Consumption Utilization Research Articles

Assessing the nexus between natural resource consumption and urban sprawl: Empirical evidence from 288 cities in China

With the rapid advancement of urbanization in China and the sharp increase in natural resources consumption, it is crucial to quantitatively study the relationship between land resources utilization and energy consumption. This paper uses the panel data at the city level from 2004 to 2019 to analyze the impact of low-density expansion of land resources on power consumption and its internal mechanism. A significant U-shaped relationship exists between urban sprawl and power consumption, which passes a variety of robustness tests. Moreover, geographical indicators are selected as instrumental variables to deal with potential endogeneity. Heterogeneity analysis shows that there are differences in the impact of urban sprawl on electricity resources for different urban locations, population sizes, sprawl characteristics, and electricity consumption types. Further mechanism analysis shows that urban sprawl affects electricity consumption by affecting variables such as real estate investment and Internet development. The conclusions of this paper bridge the conflicting viewpoints of existing research and provide empirical evidence for the utilization of land resources and the formulation of electricity regulation policies in various regions of China.

Implications of Deep Compression with Complex Neural Networks

Deep learning and neural networks have become increasingly popular in the area of artificial intelligence. These models have the capability to solve complex problems, such as image recognition or language processing. However, the memory utilization and power consumption of these networks can be very large for many applications. This has led to research into techniques to compress the size of these models while retaining accuracy and performance. One of the compression techniques is the deep compression three-stage pipeline, including pruning, trained quantization, and Huffman coding. In this paper, we apply the principles of deep compression to multiple complex networks in order to compare the effectiveness of deep compression in terms of compression ratio and the quality of the compressed network. While the deep compression pipeline is effectively working for CNN and RNN models to reduce the network size with small performance degradation, it is not properly working for more complicated networks such as GAN. In our GAN experiments, performance degradation is too much from the compression. For complex neural networks, careful analysis should be done for discovering which parameters allow a GAN to be compressed without loss in output quality.

FPGA-Based Machine Learning: Platforms, Applications, Design Considerations, Challenges, and Future Directions

Field-Programmable Gate Arrays (FPGAs) have emerged as a promising platform for accelerating machine learning tasks due to their high parallelism, low latency, and hardware customization ability. In this paper, the authors provide an overview of popular FPGA platforms for machine learning and compare the tradeoffs among FPGAs, GPUs, and CPUs for machine learning. The authors also present specific applications of machine learning based on FPGAs, including those in autonomous driving and healthcare. Additionally, the paper explores FPGA design considerations, such as architecture, resource utilization, and power consumption. Nonetheless, obstacles persist in the realm of FPGA-based machine learning that require attention. Identifying the ideal balance between adaptability and performance, considering factors such as space, energy usage, and latency, is still challenging. As the capabilities of FPGAs expand, there is a significant need for devices that have a smaller footprint, reduced power consumption, and minimized delays. The paper emphasizes the necessity of ongoing research in the field of FPGA-based machine learning to address these issues and continue enhancing the performance and effectiveness of machine learning systems.

Current Situation and Development Trend of Urban Centralized Heating

In the context of sustainable development, countries around the world are increasingly paying attention to environmental issues. In this context, the high energy consumption and high pollution of urban heating systems have become urgent issues that need to be addressed. Promoting urban centralized heating is an inevitable choice to effectively solve these problems. The heating system is an important component of urban development, and central heating plays a crucial role in energy utilization. In order to meet the requirements of sustainable development, it is necessary to actively improve the central heating system and solve the problems of low resource utilization and high energy consumption in traditional decentralized heating methods. This can reduce air pollution and improve the overall quality of the urban environment. A comprehensive analysis of the relevant content of urban central heating systems, including understanding the existing problems and development trends, helps to formulate reasonable measures to promote the development of central heating. Promoting the development of central heating systems is an important component of urban sustainable development. By improving energy utilization efficiency, reducing dependence on traditional fossil fuels and using advanced technologies, central heating systems help to achieve the Sustainable Development Goals and improve the quality of life of urban residents. A comprehensive analysis of the current situation and development trends of urban central heating can help formulate reasonable measures to promote the development of central heating, achieve a more sustainable future, and create a sustainable urban environment.

Immersive technologies and consumer behavior: A systematic review of two decades of research

Immersive technologies, including augmented reality (AR), virtual reality (VR), mixed reality (MR), and three-dimensional (3D) views, are digitally expanding consumers’ reality, enabling never-before-seen experiences. As these technologies transform from a quirky novelty into a ubiquitous utility for consumers, we analyze the advances of the past two decades of research that elaborate on the influence of immersive technologies on consumer behavior. While the past reviews in this area have focused on a limited number of immersive technologies or their application in a sole industry, we holistically analyze and compare the influence of AR, VR, MR, and 3D on consumer behavior across various sectors. We adopt an integrated TCM (Theories, Contexts, and Methods)-ADO (Antecedents, Decisions, and Outcomes) framework to systematically review 129 studies from high-quality academic journals. The findings from this analysis present implications for future research, theory, and practice. JEL Classification: M31 Marketing

Pricing Game Models of Hybrid Channel Supply Chain: A Strategic Consumer Behavior Perspective

The current sales model combining online and offline channels meets the diverse requirements of consumers. However, consumers’ inter-channel switching behavior and strategic behavior also pose significant challenges to pricing decisions in the hybrid channel. Using game theory and consumer utility theory, a retailer-driven pricing model is developed to study the optimal pricing problem for each channel in a mixed-channel supply chain considering the characteristics of channel competition and the waiting behavior of strategic consumers. Study results show there is a negative correlation between the proportion of strategic consumers and the optimal pricing and profit of each channel, and as the proportion of strategic consumers rises, the optimal pricing and profit of manufacturers and retailers all trend downward. Incorporating strategic consumers into the pricing model will assist the supply chain in elucidating the behavior of consumer heterogeneity during various decision-making periods and in making reasonable pricing decisions. Effective guiding strategies, such as pre-discount and purchase restrictions, can reduce the profit loss caused by strategic consumer behavior. The optimal combination of pre-announcement discount and strategic consumer ratio can generate the greatest profit for retailers and the supply chain.

RisCO2: Implementation and Performance Evaluation of RISC-V Processors for Low-Power CO2 Concentration Sensing.

In the field of embedded systems, energy efficiency is a critical requirement, particularly for battery-powered devices. RISC-V processors have gained popularity due to their flexibility and open-source nature, making them an attractive choice for embedded applications. However, not all RISC-V processors are equally energy-efficient, and evaluating their performance in specific use cases is essential. This paper presents RisCO2, an RISC-V implementation optimized for energy efficiency. It evaluates its performance compared to other RISC-V processors in terms of resource utilization and energy consumption in a signal processing application for nondispersive infrared (NDIR) CO2 sensors.The processors were implemented in the PULPino SoC and synthesized using Vivado IDE. RisCO2 is based on the RV32E_Zfinx instruction set and was designed from scratch by the authors specifically for low-power signal demodulation in CO2 NDIR sensors. The other processors are Ri5cy, Micro-riscy, and Zero-riscy, developed by the PULP team, and CV32E40P (derived from Ri5cy) from the OpenHW Group, all of them widely used in the RISC-V community. Our experiments showed that RisCO2 had the lowest energy consumption among the five processors, with a 53.5% reduction in energy consumption compared to CV32E40P and a 94.8% reduction compared to Micro-riscy. Additionally, RisCO2 had the lowest FPGA resource utilization compared to the best-performing processors, CV32E40P and Ri5cy, with a 46.1% and a 59% reduction in LUTs, respectively. Our findings suggest that RisCO2 is a highly energy-efficient RISC-V processor for NDIR CO2 sensors that require signal demodulation to enhance the accuracy of the measurements. The results also highlight the importance of evaluating processors in specific use cases to identify the most energy-efficient option. This paper provides valuable insights for designers of energy-efficient embedded systems using RISC-V processors.

The value of the blockchain platform for shipping logistics considering the perceived utility of consumers

Blockchain has been widely used to trace logistics information from sources to destinations in shipping logistics practices. Motivated by market practices, this study intends to discuss the potential application value of blockchain platforms in shipping logistics, considering the perceived utility of consumers. For this purpose, to investigate the shipping logistics supply chain (SLSC) of a shipping company (SC) and a freight forwarding company (FFC), in which FFC decides whether to introduce a blockchain platform and its introduction channel. Specifically, three channel models are considered: Model T (i.e., traditional logistics supply chain channel), Model B (i.e., FFCs applied a blockchain platform via a self-built platform channel), and Model J (i.e., FFCs applied a blockchain platform via joining a third-party platform channel). Meanwhile, the Nash and Stackelberg game are used to characterize the companies’ equilibrium outcomes under different power structures. By comparing these three modes, some interesting results are found: consumer perception of the level of service and privacy protection and consumer sensitivity to price has critical effects on the value of blockchain platforms. Results also show that the blockchain platform does not always motivate the members of the SLSC to become better. Whether the power structure is symmetrical or asymmetrical, FFC adopts Model B instead of Model J, in which the blockchain platform can help FFC increase profits, and the value of blockchain is significant. Moreover, the FFC can obtain at least approximately 78% more benefit when the power structure is symmetrical than asymmetrical. However, the value of blockchain platforms to SC is not necessarily significant. The value of the blockchain platform for SC is significant only in Model B with the power structure asymmetric. Therefore, the value of the blockchain platform in Model B with the power structure asymmetrical for both FFCs and SCs is significant, achieving a win-win situation. Also, an extensive numerical analysis showing the optimal channel strategy in the three models generated additional management insights.

Research on low‐carbon evaluation of clean energy use in rural residential buildings based on Analytic Hierarchy Process method

AbstractAs an important part of the construction industry, rural residential buildings are characterized by low energy utilization, unreasonable structure, and low consumption level, so it is particularly important to study the low carbon transformation and evaluation system. In this study, the coefficient of variation method was used to identify the important factors affecting the low‐carbon transformation of rural residential buildings, and the evaluation system of low‐carbonization of rural residential buildings was determined by Analytic Hierarchy Process (AHP), and the system was used in a rural residential building low‐carbonization evaluation study. The results show that “Energy utilization,” “Envelope structure,” and “Economic factors” have obvious influence on the low carbonization of buildings, with the weights of 36.4%, 24.5%, and 19.5%, respectively. Among the secondary indicators, the top four weightings are “clean energy utilization rate” (0.152), “external wall insulation system” (0.090), “window performance” (0.088), and “electricity consumption” (0.084), which are the most critical factors influencing the low carbonization level of clean energy utilization in rural residential buildings. Finally, based on the constructed low‐carbonization evaluation system, we propose targeted solution strategies to provide a theoretical basis for establishing an effective assessment system for the low‐carbonization of clean energy in rural residential buildings.

Advanced data analytics modeling for evidence-based data center energy management

Over the past few decades, the demand for Data Center (DC) services has significantly increased due to the world’s growing need for internet access, social networking, and data storage. Data Centers are among the most energy-intensive businesses, so optimizing IT operations in DC requires energy-efficient techniques. This paper presents AI based modeling strategies for effective energy management with a particular emphasis on DC’s two most energy intensive systems (i.e., cooling and IT systems). This study addresses the issues of IT equipment performance degradation, inappropriate IT room thermal conditions, inefficient workload placement, and excessive energy waste. This research entails the application of machine learning for DC thermal classification, and deployment of deep learning models to predict resource utilization and energy consumption in DC. Furthermore, a comparative analysis is performed with existing relevant methods to demonstrate the effectiveness and accuracy of the proposed AI techniques. The findings of this study also provide evidence-based recommendations for DC efficient energy management.

Optimal integration and bargaining decisions in asymmetric competing supply chains under virtual bargaining

This study examines the effects of asymmetric settings and horizontal interaction under three strategies with and without virtual bargaining, including vertical integration (VI), manufacturer’s Stackelberg (MS), and bargaining (BW) strategies—on channel members’ strategy selection in a competing supply chain (CSC) system. We also consider the effect on the performance of channel members, the overall channel, and the entire supply chain. The analysis focuses on whether a manufacturer should adopt a VI or BW strategy compared with the MS strategy. Without virtual bargaining, some counterintuitive results show that manufacturers always choose the VI strategy, but more intense competition could cause them to be trapped in a prisoner’s dilemma. It is known that the BW strategy enables channel members to achieve Pareto improvement, but we find that manufacturers will only choose it under a BW strategy with retailers’ subsidies. With virtual bargaining, we obtain similarly counterintuitive results. Overall, virtual bargaining, horizontal interaction, and asymmetric settings have a minor effect on strategy selection, while having notable ramifications for the performance of channel members, the overall channel, and the entire supply chain. Interestingly, competition between different channels as well as the double-marginal effect within a channel can be reduced by virtual bargaining. Most notably, when compared with the MS and VI strategies, virtual bargaining has the least negative effects on consumer utility and social welfare under the BW strategy.

Maresin 1 activates brown adipose tissue and promotes browning of white adipose tissue in mice

ObjectiveMaresin 1 (MaR1) is a docosahexaenoic acid-derived proresolving lipid mediator with insulin-sensitizing and anti-steatosis properties. Here, we aim to unravel MaR1 actions on brown adipose tissue (BAT) activation and white adipose tissue (WAT) browning. MethodsMaR1 actions were tested in cultured murine brown adipocytes and in human mesenchymal stem cells (hMSC)-derived adipocytes. In vivo effects of MaR1 were tested in diet-induced obese (DIO) mice and lean WT and Il6 knockout (Il6−/−) mice. ResultsIn cultured differentiated murine brown adipocytes, MaR1 reduces the expression of inflammatory genes, while stimulates glucose uptake, fatty acid utilization and oxygen consumption rate, along with the upregulation of mitochondrial mass and genes involved in mitochondrial biogenesis and function and the thermogenic program. In Leucine Rich Repeat Containing G Protein-Coupled Receptor 6 (LGR6)-depleted brown adipocytes using siRNA, the stimulatory effect of MaR1 on thermogenic genes was abrogated. In DIO mice, MaR1 promotes BAT remodeling, characterized by higher expression of genes encoding for master regulators of mitochondrial biogenesis and function and iBAT thermogenic activation, together with increased M2 macrophage markers. In addition, MaR1-treated DIO mice exhibit a better response to cold-induced BAT activation. Moreover, MaR1 induces a beige adipocyte signature in inguinal WAT of DIO mice and in hMSC-derived adipocytes. MaR1 potentiates Il6 expression in brown adipocytes and BAT of cold exposed lean WT mice. Interestingly, the thermogenic properties of MaR1 were abrogated in Il6−/− mice. ConclusionsThese data reveal MaR1 as a novel agent that promotes BAT activation and WAT browning by regulating thermogenic program in adipocytes and M2 polarization of macrophages. Moreover, our data suggest that LGR6 receptor is mediating MaR1 actions on brown adipocytes, and that IL-6 is required for the thermogenic effects of MaR1.

Energy-Efficient Task Scheduling Based on Traffic Mapping in Heterogeneous Mobile-Edge Computing: A Green IoT Perspective

As a key enabler of emerging 6G applications, mobile edge computing (MEC) improves the system performance by coordinating the computation resources between the mobile devices and edge. However, the energy consumption and carbon emission are boosted as the device number increases. This paper constructs a hierarchical network-assisted task offloading architecture for Green Internet of Things (IoT). Then the authors propose an energy-efficient scheduling based on traffic mapping with energy slack-space proportional pre-allocation algorithm (ESTMP) in the heterogeneous edge computing system to minimize the schedule length under certain energy constraints. The proposed algorithm is suitable for delay-sensitive mobile applications such as Augmented Reality (AR)/Virtual Reality (VR)/Mixed Reality (MR), which process massive multi-modal data. Simulation results demonstrate the significant advantages of the algorithm in terms of the schedule length of arriving parallel tasks, resource utilization and energy consumption.

Effects of degree of milling on japonica and glutinous rice used for huangjiu brewing physicochemical properties

Rice is one of the main ingredients in huangjiu brewing. Studying the effects of dgree of milling (DOM) and variety on the nutritional composition, physical and chemical properties, and brewing characteristics of rice will help improve the quality of huangjiu brewing. In this study, the nutrient composition, gelatinization properties, cooking properties and brewing characteristics of huangjiu of japonica rice (JR) and glutinous rice (GR) with different DOM were studied using a fast viscosity analyzer, differential scanning calorimeter, and fast gas chromatography electron nose Heracles II combined with principal component analysis. The results showed that when the DOM is in the range of 5%–15%, the quality of rice wine was relatively superior and stable. And the flavour compounds of huangjiu brewed from JR and GR could be well clustered. The liquor, reducing sugar and amino acid nitrogen contents of huangjiu made from JR and GR were relatively high. The flavour compounds and content were relatively similar. This study effectively avoided the problems of low grain utilization and high energy consumption caused by excessive milling are, which caters to the industrial production of huangjiu. The results help to explore the relationship between DOM and huangjiu quality, and provide theoretical foundation and scientific guidance for huangjiu production.

The Return of the King: The Importance of Killer Content in a Competitive OTT Market

As the over-the-top (OTT) service market continues to evolve, with new large global players entering the already crowded market, competition between various OTT services for subscribers has intensified. In this study, we aim to investigate the impact of user preference content on the selection of specific OTT services by consumers. Specifically, we employ the conjoint experiment (CE) method to examine consumer utility, relative importance, and marginal willingness to pay (MWTP) for over-the-top (OTT) subscription service attributes. Especially, the presence of users’ killer content and its impact on MWTP is the focus of our study. As a result of calculating the MWTP for each attribute, we found that users are willing to pay about 7633 KRW (5.8 USD) for the first-ranked killer content in their first preferred genre. To gain a deeper understanding of users’ willingness to pay for OTT services, we further analyzed the data by age group and the number of OTT services in use. Based on the results, we suggest strategic plans for local OTT operators to compete effectively in the fiercely competitive OTT market.

Quality changes in papaya fruit under different storage temperatures and duration

Kenya experiences high postharvest losses in the fruit and vegetable subsector, which have been estimated at 50 percent. These losses are mainly due to poor postharvest handling practices, a lack of storage facilities, and/or poor storage conditions. Papaya (Carica papaya L.) is a highly perishable tropical fruit with a short shelf life and requires proper storage and handling practices due to its delicate skin texture, high moisture content, and high respiratory rates. In this study, F hybrid and Mountain variety papaya fruits were collected at Meru County in May 2022 and stored at three different temperatures (6, 16 and 24 °C) for a duration of 0, 4, 8 and 12 days to evaluate their effects on fruit quality. The data collected was separately subjected to a two-way ANOVA in a completely randomised design using the GenStat statistical software package (14th edition). Storage temperatures and durations significantly (p < 0.05) decreased papaya quality. Mountain fruits were firmer while F fruits had more appealing peel and flesh colors. F fruits retained more potassium (1.20 g/100g) and vitamin C (0.29 g/100g) when stored at 16 °C on day 12. The optimal storage temperature and duration for successful storage and extension of shelf life of papaya fruits was found to be 16 °C for 8 days. The F hybrid fruits were more comparable to the local papaya variety, Mountain, on the various tested parameters, which could make it suitable for consumer utilization and market exploitation in Kenya.

Epigenetically altered macrophages promote development of diabetes-associated atherosclerosis.

Atherosclerosis (AS) risk is elevated in diabetic patients, but the underlying mechanism such as involvement of epigenetic control of foam macrophages remains unclear. We have previously shown the importance of immune regulation on endothelial cells to AS development in diabetes. In this study, we examined the hypothesis that diabetes may promote AS through modification of the epigenetic status of macrophages. We employed the Laser Capture Microdissection (LCM) method to evaluate the expression levels of key epigenetic regulators in both endothelial cells and macrophages at the AS lesions of patients. We then assessed the correlation between the significantly altered epigenetic regulator and serum levels of low-density Lipoprotein (LDL), triglycerides (TRIG) and high-density Lipoprotein (HDL) in patients. In vitro, the effects of high glucose on glucose utilization, lactate production, succinate levels, oxygen consumption and polarization in either undifferentiated or differentiated bone marrow-derived macrophages (BMDMs) were analyzed. The effects of depleting this significantly altered epigenetic regulator in macrophages on AS development were assessed in AS-prone diabetic mice. Histone deacetylase 3 (HDAC3) was identified as the most significantly altered epigenetic regulator in macrophages from the AS lesions in human diabetic patients. The levels of HDAC3 positively correlated with high serum LDL and TRIG, as well as low serum HDL. High glucose significantly increased glucose utilization, lactate production, succinate levels and oxygen consumption in cultured macrophages, and induced proinflammatory M1-like polarization. Macrophage depletion of HDAC3 significantly attenuated AS severity in AS-prone diabetic mice. Epigenetically altered macrophages promote development of diabetes-associated AS, which could be prevented through HDAC3 depletion.

Slime Mould Algorithm based Fuzzy Linear CFO Estimation in Wireless Sensor Networks

Orthogonal Frequency-Division Multiplexing (OFDM) is a form of digital systems and a way of encoding digital data across multiple frequency components that is used in telecommunication services. Carrier Frequency Offset (CFO) inaccuracy is a serious disadvantage of OFDM. In this research, Fuzzy based Slime Mould optimization for CFO (FSM-CFO) has been proposed. The proposed FSM-CFO not only estimates the CFO with increased precision, but also allocates resources effectively, achieving maximum utilization of dynamic resources. Initially, Slime Mould Algorithm is utilized to extract the precise CFO. Additionally, the base station (BS) manages the Resource Units (RU), which could be used to distribute resources in such a manner that the user-requests are met. The resources are assigned using fuzzy rules, with the type of resource that is most appropriate for the nodes that have requested the resources being chosen. To assign resources to a certain job, fuzzy rules are devised. Once the residence time for the duration has passed, the highest-priority tasks receive the specific resources. Finally, experiments are carried out to demonstrate the effectiveness of the proposed method. The performance of the proposed method is evaluated in terms of Resource utilization, failure rate, life span, and energy consumption. According to the simulated results, the suggested configuration can perform better compared to the existing techniques.

Construction waste resource utilization and energy consumption calculation based on Internet of things

Construction waste resource utilization and energy consumption calculation based on Internet of things

Intratemporal elasticity of substitution between private and public consumption: new evidence and implications.

This paper estimates the intratemporal elasticity of substitution (IES) between private and public consumption in private utility. Using panel data for 17 European countries over 1970-2018, we estimate the IES to lie between 0.6 and 0.74. When combined with the relevant intertemporal elasticity of substitution, our estimated IES implies that private and public consumption are Edgeworth complements. The panel estimate, however, conceals a large heterogeneity where the IES ranges from as low as 0.3 in Italy to 1.3 in Ireland. This suggests that the crowding-in (out) effect of fiscal policies involving change in government consumption will differ across countries. This cross-country variation in the IES is positively correlated the share of health spending in public expenditure but negatively correlated with the share of public order and safety in government expenditures. We also find a U-shape relationship between the size of IES and government size.