Demand Behavior Research Articles

Distributed Auto-Clustering for Residential Load Profiling Using AMI Data From the U.S. High Plains

Managing demand and building customized services are essential for power retailers, distribution network operators (DNOs), and load-serving entities (LSEs) in a competitive retail environment. Therefore, understanding residential customer power demand behaviors is essential for accurate load profiling. However, the large volume of fine-grained smart meter data and the wide variety of residential behavior make load profile extraction extremely challenging, even further exacerbated by deterministic clustering’s inherent limitations. This paper develops a distributed auto-clustering approach (DAA) for load profiling using real smart meter data based on a divide-and-conquer scheme to tackle current load profiling challenges. To improve the load profiling accuracy, temperature-based categories (TBCs) are proposed to distinguish days with similar temperatures. A Heterogeneous Distributed Local Sites (HDLS) scheme is developed based on the raster map concept to distribute smart meter data among local sites while considering their size and density. Local sites send the load representatives to a central site for global clustering to significantly reduce clustering complexity and computational burden. To verify the proposed DAA, approximately 11,000 residential 15-minute smart meter data provided by a utility company from the U.S. high plains were utilized in this paper.

Reward Modulates Visual Responses in the Superficial Superior Colliculus of Mice.

The processing of sensory input is constantly adapting to behavioral demands and internal states. The drive to obtain reward, e.g., searching for water when thirsty, is a strong behavioral demand and associating the reward with its source, a certain environment or action, is paramount for survival. Here, we show that water reward increases subsequent visual activity in the superficial layers of the superior colliculus (SC), which receive direct input from the retina and belong to the earliest stages of visual processing. We trained mice of either sex to perform a visual decision task and recorded the activity of neurons in the SC using two-photon calcium imaging and high-density electrophysiological recordings. Responses to visual stimuli in around 20% of visually responsive neurons in the superficial SC were affected by reward delivered in the previous trial. Reward mostly increased visual responses independent from modulations due to pupil size changes. The modulation of visual responses by reward could not be explained by movements like licking. It was specific to responses to the following visual stimulus, independent of slow fluctuations in neural activity and independent of how often the stimulus was previously rewarded. Electrophysiological recordings confirmed these results and revealed that reward affected the early phase of the visual response around 80 ms after stimulus onset. Modulation of visual responses by reward, but not pupil size, significantly improved the performance of a population decoder to detect visual stimuli, indicating the relevance of reward modulation for the visual performance of the animal.SIGNIFICANCE STATEMENT To learn which actions lead to food, water, or safety, it is necessary to integrate the receiving of reward with sensory stimuli related to the reward. Cortical stages of sensory processing have been shown to represent stimulus-reward associations. Here, we show, however, that reward influences neurons at a much earlier stage of sensory processing, the superior colliculus (SC), receiving direct input from the retina. Visual responses were increased shortly after the animal received the water reward, which led to an improved stimulus signal in the population of these visual neurons. Reward modulation of early visual responses may thus improve perception of visual environments predictive of reward.

More patient-centered care, better healthcare: the association between patient-centered care and healthcare outcomes in inpatients.

The objective of this study is to explore the association between patient-centered care (PCC) and inpatient healthcare outcomes, including self-reported physical and mental health status, subjective necessity of hospitalization, and physician-induced demand behaviors. A cross-sectional survey was conducted to assess patient-centered care among inpatients in comprehensive hospitals through QR codes after discharge from September 2021 to December 2021 and had 5,222 respondents in Jiayuguan, Gansu. The questionnaire included a translated 6-item version of the PCC questionnaire, physician-induced behaviors, and patients' sociodemographic characteristics including gender, household registration, age, and income. Logistic regression analyses were conducted to assess whether PCC promoted self-reported health, the subjective necessity of hospitalization, and decreased physician-induced demand. The interactions between PCC and household registration were implemented to assess the effect of the difference between adequate and inadequate healthcare resources. PCC promoted the patient's self-reported physical (OR = 4.154, p < 0.001) and mental health (OR = 5.642, p < 0.001) and subjective necessity of hospitalization (OR = 6.160, p < 0.001). Meanwhile, PCC reduced physician-induced demand in advising to buy medicines outside (OR = 0.415, p < 0.001), paying at the outpatient clinic (OR =0.349, p < 0.001), issuing unnecessary or repeated prescriptions and medical tests (OR = 0.320, p < 0.001), and requiring discharge and readmitting (OR = 0.389, p < 0.001). By improving health outcomes for inpatients and reducing the risk of physician-induced demand, PCC can benefit both patients and health insurance systems. Therefore, PCC should be implemented in healthcare settings.

A comparative study of clustering algorithms for intermittent heating demand considering time series

Occupants' behavior has a significant impact on building energy consumption. Various clustering algorithms, represented by the K-means algorithm based on the “Euclidean” distance, are widely used to extract occupants' behavior patterns. However, these commonly used algorithms cannot effectively capture the temporal characteristics of occupants' behavior. Although some studies have started to adopt clustering algorithms considering time series for research, the differences in clustering effects of different algorithms on occupants' behavior are still unclear, especially in the clustering application study of behavior with intermittent characteristics, this intermittent behavior is a behavior that does not always exist, and the timing of its occurrence changes. Therefore, this study takes the heating demand behavior of occupants with obvious intermittency as a case and compares the clustering effect of the common clustering algorithm (K-means) and three clustering algorithms considering time series (K-shape, Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW)). The study also explores possible reasons leading to differences and introduces three evaluation methods for clustering results of intermittent heating demand. This study provides a reference for selecting appropriate clustering algorithms to study occupants' intermittent behavior and has practical application value in improving the extraction and prediction of behavior patterns of occupants with intermittent characteristics.

Ventrolateral prefrontal neurons of the monkey encode instructions in the 'pragmatic' format of the associated behavioral outcomes.

The prefrontal cortex plays an important role in coding rules and producing context-appropriate behaviors. These processes necessarily require the generation of goals based on current context. Indeed, instructing stimuli are prospectively encoded in prefrontal cortex in relation to behavioral demands, but the coding format of this neural representation is, to date, largely unknown. In order to study how instructions and behaviors are encoded in prefrontal cortex, we recorded the activity of monkeys (Macaca mulatta) ventrolateral prefrontal neurons in a task requiring to perform (Action condition) or withhold (Inaction condition) grasping actions performed on real objects. Our data show that there are neurons responding in different task phases, and that the neuronal population discharge is stronger in the Inaction condition when the instructing cue is presented, and in the Action condition in the subsequent phases, from object presentation to action execution. Decoding analyses performed on neuronal populations showed that the neural activity recorded during the initial phases of the task shares the same type of format with that recorded during the final phases. We propose that this format has a pragmatic nature, that is instructions and goals are encoded by prefrontal neurons as predictions of the behavioral outcome.

The market for EuroMillions: jackpot sharing and implicit transfer of funds among countries

Multi-country (state) lottery markets have emerged worldwide as an attempt to revert the decline in lottery sales. These markets allow operators to offer appealing large prizes by combining the markets of separate lottery jurisdictions. An analysis of EuroMillions’ jackpot sharing among countries of different market size shows that some countries tend to claim higher jackpots and achieve higher payout rates, while others seem to be systematically disadvantaged. This paper elaborates on whether this unequal jackpot distribution is caused by different demand behavior and market share trends in the market for EuroMillions. Evidence shows that such distribution of the jackpot is explained by differences in jackpot elasticity of the demand among countries, which causes, at the same time, variations in the market share and draw winning odds per country. As a result, some countries may then benefit from the funds that have been contributed to the prize pool by the rest, raising some implicit funding issues (net payout balance) and questioning the optimality of jackpot sharing.

Color appearance and the end of Hering's Opponent-Colors Theory.

Hering's Opponent-Colors Theory has been central to understanding color appearance for 150 years. It aims to explain the phenomenology of colors with two linked propositions. First, a psychological hypothesis stipulates that any color is described necessarily and sufficiently by the extent to which it appears reddish-versus-greenish, bluish-versus-yellowish, and blackish-versus-whitish. Second, a physiological hypothesis stipulates that these perceptual mechanisms are encoded by three innate brain mechanisms. We review the evidence and conclude that neither side of the linking proposition is accurate: the theory is wrong. We sketch out an alternative, Utility-Based Coding, by which the known retinal cone-opponent mechanisms represent optimal encoding of spectral information given competing selective pressure to extract high-acuity spatial information; and phenomenological color categories represent an adaptive, efficient, output of the brain governed by behavioral demands.

Stimulation of investments in energy saving projects, energy awareness and energy sustainability of organizations in the context of change management

The article is devoted to the issues of stimulating investments in energy-saving projects, energy awareness, and energy sustainability of organizations in the context of change management. Based on the research, it was proposed to streamline the tools of this stimulation, considering modern security challenges. The purpose of the study tools is to stimulate investments in energy-saving projects, energy awareness, and energy sustainability of organizations in the context of change management. The paper examines energy consumption using a new topical approach based on behavior analysis. A key lesson from recent advances in behavioral economics is that people's responses to policy incentives and uncertainty may differ from classical rational optimizing behavior predictions. One of the defining characteristics is a specific support for stimulation as a successful mechanism of influencing behavior, which is confirmed by the practice and experience of foreign countries. It is noted that incentives must be tailored to the individual needs of consumers to be effective. This means that policies aimed at influencing the behavior of energy demand in the residential sector must be detailed, targeting different groups of people in specific ways. The indicated results of the practices of the leading countries make it possible to understand why recent important policy initiatives have not achieved their goals, and they offer lessons for more effective formation of Ukraine's policy based on incentives in the field of energy consumption. The results allow us to understand that the policy of stimulating investments in energy-saving projects, energy awareness, and energy sustainability of organizations in the context of change management should be focused on sustainable energy use to reduce carbon dioxide emissions effectively. In addition, it is theoretically proven that energy use is partially determined by human irrationality and politics and that government can influence energy use behavior. An interpretive analysis of consumer behavior in different countries shows that various incentives are needed to encourage behavior change. The positive role of government regulation and efforts to nudge consumers in the right direction regarding energy use political and social incentives can predict a shift in emphasis from energy efficiency to energy sustainability. Keywords: management, organizations, investment promotion, energy saving projects, energy awareness, energy sustainability, change management, energy-consuming capital.

Electricity pricing challenges in future renewables-dominant power systems

Economy-wide decarbonization will require electric power systems to rely heavily on variable renewable energy (VRE, mainly wind and solar generation), which has near zero marginal operating costs, and energy storage. We have modeled optimized, deeply decarbonized power systems in three US regions at mid-century under a wide range of plausible cost and technology assumptions. The shadow marginal values of energy (MVEs) from these optimizations approximate the wholesale spot prices of energy in simplified hypothetical competitive energy-only wholesale markets in which revenues earned by selling energy in wholesale electricity markets should be sufficient to cover all capital and operating costs. A very robust result is that under carbon constraints, very low MVEs occur much more frequently than in today's wholesale markets, and very high prices are also more frequent than today. Revenues from a relatively small number of high MVE periods are required to fully cover VRE capital and operating costs, while storage charges and discharges in many hours. In an ideal, efficient regime, a competitive energy-only wholesale market without price caps would minimize total system costs, and retail rates equal to wholesale spot prices would fully cover those costs and induce efficient demand behavior. Real power systems depart significantly from this ideal: price caps are used frequently in wholesale markets, capacity payments from organized capacity markets or bilateral contracts are relied on to supplement energy market revenues to enable full cost recovery, and most customers face retail rates that do not reflect variations in marginal operating costs. The greater volatility of future decarbonized wholesale markets will increase the costs of such politically attractive departures from the ideal regime.

How cognitive and behavioural body-functions of children with ADHD are linked to their participation and environmental supports?

BackgroundChildren with chronic conditions face participation restrictions and exclusions. AimsTo investigate how cognitive and behavioural body-functions are associated with 1) participation patterns and 2) environmental factors among children with attention deficit hyperactivity disorder (ADHD). Methods and proceduresThe study included 83 children with ADHD. Cognitive and behavioural body-functions were assessed with Conners’ Parent Rating Scale-Revised Long-Version. The participation patterns of children were assessed with Participation and Environment Measure for Children and Youth (PEM-CY). Multiple regression analyses were used to examine the relationships between cognitive and behavioural body-functions and (1) participation patterns at home, school and community settings, and (2) environmental supports and barriers. Outcome and resultsIn home setting, inattention and ADHD index which determine ADHD children from typically developing peers, were the strongest factors, explaining 24.9 % of variance in activities that mothers wanted to change. In school setting, presence/level of oppositional behaviours had the strongest effect on environmental barriers, with variation of 29.8 %. In community setting, the strongest factor explaining environmental helpfulness was anxious/shy behaviours (16.1 %). Conclusions and implicationsWhen seeking to improve participation of children with ADHD both the cognitive and behavioural demands of chosen activities should be modified while creating environments that supports these challenges.

The Influence of Repair Quality on Aircraft Spare Part Demand Variability

Accurate estimation of spare part demand is challenging in the case of intermittent or lumpy demand, characterised by infrequent demand occurrence and variability in demand size. While prior research has considered the effect of exogenous variables on spare part demand, there is a lack of research considering the effects of repair quality and aggregated spare part demand behaviour across fleets of assets under the influence of multiple simultaneously acting drivers of failure. This research provides new insights towards the problem of estimating variable spare part demand through modelling and simulation of the effects of multiple, simultaneously considered spare part demand drivers. In particular, a contribution to the state of the art is introduced by the use of a Branching Poisson Process (BPP) to model repair quality effects for spare part demand generation in conjunction with several demand drivers. The approach is applied in a numerical study which involves component failure characteristics based on real-life data from an aircraft maintenance, repair and overhaul (MRO) provider. It is shown that repair quality improvements drive down the variance in the demand and the total number of failures over time, and outperform the effect of environmental drivers of failure in terms of demand generation.

Solutions to the double burden of malnutrition also generate health and environmental benefits.

Present food consumption patterns will intensify pressure on natural resources, while poor nutrition is expected to prevail at both low and high levels of calorie consumption. To better understand the interplay between food security, environment and health, we use an integrated framework that allows for the analysis of the dynamics of the double burden of malnutrition and its health and environmental impacts by 2050. We find that excessive caloric intake will be key in rising body mass index levels, particularly in emerging economies. Because higher levels of body mass index will be reached at younger ages, future cohorts will increase their exposure to health risks, including coronary heart disease, stroke, site-specific cancers and type 2 diabetes. This framework also offers insights into the health, food and environmental security impacts of changing food demand behaviour. We find that reductions in food purchasing-associated with the mitigation of food waste and excessive food intake-are more important than changes in dietary composition in increasing food affordability and reducing pressure on cropland expansion, whereas dietary composition is critical in driving greenhouse gas emissions.

Transmission mechanisms of geopolitical risks to the crude oil market——A pioneering two-stage geopolitical risk analysis approach

As an exogenous risk, geopolitical risk drives oil price volatility indirectly through multiple factors, which are ignored by most studies. This paper establishes a two-stage approach for the first time to analyze the indirect geopolitical risk transmission mechanisms, and applies the time-varying parametric vector autoregressive (TVPVAR) model to illustrate how geopolitical risk affects oil prices via different transmission paths including micro media (supply, demand, inventories, speculative behaviors) and macro media (global economic activities). Oil prices do not always rise in response to geopolitical events, sometimes even varies to the opposite direction, a phenomenon that can be well explained and modeled by the transmission mechanisms we developed. In this paper, we have identified two clear and unique geopolitical transmission paths to enhance energy-related decisions. Aside from the conventional economic phenomenon of geopolitical risks affecting oil prices through supply and demand behaviors, speculative behaviors increase during the period of high geopolitical risk, and the impact of speculative activity on oil prices is prominent. In addition, the most influential path is that geopolitical factors can indirectly impact oil prices by influencing economic fluctuations, which provides powerful and empirical supports for energy-related financial decisions and policy implications.

Organization of orbitofrontal-auditory pathways in the Mongolian gerbil.

Sound perception is highly malleable, rapidly adjusting to the acoustic environment and behavioral demands. This flexibility is the result of ongoing changes in auditory cortical activity driven by fluctuations in attention, arousal, or prior expectations. Recent work suggests that the orbitofrontal cortex (OFC) may mediate some of these rapid changes, but the anatomical connections between the OFC and the auditory system are not well characterized. Here, we used virally mediated fluorescent tracers to map the projection from OFC to the auditory midbrain, thalamus, and cortex in a classic animal model for auditory research, the Mongolian gerbil (Meriones unguiculatus). We observed no connectivity between the OFC and the auditory midbrain, and an extremely sparse connection between the dorsolateral OFC and higher order auditory thalamic regions. In contrast, we observed a robust connection between the ventral and medial subdivisions of the OFC and the auditory cortex, with a clear bias for secondary auditory cortical regions. OFC axon terminals were found in all auditory cortical lamina but were significantly more concentrated in the infragranular layers. Tissue-clearing and lightsheet microscopy further revealed that auditory cortical-projecting OFC neurons send extensive axon collaterals throughout the brain, targeting both sensory and non-sensory regions involved in learning, decision-making, and memory. These findings provide a more detailed map of orbitofrontal-auditory connections and shed light on the possible role of the OFC in supporting auditory cognition.

Cs4 Zn5 P6 S18 I2 : the Largest Birefringence in Chalcohalide Achieved by Highly Polarizable Nonlinear Optical Functional Motifs.

Chalcohalides not only keep the balance between the nonlinear optical (NLO) coefficient and wide band gap, but also provide a promising solution to achieve sufficient birefringence for phase-matching ability in NLO crystals. In this study, a novel chalcohalide, Cs4 Zn5 P6 S18 I2 (1) is successfully synthesized, by incorporating the highly electropositive Cs and the large electronegative I element into the zinc thiophosphate. Its 3D open framework features an edge-shared by distorted [ZnS4 ], ethanol-like [P2 S6 ], and unusual [ZnS2 I2 ] polyhedrons, which is inconsistent with the soft-hard-acids-bases theory. Remarkably, compound 1 simultaneously exhibits the large second-harmonic generation (SHG, 1.1×AgGaS2 , @1.3µm) and a wide band gap (3.75eV) toward a high laser-induced damage threshold (16.7×AgGaS2 , @1.06µm), satisfying the rigorous requirements for a prominent infrared NLO material with concurrent SHG intensity (≥0.5×AGS) and band gap (≥3.5eV). Moreover, to the best of the knowledge, the experimental result shows that phase 1 has the largest birefringence (0.108, @546nm) in chalcohalide and meets phase-matching behavior demand originating from the polarizable anisotropy of NLO-functional motifs. This finding may provide great opportunities for designing birefringent chalcohalides.

Environmental sustainability through aggregate demand behavior – Does knowledge economy have global responsibility?

PurposeThis study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability.Design/methodology/approachResults are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index.FindingsU-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality.Originality/valueThis study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

The Impact of COVID-19 on Tourist Destinations in Romania

Abstract The period of the Covid 19 pandemic meant for the tourism industry a moment of general assessment of its own route. The plans and programs in which this industry was active at the time of the outbreak of the pandemic were closely related to the transformation of this industry into one that operates based on the principles of sustainability. The decrease in the number of arrivals, the practical stoppage for a period of this economic sector, attracted by itself determinations regarding the value volumes of the decreases in economic indicators but also research for the possibilities of further development of this industry. It can thus be noted that at the level of Romania, the decrease in the number of arrivals was over 50% in 2020 compared to 2019. In addition, although the forecasts of the World Tourism Organization indicated a relatively quick return in 2021, this did not happen in Romania, the value level being in 2021 at approximately 70% compared to 2019. The purpose of this article is to analyze the behavior of tourism demand and supply in the three periods, namely: pre-pandemic, pandemic and post-pandemic. The data are based on records from the National Institute of Statistics, as well as studies and research carried out on this topic. The conclusions of the determinations carried out are mainly related to the nature of isolated tourism observed during the pandemic, but also to the behavior of entrepreneurs in the sector who reacted immediately in the development of these areas.

ST-Bikes: Predicting Travel-Behaviors of Sharing-Bikes Exploiting Urban Big Data

With the development of the modern smart city, sharing-bikes require behaviors prediction for grid-level areas which is essential for intelligent transportation systems. A model which can predict bike sharing demand behaviours accurately can allocate sharing-bikes in advance to satisfy travel demands alongside saving energy, reducing traffic, cutting down waste for those sharing-bikes companies putting excessive sharing-bikes in unsaturated demand areas. In this paper, we abandon the traditional time series prediction method and use a more efficient deep learning method to solve the traffic forecasting problem. Moreover, instead of considering spatial relation and temporal relation relatively, we produced a deep multi-view spatial-temporal network to combine them into one prediction model framework. In the experimental section, we investigate in the experiment on enormous amount of real sharing-bikes application use data in the core region of Beijing to test the performance of the model framework with a 1 km <inline-formula> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> 1 km grid-level scale and compare it with other existing machine learning approaches and prediction models. And the 4G/5G/6G communication technology facilitate the real-time control of the space-time locations of sharing bikes dynamically. Thus, it provides the basis for high-frequency analysis of space-time patterns, especially supported by the 6G large-scale application in the future.

Neural correlates of visual and tactile path integration and their task related modulation

Self-motion induces sensory signals that allow to determine travel distance (path integration). For veridical path integration, one must distinguish self-generated from externally induced sensory signals. Predictive coding has been suggested to attenuate self-induced sensory responses, while task relevance can reverse the attenuating effect of prediction. But how is self-motion processing affected by prediction and task demands, and do effects generalize across senses? In this fMRI study, we investigated visual and tactile self-motion processing and its modulation by task demands. Visual stimuli simulated forward self-motion across a ground plane. Tactile self-motion stimuli were delivered by airflow across the subjects’ forehead. In one task, subjects replicated a previously observed distance (Reproduction/Active; high behavioral demand) of passive self-displacement (Reproduction/Passive). In a second task, subjects travelled a self-chosen distance (Self/Active; low behavioral demand) which was recorded and played back to them (Self/Passive). For both tasks and sensory modalities, Active as compared to Passive trials showed enhancement in early visual areas and suppression in higher order areas of the inferior parietal lobule (IPL). Contrasting high and low demanding active trials yielded supramodal enhancement in the anterior insula. Suppression in the IPL suggests this area to be a comparator of sensory self-motion signals and predictions thereof.

A unified modeling strategy of the stability and progressive damage behavior of CFRP double-blade composite stiffened structures (DCSS) under uniaxial compression

As a typical thin-walled structure, CFRP double-blade composite stiffened structure (DCSS) is widely used in the aerospace field and its stability and failure behavior demand a comprehensive understanding. In this work, a novel, unified modeling strategy is proposed for predicting the stability behavior and progressive damage processes of the DCSS under compression. A user-defined materials subroutine of the based-strain 3D-Hashin criterion and continuum damage mechanics model is developed using the FORTRAN language to simulate the intra-laminar damage initiation and propagation processes in the DCSS skin, stiffeners, and triangle zones. Moreover, the damage initiation and the extension of the inter-laminar in the skin and the skin–stiffener interface are revealed using the cohesive zone model. Furthermore, various experimental methods are employed to obtain the buckling, post-buckling and collapse behavior of the DCSS. The digital image correlation technique is used to describe the buckling and post-buckling displacement fields and the failure behavior of stiffened structures. Strain gauges are adopted to accurately acquire the buckling behavior of the DCSS. Finally, the buckling load, ultimate load, displacement data and failure modes of the stiffened structure obtained from the developed 3D finite element model are verified with the experimental results. The high correlation between the numerical and experimental results confirms the reliability of the proposed modeling strategy.