Consumption Model Research Articles

Comparative modeling of household electricity consumption in France: Insights from path analysis and classical models

Electricity consumption in Europe has risen significantly in recent years, with households being the largest consumers of final electricity. Managing and reducing residential power consumption is critical for achieving efficient and sustainable energy management, conserving financial resources, and mitigating environmental effects. Many studies have used statistical models such as linear, multinomial, ridge, polynomial, and LASSO regression to examine and understand the determinants of residential energy consumption. However, these models are limited to capturing only direct effects among the determinants of household energy consumption. This study addresses these limitations by applying a path analysis model that captures the direct and indirect effects. Numerical and theoretical comparisons that demonstrate its advantages and efficiency are also given. The results show that Sub-metering components associated with specific uses, like cooking or water heating, have significant indirect impacts on global intensity through active power and that the voltage affects negatively the global power (active and reactive) due to the physical and behavioral mechanisms. Our findings provide an in-depth understanding of household electricity power consumption. This will improve forecasting and enable real-time energy management tools, extending to the design of precise energy efficiency policies to achieve SDG 7’s objectives.

E-commerce Enterprise's Supply Chain Green Transformation Strategy

With the increasing awareness of social environmental protection and the widespread concept of sustainable development, enterprises gradually realize the importance of environmental responsibility in the process of operation. For e-commerce enterprises, their green transformation in supply chain management has become an inevitable trend. Especially in the modern consumption model, e-commerce enterprises not only need to improve operational efficiency and customer satisfaction but also must actively respond to increasingly stringent environmental regulations and consumer demand for green products. Therefore, promoting the green transformation of the supply chain not only helps to reduce the environmental impact but also enhances the social image of the enterprise and enhances the competitiveness of the brand. Taking e-commerce enterprises as the research object, this paper systematically combs the core issues of green transformation of the supply chain, including specific challenges such as cost problems, shortage of technology and talents, and changes in policies and regulations. In view of these problems, optimization strategies are put forward from the aspects of reducing cost, introducing technology and talents, and coping with policy changes. This paper aims to provide scientific reference suggestions for the green supply chain transformation of e-commerce enterprises to help them achieve sustainable development goals.

Comparative Analysis of Concession Operations in the Context of Property Rights between Chinese and American Professional Basketball Events

Concession operations in sports events, as a crucial part of the event franchise system, have become a pivotal source of revenue for event resource development. This paper, from the perspective of property rights, compares the concession models in professional basketball events between China and the United States through literature review, field research, and comparative analysis. The findings indicate that in terms of property rights structure, the property rights of professional sports events in China are attributed to individual sports associations and are under government jurisdiction; in contrast, the property rights of professional basketball events in the United States are collectively owned by the teams and are market-driven. Regarding the supply model, there is no significant difference between the concession supply of the CBA and the NBA; however, in practice, the CBA, due to the absence of a standardized concession system, exhibits low efficiency and a failure to become widespread. In terms of the consumption model, most CBA venues offer limited and sparse concession products and services, and the number of fans attending games in person is significantly lower than that of NBA events. Based on these observations, the following recommendations are proposed: firstly, to enhance the involvement of social capital by promoting cooperation between the government and social capital and encouraging social capital to actively participate in operations; secondly, to establish a standardized concession system by clarifying and confirming the interests of public and private sectors and by explicitly defining property rights and operating rights; and lastly, to improve and diversify products and services.

Adaptive Ensemble Learning for Enhancing Building Energy Consumption Prediction: Insights from COVID-19 Pandemic Energy Consumption Dynamics

Buildings account for approximately 40% of the total global energy consumption. Therefore, accurate prediction of building energy consumption is necessary to optimize resource allocation and promote sustainable energy usage. A key challenge in developing building energy consumption models is their adaptability to abrupt changes in consumption patterns owing to extraordinary events, such as the COVID-19 pandemic. Therefore, a two-layer ensemble-learning (EL) model incorporating sliding windows as input features is proposed. The model is a two-layer stacking EL consisting of two base learning methods: (1) support vector regression (SVR), and (2) random forest (RF). Temperature and humidity are included to account for the influence of weather conditions on energy consumption. The proposed model is deployed to forecast building energy consumption both before (November 2019) and during (May – October 2020) the COVID-19 pandemic and is compared with a single machine learning model. The results demonstrate that the EL model outperforms the SVR and RF methods, providing excellent prediction accuracy even during the pandemic when significant changes in energy consumption patterns occurred. The findings also highlight the effectiveness of sliding windows as input features for improving model adaptability. Additionally, the analysis reveals that temperature is more prominent than humidity for improving prediction accuracy.

Dual independent mechanisms underlying gut epithelial remodeling upon sugar substitute consumption.

Intestinal epithelial cells (IECs) are dynamically regulated by luminal contents, including dietary ingredients, food additives, and microbiota-derived metabolites. Although sugar substitutes are commonly used as food additives for their sweet taste and lower calorie content, there is limited experimental evidence regarding their potential to drive gut remodeling. In this study, we designed experimental models for short-term consumption of erythritol, a natural sugar alcohol widely used as a sugar substitute, and investigated its effects on gut remodeling and the underlying mechanisms. Our findings indicate that erythritol consumption induces hyperplasia in tuft cells (TCs) and goblet cells (GCs), as well as enhances the activity of intestinal stem cells-increases in expression levels of leucine-rich repeat containing G protein-coupled receptor 5 (Lgr5), the key intestinal stem cell marker, in the number of proliferating stem cells, and facilitation of their differentiation into villi cells-while maintaining the number of Lgr5+ intestinal stem cells. Notably, the enhanced stem cell activity was observed even in Trpm5 knockout mice, suggesting that it is mechanistically independent of TC hyperplasia. Instead, we demonstrated the functional involvement of the gut microbiota, as antibiotic treatment abolished this effect, and fecal material transfer from erythritol-consumed mice replicated the enhancement of stem cell activity in recipient mice. Furthermore, we identified acetate as the metabolite responsible for enhancing stem cell activity. These findings suggest the functional decoupling of TC hyperplasia and the enhancement of stem cell activity, providing a potential therapeutic avenue for gut epithelial diseases.

Ethical challenges in designing sustainable business models for responsible consumption and production: case studies from Jordan

Ethical challenges in designing sustainable business models for responsible consumption and production: case studies from Jordan

Evaluating AES Security: Correlation Power Analysis Attack Implementation using the Switching Distance Power Model

Cryptographic circuits play a critical role in safeguarding confidential information and ensuring secure communication, contributing to the resilience of digital infrastructure under SDG 9 (Industry, Innovation, and Infrastructure). These circuits store encryption keys for the Advanced Encryption Standard (AES) algorithm, including AES-128, AES-192, and AES-256, which are widely used in applications such as online banking and secure messaging platforms. This paper examines the effectiveness of Correlation Power Analysis (CPA), a side-channel attack technique that exploits power consumption patterns in cryptographic circuits, to highlight the challenges of implementing secure encryption systems. The study illustrates the CPA attack procedure against AES implemented on the SASEBO-GII FPGA platform. Experimental results reveal that while the CPA attack based on the Hamming Weight (HW) power consumption model fails to extract the encryption key, the Switching Distance (SD) power consumption model successfully recovers the entire key with a 100% success rate using approximately 4000 power traces. These findings underscore the vulnerability of cryptographic circuits to advanced side-channel attacks and emphasize the need for robust countermeasures to ensure secure data protection, thereby advancing secure and sustainable digital environments under SDG 11 (Sustainable Cities and Communities).

Energy consumption and emission analysis for electric container ships

The increasingly stringent international environmental protection requirements and the progressively tougher compliance requirements of shipowners have also put forward new development needs for low-emission, clean-energy vessels. At present, more than 90% of China's water transport ships still use diesel-powered propulsion systems, which have issues such as high fuel costs, high noise levels, and pollution emissions. Compared with traditional ship power systems, the ecological advantages and comprehensive benefits of electric propulsion-type ships are obvious. However, there are also issues, such as shorter range and battery life, which need to be analyzed in depth. This paper establishes a life cycle energy consumption model and emission model for the comprehensive benefits of electric ships and conducts an economic benefit analysis. The results show that electric ships have significant advantages in environmental protection, energy saving and lower costs while electric ships for containers have great prospects for future development. This paper provides a useful exploration for the international shipping industry to adopt effective measures to control ship emissions.

Architectural coatings and temperature Adaptive radiative cooling strategies applied in ten climate zones in China: Annual energy consumption Reductions and potentials

This research evaluates the energy-saving potential of high-reflectivity building materials, including three common coating types—normal (absorptivity 0.8, emissivity 0.8), cooling (absorptivity 0.2, emissivity 0.9), supercooling (absorptivity 0.05, emissivity 0.95), and Temperature-Adaptive Radiative Cooling (TARC) materials across 49 representative cities within ten climate zones in China using computational fluid dynamics simulations combined with the Demand.ninja energy consumption model. The results reveal that optimal solar absorptivity varies significantly between different climate zones, with higher absorptivity benefiting colder regions and lower absorptivity benefiting hotter regions. Urban microclimate simulations confirm that low solar absorption materials effectively reduce wall temperatures during the daytime and boost overall cooling energy savings, despite a slight increase in ambient air temperature. Building height-to-width ratios also influenced energy efficiency, with the best performance observed at a ratio of unity for both cooling and supercooling materials. Additionally, tests on the Temperature-Adaptive Radiative Cooling (TARC) materials demonstrate that TARCs offer significant energy-saving advantages, achieving an annual saving of 23.1 billion kWh across China by effectively balancing heating and cooling demands across different seasons. This superior performance is primarily attributed to TARCs’ ability to mitigate increased heating energy demands often associated with conventional cooling materials, especially in regions with substantial winter heating needs. Present research has highlighted the importance of climate-specific building material strategies and the potential of innovative materials like TARC for reducing energy consumptions, and this could favour the low carbon urban development.

Optimal task partitioning to minimize failure in heterogeneous computational platform

The increased energy consumption by heterogeneous cloud platforms surges the carbon emissions and reduces system reliability, thus, making workload scheduling an extremely challenging process. The dynamic voltage- frequency scaling (DVFS) technique provides an efficient mechanism in improving the energy efficiency of cloud platform; however, employing DVFS reduces reliability and increases the failure rate of resource scheduling. Most of the current workload scheduling methods have failed to optimize the energy and reliability together under a central processing unit - graphical processing unit (CPU-GPU) heterogeneous computing platform; As a result, reducing energy consumption and task failure are prime issues this work aims to address. This work introduces task failure minimization (TFM) through optimal task partitioning (OTP) for workload scheduling in the CPU-GPU cloud computational platform. The TFM-OTP introduces a task partitioning model for the CPU-GPU pair; then, it provides a DVFS- based energy consumption model. Finally, the energy-load optimization problem is defined, and the optimal resource allocation design is presented. The experiment is conducted on two standard workloads namely SIPHT and CyberShake workload. The result shows that the proposed TFA-OTP model reduces energy consumption by 30.35%, reduces makespan by 70.78% and reduces task failure energy overhead by 83.7% in comparison with energy minimized scheduling (EMS) approach.

Power consumption model for Unmanned Aerial Vehicles using Recurrent Neural Network techniques

Power consumption model for Unmanned Aerial Vehicles using Recurrent Neural Network techniques

Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach

Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach

EKOLOJİ ÇİRKLƏNMƏNİN İQTİSADİ TƏSİRLƏRİNİN AZALDILMASI YOLLARI

Environmental pollution emerges as one of the most pressing global challenges of our time, characterized by a multidisciplinary nature that profoundly affects both ecosystems and economic stability. This concept encompasses a multidimensional reality, illustrated by the destructive effects of toxic substances generated by industrial processes, carbon emissions from the transportation sector, and the widespread use of pesticides in agriculture. Such circumstances signify that the environment is undergoing a transformation marked by resource depletion and a decline in biodiversity. Contemporary research in Azerbaijan demonstrates that the socio-economic consequences of environmental pollution give rise to multifaceted problems not only in the short term but also in the long run. For instance, pollution of natural resources, particularly industrial waste-laden water bodies, leads to a decrease in agricultural productivity, causing significant setbacks in economic performance indicators. Furthermore, rising pollution levels adversely impact human health, imposing additional financial burdens on the healthcare system, which in turn puts the nation’s overall economic resources to the test. Analyzing environmental issues in the context of economic development highlights the necessity of adopting green technologies and sustainable consumption models. In this regard, implementing green supply chains in Azerbaijan is considered a progressive approach, both for reducing environmental pollution and enhancing economic efficiency. Key measures for ensuring ecological safety include managing radioactive waste, optimizing energy production processes, and developing recycling mechanisms. On a global scale, Azerbaijan’s engagement in international cooperation and the implementation of modern environmental standards could lead to a qualitative transformation in the country’s economic development model. Such an approach would not only mitigate the economic and ecological impacts of pollution but also pave the way for achieving strategic long-term sustainable development goals. Thus, the application of an integrated approach to the economic impacts of environmental pollution should be regarded as one of the primary mechanisms for both environmental protection and economic modernization. Keywords: Environmental pollution, economic impacts, environmental responsibility, human health, sustainable development

A low-carbon scheduling method based on improved ant colony algorithm for underground electric transportation vehicles

Improved scheduling of underground transportation vehicles in coal mines can significantly enhance work efficiency and contribute to safer production. However, the specific working conditions and limitations of electric vehicles pose significant challenges to effective vehicle scheduling. To address this issue, a constrained single-objective optimization model is developed to minimize transportation costs for low-carbon scheduling of underground electric transportation vehicles (ETVs). The model incorporates constraints related to load capacity, cruising range, and safety regulations. A specific energy consumption model for ETVs is formulated, considering factors such as road conditions, load, and driving state. To solve this problem, an improved ant colony optimization algorithm integrated with Q-learning (ACO-QL) is proposed. Specifically, ant colony optimization explores the global solution space and identifies promising regions, while the split strategy effectively distributes demand across multiple vehicles. Q-learning enhances local search by selecting the most appropriate operator, preventing premature convergence to local optima. Experimental results on four real-world instances demonstrate the superior performance of ACO-QL compared to state-of-the-art algorithms.

A Data-Driven Approach to Enhance the Prediction of Bacteria–Metabolite Interactions in the Human Gut Microbiome Using Enzyme Encodings and Metabolite Structural Embeddings

Background: The human gut microbiome is critical for host health by facilitating essential metabolic processes. Our study presents a data-driven analysis across 312 bacterial species and 154 unique metabolites to enhance the understanding of underlying metabolic processes in gut bacteria. The focus of the study was to create a strategy to generate a theoretical (negative) set for binary classification models to predict the consumption and production of metabolites in the human gut microbiome. Results: Our models achieved median balanced accuracies of 0.74 for consumption predictions and 0.95 for production predictions, highlighting the effectiveness of this approach in generating reliable negative sets. Additionally, we applied a kernel principal component analysis for dimensionality reduction. The consumption model with a polynomial kernel, and the production model with a radial basis function with 32 reduced features, showed median accuracies of 0.58 and 0.67, respectively. This demonstrates that biological information can still be captured, albeit with some loss, even after reducing the number of features. Furthermore, our models were validated on six previously unseen cases, achieving five correct predictions for consumption and four for production, demonstrating alignment with known biological outcomes. Conclusions: These findings highlight the potential of integrating data-driven approaches with machine learning techniques to enhance our understanding of gut microbiome metabolism. This work provides a foundation for creating bacteria–metabolite datasets to enhance machine learning-based predictive tools, with potential applications in developing therapeutic methods targeting gut microbes.

Methodology for developing models to estimate vehicle instantaneous energy consumption based on hub-type dyno test data

This paper describes a methodology to develop simple energy consumption models of road vehicles exploiting transient experimental datasets obtained from a vehicle/powertrain four-dyno testbed available at the Center for Automotive Research and Sustainable mobility (CARS@POLITO) of Politecnico di Torino. These models, based on a locally weighted linear regression method, can serve as a simpler alternative to more conventional methods based, for example, on engine maps obtained by steady-state characterization at engine testbeds, and combined with powertrain subsystem models. The present methodology was applied to a conventional diesel-powered vehicle. Three different modeling approaches are proposed: vehicle-based (VB), engine-based (EB) and engine-based modified (EB*). The VB approach is the simplest, being able to estimate the vehicle fuel consumption by only using, as inputs, wheel torque and speed, while the EB and EB* approaches enhance modeling accuracy by using engine speed and torque, as inputs, along with transmission-related parameters and/or by considering the moments of inertia of the powertrain rotating parts. The manuscript describes, in full, the process used to develop these models, providing significant guidance for researchers who may want to replicate the procedure with their own experimental data. These energy consumption models can be useful tools for the development and assessment of eco-driving or ADAS functions or for energy consumption comparison between different vehicles that were not tested on the same driving cycle. They can also support the estimation of the total energy consumption of vehicles along different traffic conditions or routes, based on a limited number of experiments and low computational effort.

Design and Analysis of Non-Contact Power Supply System for Wireless Monitoring Node of Ship's Water Lubricated Bearing

The wireless sensor monitoring node for the online monitoring of various parameters of the ship's water-lubricated bearing rotates with the shaft during its operation. Therefore, how to ensure a continuous power supply to the node under the rotating working condition is an urgent problem to be solved. In this paper, a non-contact power supply method is adopted to design a magnetically coupled inductive non-contact power supply system, and experimental verification is carried out. Firstly, the non-contact power supply method is described, a node energy consumption model is established for the existing wireless monitoring nodes, and the energy consumption test is conducted. Then, a magnetically coupled inductive non-contact power supply system is designed, and the output of the system circuit is simulated and analyzed. Finally, the experimental test of the power supply system is carried out to study various factors that affect the performance of the power supply system. The research results show that the rotational speed is not the key factor affecting the power supply effect of the system, and the air gap of the coupling mechanism is the key factor affecting the power of the power supply system. The output power of the power supply system increases with the increase of the air gap, and the output efficiency decreases with the increase of the air gap. Considering the influence of the efficiency factor of the power supply system, the 5mm air gap is the optimal working air gap of the power supply system, and the system output power can reach 15.9 W at this time.

Stability Analysis of Fractional-Order Nonlinear Alcohol Consumption Model and Numerical Simulation

This study explores the motivational factors behind alcohol consumption and its societal impacts. It identifies key reasons for drinking, such as improving sociability, boosting confidence, coping with challenges, participating in rituals, and seeking detoxification. The study highlights the substantial societal harm caused by the use of alcohol and drugs, with a particular emphasis on the increase in sexual violence, both within and outside families, as a result of impaired behavior. In this work, we present a newly developed mathematical model within the Caputo fractional framework, designed to reflect the lifestyle and behaviors of individuals in the alcohol model. We analyze the existence and uniqueness of solutions using the Lipschitz growth condition and fixed point theory. In addition, we explore equilibrium points, compute basic reproduction numbers, and explore the Hyers–Ulam stability of the alcohol model. Finally, we develop a numerical scheme for computational analysis of the proposed fractional model.

Research on collaborative optimization of fuel cell tractor transmission system and control strategy parameters

The distributed fuel cell tractor is a new type of power tractor. The transmission system and control strategy parameters affect the energy utilization efficiency of the entire machine. There is currently no research in this area. In order tosolve the problem of low energy utilization of the whole machine of distributed dual-motor-driven hydrogen fuel cell tractor, a cooperative optimization method based on particle swarm optimization (PSO) algorithm for the parameters of the transmission system and energy-saving control strategy of distributed dual-motor-driven hydrogen fuel cell tractor is proposed. According to the tractor dynamics analysis and equivalent hydrogen consumption theory, a fuel cell tractor transmission parameter-equivalent hydrogen consumption model is established, The wheel-side transmission ratio and the upper and lower threshold values of the hydrogen fuel cell working power are taken as control variables, and the minimum equivalent hydrogen consumption is taken as the optimization goal, the optimization method is simulated and tested based on the MATLAB simulation platform. The results show that under plowing conditions, compared with the rule-based control strategy, the proposed collaborative optimization method of the fuel cell tractor transmission system and control strategy parameters can reasonably control the operating status of the fuel cell and the power battery, ensure that the fuel cell works in a high efficiencyrange, enhance the overall performance of the fuel cell system, and control the power battery state of charge (SOC) to remain in areasonable range. The tractor equivalenthydrogen consumption is reduced by 7.84%.

The Effect of Boric Acid on Oxidative Stress, Inflammation, and Apoptosis in Embryonic and Fetal Tissues Damage Caused by Consumption of High-Fructose Corn Syrup in Pregnant Rats.

This study aimed to determine the protective role of boric acid in a pregnant rat model of high fructose corn syrup consumption. Consumption of high fructose corn syrup has been associated with adverse health outcomes in humans and animals. Twenty-eight healthy female Wistar albino rats (250-300g weight and 16-24 weeks old) were randomly distributed into four equal groups (n = 7): Control, Boric acid (BA), High Fructose Corn Syrup (HFCS), HFCS + BA. Boric acid (20mg/kg) was administered to pregnant rats via oral gavage every day during pregnancy. The prepared 30% HFCS (F30) solution (24% fructose, 28% dextrose) was added to the drinking water throughout pregnancy. At the end of pregnancy (day 19), blood, placenta, uterus, and fetuses were collected from rats. The results indicated that HFCS increases oxidative stress by increasing the level of MDA and decreasing GSH, SOD, and CAT activity in the blood of maternal. However, BA administration significantly decreased MDA levels and increased GSH levels, SOD, and CAT activity (p < 0.05). In addition, HFCS consumption significantly increased plasma TNF-α, IL-6, and leptin levels compared to control, BA, and HFCS + BA groups (p < 0.05). However, BA administration significantly decreased plasma TNF-α, IL-6, and leptin levels (p < 0.05). Furthermore, BA (20mg/kg) significantly decreased HFCS-induced histopathological and immunohistochemical alterations in the placenta, uterus, and fetal tissue. In conclusion, BA may prevent HFCS toxicity in maternal and fetal tissues, as it regulates oxidative imbalance in pregnant rat and alleviates histopathological and immunohistochemical changes. The findings indicate a need for further studies to assess the potential of boron in preventing or mitigating the effects of HFCS during pregnancy.