consumers have looked to firms to provide them with assistance when they experience problems. Recently, consumers themselves have begun to play a more active role in providing customer support to their peers, which we term peer-provided support. While emerging evidence suggests that peer-provided support can provide benefits to firms by reducing costs and providing high-quality customer support to customers (Cook, 2008), can peer-provided support have an advantage over firm-provided support?

Branded online communities have become an integral part of doing business, as customer membership can have a direct impact on the bottom line. A recent study found that customers who join online communities increase their online purchases online by 37 % and offline by 9 % (Manchanda et al. 2012). A key component of these communities is the way in which users’ reputation in the community is signaled (Lampel and Bhalla 2007). While one community might construct a hierarchy of labels to display user status such as Newbie, Citizen, and Super Citizen in the T-Mobile Support Community, another may use numerical scores such as points. For example, users in the National Multiple Sclerosis Society community gain points based on the number of comments that they post to the community. Most communities employ one reputation signal type or highlight one type over another if multiple types are used simultaneously. Even when points and labels are used to signal reputation concurrently, a pilot study (N = 80) shows that consumers tend to rely on one reputation signal type (points or labels) as the major signal (i.e., 70 % of participants in a multiple-signal condition could recall only one signal type), and directional evidence suggests that the presence of multiple signals may be confusing (MMultiple = 3.47 vs. MOne = 3.14). As such, the focus of our research is to systematically explore the differences in consumer perceptions of each reputation signal type individually.

Objetive:\ud To conduct a systematic review of the literature and meta-analyses to fill the gaps in knowledge on potassium intake and health.\ud \ud Data sources:\ud Cochrane Central Register of Controlled Trials, Medline, Embase, WHO International Clinical Trials Registry Platform, Latin American and Caribbean Health Science Literature Database, and the reference lists of previous reviews.\ud \ud Study selection:\ud Randomised controlled trials and cohort studies reporting the effects of potassium intake on blood pressure, renal function, blood lipids, catecholamine concentrations, all cause mortality, cardiovascular disease, stroke, and coronary heart disease were included.\ud \ud Data extraction and synthesis:\ud Potential studies were independently screened in duplicate, and their characteristics and outcomes were extracted. When possible, meta-analysis was done to estimate the effects (mean difference or risk ratio with 95% confidence interval) of higher potassium intake by using the inverse variance method and a random effect model.\ud \ud Results:\ud 22 randomised controlled trials (including 1606 participants) reporting blood pressure, blood lipids, catecholamine concentrations, and renal function and 11 cohort studies (127,038 participants) reporting all cause mortality, cardiovascular disease, stroke, or coronary heart disease in adults were included in the meta-analyses. Increased potassium intake reduced systolic blood pressure by 3.49 (95% confidence interval 1.82 to 5.15) mm Hg and diastolic blood pressure by 1.96 (0.86 to 3.06) mm Hg in adults, an effect seen in people with hypertension but not in those without hypertension. Systolic blood pressure was reduced by 7.16 (1.91 to 12.41) mm Hg when the higher potassium intake was 90-120 mmol/day, without any dose response. Increased potassium intake had no significant adverse effect on renal function, blood lipids, or catecholamine concentrations in adults. An inverse statistically significant association was seen between potassium intake and risk of incident stroke (risk ratio 0.76, 0.66 to 0.89). Associations between potassium intake and incident cardiovascular disease (risk ratio 0.88, 0.70 to 1.11) or coronary heart disease (0.96, 0.78 to 1.19) were not statistically significant. In children, three controlled trials and one cohort study suggested that increased potassium intake reduced systolic blood pressure by a non-significant 0.28 (-0.49 to 1.05) mm Hg.\ud \ud Conclusions:\ud High quality evidence shows that increased potassium intake reduces blood pressure in people with hypertension and has no adverse effect on blood lipid concentrations, catecholamine concentrations, or renal function in adults. Higher potassium intake was associated with a 24% lower risk of stroke (moderate quality evidence). These results suggest that increased potassium intake is potentially beneficial to most people without impaired renal handling of potassium for the prevention and control of elevated blood pressure and stroke.

[1] We perform a joint inversion of phase velocities from both earthquake and ambient noise induced Rayleigh waves to determine shear wave velocity structure in the crust and upper mantle beneath the Pacific Northwest. We focus particularly on the areas affected by mid-Miocene to present volcanic activity. The joint inversion, combined with the high density seismic network of the High Lava Plains seismic experiment and data from the EarthScope Transportable Array, provides outstanding resolution for this area. In Oregon, we find that the pattern of low velocities in the crust and uppermost mantle varies between the High Lava Plains physiographic province and the adjacent northwestern Basin and Range. These patterns may be due to the presence of the Brothers Fault Zone which separates the clockwise rotating northwest Basin and Range from the relatively undeformed areas further north. Further to the east, the Owyhee Plateau, Snake River Plain (SRP) and northeastern Basin and Range are characterized by high crustal velocities, though the depth extent of these fast wave speeds varies by province. Of particular interest is the mid-crustal high velocity sill, previously only identified within the SRP. We show this anomaly extends significantly further south into Utah and Nevada. We suggest that one possible explanation is lateral crustal extrusion due to the emplacement of the high density mafic mid-crustal sill structures within the SRP.

[1] We use data from the 118-station High Lava Plains (HLP) seismic experiment together with other regional broadband seismic data to image the 3D shear wave velocity structure in the Pacific Northwest using ambient noise tomography. This extensive data set allows us to resolve fine-scale crustal structures throughout the HLP area in greater detail than previous studies. Our results show 1) a high velocity cylinder in the crust and average velocities in the upper mantle beneath the Owyhee Plateau; 2) a mid-crustal high velocity anomaly along the Snake River Plain that also extends south into Nevada and Utah; 3) a low velocity anomaly directly beneath Yellowstone throughout the crust; and 4) low velocities beneath the HLP both in the crust and uppermost mantle, possibly indicating very thin or absent mantle lithosphere in the area. These features provide important constraints on possible models for Miocene to recent volcanism in the Pacific Northwest.

A geophysical site characterization was completed by and consisted of a land and water borne resistivity survey. Carter Lake (the Site), is located adjacent to the Omaha International Airport, and is bisected by the Nebraska-Iowa state line. The geophysical survey was designed to assist in the characterization of the local hydrogeologic framework which affects the water quality of Carter Lake. The objective of the geophysical survey was to assist in identifying lithologic units with high hydraulic conductivities (sands and gravels), aquitards (silts and clays) peripheral and within Carter Lake. Differentiation and delineation of site lithology, with respect to, electrical resistivity data provided additional information for understanding potential water seepage in delineated site areas. The data presented herein in conjunction with known hydrogeologic parameters was used to contribute to the development of a preliminary design for the watershed and in-lake treatment alternatives identified in the area Management Plan to assist in improving the overall water quality of Carter Lake. This paper presents a summary of the collection methodology, interpretation, and findings of the resistivity data collected for this study. The resistivity data was collected from two Site areas: land data collected from, the southern perimeter areas; and water borne resistivity data collected from the in-lake areas.