Consumption Model Research Articles

Online Linearized Confidence‐Weighted Learning on a Budget

Online learning aims to solve a sequence of consecutive prediction tasks by leveraging the knowledge gained from previous tasks. Linearized confidence‐weighted (LCW) learning is the first online learning algorithm introducing the concept of weight confidence into the prediction model through distributions over weights. It provides the flexibility for weights to update their values at different scales. The kernel trick in machine learning can be applied to LCW for a better prediction performance. However, the kernel‐based LCW algorithm is subject to the curse of kernelization which makes it vulnerable to the unlimited growth of the prediction model in runtime and memory consumption. In this study, we present the budgeted LCW (BLCW) algorithm which puts a limit on the growth by a predefined budget with optimization. Consequently, BLCW performs the LCW update and then reduces the information loss by projection. Based on the resource perspective that reinterprets LCW in terms of resources and utilization degrees, we demonstrated that BLCW approximates the kernel‐based LCW algorithm. We evaluate four budget maintenance strategies and suggest that the mean removal is the most stable. By various numerical experiments on real datasets, we demonstrate that BLCW performs competitively and effectively when compared to leading budgeted online algorithms.

TELOMERE LENGTH IN RHESUS MACAQUES WITH VOLUNTARY CHRONIC ETHANOL CONSUMPTION

Introduction. Alcohol abuse is associated with telomere shortening. There is no convincing evidence of a “safe” level of alcohol consumption in this regard. Long-term studies in rodents are not feasible, and clinical trials with the administration of alcohol to healthy individuals is not ethically acceptable. An approach based on a relevant model of voluntary alcohol consumption in monkeys under controlled conditions is a significant alternative. The aim of the study. To estimate the length of telomeres at long-term ethanol consumption by male rhesus macaques in under free choice with water Methods. The study was performed on fourteen mature male Rhesus macaques of groups with low (median 0.62 g/kg/day) and high (median 2.71 g/kg/day) ethanol consumption as 4% (v./v.) solution with condition of all-day access and free choice with drinking water. The duration of consumption was 920 days. The relative length of telomeres was determined by quantitative PCR according to Cawthon (2002) in blood leukocytes. Results. The relative average telomere length in the high-consumption group was 1.53±0.57 before the presentation of ethanol in the adaptation period (-32 day of the study), and at the consumption stage it was on 717 day 2.13±0.19 and on 917 day 4.61±0.7. In the low-consumption group, the average relative telomere length constituted 1.42±0.22, 1.55±0.15 and 3.3±0.47, respectively. The absolute count of leukocytes did not change significantly during the study. However, changes in the differential white cells count were revealed representing development of relative monocytosis by 917 day in both groups. Conclusion. The data obtained do not confirm the association of long-term alcohol consumption in moderate doses with telomere length. The completed study has limitations related to the lack of control without consumption and evaluation in one sex.

The Economics of Financial Stress

Abstract We study the psychological costs of financial constraints and their economic consequences. Using a representative survey of U.S. households, we document the prevalence of financial stress in U.S. households and a strong relationship between financial stress and measures of financial constraints. We incorporate financial stress into an otherwise standard dynamic model of consumption and labor supply. We emphasize two key results. First, both financial stress itself and naivete about financial stress are important components of a psychology-based theory of the poverty trap. Sophisticated households, instead, save extra to escape high-stress states because they understand that doing so alleviates the economic consequences of financial stress. Second, the financial stress channel dampens or reverses the counterfactual large negative wealth effect on labor earnings because relieving stress frees up cognitive resources for productive work. Financial stress also has macroeconomic implications for wealth inequality and fiscal multipliers.

Reliability and Power Consumption Models of Embedded Systems with a Machine Learning-based Optimization

Reliability and Power Consumption Models of Embedded Systems with a Machine Learning-based Optimization

Weather-aware energy management for unmannedaerial vehicles: a machine learning application with global data integration

This study introduces a machine learning (ML) framework to predict unmanned aerial vehicle (UAV) energy requirements under diverse environmental conditions. The framework correlates UAV flight patterns with publicly accessible weather data, to yield an energy management tool applicable to a wide range of UAV configurations. The model employs the Cross-industry standard process for data mining and advanced feature engineering, offering an in-depth analysis of meteorological factors and UAV energy demands. The study assesses several multi-regression linear and ML models, whereby ensemble models gradient boosting (GB) and eXtreme gradient boosting demonstrate superior performance and accuracy. Specifically, the GB model achieved a test mean absolute error (MAE) of 0.0395 V (V) for voltage, 0.808 A (A) for current, and 9.758 mA-hours (mAh) for discharge, with prediction accuracy of over 99.9% for voltage and discharge, and 97% for current, derived from the coefficient of determination (R2). A novel integration of real-world UAV logs and weather data underpins the development of a weather-aware ML prediction model for UAV energy consumption. Our framework is capable of concurrently predicting three components of energy and power with almost uniform accuracy, a feature not found in contemporary models. Empirical test flights show a discrepancy of only 0.005 W-hour (Wh) between total predicted and actual energy consumption. This work enhances both efficiency and safety in UAV operations. The resulting energy-predictive flight planning tool sets a new benchmark for artificial intelligence (AI) applications in intelligent automation for UAVs.

Power-consumption analysis for different IPoWDM network architectures with ZR/ZR+ and long-haul muxponders

Operators are constantly faced with the need to increase optical-network capacity to accommodate rapid traffic growth while minimizing the cost-per-bit and power-per-bit. The drastic reduction of the power consumption of IP routers and ZR/ZR+ pluggable transponders seen in the past several years has renewed the interest in “opaque” optical-network architectures, where no optical bypassing is allowed. In this work, we aim to quantify and compare the power consumption of four “IP over wavelength division multiplexing” (IPoWDM) transport network architectures employing ZR/ZR+ modules versus long-haul muxponders, considering different grooming, regeneration, and optical bypassing capabilities. We first propose a power consumption model for different IPoWDM node architectures with ZR/ZR+ modules and long-haul muxponders. Then, to obtain the power consumption of different architectures, we propose a compact auxiliary-graph-based network-design algorithm extensible to different network architectures. Moreover, we investigate how the continuous decrease in the power consumption of ZR/ZR+ and IP routers can impact the power consumption of different architectures through a sensitivity analysis. Illustrative numerical results on networks of different sizes show that, despite drastic reductions of power consumption at the IP layer, optical bypassing is still the most power-efficient solution, reducing consumption by up to 48%.

The Influence of Behavioral and ESG Drivers on Consumer Intentions for Online Fashion Renting: A Pathway Toward Sustainable Consumption in China’s Fashion Industry

As the fashion industry faces increasing scrutiny over its environmental impact, collaborative consumption models such as online fashion renting offer potential solutions for fostering sustainability. This study examines the role of environmental, social, and governance (ESG) factors alongside behavioral drivers in shaping consumer intentions toward online fashion renting in China, a model of collaborative consumption that contributes to sustainability by reducing new product demand and promoting the reuse of fashion items. The data was gathered from 403 Chinese customers using a standardized questionnaire. Structural equation modeling (SEM) was used to examine the given study hypotheses. The current study empirically demonstrates that customers’ attitudes, past sustainable behavior, and subjective norms are significant indicators of consumers’ intentions toward online fashion renting. The results further indicate that relative advantage, compatibility, perceived ownership, psychological risk, green self-identity, and experience value are the key drivers of consumers’ attitudes toward online fashion renting. Additionally, the ESG factors were found to have a significant positive impact on consumer attitudes toward online fashion renting, underscoring their importance in driving sustainable consumption patterns. By integrating behavioral and ESG perspectives, the study contributes to the growing discourse on how sustainable consumption patterns can be encouraged within the fashion industry, offering theoretical and managerial implications for fostering sustainable behavior. Directions for future research are also suggested.

Estimating Expected Asset Returns with the Present Value Model of Consumption and Fed Forecasts

Abstract This paper utilizes Greenbook forecasts of consumption and income to estimate expected asset returns through a present value model of consumption. The study finds that, despite the valuable information contained in Greenbook forecasts, the expected asset returns obtained from this approach do not provide meaningful insights into future asset returns. This contrasts with previous literature suggesting predictability using the present-value model.

Online Health Services: Attitudes and Behaviors among Adults in Israel

Objective: Online Health Services (OHS) emerged in the last decade and currently encompass technologies that facilitate patient-practitioner communication in medical fields such as diagnosis, treatment, counseling, and monitoring, including for chronic patients. The current study aims to describe attitudes and behaviors related to OHS and identify variables that explain online technology use. Methods: A descriptive cross-sectional study among Hebrew-speaking, computer-literate adults, with data collected via online questionnaires. The convenience sample included 700 respondents, of whom 57.4% were female. Respondents’ mean age was 64 years, with most being married (59.3%), secular (75.9%), academically educated at the undergraduate, graduate and postgraduate levels (59.1%), and having a chronic illness (65.7%). Results: Despite high OHS use among nearly half the participants, most were undecided whether they preferred in-person or online treatment. Preference for in-person treatment was negatively associated with the perceived efficiency and safety of online treatment, online health literacy, and the extent of OHS use. Key variables explaining OHS use were perceived efficiency and safety of online treatment, online health literacy, preference for in-person treatment, and chronic illness. The explained variation of the OHS consumption model was 40.4%. Conclusion: The variables explaining OHS use are not necessarily linked to classic background variables, e.g. gender, age, and education, but rather to variables related to the use of OHS for therapeutic purposes. OHS are not perceived as substitutes for in-person meetings with healthcare providers but as a supplementary service. Therefore, to enhance OHS use rates, decision-makers should improve online health literacy and design services integrating OHS with in-person treatment.

Development of a Prediction Model for Specific Fuel Consumption in Rotary Tillage Based on Actual Operation

Tractor fuel consumption has typically been predicted using indoor test results under specific conditions. This study analyzes the factors affecting fuel consumption during rotary tillage in actual fields and develops a prediction model. The test field was divided into sections using a grid method, and rotary tillage operations were performed to measure various parameters, including soil strength, tractor’s transmission and PTO gear stages, tillage pitch, travel speed, engine and PTO shaft torque and speed, and fuel consumption. Pearson correlation identified variables affecting specific fuel consumption, and regression analysis was used to develop a prediction model. The model’s accuracy was analyzed using the coefficient of determination (R2) and root mean square error (RMSE), and it was compared with the ASABE’s fuel consumption prediction model. The test results showed that higher transmission and PTO gear stages, and tillage pitch decreased specific fuel consumption, while soil strength had no significant effect. Thus, operating at higher gear and PTO stages within suitable conditions enhances energy efficiency in rotary tillage. Statistical analysis showed that specific fuel consumption significantly correlated with tractor travel speed, PTO shaft power, and PTO shaft speed. The prediction model, including these variables, had the highest accuracy with R2 of 0.91 and RMSE of 0.011 L/kW·h. The developed prediction model showed significantly improved accuracy compared to the ASABE model, indicating that it can predict specific fuel consumption based on key operational variables in actual rotary tillage operations.

An integrated model of electric bus energy consumption and optimised depot charging

The electrification of buses, and the depots servicing them, is a high priority for sustainability but is hindered by challenges at the intersection of the mobility and electricity systems, such as trade-offs of charging infrastructure and scheduling with the stress placed on the grid. This paper presents a model for studying and co-optimise these variables. The model has two standout features. Firstly, that it integrates a data-driven model for predicting the energy consumption of electric buses based on route details with a bus depot charging optimisation algorithm that minimises the loading on the grid. Secondly, it is compatible with the publicly available route and schedule data published by bus operators and is available open-source for extension. The results foreground the impact of route topology, temperature, and traffic congestion on energy consumption, and the interdependencies of these with timetabling, charging infrastructure and battery designs in driving grid loading.

Isotopic evidence of diet breadth hunter-gatherers changes during the Holocene in the Central Pampean Dunefields (Argentina, South America).

Based on the analysis of stable carbon and nitrogen isotopes of bone collagen, stable carbon isotopes of bone apatite and an extensive AMS dating series (~10,000-299 years cal BP), the human paleodiets of 34 individuals from the Central Pampean Dunefields (Argentina, South America) are evaluated. These data are interpreted from the isotopic ecology of animals with archaeofaunal evidence of consumption and isotopic models of human diet. Multivariate carbon and nitrogen stable isotope model and Bayesian stable isotope ellipses were used to interpret human diets. Analysis of isotopic values indicates intake of enriched lipids and/or carbohydrates in relation to the proteins consumed throughout the Holocene. The isotopic values of Middle Holocene humans in relation to the values of exploited resources point out that individuals obtained protein mainly from guanaco. Subsequently, there was an increase in the human breadth diet during the Late Holocene, with a greater relevance of small prey of high trophic levels and vegetables. This contrasts with zooarchaeological information indicating generalist human diets during the Middle Holocene and specialized human diets in guanaco during the Late Holocene. It is proposed that during the Middle Holocene arid period, the combination of low human population density and high residential mobility in wide foraging ranges allowed the guanaco to be the main source of protein. During the Late Holocene humid period, there was an increase in human population density and a decrease in residential mobility, which caused greater pressure on foraging territories and increased dietary breadth.

Global boundedness of solutions to a chemotaxis consumption model with signal dependent motility and logistic source

Global boundedness of solutions to a chemotaxis consumption model with signal dependent motility and logistic source

Oxygen Consumption in Filamentous Pellets of Aspergillus niger: Microelectrode Measurements and Modeling.

Filamentous fungi cultivated as biopellets are well established in biotechnology industries. A distinctive feature of filamentous fungi is that hyphal growth and fungal morphology affect product titers and require tailored process conditions. Within the pellet, mass transfer, substrate consumption, and biomass formation are intricately linked to the local hyphal fraction and pellet size. This study combined oxygen concentration measurements with microelectrode profiling and three-dimensional X-ray microtomography measurements of the same fungal pellets for the first time. This allowed for the precise correlation of micromorphological information with local oxygen concentrations of two Aspergillus niger strains (hyperbranching and regular branching). The generated results showed that the identified oxygen-penetrated outer pellet regions exhibited a depth of 90-290 µm, strain-specific, with the active part percentage in the pellet ranging from 18% to 69%, without any difference between strains. Using a 1D continuum diffusion consumption model, the oxygen concentration in the pellets was computed depending on the local hyphal fraction. The best simulation results were achieved by individually estimating the oxygen-related biomass yield coefficient of the consumption term within each examined pellet, with an average estimated value of 1.95 (± 0.72) kg biomass per kg oxygen. The study lays the foundation for understanding oxygen supply in fungal pellets and optimizing processes and pellet morphologies accordingly.

Development of mathematical models of power consumption at coal plants

The object of research is coal enterprises where powerful electrical equipment is used (underground installations up to 3000 kW, tunneling complexes of 500–1500 kW, technological complexes of 6–10/0.4 kV, etc.). The deficit of generated capacities, caused by growing energy consumption, can be reduced by regulating power consumption modes. The relevance of this issue is determined by the need to conduct a research of the electricity consumption system, to determine the qualitative and quantitative characteristics of electricity consumption using mathematical models. The lack of mathematical models makes it difficult to analyze energy intensity and consumption modes of each technological operation in the overall balance of electricity consumption of coal enterprises. The article considers the structure and classification of the main technological groups of energy consumers, the development of mathematical models for each type of load modes, as well as a generalized model of electricity consumption of coal enterprises, with the use of mathematical apparatus of probability theory and mathematical statistics. As a result of the work, mathematical models of the electricity consumption process for the main technological groups and for models of daily electricity consumption of coal enterprises as a whole were developed, and it was also established that technological objects of electricity consumption are divided into three different values, in terms of power consumption modes: constant, uniform and pulse. For each class of consumers their statistical characteristics were obtained. The work results can be applied for managing power consumption modes of coal enterprises

Optimizing IOT Network Performance: A Study on Low-Power Wide-Area Network (LPWAN) Technologies for Smart City Applications

Abstract: This research explores with a specific focus on smart city applications, how to optimise the execution of Web of Things (IoT) networks by utilising breakthroughs in Low-Powered Wide Area Networks (LPWAN). As IoT devices proliferate and generate vast amounts of data, having a competent network becomes essential, especially when dealing with low device thickness and limited power availability. This analysis provides a detailed energy consumption model tailored to LoRaWAN, based on a deeper understanding of its functional components in Internet of Things scenarios. The review uses MATLAB for replication and looks at two clear scenarios: the standard allocation of spreading factors without optimisation and an improved method that uses particle swarm optimisation (PSO) for dynamic spreading factor work. The results show that the network energy consumption dropped significantly, from 6.3997×10^ (- 17) Joules to 2.5230×10^(- 17) Joules, along with increased throughput, decreased parcel misfortune, and decreased idleness. Through a detailed analysis of several parameters, such as network density and data transfer efficiency, this study demonstrates the feasibility of the suggested optimisation model in terms of improving battery efficiency and overall network performance. Finally, the findings demonstrate how efficient LPWAN systems may provide robust and useful IoT environments, which are essential for the successful implementation of smart city initiatives.

Modeling Electricity Generation and Consumption in Cameroon

Currently, there is a significant gap between electricity generation and consumption in Cameroon. Research has shown that electricity consumption in the country is estimated to increase by 965.7 GWh in five years, from 2020 to 2024 due to demographic and economic growth. Hence, this study aims to find methods that can be useful in developing strategies to balance the energy supply and demand in the country. This is done by developing models that can predict future electrical power consumption and generation. Correlation analysis and regression analysis were performed by using data obtained from various databases, and related models were developed accordingly. The model parameters were carbon dioxide emissions, electricity consumption per capita, final consumption expenditures, electricity installed capacity, fossil fuel installed capacity, labor force, and GDP. The models' results demonstrated excellent performance coefficients with RMSE of 0.17041, 0.23893, 0.27571, and 0.2465 for hydroelectricity generation, fossil fuel electricity generation, net electricity generation, and net electricity consumption respectively. Also, hydroelectricity generation, net electricity generation, and net electricity consumption models showed very good RRMSE performance indicating that the models can make predictions with only 4.26%, 5.26%, and 5.77% deviation from the mean values of hydroelectricity generation, net electricity generation, and net electricity consumption, respectively.

A Survey on Heterogeneous CPU–GPU Architectures and Simulators

ABSTRACTHeterogeneous architectures are vastly used in various high performance computing systems from IoT‐based embedded architectures to edge and cloud systems. Although heterogeneous architectures with cooperation of CPUs and GPUs and unified address space are increasingly used, there are still a lot of open questions and challenges regarding the design of these architectures. For evaluation, validation and exploration of next generation of heterogeneous CPU–GPU architectures, it is essential to use unified heterogeneous simulators for analyzing the execution of CPU–GPU workloads. This article presents a systematic review on challenges of heterogeneous CPU–GPU architectures with covering a diverse set of literatures on each challenge. The main considered challenges are shared resource management, network interconnections, task scheduling, energy consumption, and programming model. In addition, in this article, the state‐of‐the‐art of heterogeneous CPU–GPU simulation platforms is reviewed. The structure and characteristics of five cycle‐accurate heterogeneous CPU–GPU simulators are described and compared. We perform comprehensive discussions on the methodologies and challenges of designing high performance heterogeneous architectures. Moreover, for developing efficient heterogeneous CPU–GPU simulators, some recommendations are presented.

The Lived Experiences and Well-Being of Female Partners Following Discovery or Disclosure of Their Male Partner’s Compulsive Sexual Behaviours: An Australian Phenomenological Study

Abstract Introduction There has been an upsurge of research articles in the last two decades about sexual addiction (SA) and compulsive sexual behaviour (CSB). In the literature, SA/CSB is mainly presented as comparable to other behavioural addictions and similarly involves a problematic consumption model: loss of control, psychosocial impairments, and risky sexual activities. Despite this recent rise in research interest in this area, only a small number of studies have focused on the partners’ lived experiences and well-being. Methods This qualitative descriptive phenomenological investigation aimed to examine female partners’ lived experiences and well-being following the discovery or disclosure of their male partners’ SA/CSB. Individual in-depth semi-structured interviews were conducted (2020–2022) with primary participants (female partners; n = 12) and supplementary participants (counsellors; n = 15). Results The analysis reveals that this group has encountered several detrimental consequences of CSB-associated betrayal trauma (BT), including but not limited to suicidal thoughts and self-harm, compulsive self-comparisons with other women, physical and behavioural difficulties, and diminished self-esteem. Conclusions This empirical research is the first study in Australia to examine the voices and experiences of female partners upon learning of their mates’ CSBs. The findings contribute to the field of SA/CSB and enhance understanding of the multifarious consequences for the emotional, physical, sexual, spiritual and/or relational well-being of intimate female partners and their general outlook on life. Policy Implications Based on the results, there are strong indications with implications for policy that this population requires validation and multifaceted support systems to better navigate the challenging, complex, and stressful processes of discovery/disclosure and subsequent recovery.

Are post-communist countries paying increased healthcare costs because of risky behaviour?

Abstract Background Based on the annual Country Health Profiles, we can see the difference in population behaviour across EU countries and the associated health risks. At first glance, the so-called post-communist countries are characterised by above-average per-capita harmful consumption of sin goods, alcohol and tobacco. Is this harmful consumption associated with increased healthcare costs? Methods The idea of the study is based on balanced panel-data published in Country Health Profiles. The member countries were divided into groups based on their health risk scores - high risk (above average), and low risk (below average). The group of countries with high risk is mostly composed of post-communist countries. The detailed per-capita annual data for the period 2013-2021 was collected, such as GDP, consumption of sin goods, alcohol, and tobacco. The analysis is based on fixed-effects linear regression models, following recent recommendations on suppressing the excessive role of statistical-inference tools. Results We built various models for different groups of countries, and in this abstract, we present results for the group of post-communist countries - a simple model of harmful consumption (sin goods, GDP) and a more complex model (alcohol, tobacco, GDP). The results show that in the case of the post-communist countries group, for a fixed level of GDP per capita, higher consumption of sin goods by EUR 1 per capita corresponds with total healthcare expenditures higher by EUR 0.244 per capita. For a fixed level of GDP per capita and tobacco consumption per capita, higher consumption of alcohol by EUR 1 per capita corresponds with total healthcare expenditures higher by EUR 1.367 per capita. Conclusions Harmful consumption has a multiplier effect in the health sector; besides direct impact, it influences an increase in healthcare expenditures. More emphasis should be placed on prevention and awareness of consumers to reduce harmful consumption. Key messages • For fixed GDP per capita, higher harmful consumption corresponds with higher healthcare costs in post-communist countries. • For fixed GDP per capita and tobacco consumption per capita, a €1 higher alcohol consumption corresponds with more than €1 higher healthcare expenditure in the post-communist countries.