Utility Of Framework Research Articles

Altruistic giving and risk taking in human affairs

AbstractThe purpose of this paper is to provide a general proposition of the relationship between altruism and risk taking. As explained in the body of the paper, we diverge from a result reported in Stark et al. (2022) and provide an expansion and a generalization of a preliminary result reported in Stark (2024). In a broad utility framework, we study the risk aversion of an altruistic person who is an active donor (benefactor) and the risk aversion of a beneficiary of an altruistic transfer. In both cases, we find that altruism lowers risk aversion. The specific case in which the utility functions of the benefactor and of the beneficiary are constant relative risk aversion (CRRA) functions constitutes a vivid example of lesser risk aversion characterization. We conclude that in terms of risk-taking behavior, a “population” endowed with altruism is uniformly more willing to take risks than a comparable “population” devoid of altruism.

An Unsupervised Learning framework for health diagnosis by incorporating Multiscale Data

Integrating real-time evaluations of health states with Structural Health Monitoring (SHM) data and environmental observations is crucial, particularly under large datasets. Not only is the volume of real-time data analysis substantial, but traditional algorithms like wavelet analysis often require extensive prior work and significant manual input, limiting the application of SHM and big data technologies in structural analysis. To overcome this limitation, this study introduces an unsupervised analytical framework coupling a Denoising Sparse Wavelet Network (DeSpaWN) and a dynamic-inner Principal Component Analysis (DiPCA) algorithm. The DeSpaWN is designed to extract sparse representation from multiscale sensor signals, and the features are subsequently analyzed using the DiPCA to assess health diagnoses. The DeSpaWN is specifically tailored for monitoring high-frequency time series dynamically and adaptively, enabling reconstruction and extraction of signals across various frequency domains. Additionally, the DiPCA effectively handles large-scale time-series data, providing precise evaluations by accounting for dynamic latent correlations. To validate the method, a 5-Degree of Freedom (DoF) structure model is established, equipped with five virtual sensors at each DoF and an anemometer at the top DoF. The sensor signals, obtained under different health states and subjected to artificial excitations, are input into the proposed framework. Validation results demonstrate the effectiveness in synthesizing multi-sensor data and assessing structural integrity. The framework's practical utility is showcased through a real-world case study that assesses the structural integrity of a wind turbine. By integrating real and virtual sensor data collected over a year and cross-referencing this with the fault log, the study validates the algorithm's efficacy in processing multisource heterogeneous data and its precision in structural health diagnosis. The study substantiates real-time tracking of health states for SHM-equipped structures, underscoring its potential to enhance safety and reliability in civil infrastructure.

Practice-enhancing publications about the medication-use process in 2023.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. This article identifies, prioritizes, and summarizes published literature on the medication-use process (MUP) from calendar year 2023 that can impact health-system pharmacy daily practice. The MUP is the foundational system that provides the framework for safe medication utilization within the healthcare environment. The MUP is defined in this article as having the following components: prescribing/transcribing, dispensing, administration, and monitoring. Articles evaluating at least one step of the MUP were assessed for their usefulness toward practice improvement. A PubMed search was conducted in January 2024 for calendar year 2023 using targeted Medical Subject Headings keywords and the tables of contents of selected pharmacy journals were searched, providing a total of 5,314 articles. A thorough review identified 40 potentially practice-enhancing articles: 8 for prescribing/transcribing, 15 for dispensing, 6 for administration, and 11 for monitoring. The trends from the articles are briefly summarized, with a mention of the importance within health-system pharmacy. The articles are listed and summarized in tables for further review and evaluation. It is important to routinely review the published literature and to incorporate significant findings into daily practice. This article assists in identifying and summarizing the most impactful literature from 2023. Health-system pharmacists have an active role in improving the MUP in their institutions, and awareness of significant published studies can assist in changing practice at the institutional level.

Empirical Study of Data-Driven Learning and Generative AI in Enhancing Meta-Cognitive Resource Utilization: A Comprehensive Analysis

This study investigates integrating data-driven learning and Generative AI within the Meta-Cognitive Resource Utilization Framework (MCRUF) and its potential to enhance educational outcomes. It highlights how AI-driven tools can personalize learning experiences, foster meta-cognitive skill development, and offer real-time feedback to improve learner autonomy and engagement. However, the study identifies key challenges, such as over-reliance on technology, digital literacy gaps, data privacy concerns, and unequal access to AI resources. The findings suggest important implications for educators and policymakers, emphasizing the need for ethical guidelines, equitable access, and a balanced approach combining AI assistance with active learner participation. Future research should focus on long-term impacts and strategies to ensure responsible implementation of these technologies.

Space, but not rocket science: A framework for capacity utilization in physical distribution

High capacity utilization of space in physical distribution, such as high fill rates of vehicles, pallets, and packaging, is essential to decarbonize supply chains. This study provides an integrated view of the extant literature on capacity utilization of space in physical distribution, which is fragmented and multidisciplinary. A systematic literature review was conducted on 212 peer-reviewed scientific publications. The paper first provides a content analysis to define six research streams on the subject. Then, using systems theory, thematic analyses identify, categorize, and consolidate key elements of capacity utilization of space into a conceptual framework. The framework defines both the root causes of and measurements used in practice. The main components include packaging-, transportation-, and logistics-related factors, all of which influence capacity utilization of space in physical distribution and its outcomes. The framework enhances researchers’ understanding of capacity utilization of space in physical distribution and serves as a guideline for practitioners in addressing this challenge. Based on the findings, we also present avenues for future research.

Directions for Effective Use of Modern Marketing Research in Increasing the Export of Leguminous Products

This paper explores the pivotal role of modern marketing research methodologies in augmenting the export potential of leguminous products. In a global marketplace, the demand for legumes continues to rise, creating an opportune moment for exporters. However, realizing this potential necessitates a nuanced understanding of market dynamics, consumer preferences, and effective marketing strategies. The paper begins by delineating the current landscape of leguminous product exports, highlighting the challenges faced by exporters in penetrating diverse international markets. It then delves into the multifaceted benefits of employing modern marketing research techniques, emphasizing their role in identifying target markets, understanding consumer behavior, and crafting tailored marketing campaigns. Moreover, the research delineates various modern marketing research methodologies, including data analytics, consumer surveys, and trend analysis, elucidating their practical applications in the leguminous export industry. By integrating these methodologies, exporters can gain insights into market trends, competitive positioning, and consumer perceptions, enabling them to develop innovative strategies for market expansion. Furthermore, the paper offers a strategic framework for the effective utilization of modern marketing research in the context of leguminous exports. It emphasizes the significance of data-driven decision-making, adaptive marketing strategies, and agile market responsiveness. In conclusion, this paper underscores the indispensable role of modern marketing research in enhancing the export prospects of leguminous products. By leveraging these methodologies effectively, exporters can navigate the complexities of global markets, capitalize on emerging trends, and foster sustained growth in the leguminous product export industry.

Integrating thermoelectric devices in pyrolysis reactors for biochar and electricity co-production

This study proposed an innovative approach to integrating thermoelectric devices with small-scale pyrolysis reactors by using Mizunara (Japanese oak) as the feedstock for biochar production. The primary objective of the study was to enhance the energy efficiency and carbon sequestration potential of the biochar production process by converting waste heat into electricity through thermoelectric devices. Comprehensive steady-state thermal balance analysis revealed that although covering the entire reactor surface with thermoelectric devices can result in excessive heat loss and reduced biochar yield, strategically limiting the installation area allows efficient power generation without considerably compromising biochar production. This balance between electricity generation and biochar yield is critical for optimizing the system’s efficiency. Historically, small-scale waste-heat power generation has been underdeveloped, but our studies have demonstrated that incorporating naturally cooled thermoelectric devices allows the generation of kilowatt-hour (kWh)-scale electricity from waste heat, which would otherwise be discarded. Furthermore, the analysis revealed that with optimized conditions, the CO2 equivalent values of the sequestered carbon can be substantially increased, providing a viable solution for long-term carbon storage. The results highlight the potential of this integrated approach to improve the energy efficiency of biochar production and provide a solution for waste-heat management. Moreover, we assessed the implementation effects by assigning reasonable values for the thermoelectric device performance and provide a robust framework for biomass utilization, waste-heat recovery, and CO2 sequestration.

Application of deep learning reduced-order modeling for single-phase flow in faulted porous media

AbstractOur research is positioned within the framework of subsurface resource utilization for sustainable economies. We concentrate on modeling the underground single-phase fluid flow affected by geological faults using numerical simulations. The study of such flows is characterized by strong uncertainites in the data defing the problem due to the difficulty of taking precise measurements in the subsoil. We aim to demonstrate the feasibility of a reduced order model that is both reliable and computationally efficient, thereby facilitating the incorporation of uncertainties. We account for the uncertainities of the properties of the rock and the geometry of the fault. The latter is achieved by using a radial basis function mesh deformation method. This approach benefits from a mixed-dimensional framework to model the rock matrix and faults as n and $${n-1}$$ n - 1 dimensional domains, allowing for non-conforming meshes. Our primary focus is on a reduced-order model capable of reproducing flow variables across the entire domain. We utilize the Deep Learning Reduced Order Model (DL-ROM), a nonintrusive neural network-based technique, and we compare it against the traditional Proper Orthogonal Decomposition (POD) method across various scenarios. The most relevant contributions of this work are: the proof of concept of the use of neural network for reduced order models for subsoil flow, dealing with non-affine problems and mixed dimensional domain. Additionally, we generalize an existing mesh deformation method for discontinuous deformation maps. Our analysis highlights the capability of reduced order model, highlighting DL-ROM’s capacity to expedite complex analyses with promising accuracy and efficiency, making multi-query analyses with various quantities of interest affordable.

Empirical study on sustainable energy development goals: Analysis of rural roof distributed photovoltaic systems in Jiangsu, China

Rural rooftop distributed photovoltaic systems (RRDPVS) play a crucial role in China's energy strategy to achieve sustainable development goals (SDGs). Over the past years, RRDPVS have experienced rapid development in China. This study focuses on identifying relevant factors and analyzing their influence on the growth of RRDPVS in Jiangsu Province. Based on county-level panel data spanning 39 months across 64 countries, an empirical model is built for analyzing the newly installed capacity of RRDPVS with the consideration of local natural resources and socio-economic conditions. It is a cross-disciplinary study in the field of economics and energy. In summary, natural resource factors have less influence and socio-economic factors have more influence on the newly installed capacity of RRDPVS at this stage. The results of regression models show that elements of natural endowment, such as solar resources, air temperature and air quality index PM2.5 have insignificant effects on the amount of newly installed capacity. The absence of government strategic guidance has resulted in the indiscriminate construction of RRDPVS in low-quality status. The results of the analyses from the socio-economic perspective show that most rural residents installed RRDPVS are the low-income group, whereas middle- and upper-income residents are less interested in installation. It is necessary to appropriately raise public awareness of the use of clean and low-carbon green energy, in order to expand the audience of RRDPVS and increase the penetration rate of the system among middle and high-income groups. The finding of this study offers insights into establishing a robust policy framework for renewable energy utilization to achieve SDGs, and promotes feasible solutions for the transformation of rural photovoltaics in China and other developing countries.

Social and Web Data Framework for Understanding Loneliness.

Loneliness can be studied through social media and web data to gain insights into the dynamic phenomenon. In this paper, we present our proposed framework through a case study on how to deploy various social media and online data sources to study loneliness comprehensively. The framework is important to understand loneliness from data perspective available online and to complement the theoretical and psychosocial understanding of loneliness. The data on loneliness is gathered through surveys and self-reporting mechanisms. This requires complementing the dynamic and vast data available on the web and through social media. Our results found that tools like Google Trend and News Analysis can give the starting point to explore loneliness in a particular region. Tools like X (formerly Twitter), Reddit, and other social media can give behavioral data on loneliness which can be analyzed through sentiment analysis and other social intelligence analysis. The framework's utility lies in its potential to inform policies, interventions, and initiatives addressing loneliness through data. However, it is crucial to acknowledge limitations, such as data availability, biases in user-generated content, and ethical considerations.

Sustainable utilization of cultivated land resources based on "element coupling-function synergy" analytical framework: A case study of Guangdong, China

Solely focusing on the agricultural production function of cultivated land resources is not conducive to the various demands for meeting the UN's Sustainable Development Goals. Recognizing the multifunctionality of cultivated land and understanding the interrelationships between individual functions are crucial for the rational planning and utilization of resources. This paper introduces an "element coupling-function synergy" analytical framework for the sustainable utilization of cultivated land resources. The proposed framework is based on the causal relationships between elements and functions within the cultivated land system. Subsequently, a causal Bayesian belief network was constructed to identify trade-offs and synergies among multiple functions of cultivated land resources in Guangdong, China. The findings reveal trade-offs between food cleanliness and food production/social security, and synergistic relationships among food production, social security, and ecological regulation, as well as among ecological regulation, habitat maintenance and landscape culture. The study area was divided into eight functional zones: Green Agricultural Zone, Agro-inputs Control Zone, Urban Agricultural Zone, Major Grain-producing Zone, Modern Agricultural Zone, Agro-ecological Preservation Zone, Agro-ecological Tourism Zone, Quality Improvement Zone. Multi-objective management plans were formulated for optimizing multifunctional relationships within each zone. The analysis result reveals the importance of nutrient conditions and ecological environments for the sustainable management of cultivated land. Consequently, specific policy recommendations are proposed accordingly. This paper may not only advance understanding of the multifunctionality of cultivated land but can also provide valuable insights for land-use planning to ensure the judicious and sustainable management of cultivated land resources.

Toward Sustainable Groundwater Management: A Comprehensive Framework for Resource Protection and Utilization

ABSTRACTThis paper presents a comprehensive framework for the sustainable management of groundwater resources in the Makutupora basin, Dodoma, Tanzania. The framework was developed through a systematic four‐phase methodology. Phase I involved a literature and document review to generate foundational insights. Phase II comprised a review of existing conceptual frameworks to identify best practices. Phase III included primary data collection through interviews, focus groups, and stakeholder surveys to understand current challenges and capacities. Phase IV involved synthesizing the findings to develop the proposed management framework. The framework includes a nested, multi‐scale structure with four hierarchical levels, overarching goals, primary management functions, specific operations, and subordinate tasks. It was informed by sustainability Principles and Intergrated Water Resources Mangagement concepts. The framework facilitates coordinated planning and implementation across relevant organizations through well‐defined roles and regular monitoring/evaluation. Key functions address resource assessment, regulatory compliance, stakeholder participation, financing, and capacity development. Validation by subject matter experts strengthened the framework's grounding in evidence and enhanced its implementation, adaptability, and long‐term sustainability. The final framework is envisioned as a dynamic decision support tool to address the complexities of groundwater utilization, protection, and conservation in an equitable, adaptive manner for current and future generations.

Efficient utilization of lignin heterogeneity: Employing a simple approach to modulate the structure-effect relationship of lignin microspheres for its high-quality applications in supercapacitors

Lignin, an abundant, sustainable, and cost-effective biomass material, is recognized for its high carbon content, making it an ideal candidate for supercapacitors electrode. However, the inhomogeneity of lignin and the amorphous structure of lignin-derived carbon limit its application in energy storage. To effectively address the aforementioned issues, this study initially fractionated alkali lignin (AL) using a green γ-valerolactone/water system to obtain two fractions (L1 and L2) with distinct molecular weights and phenolic hydroxyl contents. The aim of this initiative was to reduce the heterogeneity of the lignin. Subsequently, three groups of lignin microspheres (LMS-AL, LMS-L1 and LMS-L2) with different morphologies were prepared from AL, L1, and L2 using an industrially proven spray-drying technique. This initiative significantly increased the specific surface area of lignin. Ultimately, lignin microspheres were carbonized to prepare carbon microspheres (LMSC-AL, LMSC-L1 and LMSC-L2) that could be used as supercapacitor electrodes. Notably, LMSC-L1 demonstrated superior electrochemical performance, with a specific capacitance of 200 F g−1 at a current density of 1 A g−1, and retaining 93.34 % capacitance after 15,000 charge/discharge cycles. The excellent electrochemical performance of LMSC-L1 is attributed to its elevated specific surface area (388.1 m2 g−1) and substantial porosity (0.238 cm3 g−1). This study also explored practical application, with LMSC-L1 successfully powering LED lights and calculators. Through this research, we have developed a simple method is proposed to modulate the structure of lignin microspheres by leveraging the heterogeneity of lignin itself. Furthermore, we systematically investigated the structure-effect relationship of lignin microspheres and the electrochemical properties of their carbonized microspheres. This work lays a foundational framework for the efficient utilization of lignin in energy storge applications.

PROGRESS OF RESUSCITATIVE ENDOVASCULAR BALLOON OCCLUSION OF THE AORTA IN PREHOSPITAL EMERGENCY TREATMENT FOR PELVIC FRACTURE.

Pelvic fractures are severe traumatic injuries often accompanied by potentially fatal massive bleeding. Rapid control of hemorrhages in prehospital emergency settings is critical for improving outcomes in traumatic bleeding. Resuscitative endovascular balloon occlusion of the aorta (REBOA) is a promising technique for controlling active bleeding from pelvic fractures. By inserting a balloon catheter into the aorta, REBOA helps maintain blood flow to vital organs such as the brain and heart. This paper provides a comprehensive overview of the initial management of noncompressive trunk hemorrhage caused by pelvic fractures, introduces the technical principles and developments of REBOA, and explores its extensive application in prehospital emergency care. It delves into the operational details and outlines strategies for effectively managing potential complications. We aim to offer a theoretical framework for the future utilization of REBOA in managing uncontrollable hemorrhage associated with pelvic fractures in prehospital emergencies.

Insights on tiger nut (Cyperus Esculentus L.) oil-loaded microcapsules: characterization and oxidation stability analysis

In this paper, tiger nut oil-loaded microcapsules (TNOMs) were prepared by complexation soybean protein isolate (SPI) and maltodextrin (MD) as wall materials using the spray drying method with tiger nut oil (TNO) as the core material, and its physicochemical properties and stabilities were characterized and analyzed. Under the optimum conditions, the encapsulation efficiency (EE) of TNOMs could reach up to 91.23%. Of note, after 60 days of storage at 60 °C, the peroxide value (PV) of TNO was almost 21.8 times as much as that of TNO encapsulated. Furthermore, TNOMs had good thermal stability below 200 °C and are sufficient for the general food processing needs. By fitting Arrhenius oxidation kinetics model, it was predicted that the shelf life of the product stored at 25 °C was 352.48 d. Therefore, it is promised to be applied to the development of high oleic acid food in the future. This study offered a theoretical framework for utilization and broadening the range of applications of TNO in the food industry.

Bifunctionality of amine-modified metal-organic frameworks for CO2 capture and selective utilization in cycloaddition

In this study, copper- and chromium-based (HKUST-1 and MIL-101(Cr), respectively) metal-organic frameworks (MOF) functionalized with amine groups (HKUST-1‒NH2 and MIL-101(Cr)‒NH2, respectively) were directly synthesized using 2-aminoterephthalic acid as an organic linker via hydrothermal method without adding hydrofluoric acid. They were then investigated for their potential applications in dynamic carbon dioxide (CO2) adsorption and conversion of epoxides with CO2. The functionalized MOF (HKUST-1‒NH2 and MIL-101(Cr)‒NH2) retained their desired textural properties, while gaining a significantly enhanced Lewis basic character for CO2 capture and catalysis application. Both HKUST-1‒NH2 and MIL-101(Cr)‒NH2 not only showed an improved CO2 uptake capability, but also an excellent and stable regenerability over multiple adsorption-desorption cycles. MIL-101(Cr)‒NH2 exhibited a higher performance than the parent MOF and HKUST-1‒NH2 in the transformation of styrene oxide (SO) with CO2 to styrene carbonate (SC) and carbonate oligomers (COL) due to combined effect of its textural properties and basicity. Under solvent-free system, COL from monomeric SC was directly obtained, up to 72.4 % yield, via in situ oligomerization. Optimization of the solvent-free reaction conditions was carried out to control the selective pathway of CO2 utilization between cycloaddition and oligomerization. In the presence of acetonitrile, a > 97 % yield of SC was achieved over MIL-101(Cr)‒NH2 under a mild reaction condition (120 °C and 20 bar of CO2). Reaction mechanisms for the cycloaddition and oligomerization of SO with CO2 are also proposed to comprehend the role of MOF, amine group, and co-catalyst. The combined efficient CO2 adsorption and capability to produce CC and COL makes the synthesized MOF promising materials for CO2 capture and selective utilization.

A holistic framework to model student's cognitive process in mathematics education through fuzzy cognitive maps

This study introduces a pioneering framework for modeling students' cognitive processes in mathematics education through Fuzzy Cognitive Maps (FCMs). By integrating key educational theories—Duval's Semiotic Representation Theory, Niss's Mathematical Competencies, Marton's Variation Theory, and the broad Engagement, Motivation, and Participation framework— the model offers a comprehensive and holistic understanding of students' cognitive landscapes. This research underscores the necessity of a multidimensional approach to capturing the intricate interplay of cognitive, affective, and behavioral factors in students' mathematical learning experiences. The novelty lies in its methodological innovation, employing FCMs to transcend traditional qualitative analyzes and facilitate quantitative insights into students' cognitive processes. This approach is particularly relevant in the current era dominated by digital learning environments and artificial intelligence, where real-time, automated analysis of student interactions is increasingly vital. The proposed FCM has been developed over the years with a data-driven approach; the concepts and relationships in it have been derived from the literature and refined by the author's experience in the field. Illustrated through case studies, the framework's utility is demonstrated in diverse contexts, highlighting how the quantitative data obtained are confirmed by qualitative approach: analyzing the impact of remote learning during the Covid-19 pandemic on student engagement and exploring Augmented Reality's role in enhancing mathematical conceptualization. These applications show the framework's adaptability and its potential to integrate new technologies in educational practices. However, the transition from qualitative to quantitative methodologies poses a challenge, given the prevalent use of qualitative approaches in mathematics education research. Additionally, the technological implementation of the FCM model in educational software presents practical hurdles, necessitating further development to ensure ease of integration and use in real-time educational settings. Future work will focus on bridging these methodological gaps and overcoming technological challenges to broaden the FCM model's applicability and enhance its contribution to advancing mathematics education.

A fluid–particle decomposition approach to matching market design for crowdsourced delivery systems

This paper considers a crowdsourced delivery (CSD) system that effectively utilizes the existing trips to fulfill parcel delivery as a matching problem between CSD drivers and delivery tasks. This matching problem has two major challenges. First, it is a large-scale combinatorial optimization problem that is hard to solve in a reasonable computational time. Second, the evaluation of the objective function for socially optimal matching contains the utility of drivers for performing the tasks, which is generally unobservable private information. To address these challenges, this paper proposes a hierarchical distribution mechanism of CSD tasks that decomposes the matching problem into the task partition (master problem) and individual task-driver matching within smaller groups of drivers (sub-problems). We incorporate an auction mechanism with truth-telling and efficiency into the sub-problems so that the drivers’ perceived utilities are revealed through their bids. Furthermore, we formulate the master problem as a fluid model based on continuously approximated decision variables. By exploiting the random utility framework, we analytically represent the objective function of the problem using continuous variables, without explicitly knowing the drivers’ utilities. The numerical experiment shows that the proposed approach solved large-scale matching problems at least 100 times faster than a naive LP solver and approximated the original objective value with errors of less than 1%. The code of this work is available at https://github.com/yuki-oyama/fluid-particle-csd.

Regional variation in participation in private tutoring and the role of education system features

The use of private tutoring to enhance academic outcomes has proliferated across the globe over recent decades. Despite increased scholarly interest in these so-called shadow education activities, the understanding of how education system features relate to the prevalence of shadow education is relatively limited. Moreover, regional variation of private tutoring within countries remains largely overlooked. This study exploits the federalist structure of Switzerland's education system to investigate how education system features incentivise or discourage participation in private tutoring. Based on a subjective expected utility framework and drawing on data from two large-scale assessment studies, the analyses reveal a substantial regional variation in participation rates in private tutoring. Multilevel regression models provide evidence that the institutional modalities of selection into general secondary education contribute to this variation and the social inequalities in the use of private tutoring.

Testing a theory of strategic multi-product choice

This paper tests a theory of strategic multi-product choice (SMPC) using empirical evidence from a large-scale choice experiment, two smaller longitudinal choice experiments, and multi-market panel data. Multi-product choice involves two stages. In the first stage, the consumer chooses a set of substitutable products, where “set” refers to both the variety of alternatives and the quantities of each. In the second stage, the set is consumed. Assuming consumers are strategic, their consumption decisions will consider both the utility of whichever product is selected for consumption and the expected utility (i.e., value) of the set that remains. SMPC therefore requires a dynamic model. We test two such dynamic models in this paper. These models are derived from a basic random utility framework with a stochastic error term for the utility of each product alternative at the moment of consumption. Despite maintaining state variables for the quantity of every alternative, these SMPC dynamic models offer both a value function and optimal consumption policy in closed form. These structures allow us to test for strategic consumption in the second stage and for optimality of the choice sets selected in the first stage. Data from the large-scale choice experiment and the smaller longitudinal choice experiments support strategic consumer decision-making, consistent with SMPC theory. SMPC theory further predicts that the amount of variety consumers select will be higher for lower consumption rates and lower for higher consumption rates. Evidence from panel data of yogurt purchases supports this prediction. While we find that consumption choices are consistent with SMPC theory, they are not consistent with alternative explanations such as variety seeking or diversification bias. Viewed in its entirety, the empirical evidence presented in this paper confirms that both the choice set selected and the way it is consumed are consistent with dynamic models of future preference uncertainty.