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While scholars have explored how focal firms harness demand-side value through ecosystem development, less emphasis has been placed on understanding how peripheral complementors become competitive. Unlike focal firms, complementors can seldom set value extraction rules. How can complementors lacking critical supply-side resources establish themselves as ecosystem leaders? We conducted a longitudinal analysis of Xiaomi, which began life as a smartphone software complementor within the Android ecosystem but ultimately gained architectural control by orchestrating the creation of a derivative ecosystem. We elucidate how these resource-disadvantaged firms may begin with fueling an "identity movement" among underserved users centered around a pivotal "foothold resource." This movement serves as their entry point into an ecosystem and enables them to build a demand-side community. They then employ "demand pull" as concrete evidence to substantiate their vision, coalesce supply-side resources, and ultimately become an orchestrator. While conventional strategy research emphasizes supply-side resources as drivers of value creation, we explore the synergy between demand-side mobilization and supply-side orchestration. Finally, we illustrate how firms lacking resources craft compelling visions in conjunction with substantive actions to engage evolving target audiences. This approach allows them to secure more advantageous positions within the ecosystem, thereby transforming its structure over time. [ABSTRACT FROM AUTHOR]

The authors examine the effects of a firm's and its competitors' online reviews on its demand within the hotel industry. The authors leverage a unique data set of actual bookings from properties of a major hotel chain in six different markets in the United States, supplemented with online reviews garnered from a popular social media platform. The findings indicate that not only a hotel's own reviews but also its competitors' reviews have a significant impact on the hotel's booking performance. The impact of review sentiment is amplified if the focal hotel also charges higher prices or when the volume of reviews is high. The authors establish heterogeneous effects across consumer segments (business vs. leisure travelers) and by the type of review content (objective vs. subjective attributes to assess quality). Specifically, both a hotel's own reviews and its competitors' reviews have a larger impact on bookings for business travelers compared with leisure travelers, and for reviews that mainly discuss subjective attributes, for which consumers need to rely on the experiences of others to assess the quality of a hotel prior to their stay. The study provides a set of comprehensive insights on the impact of both own and competitors' online reviews on a focal hotel's bookings. [ABSTRACT FROM AUTHOR]

Dockless shared micromobility services have grown substantially in recent years, but their impact on consumer demand has remained largely unstudied. The authors estimate how the largest and fastest-growing segment of this market--the dockless electric scooter ("e-scooter") sharing industry--impacts spending in one of the largest segments of the local economy, the restaurant industry. Using data covering 391 companies in 98 U.S. cities, the authors find that the introduction of e-scooters in a city significantly impacts restaurant spending, increasing spending by approximately 5.2% for e-scooter users, driving incremental spending of at least $11.3 million annually across all cities that first allowed e-scooters to operate over summer 2018. Impact varies by restaurant subcategory, with a stronger positive effect on fast-food restaurant spending, and a weaker effect on sit-down restaurant spending. E-scooter entry has a larger impact on companies with higher historical revenues selling at lower prices. It facilitates discovery of new restaurants from prospective customers and repeat business from already-acquired customers. [ABSTRACT FROM AUTHOR]

A perennial challenge for executives in established firms is deciding how and when to respond to emerging technologies. This article demonstrates that the way emerging technologies play out in established industries differs according to how the business system is affected. Some have primarily a supply-side effect (on how a firm in the industry creates its product), while others have a primarily demand-side effect (on how users consume the product). Supply-side effects play out over relatively long periods of time in a predictable way, with incumbent firms executing similar strategies though at different speeds. Demand-side effects are faster-acting and more volatile, with incumbents often experimenting with a range of different business models as they seek a viable way forward in a changing market. By understanding these important differences between supply-side and demand-side effects and being able to anticipate the typical patterns of responses from incumbents, executives can make better choices in how and when to invest in emerging technologies. [ABSTRACT FROM AUTHOR]

Identifying the region's functionalities and what the specific Point-of-Interest (POI) needs is essential for effective urban planning. However, due to the diversified and ambiguity nature of urban regions, there are still some significant challenges to be resolved in urban POI demand analysis. To this end, we propose a novel framework, in which Region-of-Interest Demand Modeling is enhanced through the graph representation learning, namely Variational Multi-graph Auto-encoding Fusion, aiming to effectively predict the ROI demand from both the POI level and category level. Specifically, we first divide the urban area into spatially differentiated neighborhood regions, extract the corresponding multi-dimensional natures, and then generate the Spatial-Attributed Region Graph (SARG). After that, we introduce an unsupervised multi-graph based variational auto-encoder to map regional profiles of SARG into latent space, and further retrieve the dynamic latent representations through probabilistic sampling and global fusing. Additionally, during the training process, a spatio-temporal constrained Bayesian algorithm is adopted to infer the destination POIs. Finally, extensive experiments are conducted on real-world dataset, which demonstrate our model significantly outperforms state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Decomposition structure learning algorithms are widely adopted to recover Bayesian network structures. In the recursive process of separation phase, the network partition is obtained through recursively two steps: constructing the undirected independence graph (UIG) and decomposing with the help of partition methods. UIG as the basis for decomposition directly affects the result of the network partition and then impacts the accuracy of output structure. Existing construction algorithms adopt a fixed type of UIG in the recursive process and researches divide into two directions: constructing moral graph and moral graph with extra edges. The former suffer from the problem that computational complexity of recovering all conditional independences (CIs) is too high to divide network well due to relatively complex networks at the beginning of the recursive process, while the latter suffer from the problem that the network partition is hard to find by insufficient expression degree of CIs due to relatively simple networks at the end of the recursive process. The reason is that the fixed type of UIG can not cope with variation of network size. Therefore, this paper proposes a stage-driven construction algorithm considering variation of network size in the recursive process. Different from other construction algorithms, the proposed algorithm designs the network scale factor to achieve the stage division of the recursive process, and selects different algorithms at different stages to build appropriate UIGs through demand analysis. Experiments on different benchmark networks verify that the proposed algorithm can obtain better performances compared with other representative algorithms. [ABSTRACT FROM AUTHOR]

To improve the use experience of airport chairs, this study adopted morphological analysis to conduct systematic deconstruction, analysis, and statistics on the modeling features of existing airport chairs. The KJ method was used to determine the design requirements of airport chairs. The study combined the Kano model to classify the functional attributes and prioritize the requirements, and it merged the module and demand analysis to build a Function-Behavior-Structure mapping model. In this way, passengers' demand for airport chairs can be clearly defined, and airport chair configuration planning can be carried out according to the difference in preferences. Further, different module combination schemes can be formed to improve the functional allocation efficiency of airport chairs, optimize the overall service quality of airports, and provide some basis and reference for the future design of airport chairs. [ABSTRACT FROM AUTHOR]

This essay unpacks how Beijing-based workers' handbills and posters during the 1989 democracy movement functioned as a counterinstitution that enabled everyday workers to translate economic demands into political self-organization through groups like the Workers' Autonomous Federation. In contrast to liberal dissident writing at the time by the likes of Fang Lizhi and Liu Xiaobo, workers' organizing strategies and political vision have seldom been analyzed. The author argues that these writings represent an important effort to create an inchoate civil society under authoritarian conditions, while going beyond these conditions by empowering workers to ground a broader struggle for democracy in reorganizing the control of society and productive industries through independent politics. [ABSTRACT FROM AUTHOR]

Objective: The adoption of Public-Private Partnerships (PPPs) has sparked a surge in research interest in this area in recent years. This study aims to identify and prioritize the risks associated with PPP projects. Methodology: A questionnaire was designed to assess the significance of each of the 35 research variables (risks) derived from the literature in explaining the risks associated with PPP projects. The respondents included experts in risk associated with PPP-based projects in emerging markets, specializing in policy making, production, sales, and export. These experts were considered knowledgeable due to their practical experience in management, expert, academic, and research activities related to risks and emerging projects and markets. Using purposive sampling and following a snowball method, semistructured in-depth interviews were conducted with 19 experts until theoretical saturation was achieved. Findings: The results were analyzed using the methodology of fuzzy cognitive maps, and the relationships between the research factors were examined. Conclusion: The analysis revealed that financial constraints risk, demand risk, and government support risk are of high importance in the context of PPP projects. [ABSTRACT FROM AUTHOR]

After Japan's bubble economy collapsed in 1991, household values diversified, and, gradually, the budget shares on intangible services exceeded that on tangible goods, leading to a shift from demand for goods to services. In this study, we focus on the increased budget allocation to services in Japanese household expenditure and verify whether service-related items can be aggregated into a service group using Lewbel's (Am Econ Rev 86(3):524–543, 1996) generalized composite commodity theorem (GCCT). We first accurately reclassify into all 51 items consisting of goods and services and verify whether these aggregations are justified. Next, we verify whether these 51 items can be sub-aggregated into goods or service groups. In testing the GCCT, we incorporate panel time-series analysis with cross-sectional dependence, unlike traditional GCCT tests with time-series data. We also conduct a nonparametric revealed preference test for weak separability as a benchmark against the GCCT test results. Our findings demonstrate that the utility function can be rationalized even when the data set is reclassified into 51 items, justifying the aggregation into service groups. This suggests that in the future, specifying the functional form of a service group can be developed into a traditional demand analysis, such as calculating estimates and elasticities. [ABSTRACT FROM AUTHOR]

Online review platforms often present reviews from both critics and general users. In this research, the authors propose a measure called "topic consistency" to capture the degree of overlap between critic and user review content. High topic consistency suggests greater information recall due to repeated presentation of the same topics, which may increase the memorability of movie attributes and therefore positively affect movie demand. The authors measure the topic consistency between critic and user reviews using topic models and further study the financial consequences of this measure using data for movies released in the United States. Topic consistency is positively associated with subsequent box office revenue, suggesting a positive relationship between topic consistency and movie demand. Furthermore, the effect of topic consistency on demand is the greatest for movies with mediocre review ratings and when the review ratings from critics are close to those from users. Using lab experiments, the authors provide evidence of the causal link between topic consistency and consumers' willingness to watch a movie, and support for the potential mediation through the information recall of reviews. Movie producers and advertisers should consider highlighting or inducing a central theme for critics and users to discuss, as the more the review content of critics and users overlaps, the higher a movie's revenue. [ABSTRACT FROM AUTHOR]

Bank-created money, shadow-bank money, and Treasury bonds all satisfy investors' demand for liquidity. We measure the quantity of these forms of liquidity and their corresponding liquidity premium in a sample from 1934 to 2016, estimating the substitutability of these assets and the liquidity per unit delivered by each asset. Treasuries and bank transaction deposits are imperfect substitutes, in contrast to perfect substitutes found by Nagel (2016). Bank and nonbank non-transaction deposits are closer substitutes for Treasuries. Our empirical results inform theories of the monetary transmission mechanism running through shifts in asset supplies and models of the coexistence of the shadow banking and regulated banking system. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Time scarcity promotions (e.g., "40% off for a limited time") are mainstays of online retail marketing. Although positive effects of time scarcity promotions on consumer interest have been evidenced in the brick-and-mortar world, should retailers expect similarly robust effects online? The present research suggests the answer may be no. First, the authors report meta-analytic and experimental results suggesting that previously identified positive effects of time scarcity promotions observed offline may not emerge in online shopping contexts. Then, consistent with the prediction that online time scarcity promotions activate more persuasion knowledge than identical control promotions, the authors detail findings suggesting that providing retailer-exogeneous justifications for online time scarcity promotions' time restriction (e.g., consumers' birthdays, seasonal changes) can increase the potential of observing positive effects on consumer interest online. Further, results suggest that the positive effects of including exogenous time justification may be more likely when less time remains until the online promotion's expiration. However, results stop short of suggesting that online time scarcity promotions will consistently yield superior outcomes compared with identical online control promotions. Therefore, the authors highlight the continued need for careful managerial use as well as further research examining the optimal translation of offline tactics to online retail. [ABSTRACT FROM AUTHOR]

Literature concerning operant behavioral economics shows a strong preference for the coefficient of determination (R2) metric to (a) describe how well an applied model accounts for variance and (b) depict the quality of collected data. Yet R2 is incompatible with nonlinear modeling. In this report, we provide an updated discussion of the concerns with R2. We first review recent articles that have been published in the Journal of the Experimental Analysis of Behavior that employ nonlinear models, noting recent trends in goodness‐of‐fit reporting, including the continued reliance on R2. We then examine the tendency for these metrics to bias against linear‐like patterns via a positive correlation between goodness of fit and the primary outputs of behavioral‐economic modeling. Mathematically, R2 is systematically more stringent for lower values for discounting parameters (e.g., k) in discounting studies and lower values for the elasticity parameter (α) in demand analysis. The study results suggest there may be heterogeneity in how this bias emerges in data sets of varied composition and origin. There are limitations when using any goodness‐of‐fit measure to assess the systematic nature of data in behavioral‐economic studies, and to address those we recommend the use of algorithms that test fundamental expectations of the data. [ABSTRACT FROM AUTHOR]

Purpose: The linguistic demand of task instructions in cognitive assessments may mask or even invalidate cognitive testing results for individuals with language differences or impairments. The purpose of this study was to apply an analysis of linguistic demand to a sample of functional multitasking assessments. Method: We sampled the task instructions and materials from seven functional multitasking assessments that are based on everyday activities. We calculated indices of linguistic demand. Results: The Density Index is based on total number of words, total number of sentences, total number of different words, total number of syllables, and complex (not-simple) sentences. The Breakfast Task and the Frisch Cooking Task consistently ranked lowest in linguistic demand for both instructions and materials based on the indices reviewed. These tasks are most likely to be appropriate for individuals with language impairment. Conclusions: Clinicians have a responsibility to fairly evaluate cognitive functions of individuals with impaired language functioning. The analysis of linguistic demand provides a useful way to evaluate task instructions and materials in a systematic way, so that individuals with language impairments or language differences might be more appropriately evaluated. [ABSTRACT FROM AUTHOR]

Jacket structures play an ever-increasing role in meeting the world's burgeoning energy demand by providing support structure for exploiting offshore wind energy and oil and gas resources. Many of these structures are past their design lifetimes, and thus may have suffered significant degradation, highlighting the importance of investigating their earthquake response in seismically active regions. This study investigates the seismic performance of a typical offshore jacket platform, denoted as baseline structure, as well as four versions with two longer (25% and 50% larger) and two shorter (25% and 50% smaller) natural vibration periods accounting for two levels of degradation and retrofit effects on structural performance. For each case, eleven ensembles of ground motions compatible with the site's seismic hazard are selected using the generalized conditional intensity measure methodology. The loading uncertainty is incorporated in the performance assessment procedure via probabilistic seismic demand analysis and the demand hazard is quantified. The results provide valuable information regarding reliability of these structures both at design stage and during service life, which can aid decision-making around rehabilitation of older platforms, the suitability of reusing obsolete platforms beyond service lifetime, and risk-based design of new platforms. [ABSTRACT FROM AUTHOR]

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Assessing the fragility of intake towers using a single damage index does not allow for accurate evaluation of the potential for structural damage under seismic conditions. In this study, based on the probabilistic seismic demand analysis method, the effects of ground motion intensity on maximum displacement, local damage index, and global damage index are considered, and the seismic fragility of an intake tower structure is analyzed. First, 10 natural ground motion records were selected from the ground motion database (PEER) and 2 artificial seismic waves were synthesized. These seismic waves were amplitude-modulated for incremental dynamic analysis (IDA). The trends of the IDA curves were analyzed to divide the performance levels of the intake tower structure. Furthermore, a two-dimensional fragility curve for the intake tower structure was plotted in this study. The maximum displacement in the direction of parallel flow and the damage index were taken into account in the two-dimensional fragility curve. The results show that, under the designed seismic acceleration, the two-dimensional fragility curve for the intake tower structure was lower than the one-dimensional curve. This indicates that the seismic design based on the one-dimensional performance index was unstable. This provides a theoretical reference for seismic optimization design and the strengthening of intake towers. Therefore, it is recommended to use multidimensional fragility analysis to study the seismic performance of intake tower structures in seismic design. [ABSTRACT FROM AUTHOR]

The specific learning disabilities (SLD) identification literature is replete with competing narratives concerning the advantages and disadvantages of various techniques and methods. Until a widely accepted and empirically proven SLD identification methodology is universally supported, evaluators should seek to improve the existing alternatives. This article describes the value of using norm-referenced testing of intellectual development to comprehensively identify specific learning disabilities (SLD) as advocated by the Core-Selective Evaluation Process (C-SEP). To this end, we will define intellectual development and describe practices such as integrated data analysis and task demand analysis. [ABSTRACT FROM AUTHOR]

Water distribution is a critical system that involves engineered hydrologic and hydraulic components to provide water supply to a continuously growing population. Ensuring a sufficient and uniform water supply through a well-designed network is essential to meet the increasing water demand. The present study focuses on analyzing the water demand of the public water supply to facilitate effective planning, development, and operation of water supply and distribution networks. The main objective of the study is to analyze the existing water distribution network at West Moorings using the Watergems Software. To conduct this analysis, various data points are needed, such as the population of the area, water demand, distribution network layout, and water pump information. Additionally, details regarding the length, nodes, and diameter of the pipes are essential for the analysis. These data are input into the Watergems Software to perform analyses related to pressure, head loss, and elevation. The results of the analysis provide valuable information on pressure and flowrate at different nodes and head loss along various pipes in the network. By comparing the results obtained from the Watergems Software with actual data, the study aims to locate the leaks within the water distribution network at Westmooring, Trinidad and Tobago. In conclusion, Watergems identified leakage nodes within the West Moorings Network, and data analysis helped narrow down the leak's location in the field. This process involved model calibration using field pressure and flowrate data from WASA, and emitter coefficients were categorized by the Darwin Calibrator function. Accurate boundary inputs, such as the number of leakage nodes, Initial Emitter Coefficient Spacing, and time intervals, were essential for precise data assumptions. [ABSTRACT FROM AUTHOR]

This study investigated the relationship between stress exposure and subsequent ethanol use, focusing on individual differences among male rats. We combined operant self-administration with behavioral economics to assess how intermittent swim stress affects ethanol consumption. This approach allowed for a nuanced analysis of the transition from regular ethanol intake to stressinduced escalation in economic demand. Results showed a consistent rise in ethanol demand post-stress among subjects, irrespective of exposure to actual swim stress or a sham procedure. This increase may result from a two-week abstinence or an inherent rise in demand over time. Significantly, we identified a direct link between post-stress corticosterone levels and the demand for ethanol, considering baseline levels. This correlation was particularly pronounced when examining the shifts in both corticosterone levels and demand for ethanol poststress. However, neither post-stress corticosterone levels nor their change over time correlated significantly with changes in ethanol demand following a forced swim test that was administered 24 h after the intermittent swim stress test. This suggests potential context-specific or stressor-specific effects. Importantly, pre-stress ethanol demand did not significantly predict the corticosterone response to stress, indicating that high ethanol-demand rats do not inherently exhibit heightened stress sensitivity. Our research brings to light the complex interplay between stress and ethanol consumption, highlighting the critical role of individual differences in this relationship. This research introduces a nuanced perspective, underscoring the need for future studies in the realm of stress and substance use to give greater consideration to individual variability. [ABSTRACT FROM AUTHOR]

With the gradual development of new distribution system, the standard of traditional distribution system has appeared lag, so people need to explore the standard demand of new distribution system. This paper mainly discusses the development background and development of new distribution system standard demand, and then carries out demand analysis from the perspective of coordination and demand, aiming at providing support for the subsequent development of new distribution system. The research on this has practical significance to promote the development of new distribution standard. [ABSTRACT FROM AUTHOR]

"Demand traction, technology promotion" is the main rule of the development of equipment system construction. This paper focuses on the analysis of system requirements, and puts forward the process of system requirements analysis on the basis of analyzing the basic connotation of system requirements analysis, combining the advantages of threat and capability; aiming at the key technical problems such as capability gap measurement and mapping analysis in system requirements analysis. The capacity gap measurement model is established, and the QFD method is used to realize the mapping between the capability requirement-system requirement-technology requirement, and the analysis of the typical space target monitoring system is taken as an example to verify the system demand analysis method proposed in this paper, which provides a theoretical basis for carrying out the system demand analysis. [ABSTRACT FROM AUTHOR]

The probabilistic seismic demand model (PSDM) is essential to identify the seismic demand of the highway bridge during and after an earthquake. This paper aims to review the probabilistic seismic demand estimation and modeling methodology options associated with the procedure, analytical analysis, and mathematical framework for a highway bridge. As a result of the review, different techniques with features, applications, and limitations on highway bridges are reviewed and presented. A review has investigated the current PSDM and provides a comprehensive summary with formulas, tables, figures, and frameworks. PSDM steps are constructed and introduced to how scholars use them. Besides, analytical methods are the best choice for investigating the PSDA and PSDM for critical bridge components when damage data is insufficient. They are determined to predict each component's seismic response for a given deterministic or random variable. This work helps and motivates the decision-makers and stakeholders to extend the application of the PSDM methodology option for a more informed decision. [ABSTRACT FROM AUTHOR]

The container shipping industry has undergone significant changes in the post‐COVID‐19 era. This study examines the impact of the pandemic on container freight rates (CFR) through a demand analysis approach, using 11 major international container trade lanes from Shanghai, China as a case study. We analyze the CFR data on both weekly and monthly bases, with a focus on retaining more individual and time information using long panel data. To better reveal the pandemic's impact on CFR, we introduce interactive terms and simultaneous equations in our model. Our empirical analysis shows that the pandemic has not only directly and positively affected CFR, but, more importantly, has also indirectly promoted the growth of container shipping cost by stimulating the demand for containerized goods exported from China. Additionally, the connectivity or capacity of a given trade lane has inhibited the pandemic's effects on CFR to a certain extent. These findings provide insights into the remarkable growth of CFR from the demand side in 2020. [ABSTRACT FROM AUTHOR]

Rapid growth rate indicates that the free-floating electric vehicle sharing (FFEVS) system leads to a new carsharing idea. Like other carsharing systems, the FFEVS system faces significant regional demand fluctuations. In such a situation, the rental stations and charging stations should be constructed in high-demand areas to reduce the scheduling costs. However, the planning of the FFEVS system includes a series of aspects of rental stations and charging stations, such as the location, size, and number, which interact with each other. In this paper, we first provide a method for forecasting the demand for car sharing based on the land characteristics of Beijing FFEVS station catchment areas. Then, the multi-objective MILP model for planning FFEVS systems is developed, which considers the requirements of vehicle relocation and electric vehicle charging. Afterward, the capabilities of the proposed models are demonstrated by the real data obtained from Beijing, China. Finally, the sensitivity analysis of the model is made based on varying demand and subsidy levels. From the results, the proposed model can provide decision-makers with useful insights about the planning of FFEVS systems, which bring great benefits to formulating more rational policies. [ABSTRACT FROM AUTHOR]

Worker safety and productivity and the factors that affect them, such as ergonomics, are essential aspects of construction projects. The application of ergonomics and the identification of the connections between workers and assigned tasks have led to a decrease in worker injuries and discomfort, beneficial effects on productivity, and a reduction in project costs. Nevertheless, workers in the construction area are often subjected to awkward body postures and repetitive motions that cause musculoskeletal disorders, in turn leading to delays in production. As a systematic and widely used procedure that generates a final document or form, physical demand analysis (PDA) assesses the health and safety of workers engaged in construction or manufacturing activities and proactively evaluates ergonomic risks. However, to gather the necessary information, traditional PDA methods require ergonomists to spend significant time observing and interviewing workers. To increase the speed and accuracy of PDA, this study focuses on developing a systematic PDA framework to automatically fill a posture-based PDA form and address the physiological aspects of task demands. In contrast to the traditional observation-based approach, the proposed framework uses a motion capture (MOCAP) system and a rule-based expert system to obtain joint angles and body segment positions in different work situations, convert the measurements to objective identification of activities and their frequencies, and then automatically populate the PDA forms. The framework is tested and validated in both laboratory and on-site environments by comparing the generated forms with PDA forms filled out by ergonomists. The results indicate that the MOCAP-/AI-based automated PDA framework successfully improves the performance of PDA in terms of accuracy, consistency, and time consumption. Ultimately, this framework can aid in the design of job tasks and work environments with the goal of promoting health, safety, and productivity in the workplace. [ABSTRACT FROM AUTHOR]

In this paper, closed-form expressions to calculate both the mean annual failure rate and the confidence factor are proposed. Reliability indicators are estimated assuming a normalization between capacity and demand called I DC . Simplified closed-form expressions are obtained in accordance with the probability seismic demand analysis used in SAC/FEMA. Uncertainties associated to mechanical, geometrical, and epistemic properties are taken into account, as well as uncertainties related to the occurrence of earthquakes. A comparison of both the mean annual failure rate and the confidence factor with I DC and the expressions proposed by Cornell and collaborators is performed. The numerical approach for the mean annual failure rate is obtained to verify the approximation of the closed-form solutions. Reliability indicators are obtained using six continuous reinforced concrete bridges designed to comply with three drift thresholds (0.002, 0.003 and 0.004). The bridge systems are situated in transition soil of Mexico City. Maximum differences of 4.1% and 10.6% are obtained between the proposed expression and the numerical solution for the mean annual rate of failure, estimated for two limit states: serviceability and collapse. The confidence factor presents differences of 5.2% between the present study and the original formulation. [ABSTRACT FROM AUTHOR]

Benchmarking incentivizes fund managers to invest a fraction of their funds' assets in their benchmark indexes, and such demand is inelastic. We construct a measure of inelastic demand a stock attracts, benchmarking intensity (BMI) , computed as its cumulative weight in all benchmarks, weighted by assets following each benchmark. Exploiting the Russell 1000/2000 cutoff, we show that changes in stocks' BMIs instrument for changes in ownership of benchmarked investors. The resultant demand elasticities are low. We document that both active and passive fund managers buy additions to their benchmarks and sell deletions. Finally, an increase in BMI lowers future stock returns. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

The interplay between investors' demand and providers' incentives has shaped the evolution of exchange-traded funds (ETFs). While early ETFs invested in broad-based indexes and therefore offered diversification at low cost, more recent products track niche portfolios and charge high fees. Strikingly, over their first 5 years, specialized ETFs lose about 30 |$\%$| (risk-adjusted). This underperformance cannot be explained by high fees or hedging demand. Rather, it is driven by the overvaluation of the underlying stocks at the time of the launch. Our results are consistent with providers catering to investors' extrapolative beliefs by issuing specialized ETFs that track attention-grabbing themes. [ABSTRACT FROM AUTHOR]

This article examines the differential impact of variances in the quality and taste comments found in online customer reviews on firm sales. Using an analytic model, the authors show that although increased variance in consumer reviews about taste mismatch normally decreases subsequent demand, it can increase demand when mean ratings are low and/or quality variance is high. In contrast, increased variance in quality always decreases subsequent demand, although this effect is moderated by the amount of variance in tastes. Since these theoretical demand effects are predicated on the assumption that consumers can differentiate between the two sources of variation in ratings, the authors conduct a survey to test this assumption, demonstrating that participants are indeed able to reliably distinguish quality from taste evaluations in two subsets of 5,000 reviews taken from larger data sets of reviews for 4,305 restaurants and 3,460 hotels. The authors use these responses to construct sets of reviews that they use in a controlled laboratory experiment on restaurant choice, finding strong support for the theoretical predictions. These responses are also used to train classifiers using a bag-of-words model to predict the degree to which each review in the larger data sets relates to quality and/or taste, allowing the authors to estimate the two types of review variances. Finally, the authors estimate the effects of these variances in overall ratings on establishment sales, again finding support for the theoretical results. [ABSTRACT FROM AUTHOR]

Short videos have become a powerful medium in health communication. This study explores the information needs and satisfaction of Chinese urban youth regarding short health videos, guided by the KANO model. The research was conducted in four stages: text mining, online survey, demand analysis, and emotional strategy analysis. During the text mining stage, we used GooSeeker software to extract 26,108 health-tagged short video entries from Douyin and identified 21 unique themes through a collinear network analysis. In the online survey stage, we gathered data on urban youth's preferences for these themes. Using the KANO model and Better-Worse analysis in the demand analysis stage, we categorized health video demands and gained insights into the preferred content. In the emotional strategy analysis stage, we examined how different emotional strategies like appeals to fear and hope influenced content effectiveness. Findings show that content related to health science, tea drinking, popular news, and food safety significantly enhances satisfaction. Conversely, information on refuting rumors, epidemic prevention, and control, as well as authoritative views, tends to lower satisfaction, possibly due to a trust crisis caused by a mismatch between demand and supply. This study suggests that content creators can boost engagement and satisfaction by focusing on preferred themes. It also highlights the varying impacts of information sources and emotional strategies on the health video preferences of Chinese urban youth. The insights from this research provide a foundation for user-centric content creation and platform development in health communication. [ABSTRACT FROM AUTHOR]

Conditional Spectrum (CS), characterized by a conditional mean and conditional standard deviation, serves as a target spectrum linking seismic hazard information to ground motion selection for seismic demand analysis. Current Ground Motion Selection and Modification (GMSM) methods aim to align with this target spectrum but face challenges due to the limited availability of recorded ground motions and potential modifications, leading to difficulties in ensuring selected ground motions accurately reflect site characteristics. Addressing this issue, the study presents an algorithm to select hazard-consistent CS-based records utilizing a stochastic ground motion model with a dual objective: (1) alleviate the need for scaling during the selection process, and (2) select records consistent with the target hazard and the contributing causal scenarios. To achieve both objectives, the study generates a database of hazard-targeted ground motions utilizing the kriging surrogate with scenarios sampled from the disaggregation matrix. Then, a postprocessing step explicitly considers causative parameters in the selection process. The algorithm's potential to select site-specific ground motions is demonstrated using sites in the Western United States, providing insights into the computational cost and accuracy. Additionally, statistical comparisons are conducted to explore circumstances where similar hazard consistency can be achieved without the postprocessing step, reducing the overall computational cost. Finally, the selected ground motions are favorably compared to those selected through traditional conditional spectrum-based approaches based on their spectral shape and ground motion intensity measures. [ABSTRACT FROM AUTHOR]

Increased construction activity around the world to meet rising economic demand depletes natural resources, mainly river sand. River sand remains the first choice for fine aggregate because of its superior quality and the lack of a suitable alternative. This situation resulted in haphazard sand exploration. Also, demolition waste generated by construction industries and its growth rate has become a major issue for many countries. Although the utilization of construction and demolition waste (CDW) has proved as a sustainable option, the inferior properties of CDW limit its utility as a construction material, especially the fine portion of CDW. This study investigates the effects of CDW as a fine aggregate replacement on the workability and mechanical performance of the mortar by using three types of recycled fine aggregate (RFA) derived from the same CDW by applying different processing techniques. Firstly, the effect of different processing techniques in washed and unwashed state of RFA at 0%, 50% and 100% replacement ratios on mechanical properties of mortar was examined. Further, the most appropriate RFA was selected to analyze the effect of moisture conditions on the compressive strength of mortar at 25%, 50%, 75% and 100% replacement levels. Superior results were achieved in the case of a treated aggregate with improved mechanical properties, whereas there was a decrement in properties in saturated moisture conditions than in dry conditions. This study concludes that by adopting suitable processing techniques results in the enhancement of mechanical properties of mortar at all moisture states with respect to unprocessed aggregate. Further, improved results conclude the valid potential of RFA in producing sustainable mortar. [ABSTRACT FROM AUTHOR]

The power system using hydrogen fuel cells (FCs) as the primary energy source holds significant potential for applications in emergency fields such as post-disaster relief and outdoor power supply. The operational environment of the FC emergency power supply system (FCEPSS) is challenging, with long continuous operation time, difficulties in hydrogen supply, high maintenance costs, and, accordingly, requirements for hydrogen consumption and useful life of FCs are heightened. Hence, this paper proposes a multi-objective energy management strategy (EMS) for FCEPSS. First, the structure of FCEPSS is designed based on the demand analysis in emergency scenarios. Then, the hydrogen consumption and life degradation models of FCEPSS are established. Aiming at the multi-objective optimization problem of minimizing hydrogen consumption and life degradation value in the FCEPSS, an improved multi-objective optimization algorithm is proposed. This paper converts complex constraints into the penalty term and incorporates it as one of the objectives, constructing a three-objective optimization model. To enhance the solution of multi-objective optimization problems, an improved strategy based on relaxation-tightening and three-objective sorting is proposed. Combining the fast non-dominated sorting genetic algorithm-II and the above improved strategy, an improved multi-objective optimization algorithm (IMOOA) is constructed. In the simulation, the advantages of the proposed IMOOA algorithm are verified. In the experimental part, compared with the logical threshold strategy, the proposed strategy saves 0.60% of hydrogen consumption and reduces 5.65% of life degradation within 24 h, respectively. Finally, the superiority of the multi-objective EMS utilizing the IMOOA is demonstrated. [Display omitted] • The demand analysis and scheme design of fuel cell emergency power supply system are carried out. • A multi-objective energy management strategy for low hydrogen consumption and long useful life is designed. • An algorithm based on the relaxation-tightening and three-sorting principle is constructed. [ABSTRACT FROM AUTHOR]

The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development. [ABSTRACT FROM AUTHOR]

The perishable nature of vegetable commodities poses challenges for superstores, as reselling them is often unfeasible due to their short freshness period. Reliable market demand analysis is crucial for boosting revenue. This study simplifies the pricing and replenishment decision-making process by making reasonable assumptions about the selling time, wastage rate, and replenishment time for vegetable commodities. A single-objective planning model with the objective of profit maximization was constructed by fitting historical data using the nonparametric method of support vector regression (SVR). The study reveals a specific relationship between sales volume and cost-plus pricing for each category and predicts future cost changes using an LSTM model. Combining these findings, we substitute the relationship between sales volume and pricing as well as the LSTM prediction data into the model, and solve it using genetic algorithms in machine learning to derive the optimal replenishment volume and pricing strategy. Practical results show that the method can provide reasonable pricing and replenishment strategies for vegetable superstores, and after careful accounting, we arrive at an expected profit of RMB 22,703.14. The actual profit of the supermarket was RMB 19,732.89. The method, therefore, increases the profit of the vegetable superstore by 13.08%. By optimizing inventory management and pricing decisions, the superstore can better meet the challenges of vegetable commodities and achieve sustainable development. [ABSTRACT FROM AUTHOR]

Using the Heckman framework, we develop profiles of households who purchase Greek yogurt and non-Greek yogurt and estimate own-price, cross-price, and income elasticities of demand. Attention is centered on the impacts of age, race, education, and ethnicity of the household head, household income, household size, region, the presence of children, and prices of Greek yogurt and non-Greek yogurt. This analysis rests on data acquired from Nielsen pertaining to 164,484 households over calendar years 2018–2020. Own-price elasticities are estimated to be −1.36 for Greek yogurt and −0.70 for non-Greek yogurt. Additionally, these yogurt products are not only substitutes but also necessities. [ABSTRACT FROM AUTHOR]

We hypothesize that the demand for auditing in the registered investment management industry arises from the auditor's ability to act as a solvent indemnifier when outside parties incur losses because of financial misrepresentations. Consistent with this insurance demand, we find that, relative to financial companies, registered investment companies are more likely to retain Big 4 auditors. Restricting the sample to the registered investment management industry, we construct three direct tests of the insurance demand hypothesis. We find that (a) the market share of a Big 4 firm is positively associated with the firm's wealth, (b) changes in audit fees are positively associated with changes in the audit firms' wealth, and (c) net fund flows increase when clients switch from non‐Big 4 to Big 4 auditors. Our results highlight an unusually high demand for Big 4 auditors in the registered investment industry, which we attribute to the insurance demand for auditing services. [ABSTRACT FROM AUTHOR]

With the greatly increased penetration rate of wind power, photovoltaic, and other new energy sources in the power system, the proportion of controllable units gradually decreased, resulting in increased system uncertainty. The biogas power generation system can effectively alleviate the pressure caused by source-load uncertainty in such high-permeability systems of new energy sources such as wind power and photovoltaic. Hence, from the perspective of the power system, this paper introduces a capacity demand analysis method for a rural biogas power generation system capable of independent operation amidst source-load uncertainty. To enhance the depiction of pure load demand uncertainty, a scene set generation method is proposed, leveraging quantile regression analysis and Gaussian mixture model clustering. Each scene's data and probability of occurrence elucidate the uncertainty of pure load demand. An integrated optimal operation model for new energy and biogas-generating units, free from energy storage capacity constraints, is established based on the generated scenario set. Addressing considerations such as biogas utilization rate and system operation cost, a biogas storage correction model, utilizing the gas storage deviation degree index and the cost growth rate index, is developed to determine biogas demand and capacity. The example results demonstrate the significant reduction in gas storage construction costs and charging and discharging imbalances achieved by the proposed model while ensuring systemic operational cost effectiveness. [ABSTRACT FROM AUTHOR]

Thorough understanding of user demands and formulation of product development strategies are crucial in product design, and can effectively stimulate consumer behavior. Scientific categorization and classification of demands contribute to accurate design development, design efficiency, and success rates. In recent years, e-commerce has become important consumption platforms for smart wearable products. However, there are few studies on product design and development among those related to promoting platform product services and sales. Meanwhile, design strategies focusing on real consumer needs are scarce among smart wearable product design studies. Therefore, an empirical consumer demand analysis method is proposed and design development strategies are formulated based on a categorized interpretation of demands. Using representative smart bracelets from wearable smart products as a case, this paper classifies consumer demands with three methods: big data semantic analysis, KANO model analysis, and satisfaction analysis. The results reveal that analysis methods proposed herein can effectively classify consumer demands and confirm that differences in consumer demand categories have varying impacts on consumer behavior. On this basis, corresponding design strategies are proposed based on four categories of consumer demands, aiming to make product design the leading factor and promote consumer behavior on e-commerce platforms. This research further enriches demand research on smart wearable products on e-commerce platforms, and optimizes products from a design perspective, thereby promoting consumption. In future research, different data analysis methods will be tried to compare and analyze changes in consumer demands and influencing factors, thus improving research on impact factors of product design in e-commerce. [ABSTRACT FROM AUTHOR]

This paper studies the pre‐Federal Open Market Committee (FOMC) announcement drift at the stock level. We hypothesize that investors have a higher propensity to speculate before the monetary policy announcements by the FOMC, due to the resolution of uncertainty and associated reduction in investors' fear. Indeed, we find evidence that there exists a drift of lottery‐like stocks in the pre‐FOMC window, when investors' fear gauge is lower, together with higher demand for lottery‐like stocks and higher realized skewness. Moreover, we show that the demand for lottery‐like stocks ahead of FOMC announcements is more prominent among institutional investors than retail investors. Our findings also identify the key role of transient and quasi‐index institutional investors in our documented flight‐to‐lottery effect. Our findings advance the ongoing debates about the role of firms' investor heterogeneity in determining how monetary policy affects corporate managers' decisions. Our paper has important implications for central banks and managers by showing that investors' preference for lottery‐like stocks increases before FOMC announcements. [ABSTRACT FROM AUTHOR]

Background: Alcohol use in adolescence may increase susceptibility to substance use disorders (SUDs) in adulthood. This study determined if voluntary ethanol (EtOH) consumption during adolescence, combined with social isolation, alters the trajectory of EtOH and nicotine intake during adulthood, as well as activating brain neuroinflammation. Methods: Adolescent male isolate- and group-housed rats were given 0.2 % saccharin/20 % EtOH (Sacc/EtOH) or water using intermittent 2-bottle choice; controls were given water in both bottles (n=17–20 per group). Some rats from each group (n=5–6) were euthanized one week later to measure autoradiographic [3H]PK-11195 binding, an indicator of microglial reactivity, and the remainder (n=11–14 per group) were tested in adulthood in 2-bottle choice, followed by nicotine self-administration using an incremental fixed ratio (FR) schedule with Sacc/EtOH and water concurrently available. Results: Isolation housing increased adolescent intake of Sacc/EtOH, but the increase did not produce an observable neuroimmunological response in brain. Adolescent EtOH exposure decreased adult intake of both Sacc/EtOH and unsweetened EtOH, with isolate-housed rats showing a greater effect than group-housed rats. In the co-use model, a cross-price economic demand analysis revealed a substitutional relationship between Sacc/EtOH and nicotine, but no effect of adolescent Sacc/EtOH exposure. Compared to group-housed rats, isolate-housed rats were more sensitive to the changing price of nicotine and showed greater substitutability of Sacc/EtOH for nicotine. Conclusion: The current results suggest that adolescent EtOH exposure per se, with or without isolation stress, does not likely explain the enhanced risk for either alcohol or nicotine use later in life.

This study investigated the relationship between stress exposure and subsequent ethanol use, focusing on individual differences among male rats. We combined operant self-administration with behavioral economics to assess how intermittent swim stress affects ethanol consumption. This approach allowed for a nuanced analysis of the transition from regular ethanol intake to stress-induced escalation in economic demand. Results showed a consistent rise in ethanol demand post-stress among subjects, irrespective of exposure to actual swim stress or a sham procedure. This increase may result from a two-week abstinence or an inherent rise in demand over time. Significantly, we identified a direct link between post-stress corticosterone levels and the demand for ethanol, considering baseline levels. This correlation was particularly pronounced when examining the shifts in both corticosterone levels and demand for ethanol post-stress. However, neither post-stress corticosterone levels nor their change over time correlated significantly with changes in ethanol demand following a forced swim test that was administered 24 h after the intermittent swim stress test. This suggests potential context-specific or stressor-specific effects. Importantly, pre-stress ethanol demand did not significantly predict the corticosterone response to stress, indicating that high ethanol-demand rats do not inherently exhibit heightened stress sensitivity. Our research brings to light the complex interplay between stress and ethanol consumption, highlighting the critical role of individual differences in this relationship. This research introduces a nuanced perspective, underscoring the need for future studies in the realm of stress and substance use to give greater consideration to individual variability.