Consumption Model Research Articles

Synergy of Real and Digital Worlds - Promising Insights for the Future Generations of Fashion

Digital transformation (DT) is reshaping the fashion industry, revolutionizing the way brands, retailers, and consumers interact. It is not a fad but a necessary step towards innovation and survival in today's business context. Implementing digital transformation requires a bespoke approach that addresses specific challenges and goals within the existing framework of each brand or retailer. Fashion brands are leveraging digital technologies throughout their value chain, from product design and production to marketing and distribution. Computer-aided design (CAD) software, predictive analytics, and 3D printing are streamlining the design and prototyping phases, reducing time-to market and improving product quality. Technologies like 3D body scanning, seamless garments and digitized looms are also being adopted. Brands like Hugo Boss are using smart factories that incorporate robotics, artificial intelligence (AI), analytics, and the Internet of Things (IoT) to increase efficiency. Moreover, digital integrated supply chain platforms, such as blockchain, are enabling faster and more efficient production by connecting brands with manufacturers and suppliers globally. The adoption of digital solutions extends to the production of futuristic textiles, digital clothing and virtual model / digital show stoppers. Retailers are also embracing DT to enhance the customer experience and expand their reach. E-commerce has become a crucial channel, allowing retailers to sell products online and reach a wider customer base. Many retailers are investing in robust online platforms and incorporating augmented reality (AR) and virtual reality (VR) technologies to offer immersive in-store shopping experiences. Data analytics and AI are being used to personalize recommendations and offers, enhancing customer engagement and loyalty. In-store virtual assistants or kiosks are worth mentioning in light of digital clientele. Social media platforms play a vital role in the fashion industry's digital transformation. Fashion brands are partnering with social media influencers to promote their products and reach a larger audience. Additionally, collaborative consumption models are emerging through digital apps, supporting sustainability efforts such as zero waste, recycling, and sharing goods or services. Consumers are benefiting from greater convenience, choice, and personalized experiences in the digital fashion landscape. They have access to a wide array of products from around the world and can find inspiration and style ideas on social media platforms. User generated content and reviews facilitate informed purchase decisions. Mobile technology advancements have blurred the lines between online and offline retail experiences, enabling consumers to shop anytime and anywhere. Adaptability, inclusivity, diversity, authenticity and traceability of product life cycle (Blockchain) have become driving instincts of Gen Zers and Millennials at purchase points. However, traditional industry players and small independent retailers face increased competition from e-commerce giants, necessitating their adaptation to digital technologies to remain relevant. The COVID 19 pandemic has accelerated this shift and normalized the idea of going digital. This paper presents an in-depth review of how digital transformations have various players within the fashion Industry with special reference to the brands, designers and enterprises implementing these for their evolution and progression to thrive in this rapidly evolving landscape.

Energy efficiency improvement of heavy-load hydraulic fine blanking press for sustainable manufacturing assisted by multi-stages pressure source system

The fine blanking process has the highest forming accuracy in the field of metal forming and its production volume has increased sharply. However, hydraulic fine blanking presses (HFBP) – the core equipment of the fine blanking process, have suffered from significant energy loss due to the inefficient hydraulic system. To enhance the energy efficiency of the hydraulic system, this study introduced a novel approach – a multi-stage pressure source system comprised of distinct pump-accumulator groups, each operating at varied supplied pressures to narrow the mismatch of the installed and demanded power. This adaptive system is capable of automatic adjustments to align with the required pressure, thereby mitigating energy losses while concurrently preserving superior dynamic working performance. First, this study commenced with a theoretical analysis of the system configuration and energy characteristics of HFBP. Subsequently, a comprehensive exploration into the inherent inefficiencies of the hydraulic system is undertaken by formulating a precise energy consumption model. Finally, a novel energy-saving optimization design was carried out on the fast-approaching subsystem. The results demonstrated that the implementation of the proposed system led to a notable reduction of 76.87% in energy loss within the flow-regulating module, and an overall diminution of 13.53% in energy dissipation compared to the conventional system. This study will significantly advance sustainable manufacturing practices within the metal-forming domain by reducing electricity consumption and production cost.

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

BATSCCD: a new change detection method for mapping building age in rapidly changing urban areas using Landsat time series data

ABSTRACT The building age is an important feature in the vulnerability assessment of disasters and building energy consumption modeling. It provides indispensable information for the study of built environment. However, obtaining high-precision maps of building ages is challenging, which impedes the development of relevant research and applications. In this study, a new building age mapping method based on Landsat time series classification and change detection (BATSCCD) was proposed, which integrates Landsat time series data with random forest and LandTrendr algorithm to reflect non-building to building changes, building reconstruction, and corresponding change characteristics. The proposed method achieved overall building age mapping accuracies of 80.45% within a 2-year tolerance and 87.66% within a 3-year tolerance in Guangzhou and accuracies of 78.08% and 87.52%, respectively, in Beijing. The improvement was notable, especially compared with the continuous change detection and classification (CCDC) method, with corresponding accuracy values of 36.22% and 46.31%, respectively, in Guangzhou and 49.7% and 60.19%, respectively, in Beijing. The results suggest that the BATSCCD method can be used to accurately estimate the building age and provide reliable mapping results. Thanks to the accessibility of required data and its high accuracy of building age estimation, the proposed method exhibits great application potential.

Grid Model of Energy Consumption Using Random Forest by Integrating Data on the Nighttime Light, Population, and Urban Impervious Surface (2000–2020) in the Guangdong–Hong Kong–Macau Greater Bay Area

Energy consumption is an important indicator for measuring economic development and is closely related to the atmospheric environment. As a demonstration zone for China’s high-quality development, the Guangdong–Hong Kong–Macao Greater Bay Area imposes higher requirements on ecological environment and sustainable development. Therefore, accurate data on energy consumption is crucial for high-quality green development. However, the statistical data on local energy consumption in China is insufficient, and the lack of data is severe, which hinders the analysis of energy consumption at the metropolitan level and the precise implementation of energy policies. Nighttime light data have been widely used in the inversion of energy consumption, but they can only reflect socio-economic activities at night with certain limitations. In this study, a random forest model was developed to estimate metropolitan-level energy consumption in the Guangdong–Hong Kong–Macao Greater Bay Area from 2000 to 2020 based on nighttime light data, population data, and urban impervious surface data. The estimation results show that our model shows good performance with an R2 greater than 0.9783 and MAPE less than 9%. A long time series dataset from 2000 to 2020 on energy consumption distribution at a resolution of 500 m in the Guangdong–Hong Kong–Macao Greater Bay Area was built using our model with a top-down weight allocation method. The spatial and temporal dynamics of energy consumption in the Greater Bay Area were assessed at both the metropolitan and grid levels. The results show a significant increase in energy consumption in the Greater Bay Area with a clear clustering, and approximately 90% of energy consumption is concentrated in 22% of the area. This study established an energy consumption estimation model that comprehensively considers population, urban distribution, and nighttime light data, which effectively solves the problem of missing statistical data and accurately reflects the spatial distribution of energy consumption of the whole Bay Area. This study provides a reference for spatial pattern analysis and refined urban management and energy allocation for regions lacking statistical data on energy consumption.

Energy extractivism and water dispossession by contamination: an essay of fracking in mexican legal order

The article analyzes the extraction of hydrocarbons through fracking in Mexico. Energy extractivism is discussed as the hegemonic model of production and consumption of fossil fuels and, in particular, as a neoliberal prototype of energy management. The water-energy relationship is examined from a critical perspective, highlighting the granting of water rights for the stimulation of unconventional reservoirs. It is documented that the explicit regulation of fracking intervened in the allocation of hydraulic uses and the order of priority of uses. In any case, these issues sharpen competition for water on lands of collective social property. It is concluded that fracking constitutes a mechanism of water dispossession by contamination.

Predictive Model of Energy Consumption Using Machine Learning: A Case Study of Residential Buildings in South Africa

Predictive Model of Energy Consumption Using Machine Learning: A Case Study of Residential Buildings in South Africa

Quality of Life as a Factor in Maintaining the National-State Identity Steadiness

The paper discusses the quality of life capacity as a factor in maintaining the stability of national-state identity in modern Russia. The authors reveal the topic based on the analysis of data from quantitative sociological research for the period 2013–2021 (surveys by the All-Russian Center for the Study of Public Opinion and the Institute of Sociology of the Russian Academy of Sciences). The theoretical basis of the study is the concept of social identity by G. Tajfel and D. Turner. The authors conclude that, by the mid-2010s, a consensus formed among most adult citizens in a Russian society that the quality of life was the key or one of the primary criteria for the national greatness of the country. The model of irrational prestige consumption has become widespread, the extrapolation of which was facilitated by increased access to relevant media content through increased Internet penetration and improved methods of simulation getting status goods and services. The described situation creates risks of destroying the empirical basis for supporting in-group favoritism of Russians and out-group discrimination of communities speaking on behalf of the principal opponents of the Russian Federation in the external domain. Because of this, the quality of life in the medium term gains the role of one of the key factors providing the sustainability of the national-state identity of Russians.

Simulation Study on Student Residential Energy Use Behaviors: A Case Study of University Dormitories in Sichuan, China

Studying the energy use behavior of occupants is crucial for accurately predicting building energy consumption. However, few studies have considered the impact of occupant behaviors on energy consumption in university dormitories. The objective of this study is to establish an agent-based model of energy consumption for university dormitories based on energy use behavior. The dormitories of a typical university in Sichuan, China, were subdivided into three clusters using a two-step cluster analysis. Subsequently, the energy use behaviors of occupants in each type of dormitory were characterized to establish a stochastic energy use behavior model. On the basis of the above, NETLOGO was used to construct an agent-based model for dormitories’ energy consumption to dynamically simulate energy use behavior. The accuracy of the model was verified by comparing the simulated values with the measured data. Finally, a building-energy-friendly retrofit scheme was proposed, and it was found that the optimized dormitory reduced energy consumption by 16.07%. Therefore, the results can provide information support for energy-saving decisions during the early design and retrofit phases of buildings. With the popularity of centralized supply, the research methodology may provide an extensive reference for energy management policies and sustainable strategies in the building sector.

Predicting daily heating energy consumption in residential buildings through integration of random forest model and meta-heuristic algorithms

This research utilizes a sophisticated hybrid model integrating the Random Forest algorithm with meta-heuristic optimization techniques to estimate heating energy consumption in residential buildings. The study addresses key variables including architectural characteristics, occupancy, and ambient temperature. The primary objective is to enhance the prediction accuracy of heating energy consumption using a novel approach combining Random Forest with various meta-heuristic algorithms. The study employs six combinations of the Random Forest algorithm and meta-heuristic optimizers. To mitigate overfitting, K-Fold cross-validation is implemented during model training. The model's performance is evaluated using five statistical indices: coefficient of determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Relative Absolute Error (RAE), and Theil Inequality Coefficient (TIC). Results demonstrate the hybrid model's high predictive accuracy, with the Arithmetic Optimization Algorithm enhancing Random Forest's performance significantly. Notable statistical achievements include R2 = 0.977201, RMSE = 0.1179, MAE = 0.0573, RAE = 0.0930, and TIC = 0.0187. Additionally, the Ant Lion Optimizer shows excellent convergence, achieving a TIC value of 0.014986 after 101 iterations. The proposed hybrid model significantly outperforms traditional methods in predicting residential heating energy consumption. The integration of Random Forest with advanced meta-heuristic algorithms offers a robust framework for enhancing prediction accuracy in energy consumption modeling.

Performance evaluation of a wet air oxidation reactor with external circulation for high-chemical oxygen demand wastewater treatment

The presence of high-concentration organic contaminants in wastewater poses considerable risks to the health of both humans and ecosystems. Wet air oxidation (WAO) is an attractive treatment option, but current dilution and retreatment processes do not align with economic and environmental principles. Here we report an external circulation wet oxidation treatment process that utilizes oxidizing liquids to reduce feed concentration. By coupling energy consumption model with computational fluid dynamics model, we explore the optimal parameters for an industrial external circulation reactor. A 90 kt/a external circulation wastewater treatment system has been established, achieving a 21.6% increase in chemical oxygen demand (COD) removal efficiency and a 34.8% reduction in energy consumption. These findings provide a promising pathway for the efficient, low-energy treatment of high-concentration organic wastewater while minimizing freshwater resource consumption.

Modelling, Analysis and Comparison of Robot Energy Consumption for Three-Dimensional Concrete Printing Technology

The technology used for the 3D printing of buildings from concrete is currently a very relevant and developing topic and appears to be especially advantageous in terms of sustainable production. An important aspect of the sustainability assessment is the energy efficiency of the printing robots. Printing robots consume a significant amount of energy when printing. It is important to analyse this energy thoroughly and to be able to predict it in order to optimise the movement and control of printing robots to reduce energy consumption. In that paper, we analyse in detail the energy consumption of printing robots, which has not yet been thoroughly investigated in the context of 3D printing building applications. We present a methodology to develop an energy consumption model for a printing robot, specifically developed and optimized for this technology. Our methodology incorporates an innovative approach to determine reduced-efficiency maps, allowing for the inclusion of difficult-to-measure drive efficiency parameters in the model. This results in a comprehensive model of the energy consumption of the printing robot, reflecting its operating characteristics in a real-world environment. An open control system of the printing robot is used for the measurement of energy quantities, and specially developed software tools are introduced. We also present the first direct comparison of the energy consumption of different printing robots when following a uniform printing trajectory. The comparison is made based on the presented methodology to obtain and compare actual energy data from workplaces with printing robots. The methodology combines measured data with energy simulations from ABB RobotStudio, enabling energy comparisons between industrially articulated robots and real printing robots, including the ABB IRB4600, the gantry printing robot, and the printing robot. The experiments clearly demonstrate that the kinematic structure of printing robots significantly affects their energy consumption in 3D printing concrete. Based on the conducted methodologies and analyses, we identify key aspects of energy consumption of printing robots in 3D Construction Printing or 3D Concrete Printing (3DCP) technology. In doing so, we bring a new perspective and provide a basis for further research and development in this previously understudied area.

Consumer sentiment of the regional youth

In today’s rapidly developing world, young people play an important role in the segment of active consumers of goods and services. Their behaviour in the consumer market is formed under the influence of subjective moods, ideas, assessments, and expectations. The purpose of this work is to study the state and dynamics of consumer sentiment among young people in the Vologda region using the results of public opinion monitoring and using the index method of processing sociological information. The results of the study showed that the consumer behaviour of young people has a number of specific characteristics: rationality, responsiveness to new products, spontaneity, desire to follow fashion, status. The formation of various consumption models and practices among representatives of the younger generation is largely determined by consumer sentiment. During the measurement period, young people’s assessments were dominated by negative characteristics of their financial situation, opportunities for purchasing goods and services, current economic situation and prospects for its further development. The unfavorable consumer sentiment of the youth significantly deforms its typical features of consumer behaviour, hinders the formation of modern consumer practices and the development of the regional consumer market in accordance with new all-Russian and global trends. Improving the consumer sentiment of young people is directly related to enhancement of the state’s youth and economic policies aimed at improving the socio-economic situation of young members of the Russian society and creating sufficient guarantees for young citizens to exercise their rights and freedoms.

The time code of online consumption: Exploring the impact of work hours on online consumption in the retail industry

The growth of e-commerce has led to a rise in online consumption, while the online environment usually muddles consumers’ sense of time, which easily brings about rising on time during consumption. Building on the Beckerian model of consumption, we find that increasing work hours may constrain employees’ online consumption. Using 15,529 employee samples from China Family Panel Studies (CFPS), this paper examines the effect of work hours on consumers’ online consumption in the retail industry. The IV-Heckman method and the stimulus-organism-response (S–O-R) framework have been applied. Empirical findings show that increasing work hours significantly reduces employees’ online consumption. This study reveals the causal effect of work hours on online consumption both theoretically and empirically.

Aid and developing regional integration

This paper examines megatrends and interdependence of the loan aid-capital investment links across developing regions. Theoretically, it contributes to the existing studies by providing a novel theoretical framework with a dual-objective donor-giving grant (driven by a benevolent objective) and loan aid (profitability motive), as well as a public–private partnership-like investment structure and multi-region feedback mechanism. Empirically, this paper adds to the literature by allowing for cross-heterogeneity and interdependencies across regions of loan aid-capital investment link. We estimate a panel structural VAR model of grant aid, loan aid, private consumption, and aggregate capital stock using data from nine developing regional country blocs covering the 1961-2017 period. Amidst a generally weak aid-investment link, we find loan aid to Granger causes capital accumulation, and among African regions, it is the most (least) productive – in terms of within- and spillover effects – in the IGAD (ECOWAS) region. In addition, our results suggest deeper regional integration between the Sub-Saharan African regions than the collective Latin American & Caribbean regions and the two Southern Asian regions (Southeast Asia and South Asia) examined.

Boundedness of classical solutions to a chemotaxis consumption model with signal-dependent motility

Boundedness of classical solutions to a chemotaxis consumption model with signal-dependent motility

Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy model

Quantifying energy consumption of database operations is the foundation of building energy-efficient database systems. Existing approaches only focused on stand-alone database servers, while we are interested in modeling energy consumption of database operations in the distributed environments. In this study, we aim at providing an accurate energy consumption model for queries executed in distributed database systems, profiling energy consumption characteristics of both the individual queries and the system as a whole, to guide the design of green database systems and to reveal opportunities for energy-efficient computing. As the execution of a distributed query is a combination of a set of subqueries that decomposed from it, we start from building energy models for individual subqueries by extracting basic operations that can effectively reflect their energy consumption. Then we use a bottom-up measuring and modeling method as the basis to provide a comprehensive energy estimation model for the entire distributed query. To validate the accuracy of the model, we use queries with a variety complexity that generated from three standard benchmarks (TPC-H, SSB, and Sysbench) on real distributed databases. Extensive experimental results show that our solution can achieve a high average accuracy of 94.63% for distributed queries. More importantly, based on these results we further explored energy consumption patterns for distributed queries and presented several important implications. And finally, a significant role of distributed joins has been discovered in saving both idle and dynamic energy cost of the system. We hope that taking advantage of our observations can help readers who wish to substantially improve energy efficiency for distributed database systems.

A bilevel fast-convergent optimizer via high-fidelity convex models: Application on optimal operation of all-parallel heterogeneous chiller-pump systems

To cope with buildings’ growing and diverse cooling demand, heterogeneous multi-chiller systems with non-dedicated pumps are commonly applied in large-scale centralized chiller plants. All-parallel heterogeneous systems offer high operation flexibility but pose challenges to optimal operation planning. For the deficiencies in convexity tractability, the classical neural networks (NNs) achieved great success in system identification yet were restricted in model-based optimization. In this study, an optimization-oriented convex modeling approach based on input convex neural networks (ICNN) to identify energy consumption models for energy units of chiller-pump systems was presented, which provides convex input–output mappings while leveraging the high-fidelity capability of NNs. Using the convex models, the energy minimization issue subjected to multiple constraints was formulated, the optimality of which was mathematically proven. A bilevel deterministic optimizer was developed to determine the global optimal. Comprehensive data experiments were conducted using practical and simulated operation data to evaluate the modeling and optimization performances of the proposed methods compared with conventional candidates. Numerical results suggest that the ICNN-based convex models exhibit superior modeling performances than physics-based models. A better overall energy saving ratio of 8.86 % and faster convergence time of 3.53 s for average achieved by the proposed bilevel optimizer compared with rule-based and meta-heuristic optimizers under identical external conditions.

Materials and Structural Designs toward Motion Artifact-Free Bioelectronics.

Bioelectronics encompassing electronic components and circuits for accessing human information play a vital role in real-time and continuous monitoring of biophysiological signals of electrophysiology, mechanical physiology, and electrochemical physiology. However, mechanical noise, particularly motion artifacts, poses a significant challenge in accurately detecting and analyzing target signals. While software-based "postprocessing" methods and signal filtering techniques have been widely employed, challenges such as signal distortion, major requirement of accurate models for classification, power consumption, and data delay inevitably persist. This review presents an overview of noise reduction strategies in bioelectronics, focusing on reducing motion artifacts and improving the signal-to-noise ratio through hardware-based approaches such as "preprocessing". One of the main stress-avoiding strategies is reducing elastic mechanical energies applied to bioelectronics to prevent stress-induced motion artifacts. Various approaches including strain-compliance, strain-resistance, and stress-damping techniques using unique materials and structures have been explored. Future research should optimize materials and structure designs, establish stable processes and measurement methods, and develop techniques for selectively separating and processing overlapping noises. Ultimately, these advancements will contribute to the development of more reliable and effective bioelectronics for healthcare monitoring and diagnostics.

Sex Differences in Alcohol-related Cardiomyopathy

Alcohol-related Cardiomyopathy (ACM) manifests in humans with a significant history of alcohol use and is characterized by ventricular dilation and cardiac function impairment. Although the most common age group for ACM is males between the ages of 30-55, women require a lesser lifetime alcohol exposure to develop ACM than men. The objective of this study is to assess sexual differences in a mouse model of chronic plus binge alcohol consumption. We hypothesize that female mice exposed to chronic plus binge alcohol will not develop as severe cardiac dysfunction as their male counterparts. In this study, male (n=19) and female (n=14) C57BL/6J mice were provided chronic plus binge alcohol feeding. After 5 days of acclimation to the liquid diet, mice are fed ad libitum 5% ethanol (EtOH) liquid or isocaloric control liquid diet for 30 days. At 10 and 30 days, mice received an oral binge dose of EtOH (5 g/kg body weight), or isocaloric maltose dextrin solution (9 g/kg body weight). Noninvasive cardiac structural and functional data were obtained via echocardiography at baseline and following binges. After 30 days, cardiac hemodynamics was measured by open chest left ventricle catheterization. Two-way ANOVA with Tukey’s post hoc analysis was performed for each cardiac parameter, and significance was defined as p<0.05. Male EtOH mice exhibited a significant decrease in stroke work (1119 ± 297 mmHg/mL when compared to male controls, 1622 ± 365 mmHg/mL; p= 0.03). Female EtOH mice had a nonsignificant decrease in stroke work (1242 ± 374 mmHg/mL when compared to female controls, 1423 ± 469 mmHg/mL; p= 0.80). Maximum left ventricular pressure was decreased 20% in male EtOH mice (p=0.003) but increased 10% in female EtOH mice (p=0.46). The maximum rate of pressure change within the ventricle was 24% lower in the male EtOH mice (p=0.02) and 9% higher in female EtOH mice (p=0.87). The minimum rate of pressure change within the ventricle was 24% higher in male EtOH mice (p=0.02) and 17% lower in female ETOH mice (p=0.44). After 30 days of EtOH exposure, male mice showed significant systolic and diastolic dysfunction. Female mice on the other hand, did not show substantial cardiac dysfunction after chronic plus binge ethanol exposure. Further experiments involving qPCR of proinflammatory and profibrotic markers will elucidate the molecular mechanism of sex differences in ACM development. R21AA029747 (JG & CB) & T32AA007577. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.