Multi-Connectivity for 5G Networks and Beyond: A Survey

To manage a growing number of users and an ever-increasing demand for bandwidth, future 5th Generation (5G) cellular networks will combine different radio access technologies (cellular, satellite, and WiFi, among others) and different types of equipment (pico-cells, femto-cells, small-cells, macro-cells, etc.). Multi-connectivity is an emerging paradigm aiming to leverage this heterogeneous architecture. To achieve this, multi-connectivity proposes to enable UE to simultaneously use component carriers from different and heterogeneous network nodes: base stations, WiFi access points, etc. This could offer many benefits in terms of quality of service, energy efficiency, fairness, mobility, and spectrum and interference management. Therefore, this survey aims to present an overview of multi-connectivity in 5G networks and beyond. To do so, a comprehensive review of existing standards and enabling technologies is proposed. Then, a taxonomy is defined to classify the different elements characterizing multi-connectivity in 5G and future networks. Thereafter, existing research works using multi-connectivity to improve the quality of service, energy efficiency, fairness, mobility management, and spectrum and interference management are analyzed and compared. In addition, lessons common to these different contexts are presented. Finally, open challenges for multi-connectivity in 5G networks and beyond are discussed.

Energy Intensity of Final Consumption: the Richer, the Poorer the Efficiency

To maintain perpetual economic growth, most energy transition scenarios bet on a break in the historical relationship between energy use and gross domestic product (GDP). Practical limits to energy efficiency are overlooked by such scenarios, in particular the fact that high-income individuals tend to buy goods and services that are more energy intensive. Detailed assessments of the energy embodied in regional final consumption are needed to better understand the relationship between energy and GDP. Here, we calculate the energy necessary to produce households and governments' final consumption in 49 world regions in 2017. We correct prices at the sector level and account for the energy embodied in the whole value chain, including capital goods. We find that high-income regions use more energy per unit of final consumption than low-income ones. This result contradicts the common belief that a higher GDP is correlated with a better efficiency and questions the feasibility of mainstream energy transition scenarios based on universal GDP growth.

Continuous lighting can improve yield and reduce energy costs while increasing or maintaining nutritional contents of microgreens

Microgreens represent a fast growing segment of the edible greens industry. They are prized for their colour, texture, and flavour. Compared to their mature counterparts, microgreens have much higher antioxidant and nutrient content categorizing them as a functional food. However, current production practices in plant factories with artificial light are energy intensive. Specifically, the lack of sunlight within the indoor structure means all of the light must be provided via energy consuming light fixtures, which is energy intensive and costly. Plant growth is usually increased with the total amount of light provided to the plants - daily light integral (DLI). Long photoperiods of low intensity lighting (greater than 18h) providing the desired/target DLI can reduce the capital costs for light fixtures and electricity costs. This is achieved by moving the electricity use from peak daytime hours (high price) to off-peak hours (low price) during the night in regions with time-based pricing scheme and lowering the electricity use for air conditioning, if plant growth is not compromised. However, lighting with photoperiods longer than tolerance thresholds (species/cultivar specific) usually leads to plant stress/damage. Therefore, we investigated the effects of continuous 24h white light (CL) at two DLIs (~14 and 21 mol m^-2 d^-1) on plant growth, yield, and antioxidant content on 4 types of microgreens - amaranth, collard greens, green basil, and purple basil to see if it compromises microgreen production. It was found that amaranth and green basil had larger fresh biomass when grown under CL compared to 16h when the DLIs were the same. In addition, purple basil had higher biomass at higher DLI, but was unaffected by photoperiods. Plants grown under the CL treatments had higher energy-use-efficiencies for lighting (10-42%) than plants grown under the 16h photoperiods at the same DLI. Notably, the electricity cost per unit of fresh biomass ($ g^-1) was reduced (8-38%) in all microgreens studied when plants were grown under CL lighting at the same DLIs. Amaranth and collard greens also had higher antioxidant content. Taken together, growing microgreens under CL can reduce electricity costs and increase yield while maintaining or improving nutritional content.

Energy Harvesting in a System with a Two-Stage Flexible Cantilever Beam

The subject of the research contained in this paper is a new design solution for an energy harvesting system resulting from the combination of a quasi-zero-stiffness energy harvester and a two-stage flexible cantilever beam. Numerical tests were divided into two main parts-analysis of the dynamics of the system due to periodic, quasiperiodic, and chaotic solutions and the efficiency of energy generation. The results of numerical simulations were limited to zero initial conditions as they are the natural position of the static equilibrium. The article compares the energy efficiency for the selected range of the dimensionless excitation frequency. For this purpose, three cases of piezoelectric mounting were analyzed-only on the first stage of the beam, on the second and both stages. The analysis has been carried out with the use of diagrams showing difference of the effective values of the voltage induced on the piezoelectric electrodes. The results indicate that for effective energy harvesting, it is advisable to attach piezoelectric energy transducers to each step of the beam despite possible asynchronous vibrations.

Functionalized Ag with Thiol Ligand to Promote Effective CO2 Electroreduction

It is challenging while critical to develop efficient catalysts that can achieve both high current density and high energy efficiency for electrocatalytic CO₂ reduction (CO₂R). Herein, we report a strategy of tailoring the surface electronic structure of an Ag catalyst via thiol ligand modification to improve its intrinsic activity, selectivity, and further energy efficiency toward CO₂R. Specifically, interconnected Ag nanoparticles with residual thiol ligands on the surface were prepared through electrochemical activation of a thiol-ligand-based Ag complex. When it was used as a catalyst for CO₂R, the thiol-ligand modified Ag exhibited high CO selectivity (>90%) throughout a wide electrode-potential range; furthermore, high cathodic energy efficiencies of >90% and >70% were obtained for CO formation at high current densities of 150 and 750 mA cm^-2, respectively, outperforming the state-of-the-art Ag-based electrocatalysts for CO₂ to CO conversion. The first-principle calculations on the reaction energetics suggest that the binding energies of the key intermediate -*COOH on Ag are optimized by the adsorbed thiol ligand, thus favoring CO formation while suppressing the competing H₂ evolution. Our findings provide a rational design strategy for CO₂ reduction electrocatalyst by electronic modulation through surface-adsorbed ligands.

Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI

Neuromorphic hardware, the new generation of non-von Neumann computing system, implements spiking neurons and synapses to spiking neural network (SNN)-based applications. The energy-efficient property makes the neuromorphic hardware suitable for power-constrained environments where sensors and edge nodes of the internet of things (IoT) work. The mapping of SNNs onto neuromorphic hardware is challenging because a non-optimized mapping may result in a high network-on-chip (NoC) latency and energy consumption. In this paper, we propose NeuMap, a simple and fast toolchain, to map SNNs onto the multicore neuromorphic hardware. NeuMap first obtains the communication patterns of an SNN by calculation that simplifies the mapping process. Then, NeuMap exploits localized connections, divides the adjacent layers into a sub-network, and partitions each sub-network into multiple clusters while meeting the hardware resource constraints. Finally, we employ a meta-heuristics algorithm to search for the best cluster-to-core mapping scheme in the reduced searching space. We conduct experiments using six realistic SNN-based applications to evaluate NeuMap and two prior works (SpiNeMap and SNEAP). The experimental results show that, compared to SpiNeMap and SNEAP, NeuMap reduces the average energy consumption by 84% and 17% and has 55% and 12% lower spike latency, respectively.

A Hybrid PAPR Reduction Scheme in OFDM-IM Using Phase Rotation Factors and Dither Signals on Partial Sub-Carriers

As a multi-carrier modulation technique, a high peak-to-average power ratio (PAPR) is a common issue suffered by orthogonal frequency division multiplexing with index modulation (OFDM-IM) due to its system structure. High PAPR may cause signal distortion, which affects correct symbol transmission. This paper tries to inject dither signals to the inactive (idle) sub-carriers, which is a unique transmission structure of OFDM-IM, to reduce PAPR. Unlike the previous works, which utilize all idle sub-carriers, the proposed PAPR reduction scheme utilizes selected partial sub-carriers. This method performs well in terms of bit error rate (BER) performance and energy efficiency, which are obvious drawbacks of the previous PAPR reduction works due to the introduction of dither signals. In addition, in this paper, phase rotation factors are combined with the dither signals to compensate for the PAPR reduction performance degradation due to the insufficient use of partial idle sub-carriers. Moreover, an energy detection scheme is designed and proposed in this paper in order to distinguish the index of phase rotation factor used for transmission. It is shown by extensive simulation results that the proposed hybrid PAPR reduction scheme is able to implement an impressive PAPR reduction performance among existing dither signa-based schemes as well as classical distortion-less PAPR reduction schemes. In addition, the proposed method obtains better error performance and energy efficiency than that of the previous works. At the error probability 10−4, the proposed method can achieve around 5 dB gain compared to the conventional dither signal-based schemes

JDAPCOO: Resource Scheduling and Energy Efficiency Optimization in 5G and Satellite Converged Networks for Power Transmission and Distribution Scenarios

Along with the continuous revolution of energy production and energy consumption structures, the information data of smart grids have exploded, and effective solutions are urgently needed to solve the problem of power devices resource scheduling and energy efficiency optimization. In this paper, we propose a fifth generation (5G) and satellite converged network architecture for power transmission and distribution scenarios, where power transmission and distribution devices (PDs) can choose to forward power data to a cloud server data center via ground networks or space-based networks for power grid regulation and control. We propose a Joint Device Association and Power Control Online Optimization (JDAPCOO) algorithm to maximize the long-term system energy efficiency while guaranteeing the minimum transmission rate requirement of PDs. Since the formulated issue is a mixed integer nonconvex optimization problem with high complexity, we decompose the original problem into two subproblems, i.e., device association and power control, which are solved using a genetic algorithm and improved simulated annealing algorithm, respectively. Numerical simulation results show that when the number of PDs is 50, the proposed algorithm can improve the system energy efficiency by 105%, 545.05% and 835.26%, respectively, compared with the equal power allocation algorithm, random power allocation algorithm and random device association algorithm.

Does Energy Efficiency Realize Energy Conservation in the Iron and Steel Industry? A Perspective of Energy Rebound Effect

The energy rebound effect may weaken the impact of energy efficiency improvement on energy consumption. Therefore, the rebound effect is an important consideration in energy and environmental policies. This study takes the iron and steel industry as the research object, which is a large energy consumption sector in China, and the improved technique is used to estimate the energy rebound effect. The study constructs the dynamic energy efficiency utilizing provincial data from 2000 to 2019. The energy rebound effect from factor substitution and output expansion is then calculated. The research further discusses regional differences in the energy rebound effect. The results indicate that the technical progress of the iron and steel industry promotes energy efficiency improvements. The eastern region shows the best energy efficiency performance, followed by the central area, and the western region performs the worst in energy efficiency. The industrial energy rebound effect is 0.4297, which partially offsets the energy reduction caused by energy efficiency improvements. Factor substitution and output growth produce the industrial energy rebound effect. Furthermore, the rebound effect exhibits distinct geographical features. The policy suggestions are finally proposed to mitigate the industrial rebound effect and achieve energy and carbon reductions.

An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking

Target tracking is an essential issue in wireless sensor networks (WSNs). Compared with single-target tracking, how to guarantee the performance of multi-target tracking is more challenging because the system needs to balance the tracking resource for each target according to different target properties and network status. However, the balance of tracking task allocation is rarely considered in those prior sensor-scheduling algorithms, which may result in the degradation of tracking accuracy for some targets and additional system energy consumption. To address this issue, we propose in this paper an improved Q-learning-based sensor-scheduling algorithm for multi-target tracking (MTT-SS). First, we devise an entropy weight method (EWM)-based strategy to evaluate the priority of targets being tracked according to target properties and network status. Moreover, we develop a Q-learning-based task allocation mechanism to obtain a balanced resource scheduling result in multi-target-tracking scenarios. Simulation results demonstrate that our proposed algorithm can obtain a significant enhancement in terms of tracking accuracy and energy efficiency compared with the existing sensor-scheduling algorithms.

A step towards green economic policy framework: role of renewable energy and climate risk for green economic recovery

According to the World Bank, energy efficiency is a critical facilitator of most Sustainable Development Goals. Its contribution to CO2 emission reduction is astounding. Environmentalists have recently emphasized the essential need to determine energy efficiency causes. This research broadens the debate's horizons by proposing additional possible energy efficiency factors using data from the Chinese economy. From 1990 to 2020, we examined the influence of investment in renewable energy resources, financial inclusion, industrial production, and trade openness on China's energy efficiency and climate risk. Additionally, this study is added to the literature by examining the causal relationships between variables while considering the temporal dimension. The findings indicate that industrial production, financial inclusion, public R&D on renewable energy, and trade openness contribute significantly to China's energy efficiency and climate risk. All other factors, except industrial production, are positively associated with energy efficiency. The path of causality is established from energy efficiency and climate risk to financial inclusion, industrial production, renewable energy, public research and development budgets, and trade openness. According to the findings, changes in energy performance have frequency-changing impacts on all variables. Policymakers believe that the financial system must be strengthened since this will significantly influence renewable energy.

Joint User Scheduling and Hybrid Beamforming Design for Massive MIMO LEO Satellite Multigroup Multicast Communication Systems

In the satellite multigroup multicast communication systems based on the DVB-S2X standard, due to the limitation of the DVB-S2X frame structure, user scheduling and beamforming design have become the focus of academic research. In this work, we take the massive multi-input multi-output (MIMO) low earth orbit (LEO) satellite communication system adopting the DVB-S2X standard as the research scenario, and the LEO satellite adopts a uniform planar array (UPA) based on the fully connected hybrid structure. We focus on the coupling design of user scheduling and beamforming; meanwhile, the scheme design takes the influence of residual Doppler shift and phase disturbance on channel errors into account. Under the constraints of total transmission power and quality of service (QoS), we study the robust joint user scheduling and hybrid beamforming design aimed at maximizing the energy efficiency (EE). For this problem, we first adopt the hierarchical clustering algorithm to group users. Then, the semidefinite programming (SDP) algorithm and the concave convex process (CCCP) framework are applied to tackle the optimization of user scheduling and hybrid beamforming design. To handle the rank-one matrix constraint, the penalty iteration algorithm is proposed. To balance the performance and complexity of the algorithm, the user preselected step is added before joint design. Finally, to obtain the digital beamforming matrix and the analog beamforming matrix in a hybrid beamformer, the alternative optimization algorithm based on the majorization-minimization framework (MM-AltOpt) is proposed. Numerical simulation results show that the EE of the proposed joint user scheduling and beamforming design algorithm is higher than that of the traditional decoupling design algorithms.

Tangential effluent inlet in a cylindrical electrocoagulation reactor containing curved electrodes, and its use in crude oil in water treatment

A continuous electrocoagulation reactor, with curved electrodes, polarity switch, and cylindrical geometry, was used for emulsified crude oil in water separation. Apparatus novelty consists of an inlet arranged to promote a circular flow regime. The effects of flow rate (2 and 6 mL.s^-1), electrical current (2 and 4 A), and distance between electrodes (1.5 and 2.5 cm) were investigated using a full factorial design and statistical analysis. Using 6 mL.s^-1 flow rate, 2 A electric current and 2.5 cm electrode distance; 86% oil removal was obtained at a pH < 9.0. For this configuration, the system will process 21.6 L of oily emulsion while consuming 6.92 Wh. Oil removal increased with flow rate, a novel characteristic created by the unusual geometry of the system.

Use of Polar Heliostats to Improve Levels of Natural Lighting inside Buildings with Little Access to Sunlight

The growing need to increase environmental and energy sustainability in buildings (housing, offices, warehouses, etc.) requires the use of solar radiation as a renewable source of energy that can help to lower carbon footprint, making buildings more efficient and thereby contributing to a more sustainable planet, while enhancing the health and wellbeing of its occupants. One of the technologies deployed in the use of solar energy in buildings is heliostats. In this context, this paper presents an analysis of the performance of a heliostat illuminator to improve illumination in a classroom at the Campus of Rabanales of the University of Cordoba (Spain). A design of a system in charge of monitoring and measuring daylighting variables using Arduino hardware technology and free software is shown. This equipment develops the communications, programming and collection of lighting data. In parallel, installation of an artificial lighting system complementary to the natural lighting system is implemented. Finally, an analysis of the impact of the proposed solution on the improvement of energy efficiency is presented. Specifically, it is estimated that up to 64% of savings in artificial lighting can be achieved in spaces with heliostatic illuminators compared to those without them.

Progressive and Stable Synaptic Plasticity with Femtojoule Energy Consumption by the Interface Engineering of a Metal/Ferroelectric/Semiconductor

In the era of "big data," the cognitive system of the human brain is being mimicked through hardware implementation of highly accurate neuromorphic computing by progressive weight update in synaptic electronics. Low-energy synaptic operation requires both low reading current and short operation time to be applicable to large-scale neuromorphic computing systems. In this study, an energy-efficient synaptic device is implemented comprising a Ni/Pb(Zr_0.52 Ti_0.48 )O₃ (PZT)/0.5 wt.% Nb-doped SrTiO₃ (Nb:STO) heterojunction with a low reading current of 10 nA and short operation time of 20-100 ns. Ultralow femtojoule operation below 9 fJ at a synaptic event, which is comparable to the energy required for synaptic events in the human brain (10 fJ), is achieved by adjusting the Schottky barrier between the top electrode and ferroelectric film. Moreover, progressive domain switching in ferroelectric PZT successfully induces both low nonlinearity/asymmetry and good stability of the weight update. The synaptic device developed here can facilitate the development of large-scale neuromorphic arrays for artificial neural networks with low energy consumption and high accuracy.

A Reinforcement Learning Based Transmission Parameter Selection and Energy Management for Long Range Internet of Things

Internet of Things (IoT) landscape to cover long-range applications. The LoRa-enabled IoT devices adopt an Adaptive Data Rate-based (ADR) mechanism to assign transmission parameters such as spreading factors, transmission energy, and coding rates. Nevertheless, the energy assessment of these combinations should be considered carefully to select an accurate combination. Accordingly, the computational and transmission energy consumption trade-off should be assessed to guarantee the effectiveness of the physical parameter tuning. This paper provides comprehensive details of LoRa transceiver functioning mechanisms and provides a mathematical model for energy consumption estimation of the end devices EDs. Indeed, in order to select the optimal transmission parameters. We have modeled the LoRa energy optimization and transmission parameter selection problem as a Markov Decision Process (MDP). The dynamic system surveys the environment stats (the residual energy and channel state) and searches for the optimal actions to minimize the long-term average cost at each time slot. The proposed method has been evaluated under different scenarios and then compared to LoRaWAN default ADR in terms of energy efficiency and reliability. The numerical results have shown that our method outperforms the LoRa standard ADR mechanism since it permits the EDs to gain more energy. Besides, it enables the EDs to stand more, consequently performing more transmissions.

Energy-Efficient Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks

In a wireless sensor network (WSN), reducing the energy consumption of battery-powered sensor nodes is key to extending their operating duration before battery replacement is required. Message bundling can save on the energy consumption of sensor nodes by reducing the number of message transmissions. However, bundling a large number of messages could increase not only the end-to-end delays and message transmission intervals, but also the packet error rate (PER). End-to-end delays are critical in delay-sensitive applications, such as factory monitoring and disaster prevention. Message transmission intervals affect time synchronization accuracy when bundling includes synchronization messages, while an increased PER results in more message retransmissions and, thereby, consumes more energy. To address these issues, this paper proposes an optimal message bundling scheme based on an objective function for the total energy consumption of a WSN, which also takes into account the effects of packet retransmissions and, thereby, strikes the optimal balance between the number of bundled messages and the number of retransmissions given a link quality. The proposed optimal bundling is formulated as an integer nonlinear programming problem and solved using a self-adaptive global-best harmony search (SGHS) algorithm. The experimental results, based on the Cooja emulator of Contiki-NG, demonstrate that the proposed optimal bundling scheme saves up to 51.8% and 8.8% of the total energy consumption with respect to the baseline of no bundling and the state-of-the-art integer linear programming model, respectively.

Multi-Criteria Evaluation for Sorting Motion Planner Alternatives

Automated vehicles are expected to push towards the evolution of the mobility environment in the near future by increasing vehicle stability and decreasing commute time and vehicle fuel consumption. One of the main limitations they face is motion sickness (MS), which can put their wide impact at risk, as well as their acceptance by the public. In this direction, this paper presents the application of motion planning in order to minimise motion sickness in automated vehicles. Thus, an optimal control problem is formulated through which we seek the optimum velocity profile for a predefined road path for multiple fixed journey time (JT) solutions. In this way, a Pareto Front will be generated for the conflicting objectives of MS and JT. Despite the importance of optimising both of these, the optimum velocity profile should be selected after taking into consideration additional objectives. Therefore, as the optimal control is focused on the MS minimisation, a sorting algorithm is applied to seek the optimum solution among the pareto alternatives of the fixed time solutions. The aim is that this solution will correspond to the best velocity profile that also ensures the optimum compromise between motion comfort, safety and driving behaviour, energy efficiency, journey time and riding confidence.

Energy Efficiency Optimization for SWIPT-Enabled IoT Network with Energy Cooperation

As an advanced technology, simultaneous wireless information and power transfer (SWIPT), combined with the internet of things (IoT) devices, can effectively extend the online cycle of the terminal. To cope with the fluctuation of energy harvesting by the hybrid access points (H-AP), the energy cooperation base station is introduced to realize the sharing of renewable energy. In this paper, we study the SWIPT-enabled IoT networks with cooperation. Our goal is to maximize the energy efficiency of the system, and at the same time, we need to meet the energy harvesting constraints, user quality of service (QoS) constraints and transmission power constraints. We jointly solve the power allocation, time switching and energy cooperation problems. Because this problem is a nonlinear programming problem, it is difficult to solve directly, so we use the alternating variable method, the iterative algorithm is used to solve the power allocation and time switching problem, and the matching algorithm is used to solve the energy cooperation problem. Simulation results show that the proposed algorithm has obvious advantages in energy efficiency performance compared with the comparison algorithm. At the same time, it is also proved that the introduction of energy cooperation technology can effectively reduce system energy consumption and improve system energy efficiency.

Excellent Stability in Polyetherimide/SiO2 Nanocomposites with Ultrahigh Energy Density and Discharge Efficiency at High Temperature

Polymer dielectrics with excellent thermal stability are the essential core material for thin film capacitors applied in a harsh-environment. However, the dielectric and mechanical properties of polymers are commonly deteriorated with temperature rising. Herein, polyetherimide (PEI)-based nanocomposites contained with SiO₂ nanoparticles (SiO₂ -NPs) are fabricated by a solution casting method. It is found that the introduction of SiO₂ -NPs decreases the electric conductivity and significantly enhances the breakdown strength of the nanocomposites, especially under high temperatures. As a result, the 5 vol% PEI/SiO₂ -NPs nanocomposite film displays a superior dielectric energy storage performance, e.g., a discharged energy density of 6.30 J cm^-3 and a charge-discharge efficiency of 90.5% measured at 620 MV m^-1 and 150 °C. In situ scanning Kelvin probe microscopy characterization indicates that the charge carriers can be trapped in the interfacial regions between the polymer matrix and the SiO₂ -NPs till the temperature reaches as high as 150 °C. This work demonstrates an effective strategy to fabricate high-temperature dielectric polymer nanocomposites by embedding inorganic nanoparticles and provides a method for directly detecting charge behavior at the nanoscale inside the matrix.

Pareto optimality, economy-effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons

Neurons encounter unavoidable evolutionary trade-offs between multiple tasks. They must consume as little energy as possible while effectively fulfilling their functions. Cells displaying the best performance for such multi-task trade-offs are said to be Pareto optimal, with their ion channel configurations underpinning their functionality. Ion channel degeneracy, however, implies that multiple ion channel configurations can lead to functionally similar behaviour. Therefore, instead of a single model, neuroscientists often use populations of models with distinct combinations of ionic conductances. This approach is called population (database or ensemble) modelling. It remains unclear, which ion channel parameters in the vast population of functional models are more likely to be found in the brain. Here we argue that Pareto optimality can serve as a guiding principle for addressing this issue by helping to identify the subpopulations of conductance-based models that perform best for the trade-off between economy and functionality. In this way, the high-dimensional parameter space of neuronal models might be reduced to geometrically simple low-dimensional manifolds, potentially explaining experimentally observed ion channel correlations. Conversely, Pareto inference might also help deduce neuronal functions from high-dimensional Patch-seq data. In summary, Pareto optimality is a promising framework for improving population modelling of neurons and their circuits.

Evaluation of Strong Cation Ion-Exchange Resin Cost Efficiency in Manufacturing Applications-A Case Study

The effective ionic capacities of strong cation ion-exchange resins were investigated and compared using conditions similar to those found in white goods, in order to establish behavioral differences between commercial products and evaluate their capacity in a broader business context. Nine different products of equivalent TDS (Technical Data Sheet) capacity were observed to examine their differences in approximately real-life conditions. For a broader context of applicability analysis, besides the absolute ionic operating capacity, the following additional factors were included in the evaluation: the standard deviation in the resins’ performances and their relative prices. A complete method for material applicability evaluation was hereby proposed and shown to offer cost factor benefits of up to 21.1% within the range of products examined, in comparison to a cost-only evaluation for equivalent materials.

Energy efficiency: The evolution of a motherhood concept

Energy efficiency is a popular policy strategy to reduce energy consumption and greenhouse gas emissions. The concept of energy efficiency is relatively simple - to use less energy for the same level of output. However, complexities emerge when applying efficiency concepts to real world processes and practices of energy consumption: subjective judgments when defining, measuring and applying energy efficiency principles, how efficiency is conceptualized and applied in policy, and how policy is designed and implemented, given social and environmental tradeoffs. This article traces the evolution of EU energy efficiency policy over seven decades to better understand underlying values and tradeoffs from a sociological perspective. Using insights from critical policy studies, the article reveals how certain values are reflected in how energy efficiency is defined and measured over time. It highlights how the conceptualization of energy efficiency has been used as an effective rhetorical device - and how some potentially relevant concepts and issues get sidelined in favor of others. The analysis illustrates how narrow conceptualizations of energy efficiency has put blinders on wider environmental and social issues. This points to the need for a more nuanced policy approach that takes into account the complexities and uncertainties of societal and policy challenges. The findings point to the need for energy efficiency policy that pays closer attention to citizens' views and collective solutions in order to formulate more effective policy to reduce energy consumption.

Performance Evaluation of Combined Hydrothermal-Mechanical Pretreatment of Lignocellulosic Biomass for Enzymatic Enhancement

Pretreatment is a crucial process in a lignocellulosic biorefinery. Corncob is typically considered as a natural renewable carbon source to produce various bio-based products. This study aimed to evaluate the performance of the hydrothermal-mechanical pretreatment of corncob for biofuels and biochemical production. Corncob was first pretreated by liquid hot water (LHW) at different temperatures (140–180 °C) and duration (30, 60 min) and then subjected to centrifugal milling to produce bio-powders. To evaluate the performance of this combined pretreatment, the energy efficiency and waste generation were investigated. The results indicated that the maximum fermentable sugars (FS) were 0.488 g/g biomass obtained by LHW at 180 °C, 30 min. In order to evaluate the performance of this combined pretreatment, the energy efficiency and waste generation were 28.3 g of FS/kWh and 7.21 kg of waste/kg FS, respectively. These obtained results indicate that the combined hydrothermal-mechanical pretreatment was an effective pretreatment process to provide high energy efficiency and low waste generation to produce biofuels. In addition, the energy efficiency and waste generation will be useful indicators for process scaling-up into the industrial scale. This combined pretreatment could be a promising pretreatment technology for the production of biofuels and biochemicals from lignocellulosic valorization.

Decomposition of VOCs using serial surface dielectric barrier discharge reactors

Decomposition of two typical volatile organic compounds (VOCs): toluene and methyl ethyl ketone (MEK), was investigated by introducing the complete energy yield (i.e. amount of VOCs totally converted to CO and CO₂ for every 1 kWh of electrical usage) and specific energy density (SED) for the non-thermal plasma technology. Three dielectric barrier discharge reactors of which SED range was 55 J/L to 283 J/L at a flow rate of 1 L/min were connected in a 3-reactor arranged in series using interconnecting tubes in the inlets and outlets. As a result of the serial reactor design, the energy efficiency of the system improved, and in particular, 7% of increase was found in at the same level of energy consumption. The mixed phase of VOCs (toluene and MEK) showed MEK as a limiting species in this study. As the inlet concentration rose from 20 to 100 ppmv, both energy yield and complete energy yield increased from 0.8 to 9.2 g/kWh and from 0.8 to 2.3 g/kWh, respectively.

How Do Green Finance and Energy Efficiency Mitigate Carbon Emissions Without Reducing Economic Growth in G7 Countries?

Climate change is one of the most serious threats facing the world today. Environmental pollution and depletion of natural resources have been highlighted by the United Nations Sustainable Development Goals (SDGs), paving the way for modern concepts such as sustainable growth to be introduced. Therefore, this research explores the relationship between green finance, energy efficiency, and CO₂ emissions in the G7 countries. The study uses panel data model technique to examine the dependence structure of green finance, energy efficiency, and CO₂ emissions. Moreover, we use DEA to construct an energy efficiency index of G7 countries. A specific interval exists between the values of the energy efficiency indexes. Japan, the United Kingdom, and the United States were named the most energy-efficient countries in the world, based on results obtained for five consecutive years in this category. However, according to the comparative rankings, France and Italy are the most successful of all the G7 members, followed by the United Kingdom and Germany. Our overall findings of the econometric model confirm the negative impact of green finance and energy efficiency on CO₂ emissions; however, this relationship varies across the different quantiles of the two variables. The findings in the study confirm that green finance is the best financial strategy for reducing CO₂ emissions.

Modeling of Polymer Composite Materials Chaotically Reinforced with Spherical and Cylindrical Inclusions

The technical and economic efficiency of new PCMs depends on the ability to predict their performance. The problem of predicting the properties of PCMs can be solved by computer simulation by the finite element method. In this work, an experimental determination of the physical and mechanical properties of PTFE PCMs depending on the concentration of fibrous and dispersed filler was carried out. A finite element model in ANSYS APDL was built to simulate the strength and load-bearing capacity of the material with the analysis of damage accumulation. Verification of the developed computer model to predict the mechanical properties of composite materials was performed by comparing the results obtained during field and model experiments. It was found that the finite element model predicts the strength of chaotically reinforced spherical inclusions of composite materials. This is due to the smoothness of the filler surfaces and the lack of filler dissection in the model. Instead, the prediction of the strength of a finite element model of chaotically reinforced cylindrical inclusions of composite materials requires additional analysis. The matrix and the fibrous filler obviously have stress concentrators and are both subject to the difficulties of creating a reliable structural model.

Self-Stabilizing Capacitated Vertex Cover Algorithms for Internet-of-Things-Enabled Wireless Sensor Networks

Wireless sensor networks (WSNs) achieving environmental sensing are fundamental communication layer technologies in the Internet of Things. Battery-powered sensor nodes may face many problems, such as battery drain and software problems. Therefore, the utilization of self-stabilization, which is one of the fault-tolerance techniques, brings the network back to its legitimate state when the topology is changed due to node leaves. In this technique, a scheduler decides on which nodes could execute their rules regarding spatial and temporal properties. A useful graph theoretical structure is the vertex cover that can be utilized in various WSN applications such as routing, clustering, replica placement and link monitoring. A capacitated vertex cover is the generalized version of the problem which restricts the number of edges covered by a vertex by applying a capacity constraint to limit the covered edge count. In this paper, we propose two self-stabilizing capacitated vertex cover algorithms for WSNs. To the best of our knowledge, these algorithms are the first attempts in this manner. The first algorithm is stabilized under an unfair distributed scheduler (that is, the scheduler which does not grant all enabled nodes to make their moves but guarantees the global progress of the system) at most O(n2) step, where n is the count of nodes. The second algorithm assumes 2-hop (degree 2) knowledge about the network and runs under the unfair scheduler, which subsumes the synchronous and distributed fair scheduler and stabilizes itself after O(n) moves in O(n) step, which is acceptable for most WSN setups. We theoretically analyze the algorithms to provide proof of correctness and their step complexities. Moreover, we provide simulation setups by applying IRIS sensor node parameters and compare our algorithms with their counterparts. The gathered measurements from the simulations revealed that the proposed algorithms are faster than their competitors, use less energy and offer better vertex cover solutions.

Does Intensive Land Use Contribute to Energy Efficiency?-Evidence Based on a Spatial Durbin Model

In order to ensure the safety of cultivated land and promote urban productivity, the Chinese government began to promote intensive land use at the legislative level from 2014. At the same time, China faces problems of carbon emissions and energy, so we need to improve energy efficiency. Therefore, this paper aims to verify the spatial effects of intensive land use on energy efficiency of China from 2009 to 2018. We further use an index system to quantify intensive land use and use chain DEA (data envelope analysis) to quantify energy efficiency. This paper finds that: (1) intensive land use can significantly improve energy efficiency. A 1% increase in the level of intensive land use will increase energy efficiency by 1.3%. (2) The intensive use of land in one city will have a negative impact on the energy efficiency of surrounding cities. The reason is that the intensive use of land in a single city may lead to the transfer of energy-consuming industries to surrounding cities. (3) The impact of intensive land use on the energy efficiency of surrounding cities has negative threshold characteristics, and the negative impact will be weakened as the level of integration of the city increases.

Reliable and Energy-Efficient LEO Satellite Communication with IR-HARQ via Power Allocation

This paper examines reliable and energy-efficient transmission in low earth orbit (LEO) satellite communication systems. In particular, we analyze the link transmission characteristics of the LEO satellite to the ground user and model the channel as a combination of large-scale fading and small-scale fading. Based on this, we consider an incremental redundancy hybrid automatic repeat request (IR-HARQ) technique with a variable-power allocation method, and we call it the IR-HARQ-VPA scheme. In this method, the outage probability after each IR-HARQ round can be obtained through numerical integration based on the fast Fourier transform (NI-FFT). This method is suitable for any number of HARQ transmission rounds and can improve the accuracy compared with previous approximation methods. In addition, variable-power allocation based on the genetic algorithm (VPA-GA) is introduced to reduce the energy consumption. The simulation results show that the proposed IR-HARQ-VPA scheme cannot only meet the requirements of transmission reliability but also achieves higher energy efficiency than IR-HARQ with equal power (IR-HARQ-EP) transmission and a previously proposed variable-power allocation method. Moreover, the simulation results in a LEO satellite communication window also confirm the effectiveness of the proposed IR-HARQ-VPA scheme.

Atmospheric Water Harvesting by Large-Scale Radiative Cooling Cellulose-Based Fabric

Atmospheric water harvesting (AWH) has received tremendous interest because of population growth, limited freshwater resources, and water pollution. However, key challenges remain in developing efficient, flexible, and lightweight AWH materials with scalability. Here, we demonstrated a radiative cooling fabric for AWH via its hierarchically structured cellulose network and hybrid sorption-dewing mechanisms. With 8.3% solar absorption and ∼0.9 infrared (IR) emissivity, the material can drop up to 7.5 °C below ambient temperature without energy consumption via radiative cooling. Water adsorption onto the hydrophilic functional groups of cellulose is dominated by sorption at low relative humidity (RH) and dewing at high RH. The cellulose network provides desirable mechanical properties with entangled high-aspect-ratio fibers over tens of adsorption-extraction cycles. In the field test, the cellulose sample exhibited water uptake of 1.29 kg/kg at 80% RH during the night. The profusion of radiative cooling fabric features desirable cost effectiveness and allows fast deployment into large-scale AWH applications.

Effect of Load Spatial Configuration on the Heating of Chicken Meat Assisted by Radio Frequency at 40.68 MHz

Food heating assisted by radio frequencies has been industrially applied to post-harvest treatment of grains, legumes and various kind of nuts, to tempering and thawing of meat and fish products and to post-baking of biscuits. The design of food processes based on the application of radiofrequencies was often based on rules of thumb, so much so that their intensification could lead significant improvements. One of the subjects under consideration is the shape of the food items that may influence their heating assisted by radiofrequency. In this work, a joint experimental and numerical study on the effects of the spatial configuration of a food sample (chicken meat shaped as a parallelepiped) on the heating pattern in a custom RF oven (40.68 MHz, 50 Ohm, 10 cm electrodes gap, 300 W) is presented. Minced chicken breast samples were shaped as cubes (4 × 4 × 4 cm3) to be organized in different loads and spatial configurations (horizontal or vertical arrays of 2 to 16 cubes). The samples were heated at two radiofrequency operative power levels (225 W and 300 W). Heating rate, temperature uniformity and heating efficiency were determined during each run. A digital twin of the experimental system and process was developed by building and numerically solving a 3D transient mathematical model, taking into account electromagnetic field distribution in air and samples and heat transfer in the food samples. Once validated, the digital tool was used to analyze the heating behavior of the samples, focusing on the most efficient configurations. Both experiments and simulations showed that, given a fixed gap between the electrodes (10 cm), the vertically oriented samples exhibited a larger heating efficiency with respect to the horizontally oriented ones, pointing out that the gap between the top electrode and the samples plays a major role in the heating efficiency. The efficiency was larger (double or even more; >40% vs. 10−15%) in thicker samples (built with two layers of cubes), closer to the top electrode, independently from nominal power. Nevertheless, temperature uniformity in vertical configurations was poorer (6−7 °C) than in horizontal ones (3 °C).

Three-dimensional BiVO4-based semiconductor photocathode for high efficiency photo-assisted Zn-iodine redox flow batteries

Aqueous Zn-iodine redox flow batteries have aroused great interest for the features of high capacity, excellent stability, low cost, and high safety, yet the dissatisfying energy efficiency still limits their future advancement. In this work, three-dimensional semiconductor BiVO₄nanoparticles decorated hierarchical TiO₂/SnO₂arrays (BiVO₄@TiO₂/SnO₂) were applied as photocathode in Zn-iodine redox flow batteries (ZIRFBs) for the realization of efficient photo-assisted charge/discharge process. The photogenerated carriers at the solid/liquid interfaces boosted the oxidation process of I^-, and thus contributed to a significant elevation in energy efficiency of 14.9% (@0.5 mA cm^-2). A volumetric discharge capacity was extended by 79.6% under light illumination, owing to a reduced polarization. The photocathode also exhibited an excellent durability, leading to a stable operation for over 80 h with a maintained high energy efficiency of ∼90% @0.2 mA cm^-2. The research offers a feasible approach for the realization of high-energy-efficiency aqueous Zn-iodine batteries towards high-efficiency energy conversion and utilization.

Asymmetric and Symmetric Redox Flow Batteries for Energy-Efficient, High-Recovery Water Desalination

Electrochemical separation offers an energy-efficient means to desalinate brackish water, a relatively untapped but increasingly utilized water source for freshwater supply. Several electrochemical techniques are being developed to enable low-energy desalination combined with energy storage. We report a new approach that produced a peak power density of 6.0 mW cm^-2 from the energy stored in iron cyanide (Fe-CN) and iron citrate (Fe-Cit) redox couples during water desalination, using asymmetric redox flow batteries (RFBs). Desalination and the charging of the redox couples occurred in a four-channel RFB cell. The stored energy was extracted in a two-channel RFB cell. Desalination of model brackish water (2.9 g L^-1) to freshwater (0.5 g L^-1) was also studied in a symmetric system using the environmentally benign Fe-Cit. The process was characterized by low energy consumption (0.56 kW h m^-3), high productivity (41.1 L freshwater m^-2 area h^-1, representing practical operating conditions for brackish water desalination), and high water recovery (91% product-to-intake water ratio, addressing the environmental and economic challenges of brine disposal). The low cell voltage (<0.5 V) required in the reported system is ideally suited for developing modular desalination systems powered by renewables, including solar energy. Collectively, water-based RFBs for desalination and power production would lead to sustainable water-energy infrastructure.

Nonlinear Dynamics of a Star-Shaped Structure and Variable Configuration of Elastic Elements for Energy Harvesting Applications

The subject of the model research contained in this paper is a new design solution of the energy harvesting system with a star-shaped structure of elastic elements and variable configuration. Numerical experiments focused mainly on the assessment of the configuration of elastic elements in the context of energy harvesting efficiency. The results of computer simulations were limited to zero initial conditions as it is the natural position of the static equilibrium. The article compares the energy efficiency for the selected range of the dimensionless excitation frequency. For this purpose, four cases of elastic element configurations were compared. The results are visualized based on the diagram of RMS voltage induced on piezoelectric electrodes, bifurcation diagrams, Lyapunov exponents, and Poincaré maps, showing the impact of individual solutions on the efficiency of energy harvesting. The results of the simulations show that the harvester's efficiency ranges from 4 V to 20 V depending on the configuration and the frequency range of the excitation, but the design allows for a smooth adjustment to the given conditions.

Combined Membrane Dehumidification with Heat Exchangers Optimized Using CFD for High Efficiency HVAC Systems

Traditional air conditioning systems use a significant amount of energy on dehumidification by condensing water vapor out from the air. Membrane-based air conditioning systems help overcome this problem by avoiding condensation and treating the sensible and latent loads separately, using membranes that allow water vapor transport, but not air (nitrogen and oxygen). In this work, a computational fluid dynamics (CFD) model has been developed to predict the heat and mass transfer and concentration polarization performance of a novel active membrane-based energy exchanger (AMX). The novel design is the first of its kind to integrate both vapor removal via membranes and air cooling into one device. The heat transfer results from the CFD simulations are compared with common empirical correlations for similar geometries. The performance of the AMX is studied over a broad range of operating conditions using the compared CFD model. The results show that strong tradeoffs result in optimal values for the channel length (0.6–0.8 m) and the ratio of coil diameter to channel height (~0.5). Water vapor transport is best if the flow is just past the turbulence transition around 3000–5000 Reynolds number. These trends hold over a range of conditions and dimensions.

The Energy Transition between Desideratum and Challenge: Are Cogeneration and Trigeneration the Best Solution?

Climate change has brought to the attention of politicians, researchers, and other stakeholders the need to protect the environment. The concerns at the international level are more and more intense, and the solutions found are multiple. One of the directions to follow is a new energy transition, which involves the use of renewable energy, but also techniques of cogeneration and trigeneration. This study presents the main research on increasing energy efficiency in the use of a primary energy source and the impact on the environment. Compared to the classical methods of obtaining heat and electricity from burning fossil fuels through separate technologies, the study brings to the fore two methods (cogeneration and trigeneration) that have much higher yields by obtaining two or even three forms of energy from the use of a single source of combustion. The impact on the environment is also significantly reduced by eliminating additional sources of pollution (reducing greenhouse gas emissions). Taking into account the evolutions of the energy market during this period, this article aims to analyze, from the point of view of the two most important influencing factors, the economic efficiency of processes and the reduction of greenhouse gas emissions by overlapping their effect, in case of the use of modern technologies (cogeneration and trigeneration), for the combined production of various forms of energy.

Broadband Zero-Power Wakeup MEMS Device for Energy-Efficient Sensor Nodes

A zero-power wakeup scheme for energy-efficient sensor applications is presented in this study based on a piezoelectric MEMS energy harvester featuring wafer-level-integrated micromagnets. The proposed setup overcomes a hybrid assembly of magnets on a chip-level, a major drawback of similar existing solutions. The wakeup device can be excited at low frequencies by frequency up-conversion, both in mechanical contact and contactless methods due to magnetic force coupling, allowing various application scenarios. In a discrete circuit, a wakeup within 30−50 ms is realized in frequency up-conversion at excitation frequencies < 50 Hz. A power loss in the off state of 0.1 nW renders the scheme virtually lossless. The potential extension of battery lifetime compared to cyclical wakeup schemes is discussed for a typical wireless sensor node configuration.

Hardware Efficient Massive MIMO Systems with Optimal Antenna Selection

An increase in the number of transmit antennas (M) poses an equivalent rise in the number of Radio Frequency (RF) chains associated with each antenna element, particularly in digital beamforming. The chain exhibits a substantial amount of power consumption accordingly. Hence, to alleviate such problems, one of the potential solutions is to reduce the number of RFs or to minimize their power consumption. In this paper, low-resolution Digital to Analogue Conversion (DAC) and transmit antenna selection at the downlink are evaluated to favour reducing the total power consumption and achieving energy efficiency in mMIMO with reasonable complexity. Antenna selection and low-resolution DAC techniques are proposed to leverage massive MIMO systems in free space and Close In (CI) path-loss models. The simulation results show that the power consumption decreases with antenna selection and low-resolution DAC. Then, the system achieves more energy efficiency than without low-resolution of DAC and full array utilization.

Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks

The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power shortages in the WSNs, which eventually results in the inefficiency of the overall IoT network. In addition, the density of the available sensor nodes affects the existence of feasible routes and the level of path multiplicity in the WSNs. Therefore, an efficient routing mechanism is expected to extend the lifetime of the WSNs by adaptively selecting the best routes for the data transfer between interconnected IoT devices. In this work, we propose a novel routing mechanism to balance the energy consumption among all the nodes and elongate the WSN lifetime, which introduces a score value assigned to each node along a path as the combination of evaluation metrics. Specifically, the scoring scheme considers the information of the node density at a certain area and the node energy levels in order to represent the importance of individual nodes in the routes. Furthermore, our routing mechanism allows for incorporating non-cooperative nodes. The simulation results show that the proposed work gives comparatively better results than some other experimented protocols.

Scalable Gas Diffusion Electrode Fabrication for Electrochemical CO2 Reduction Using Physical Vapor Deposition Methods

Electrochemical CO₂ reduction (ECR) promises the replacement of fossil fuels as the source of feedstock chemicals and seasonal storage of renewable energy. While much progress has been made in catalyst development and electrochemical reactor design, few studies have addressed the effect of catalyst integration on device performance. Using a microfluidic gas diffusion electrolyzer, we systematically studied the effect of thickness and the morphology of electron beam (EB) and magnetron-sputtered (MS) Cu catalyst coatings on ECR performance. We observed that EB-Cu outperforms MS-Cu in current density, selectivity, and energy efficiency, with 400 nm thick catalyst coatings performing the best. The superior performance of EB-Cu catalysts is assigned to their faceted surface morphology and sharper Cu/gas diffusion layer interface, which increases their hydrophobicity. Tests in a large-scale zero-gap electrolyzer yielded similar product selectivity distributions with an ethylene Faradaic efficiency of 39% at 200 mA/cm², demonstrating the scalability for industrial ECR applications.

Contrasting roles for two conserved arginines: stabilizing flavin semiquinone or quaternary structure, in bifurcating electron transfer flavoproteins

Bifurcating electron transfer flavoproteins (Bf ETFs) are important redox enzymes that contain two flavin adenine dinucleotide (FAD) cofactors, with contrasting reactivities and complementary roles in electron bifurcation. However, for both the “electron transfer” (ET) and the “bifurcating” (Bf) FADs, the only charged amino acid within 5 Å of the flavin is a conserved arginine (Arg) residue. To understand how the two sites produce different reactivities utilizing the same residue, we investigated the consequences of replacing each of the Arg residues with lysine, glutamine, histidine, or alanine. We show that absence of a positive charge in the ET site diminishes accumulation of the anionic semiquinone (ASQ) that enables the ET flavin to act as a single electron carrier, due to depression of the oxidized versus. ASQ reduction midpoint potential, E°_OX/ASQ. Perturbation of the ET site also affected the remote Bf site, whereas abrogation of Bf FAD binding accelerated chemical modification of the ET flavin. In the Bf site, removal of the positive charge impaired binding of FAD or AMP, resulting in unstable protein. Based on pH dependence, we propose that the Bf site Arg interacts with the phosphate(s) of Bf FAD or AMP, bridging the domain interface via a conserved peptide loop (“zipper”) and favoring nucleotide binding. We further propose a model that rationalizes conservation of the Bf site Arg even in non-Bf ETFs, as well as AMP's stabilizing role in the latter, and provides a mechanism for coupling Bf flavin redox changes to domain-scale motion.

An Adaptive Sampling Period Approach for Management of IoT Energy Consumption: Case Study Approach

The Internet of Things (IoT) opens opportunities to monitor, optimize, and automate processes into the Agricultural Value Chains (AVC). However, challenges remain in terms of energy consumption. In this paper, we assessed the impact of environmental variables in AVC based on the most influential variables. We developed an adaptive sampling period method to save IoT device energy and to maintain the ideal sensing quality based on these variables, particularly for temperature and humidity monitoring. The evaluation on real scenarios (Coffee Crop) shows that the suggested adaptive algorithm can reduce the current consumption up to 11% compared with a traditional fixed-rate approach, while preserving the accuracy of the data.

Balanced Energy-Aware and Fault-Tolerant Data Center Scheduling

Fault tolerance, performance, and throughput have been major areas of research and development since the evolution of large-scale networks. Internet-based applications are rapidly growing, including large-scale computations, search engines, high-definition video streaming, e-commerce, and video on demand. In recent years, energy efficiency and fault tolerance have gained significant importance in data center networks and various studies directed the attention towards green computing. Data centers consume a huge amount of energy and various architectures and techniques have been proposed to improve the energy efficiency of data centers. However, there is a tradeoff between energy efficiency and fault tolerance. The objective of this study is to highlight a better tradeoff between the two extremes: (a) high energy efficiency and (b) ensuring high availability through fault tolerance and redundancy. The main objective of the proposed Energy-Aware Fault-Tolerant (EAFT) approach is to keep one level of redundancy for fault tolerance while scheduling resources for energy efficiency. The resultant energy-efficient data center network provides availability as well as fault tolerance at reduced operating cost. The main contributions of this article are: (a) we propose an Energy-Aware Fault-Tolerant (EAFT) data center network scheduler; (b) we compare EAFT with energy efficient resource scheduling techniques to provide analysis of parameters such as, workload distribution, average task per servers, and energy consumption; and (c) we highlight effects of energy efficiency techniques on the network performance of the data center.

Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach

In this paper, an intelligent data analysis method for modeling and optimizing energy efficiency in smart buildings through Data Analytics (DA) is proposed. The objective of this proposal is to provide a Decision Support System (DSS) able to support experts in quantifying and optimizing energy efficiency in smart buildings, as well as reveal insights that support the detection of anomalous behaviors in early stages. Firstly, historical data and Energy Efficiency Indicators (EEIs) of the building are analyzed to extract the knowledge from behavioral patterns of historical data of the building. Then, using this knowledge, a classification method to compare days with different features, seasons and other characteristics is proposed. The resulting clusters are further analyzed, inferring key features to predict and quantify energy efficiency on days with similar features but with potentially different behaviors. Finally, the results reveal some insights able to highlight inefficiencies and correlate anomalous behaviors with EE in the smart building. The approach proposed in this work was tested on the BlueNet building and also integrated with Eugene, a commercial EE tool for optimizing energy consumption in smart buildings.

Innovative Structural Fuse Systems for Various Prototype Applications

To resist the imposed lateral forces on the structures, hysteric dampers are developed from steel plates and strategically implemented within various structural applications. Structural shear dampers have recently been used to alleviate damage, while remaining members remain intact and undamaged. The practical use of the innovative dampers in structural applications is investigated in this study. For this purpose, the design methodology for a set of innovative shear dampers is initially elaborated, for which the dampers are designed considering the governing shear and flexural ductile limit states, while the brittle buckling limit state is prevented. Subsequently, the finite element modeling methodology is verified and compared to laboratory tests for computationally analyzing various shapes of the shear damper in structural applications. Three major general prototype structures are established, and shear dampers are designed to be incorporated in prototype applications. For each of the proposed applications, at least six different shapes of shear dampers are designed and subsequently compared with conventional systems. The results determined that the use of innovative shear dampers could effectively reduce demand forces on the boundary elements by more than 40% on average, while the strength and the stiffness alter within margin of difference less than 5%.

An Energy Reward-Based Caching Mechanism for Information-Centric Internet of Things

Existing information-centric networking (ICN) designs for Internet of Things (IoT) mostly make caching decisions based on probability or content popularity. From the energy-efficient perspective, those strategies may not always be energy efficient in resource-constrained IoT because without considering the energy reward of caching decisions, inappropriate routers and content objects may be selected for caching, which may lead to negative energy rewards. In this paper, we analyze the energy consumption of content caching and content retrieval in resource-constrained IoT and calculate caching energy reward as a key metric to measure the energy efficiency of a caching decision. We then propose an efficient cache placement and cache replacement mechanism based on the caching energy reward to improve the energy efficiency of caching decisions. Through analysis and experimental results, we show that the proposed mechanism achieves a significant improvement in terms of energy efficiency, stretch ratio, and cache hit ratio compared to state-of-the-art caching schemes.

Environmental Consequences of Potential Strategies for China to Prepare for Natural Gas Import Disruptions

Worldwide efforts to switch away from coal have increased the reliance on natural gas imports for countries with inadequate domestic production. In preparing for potential gas import disruptions, there have been limited attempts to quantify the environmental and human health impacts of different options and incorporate them into decision-making. Here, we analyze the air pollution, human health, carbon emissions, and water consumption impacts under a set of planning strategies to prepare for potentially fully disrupted natural gas imports in China. We find that, with China's current natural gas storage capacity, compensating for natural gas import disruptions using domestic fossil fuels (with the current average combustion technology) could lead up to 23,300 (95% CI: 22,100-24,500) excess premature deaths from air pollution, along with increased carbon emissions and aggravated water stress. Improving energy efficiency, more progressive electrification and decarbonization, cleaner fossil combustion, and expanding natural gas storage capacity can significantly reduce the number of excess premature deaths and may offer opportunities to reduce negative carbon and water impacts simultaneously. Our results highlight the importance for China to increase the domestic storage capacity in the short term, and more importantly, to promote a clean energy transition to avoid potentially substantial environmental consequences under intensifying geopolitical uncertainties in China. Therefore, mitigating potential negative environmental impacts related to insecure natural gas supply provides additional incentives for China to facilitate a clean and efficient energy system transition.

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.