Search

Firms adopt randomized experiments to evaluate various interventions (e.g., website design, creative content, and pricing). However, most randomized experiments are designed to identify the impact of one specific intervention. The literature on randomized experiments lacks a holistic approach to optimize a sequence of interventions along the customer journey. Specifically, locally optimal interventions unveiled by randomized experiments might be globally suboptimal when considering their interdependence as well as the long-term rewards. Fortunately, the accumulation of a large number of historical experiments creates exogenous interventions at different stages along the customer journey and provides a new opportunity. This study integrates multiple experiments within the reinforcement learning (RL) framework to tackle the questions that cannot be answered by stand-alone randomized experiments. How can we learn optimal policy with a sequence of interventions along the customer journey based on an ensemble of historical experiments? Additionally, how can we learn from multiple historical experiments to guide future intervention trials? We propose a Bayesian recurrent Q-network model that leverages the exogenous interventions from multiple experiments to learn their effectiveness at different stages of the customer journey and optimize them for long-term rewards. Beyond optimization within the existing interventions, the Bayesian model also estimates the distribution of rewards, which can guide subject allocation in the design of future experiments to optimally balance exploration and exploitation. In summary, the proposed model creates a two-way complementarity between RL and randomized experiments, and thus, it provides a holistic approach to learning and optimizing interventions along the customer journey. This paper was accepted by Anindya Ghose, information systems. Funding: This work was supported by Adobe Faculty Research Award and the Marketing Science Institute Research Grant. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4914. [ABSTRACT FROM AUTHOR]

With the continuous intensification of competition among major countries and the resurgence of anti-globalization trends, countries around the world have strengthened their strategic deployment for digital transformation in the manufacturing industry. Digital development enables enterprises to transform and upgrade, and the digital transformation and green transformation of enterprises have a historic intersection. This study uses the panel data of 1900 A-share listed companies to explain the theoretical mechanism of digital transformation to promote carbon emission reduction from the perspective of green technology progress. In addition, the threshold model is used to analyze the nonlinear relationship. The results show that the digital transformation has significantly reduced the carbon emission level of Chinese manufacturing enterprises, and the conclusion is still true after the instrumental variable estimation and robustness test. Heterogeneity analysis found that the carbon emission reduction in state-owned enterprises is more obvious. Green technology innovation capability shows the technology dividend effect, which is an important way for digital transformation to promote carbon emission reduction. This study expands the research on the sustainable development of China's manufacturing industry and provides guidance for policy makers and business decision makers. [ABSTRACT FROM AUTHOR]

The digital economy (DE) has become a major driving force behind the optimization and upgrading of industrial structures (ISOU), but research on its driving mechanisms has yet to be verified. To this end, the driving influence of the DE on ISOU is targeted and the role played by economic resilience (ER) and innovation is explored. Based on the panel data of 31 provinces in China from 2011 to 2020, the driving influence of the DE on ISOU is analyzed using a two-way fixed-effects model. This empirical study finds that the DE has a positive driving effect on both industrial structure advancement and rationalization and that the driving effect is still significant after the addition of control variables and the robust-type test with one period of lag. The DE presents regional differences in ISOU, with the driving effect of the DE on the industrial structure advancement in the eastern region having a more significant performance than that in the central and western regions and the driving influence on the rationalization of the industrial structure in the eastern and western regions exceeds that in the central region. The impact of the DE on ISOU has a mediating role through innovation. ER has a moderating role in the process and innovation, as a partial mediator, also has a threshold effect. Finally, based on the research and discussion, conclusions and countermeasure recommendations are presented. [ABSTRACT FROM AUTHOR]

Due to the complex mesostructure and components of composite active layers in lithium-ion battery (LIB) electrodes, coupled with the concentration-dependent material properties and eigenstrains, efficiently estimating the effective modulus of the active layers remains a great challenge. In this work, the classic Mori–Tanaka method is found to be unable to estimate the modulus of the active layer. By realizing the importance of the mesostructure feature, a rod-rod model is proposed. The resulting modulus is expressed analytically. It is shown that the rod-rod model can accurately estimate the modulus evolution of the active layer if the material properties of the components and the evolution of volume fractions are known in advance. Moreover, a simplified rod-rod model is also developed to reduce the complexity of the proposed method. By knowing the volume fractions at two arbitrary states of charge and subsequently determining two constants, the simplified model can estimate the modulus efficiently. Considering both its accuracy and its simplicity, the simplified rod-rod model is the most suitable for the estimation. Thus, the methods developed in this work provide a new perspective for analyzing the material properties of composite active layers in LIB electrodes. [ABSTRACT FROM AUTHOR]

From the overall framework of battery development, the battery structures have not received enough attention compared to the chemical components in batteries. The mechanical–electrochemical coupling behavior is a starting point for investigation on battery structures and the subsequent battery design. This perspective systematically reviews the efforts on the mechanics-based design for lithium-ion batteries (LIBs). Two typical types of mechanics-based LIB designs, namely the design at the preparation stage and that at the cycling stage, have been discussed, respectively. The former systemizes the structure design of multiscale battery components from the particle level to the cell level. The latter focuses on the external mechanics-related control, including external pressures and charge–discharge protocols, of in-service LIBs. Moreover, the general problems currently being faced in the mechanics-based LIB design are summarized, followed by the outlook of possible solutions. [ABSTRACT FROM AUTHOR]

This article proposes model-based homogeneity (MBH) to extend compressed sensing (CS) for landmine-detection ground penetrating radar (GPR). Conventional CS methods have difficulty in distinguishing landmines from clutter, since it principally pays attention to signal magnitude. In contrast, our method visualizes landmines based on homogeneity of high-dimensional scattering features in a spatial model. It realizes both the exclusion of clutter and the reduction of measurement points. Experiments demonstrate that the total measurement and processing time are reduced to one-twentieth of a conventional dense measurement case. We also investigate the influence of model size and number of landmines on the performance. The proposed method is capable of visualizing any objects having respective shapes by configuring corresponding models. [ABSTRACT FROM AUTHOR]

We propose an approach for uncovering characteristic response paths of a population from an individual-level multivariate time series data set. The approach is based on a model that accommodates a set of arbitrary distributions for endogenous variables and interstep intervals and variables. The model enables reliable estimation of individual-level parameters by uncovering and statistically pooling clusters of similar individuals. We show that using such a model one can distribute the response of an outcome variable to an impulse over all possible preceding activity sequences. When a few such sequences explain most of the response, they describe the population's characteristic response paths from the impulse to the outcome. We apply the proposed approach to a customer touchpoint data set from a large multichannel specialty retailer. This application uncovers six customer segments, each with unique characteristic paths to purchase. These paths provide insights into the behavior of customers and the optimal over-time communication strategy for different customer segments. Summary of Contribution: Uncovering users' paths through physical and virtual spaces has been of considerable interest in the computing and operations research domain. The existing research suggests a demand for visualizing the primary paths of agents through geographic, online, and activity spaces. Thus far, most of the research have developed approaches that are unique to specific domains providing insight into the domain in the process. There is a need for a general statistically robust approach that can be applied to a broad range of domains to uncover variable sequences that lead to outcomes of interest. We propose a computational approach to uncover characteristic response paths of a population from an individual-level multivariate time series data set. The approach is based on a statistical model that accommodates arbitrary and mixed set of distributions for the endogenous variables, accommodates intersession intervals and variables, and reliably estimates individuals' parameters through statistical pooling by uncovering clusters of similar members. These features make the proposed model suitable for a large variety of real-world datasets. We show that using such a model one can extract characteristic paths over possible activity sequences starting from an impulse leading up to a target variable of interest. [ABSTRACT FROM AUTHOR]

We propose an approach to match returning donors to fundraising campaigns on philanthropic crowdfunding platforms. It is based on a structural econometric model of utility-maximizing donors who can derive both altruistic (from the welfare of others) and egoistic (from personal motivations) utilities from donating—a unique feature of philanthropic giving. We estimate our model using a comprehensive data set from DonorsChoose.org—the largest crowdfunding platform for K–12 education. We find that the proposed model more accurately identifies the projects that donors would like to donate to on their return in a future period, and how much they would donate, than popular personalized recommendation approaches in the literature. From the estimated model, we find that primarily egoistic factors motivate over two-thirds of the donations, but, over the course of the fundraising campaign, both motivations play a symbiotic role: egoistic motivations drive the funding in the early stages of a campaign when the viability of the project is still unclear, whereas altruistic motivations help reach the funding goal in the later stages. Finally, we design a recommendation policy using the proposed model to maximize the total funding each week considering the needs of all projects and the heterogeneous budgets and preferences of donors. We estimate that over the last 14 weeks of the data period, such a policy would have raised 2.5% more donation, provided 9% more funding to the projects by allocating them to more viable projects, funded 17% more projects, and provided 15% more utility to the donors from the donations than the current system. Counterintuitively, we find that the policy that maximizes total funding each week leads to higher utility for the donors over time than a policy that maximizes donors' total utility each week. The reason is that the funding-maximizing policy focuses donations on more viable projects, leading to more funded projects, and, ultimately, higher realized donors' utility. This paper was accepted by Kartik Hosanagar, information systems. [ABSTRACT FROM AUTHOR]

Volatile organic solids, such as cyclododecane or menthol, have been employed as temporary reinforcement material during archaeological excavations. They are usually applied as melts and reinforcement is achieved once the melts solidify. Such solidification process can induce internal stress on the artifacts, which can be a big concern, especially to those very precious and fragile ones. However, information about such stress is still extremely limited at present. This paper proposes an experimental method based on resistance strain gauge technique to monitor the deformation induced by solidification of menthol melt. Bending tests are performed on very thin glass slides. The solidification process of menthol melt is well characterized by the development of mechanical strains. Then, menthol melts are applied to three kinds of simulated samples, i.e. glass, sandstone and rice paper, to investigate the mechanical response of preserved bodies upon solidification. It is found that menthol melt will generate certain amount expansion or contraction of the objects upon solidification. The stresses induced, evaluated according to obtained strains, are generally quite small, indicating that application of menthol as reinforcement material is safe in mechanics for cultural relics. [ABSTRACT FROM AUTHOR]

Sometimes we desire change, a break from the same, or an opportunity to fulfill different aspects of our needs. Noting that consumers seek variety, several approaches have been developed to diversify items recommended by personalized recommender systems. However, current diversification strategies operate under a one-shot paradigm without considering the evolution of preferences resulting from recent consumption. Therefore, such methods often sacrifice accuracy. In the context of online media, we show that by recognizing that consumption in a session is the result of a sequence of utility-maximizing selections from various categories, one can increase recommendation accuracy by dynamically tailoring the diversity of suggested items to the diversity sought by the consumer. Our approach is based on a multicategory utility model that captures a consumer's preference for different categories of content, how quickly the consumer satiates with one category and wishes to substitute it with another, and how the consumer trades off costly search efforts with selecting from a recommended list to discover new content. Taken together, these three elements allow us to characterize how an individual selects a diverse set of items to consume over the course of a session and how likely the individual is to click on recommended content. We estimate the model using a clickstream data set from a large media outlet and apply it to determine the most relevant content to recommend at different stages of an online session. We find that our approach generates recommendations that are on average about 10% more accurate than optimized alternatives and about 25% more accurate than those diversified using existing diversification strategies. Moreover, the proposed method recommends content with diversity that more closely matches the diversity sought by readers, exhibiting lower concentration–diversification bias than other personalized recommender systems. Using a policy simulation, we estimate that recommending content using the proposed approach would result in visitors reading 23% additional articles at the studied website and deriving 35% higher utility. This could lead to immediate gains in revenue for the publisher and longer-term improvements in customer satisfaction and retention at the site. [ABSTRACT FROM AUTHOR]

The crucial role of mechanical stress in voltage hysteresis of lithium ion batteries in charge–discharge cycles is investigated theoretically and experimentally. A modified Butler–Volmer equation of electrochemical kinetics is proposed to account for the influence of mechanical stresses on electrochemical reactions in lithium ion battery electrodes. It is found that the compressive stress in the surface layer of active materials impedes lithium intercalation, and therefore, an extra electrical overpotential is needed to overcome the reaction barrier induced by the stress. The theoretical formulation has produced a linear dependence of the height of voltage hysteresis on the hydrostatic stress difference between lithiation and delithiation, under both open-circuit conditions and galvanostatic operation. Predictions of the electrical overpotential from theoretical equations agree well with the experimental data for thin film silicon electrodes. [ABSTRACT FROM AUTHOR]

In this article, we propose a unified geolocation framework to automatically determine where on the earth a web video was shot. We analyze different social, visual, and textual relationships from a real-world dataset and find four relationships with apparent geography clues that can be used for web video geolocation. Then, the geolocation process is formulated as an optimization problem that simultaneously takes the social, visual, and textual relationships into consideration. The optimization problem is solved by an iterative procedure, which can be interpreted as a propagation of the geography information among the web video social network. Extensive experiments on a real-world dataset clearly demonstrate the effectiveness of our proposed framework, with the geolocation accuracy higher than state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

ABSTRACT On the basis of the experimental studies on viscoelastic properties of poly vinyl alcohol (PVA) films at various relative humidity (RH) and temperature conditions by dynamic mechanical analysis (DMA), the influence of both temperature and RH on the glass transition are discussed and an improved property model is developed to relate the dynamic modulus to RH and temperature. The results indicate that (1) with increasing the RH, the storage modulus of PVA decrease remarkably, while both loss modulus and tanδ sharply increase to reach the peak and then markedly drops. The intensity of this variation is highly dependent upon temperature. (2) Moisture increase will cause the glass transition of PVA at isothermal condition and the transition point can be detected by glass transition relative humidity (RH g) that obtained by isothermal RH scans. (3) Similar to the relationship between Tg and RH, the RH g of PVA vary linearly with temperature. The state diagram of RH g versus temperatures is nearly consistent with that of Tg versus RH. (4)The present equation based on model of Mahieux and Reifsnider (Mahieux and Reifsnider, Polymer 2001, 42, 3281) can predict well the dynamic modulus of PVA at various RHs and temperatures. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 130: 3161-3167, 2013 [ABSTRACT FROM AUTHOR]

An analytical model of mechanical contact problems in composite electrodes of lithium-ion batteries is developed in this article. Two typical types of mechanical contact, namely contact between particles and contact between particle and current collector, are investigated. Key parameters that affect the contact problem are identified from the analytical solution. High uniformity of the particle size is found to be critical to the electrode. Furthermore, a soft current collector could significantly reduce the contact stress and hence is also suggested. It is figured out that contact stress is comparable to or even higher than the diffusion-induced stress under free-expansion state, even if the mechanical constraint in electrodes is weak. To highlight the significance of contact stress, an electrochemical cycling verification experiment which involves a charging pause is conducted. Both analytical and experimental results indicate that the mechanical contact plays a crucial role in the evaluation of mechanical stability of lithium-ion battery electrodes. • Analytical solutions of PP and PC contact are established. • The contact stress can be higher than the diffusion-induced stress. • The stress within the active particle is mainly induced by mechanical contacts. [ABSTRACT FROM AUTHOR]

In metal metallurgy, precipitation strengthening is widely used to increase material strength by utilizing the impediment effect of the second-phase particles on dislocation movements. Inspired by this mechanism, in this paper, novel multiphase heterogeneous lattice materials are developed with enhanced mechanical properties utilizing a similar hindering effect of second-phase lattice cells on the shear band propagation. For this purpose, biphase and triphase lattice samples are fabricated using high-speed multi jet fusion (MJF) and digital light processing (DLP) additive manufacturing techniques, and a parametric study is carried out to investigate their mechanical properties. Different from the conventional random distribution, the second-phase and third-phase cells in this work are continuously distributed along the regular pattern of a larger-scale lattice to form internal hierarchical lattice structures. The results show that the triphase lattices possess balanced mechanical properties. Interestingly, this indicates that introducing a relatively weak phase also has the potential to improve the stiffness and plateau stress, which is distinct from the common mixed rule. This work is aimed at providing new references for the heterogeneous lattice design with outstanding mechanical properties through material microstructure inspiration.