Cooperative activation of sodium channels for downgrading the energy efficiency in neuronal information processing

The Hodgkin–Huxley model assumes independent ion channel activation,although mutual interactions are common in biological systems.This raises the problem why neurons would favor independent over cooperative channel activation.In this study,we evaluate how cooperative activation of sodium channels affects the neuron’s information processing and energy consumption.Simulations of the stochastic Hodgkin–Huxley model with cooperative activation of sodium channels show that,while cooperative activation enhances neuronal information processing capacity,it greatly increases the neuron’s energy consumption.As a result,cooperative activation of sodium channel degrades the energy efficiency for neuronal information processing.This discovery improves our understanding of the design principles for neural systems,and may provide insights into future designs of the neuromorphic computing devices as well as systematic understanding of pathological mechanisms for neural diseases.

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

eQUEST Based Building Energy Modeling Analysis for Energy Efficiency of Buildings

Building energy performance is a function of numerous building parameters.In this study,sensitivity analysis on twenty parameters is performed to determine the top three parameters that have the most significant impact on the energy performance of buildings.Actual data from two fully operational commercial buildings were collected and used to develop a building energy model in the Quick Energy Simulation Tool(eQUEST).The model is calibrated using the Normalized Mean Bias Error(NMBE)and Coefficient of Variation of Root Mean Square Error(CV(RMSE))method.The model satisfies the NMBE and CV(RMSE)criteria set by the American Society of Heating,Refrigeration,and Air-Conditioning(ASHRAE)Guideline 14,Federal Energy Management Program(FEMP),and International Performance Measurement and Verification Protocol(IPMVP)for building energy model calibration.The values of the parameters are varied in two levels,and then the percentage change in output is calculated.Fractional factorial analysis on eight parameters with the highest percentage change in energy performance is performed at two levels in a statistical software JMP.For building A,the top 3 parameters from the percentage change method are:Heating setpoint,cooling setpoint and server room.From fractional factorial design,the top 3 parameters are:heating setpoint(p-value=0.00129),cooling setpoint(p-value=0.00133),and setback control(p-value=0.00317).For building B,the top 3 parameters from both methods are:Server room(pvalue=0.0000),heating setpoint(p-value=0.00014),and cooling setpoint(p-value=0.00035).If the best values for all top three parameters are taken simultaneously,energy efficiency improves by 29%for building A and 35%for building B.

High Energy Efficiency Dynamic Connected Hybrid Precoding for mm Wave Massive MIMO Systems

This paper considers a high energy efficiency dynamic connected(HEDC)structure,which promotes the practicability and reduces the power consumption of hybrid precoding system by lowresolution phase shifters(PSs).Based on the proposed structure,a new hybrid precoding algorithm is presented to optimize the energy efficiency,namely,HP-HEDC algorithm.Firstly,via a new defined effective optimal precoding matrix,the problem of optimizing the analog switch precoding matrix is formulated as a sparse representation problem.Thus,the optimal analog switch precoding matrix can be readily obtained by the branch-and-bound method.Then,the digital precoding matrix optimization problem is modeled as a dictionary update problem and solved by the method of optimal direction(MOD).Finally,the diagonal entries of the analog PS precoding matrix are optimized by exhaustive search independently since PS and antenna is one-to-one.Simulation results show that the HEDC structure enjoys low power consumption and satisfactory spectral efficiency.The proposed algorithm presents at least 50%energy efficiency improvement compared with other algorithms when the PS resolution is set as 3-bit.

Carbon efficiency evaluation method for urban energy system with multiple energy complementary

Urban energy systems(UESs)play a pivotal role in the consumption of clean energy and the promotion of energy cascade utilization.In the context of the construction and operation strategy of UESs with multiple complementary energy resources,a comprehensive assessment of the energy efficiency is of paramount importance.First,a multi-dimensional evaluation system with four primary indexes of energy utilization,environmental protection,system operation,and economic efficiency and 21 secondary indexes is constructed to comprehensively portray the UES.Considering that the evaluation system may contain a large number of indexes and that there is overlapping information among them,an energy efficiency evaluation method based on data processing,dimensionality reduction,integration of combined weights,and gray correlation analysis is proposed.This method can effectively reduce the number of calculations and improve the accuracy of energy efficiency assessments.Third,a demonstration project for a UES in China is presented.The energy efficiency of each scenario is assessed using six operational scenarios.The results show that Scenario 5,in which parks operate independently and investors build shared energy-storage equipment,has the best results and is best suited for green and low-carbon development.The results of the comparative assessment methods show that the proposed method provides a good energy efficiency assessment.This study provides a reference for the optimal planning,construction,and operation of UESs with multiple energy sources.

Industrialization,Efficiency and Social Housing:Sustainable Prefabrication for the Colombian Social and Cultural Context

This research aims to generate processes of industrialization and qualification of social housing in Colombia through prefabrication,low energy consumption and high environmental quality to reduce the current deficit and improve the quality of life in communities with fewer economic resources.Modular coordination and the use of materials with thermal behaviors,appropriate to the country’s climatic characteristics,reduce production costs,avoid waste and improve the architectural and environmental quality of social housing.Passive cooling strategies,such as controlling solar incursion and generating natural cross ventilation eliminate energy consumption and allow reaching desired standards of comfort.In a context in which traditional construction systems have still not resolved the social housing deficit in Colombia and which generate high costs for energy consumption in the search for thermal comfort,industrialized and efficient construction with high environmental quality provides solutions according to the economic,geographic,social and cultural context in Colombia.

Research on the Impact of Digital Finance on Energy Efficiency in China

Digital finance and green technology innovation(GTI)serve as powerful engines for promoting energy efficiency(EE)and economic development.This paper explores the mechanism by which digital finance impacts EE based on panel data from 30 provinces in China spanning from 2011 to 2019.The results demonstrate that digital finance can significantly enhance EE,with a particularly pronounced effect in the eastern region.Through mechanistic analysis,it is evident that GTI serves as the transmission pathway through which digital finance influences EE,accounting for 45.3%of the effect.The policy implication of this study suggests that China should expedite the digitization of financial markets to further harness the development of digital finance,particularly in pursuit of its technological innovation and green,lowcarbon environmental protection effects.

Meta-analysis of CO_(2) conversion,energy efficiency,and other performance data of plasma-catalysis reactors with the open access PIONEER database

This paper brings the comparison of performances of CO_(2)conversion by plasma and plasma-assisted catalysis based on the data collected from literature in this field,organised in an open access online database.This tool is open to all users to carry out their own analyses,but also to contributors who wish to add their data to the database in order to improve the relevance of the comparisons made,and ultimately to improve the efficiency of CO_(2)conversion by plasma-catalysis.The creation of this database and database user interface is motivated by the fact that plasma-catalysis is a fast-growing field for all CO_(2)conversion processes,be it methanation,dry reforming of methane,methanolisation,or others.As a result of this rapid increase,there is a need for a set of standard procedures to rigorously compare performances of different systems.However,this is currently not possible because the fundamental mechanisms of plasma-catalysis are still too poorly understood to define these standard procedures.Fortunately however,the accumulated data within the CO_(2)plasma-catalysis community has become large enough to warrant so-called“big data”studies more familiar in the fields of medicine and the social sciences.To enable comparisons between multiple data sets and make future research more effective,this work proposes the first database on CO_(2)conversion performances by plasma-catalysis open to the whole community.This database has been initiated in the framework of a H_(2)0_(2)0 European project and is called the“PIONEER Data Base”.The database gathers a large amount of CO_(2)conversion performance data such as conversion rate,energy efficiency,and selectivity for numerous plasma sources coupled with or without a catalyst.Each data set is associated with metadata describing the gas mixture,the plasma source,the nature of the catalyst,and the form of coupling with the plasma.Beyond the database itself,a data extraction tool with direct visualisation features or advanced filtering functionalities has been developed and is available online to the public.The simple and fast visualisation of the state of the art puts new results into context,identifies literal gaps in data,and consequently points towards promising research routes.More advanced data extraction illustrates the impact that the database can have in the understanding of plasma-catalyst coupling.Lessons learned from the review of a large amount of literature during the setup of the database lead to best practice advice to increase comparability between future CO_(2)plasma-catalytic studies.Finally,the community is strongly encouraged to contribute to the database not only to increase the visibility of their data but also the relevance of the comparisons allowed by this tool.

Model Establishment of Whole Life Cycle for Energy Efficiency of Rural Residential Buildings in Northern China

The building energy efficiency is determined by the climatic region and the energy-saving measures. In this paper an assessment model for energy efficiency of the rural residential buildings in the northern China was established by the method of whole life cycle. The energy consumption of the rural residential buildings in different stages was analyzed through quantitative method in this model. At the same time, the corresponding energy efficiency assessment system was developed.

Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent integration.The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and processing.However,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion terminals.Simultaneously,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart grid.In response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these issues.The scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT terminals.It then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy states.This comprehensive approach reduces the impact of subjective factors on trust measurements.Additionally,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious terminals.This,in turn,enhances the security and reliability of the smart grid environment.The effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation experiments.Notably,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

A Traffic-Aware and Cluster-Based Energy Efficient Routing Protocol for IoT-Assisted WSNs

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart agriculture.Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes.This paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks.The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies.The protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base station.The performance of the proposed protocol is evaluated through the network simulator NS3.40.For different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network lifetime.For 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.

Hydrodynamic Performance of An Integrated System of Breakwater and A Multi-Chamber OWC Wave Energy Converter

A multi-chamber oscillating water column wave energy converter(OWC-WEC)integrated to a breakwater is investigated.The hydrodynamic characteristics of the device are analyzed using an analytical model based on the linear potential flow theory.A pneumatic model is employed to investigate the relationship between the air mass flux in the chamber and the turbine characteristics.The effects of chamber width,wall draft and wall thickness on the hydrodynamic performance of a dual-chamber OWC-WEC are investigated.The results demonstrate that the device,with a smaller front wall draft and a wider rear chamber exhibits a broader effective frequency bandwidth.The device with a chamber-width-ratio of 1:3 performs better in terms of power absorption.Additionally,results from the analysis of a triplechamber OWC-WEC demonstrate that reducing the front chamber width and increasing the rearward chamber width can improve the total performance of the device.Increasing the number of chambers from 1 to 2 or 3 can widen the effective frequency bandwidth.

Intelligent UAV Based Energy Supply for 6G Wireless Powered IoT Networks

In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base station.In this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working duration.With the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of concurrently.To tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach algorithm.The simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.

AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance

In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality.This research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in WSNs.The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination.Employing a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication chain.Through comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced performance.Furthermore,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.

Effect of building energy efficiency standards on carbon emission efficiency in commercial buildings

The building sector plays a crucial role in the worldwide shift toward achieving net-zero emissions.Building energy efficiency standards(BEESs)are highly effective policies for reducing carbon emissions.Therefore,exploring the provincial variations in carbon emission efficiency(CEE)in the building sector and identifying the effect of BEESs on CEE is crucial.This study focuses on commercial buildings in China and applies a difference in differences model to evaluate the impact of BEESs on the CEE of commercial buildings.The slacks-based measure–data envelopment analysis model is employed to assess the CEE of commercial buildings in 30 Chinese provinces from 2000 to 2019.Furthermore,heterogeneous tests are used to explore how climate characteristics and economic conditions affect the efficiency of BEESs.The results indicate that BEESs positively influence the CEE of commercial buildings.Specifically,a 1%increase in the intensity of BEESs causes a 0.1484%increase in the CEE of commercial buildings.Moreover,the impact of BEESs is particularly pronounced in the southern and western provinces.This study provides valuable scientific evidence for governments to enhance BEESs implementation.

Spatiotemporal variations,influencing factors,and configurational promotion paths of energy efficiency in China’s cities

The promotion of energy efficiency(EE)helps address energy constraints and promote environmental sustainability.This study comprehensively explores the spatiotemporal variations,influencing factors,and configuration promotion paths of EE in 284 Chinese cities during 2003‒2019 using the global super-efficiency minimum distance to strong efficient frontier(G-S-MinDS),exploratory spatial data analysis(ESDA),multiscale geographically weighted regression(MGWR),and fuzzy set qualitative comparative analysis(fsQCA)methods.The findings are:①China’s cities have an annual average EE of 0.658 with a growth rate of 0.53%,showing considerable promotion potential.②Industrial structure optimization,population agglomeration,economic development,and increased green coverage contribute positively,while government intervention and openness hinder China’s urban EE.③Four configurational promotion paths for enhancing China’s urban EE are identified,where among those paths population density is a core condition,while government intervention is not.This study provides valuable insights into substantially improving urban EE,emphasizing the need for targeted policies to address energy and environmental crises in China.

Energy-Efficient Traffic Offloading for RSMA-Based Hybrid Satellite Terrestrial Networks with Deep Reinforcement Learning

As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.

Do smart city policies improve energy efficiency?Evidence from China

To address air pollution and offer a convenient and comfortable living environment,the Chinese government launched a smart city pilot(SCP)project in 2012,accompanied by a comprehensive set of environmental and energy-related laws and regulations.Although academic interest in smart cities has surged,there remains a notable gap in empirical research exploring the economic,environmental,and energy effects of such initiatives.Taking 232 prefecture-level cities from 2003 to 2017 as research subjects,this study measures energy effi‐ciency by using energy consumption per unit of GDP and adopts a difference-in-differences(DID)analysis to investigate the impact of SCPs on energy efficiency.The empirical results indicate that SCPs improved energy efficiency by promoting urban technological innovation capabilities and green total factor productivity,and this effect was more pronounced in cities that were more dependent on traditional fossil fuel energy sources and had more developed fiscal and financial levels.Studying the impact of smart city construction on energy utilization efficiency in developing countries,such as China,is not only significantly enlightening for China’s green and low-carbon transition but also provides reference opinions for constructing smart cities and the path to enhancing energy efficiency in other developing countries.The findings provide valuable insights into the global development of smart cities,urban sustainability,and high-quality economic growth.

Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer processes.However,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy costs.This paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research process.The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data centers.These sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for analysis.The data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center environment.Through the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT sensors.The model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a Mean Squared Error(MSE)of 5.33%and a Mean Absolute Error(MAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power costs.Furthermore,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector Machine(SVM),Decision Trees,and AlexNet.The NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark algorithms.These findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive capabilities of cloud computing systems.The IoT plays a critical role in driving these advancements by providing real-time data insights into the operational aspects of data centers.

Maximum initial primary wave model for low-Froude-number reservoir landslides based on wave theory

The impulse waves induced by large-reservoir landslides can be characterized by a low Froude number.However,systematic research on predictive models specifically targeting the initial primary wave is lacking.Taking the Shuipingzi 1#landslide that occurred in the Baihetan Reservoir area of the Jinsha River in China as an engineering example,this study established a large-scale physical model(with dimensions of 30 m×29 m×3.5 m at a scale of 1:150)and conducted scaled experiments on 3D landslide-induced impulse waves.During the process in which a sliding mass displaced and compressed a body of water to generate waves,the maximum initial wave amplitude was found to be positively correlated with the sliding velocity and the volume of the landslide.With the increase in the water depth,the wave amplitude initially increased and then decreased.The duration of pressure exertion by the sliding mass at its maximum velocity directly correlated with an elevated wave amplitude.Based on the theories of low-amplitude waves and energy conservation,while considering the energy conversion efficiency,a predictive model for the initial wave amplitude was derived.This model could fit and validate the functions of wavelength and wave velocity.The accuracy of the initial wave amplitude was verified using physical experiment data,with a prediction accuracy for the maximum initial wave amplitude reaching 90%.The conversion efficiency(η)directly determined the accuracy of the estimation formula.Under clear conditions for landslide-induced impulse wave generation,estimating the value ofηthrough analogy cases was feasible.This study has derived the landslide-induced impulse waves amplitude prediction formula from the standpoints of wave theory and energy conservation,with greater consideration given to the intrinsic characteristics in the formation process of landslide-induced impulse waves,thereby enhancing the applicability and extensibility of the formula.This can facilitate the development of empirical estimation methods for landslide-induced impulse waves toward universality.