Unbalanced Energy Research Articles

GDR: A Game Algorithm Based on Deep Reinforcement Learning for Ad Hoc Network Routing Optimization

Ad Hoc networks have been widely used in emergency communication tasks. For dynamic characteristics of Ad Hoc networks, problems of node energy limited and unbalanced energy consumption during deployment, we propose a strategy based on game theory and deep reinforcement learning (GDR) to improve the balance of network capabilities and enhance the autonomy of the network topology. The model uses game theory to generate an adaptive topology, adjusts its power according to the average life of the node, helps the node with the shortest life to decrease the power, and prolongs the survival time of the entire network. When the state of the node changes, reinforcement learning is used to automatically generate routing policies to improve the average end-to-end latency of the network. Experiments show that, under the condition of ensuring connectivity, GDR has smaller residual energy variance, longer network lifetime, and lower network delay. The delay of the GDR model is 10.5% higher than that of existing methods on average.

Gradient normalized least-squares reverse-time migration imaging technology

Least-squares reverse-time migration (LSRTM) can overcome the problems of low resolution and unbalanced amplitude energy of deep formation imaging in reverse-time migration (RTM); hence, it can obtain a more accurate imaging profile. In the conventional conjugate gradient LSRTM, the gradient is obtained based on cross correlation without a precondition operator, and the source has a great influence on the gradient, causing the convergence rate to be slow. In the framework of conventional conjugate gradient LSRTM, a normalized cross-correlation of the source wavefield was used in this study to effectively weaken the influence of the source effect and reduce the low-frequency noise. The idea of normalized cross-correlation of the source wavefield was adopted to improve the steepest descent gradient to further accelerate the iterative convergence speed and complete the final migration imaging. Model and field data examples verify the advantages of the proposed methods over conventional methods in reducing source effects, improving convergence speed, and enhancing underground deep illumination.

Novel Plant Extract Ameliorates Metabolic Disorder through Activation of Brown Adipose Tissue in High-Fat Diet-Induced Obese Mice.

Obesity is characterized by excessive body fat accumulation due to unbalanced energy intake and expenditure. Potential therapeutic targets for anti-obesity include the inhibition of white adipose tissue (WAT) hypertrophy and hyperplasia and the activation of brown adipose tissue (BAT). Not only the activation of BAT but also the browning of WAT have gained increasing attention in research fields as an alternative method in the prevention and treatment of obesity. Here, we investigated possible mechanisms underlying the anti-obesity effect of Phlomis umbrosa Turcz. root ethanol extract (PUE) in an obesogenic animal model. PUE treatment can reduce diet-induced obesity and modulate obesity-associated metabolic disorders, including insulin resistance, hepatic steatosis, and inflammation. In the liver, PUE improved hepatic steatosis by suppressing hepatic lipogenesis and lipid absorption while increasing biliary sterol excretion and hepatic fatty acid oxidation compared to the high-fat group. Moreover, PUE increased energy expenditure and regulated fecal lipid excretion, leading to reduced body weight gain. In particular, PUE remarkably activated the browning of subWAT via upregulation of the browning-related protein and gene expression and promoted BAT activation. In conclusion, these findings provide the potential therapeutic usefulness into the effects of PUE in the treatment of obesity and metabolic disorders. Furthermore, it suggests that PUE treatment can regulate energy metabolism via activating BAT and browning subWAT.

Building one-dimensional hole transport channels in cross-linked polymers to enable efficient deep blue QLED

Quantum dot light-emitting diodes (QLEDs) emerged as very competitive candidates for next generation display and lighting applications. Cross-linked hole transport materials (HTMs) permit the flourish of solution-processed QLEDs. Whereas, unbalanced charge injection and energy level mismatch happened to cross-linked HTMs severely suppress the efficiency of blue QLEDs, which limits the full-color display application. Here, discoid molecules with self-assembly behavior were firstly proposed to construct one-dimensional transport channels in cross-linked hole transport layer (HTL) to advance the carrier transport property of the target composite HTL (CHTL). Compared with CHTLs prepared from amorphous and disordered crystalline discoid molecules, the carrier mobility of CHTL (T5DP36: V-CBP) with one-dimensional transport channels was signally boosted by two orders of magnitude from 6.6 × 10−5 to 2.16 × 10−3 cm2 V−1 s−1. The relevant blue QLED was qualified with notably enhanced external quantum efficiency of 18.16 %, deep blue emission with Commission International de I’Eclaiiage of (0.14, 0.04) and extended T50 lifetime from 177 to 417 h under 100 cd m−2. This is at a cutting edge of deep blue light and eligible for high-end display applications.

Distribution Voltage and Output Power Sharing Control for DC Microgrids using Fuzzy Control and Gain-Scheduling

Abstract: A dc microgrid has gained popularity in recent years due to dc sources such as solar panels, fuel cells, and batteries. Interconnections can be made without AC to DC conversion, which improves system efficiency. Furthermore, when a utility grid is experiencing voltage sags or blackouts, A continuous supply of high-quality power is possible. Parallel operations have been proposed with several types of droop controls and microgrids, including DC and AC, were also used in some cases. Sharing the storage unit outputs via a gain scheduling scheme would result in unbalanced storage energy. Therefore, this research integrates fuzzy control with gain-scheduling techniques to create a new voltage control system that can both share energy and optimize power consumption. A microgrid consists of a variety of distributed energy sources used for the energy storage system. Fuzzy control is used to maintain power quality. Controlling fuzzy is based on different rules with the goal of maintaining constant droop.

Performance Analysis of Wireless Sensor Network by Variation in Cluster Head Selection through mLEACH-CS Algorithm

Abstract: The energy depletion at the nodes due to large volume of data transmission and data reception for large scale Wireless Sensor Networks (WSN) is one of the important areas of concern. Unbalanced energy consumption due to data traffic overhead at the nodes near the base station (BS) results in early node death and reduction in network lifetime. To balance the load at the wireless nodes during clustering, a novel hybrid clustering and routing protocol has been proposed based on mLEACH (modified Low-Energy Adaptive Clustering Hierarchy) and Cuckoo Search (CS) algorithm. Initially, mLEACH protocol is utilized to solve a novel equation designed to select the cluster heads by rotation at every round of data transmission based on the maximum permissible cluster heads in the network. In the second phase, CS algorithm is implemented for uniform distribution of the cluster heads in the network for efficient multi-hop data routing in the network based on several weighted factors. The performance of the proposed algorithm is compared with other algorithms in literature in terms of energy consumption and network lifetime for different positions of the base station and varied number of cluster heads in the model application area. The proposed mLEACH-CS algorithm shows a significant improvement by 60% considering 300 nodes in the WSN architecture while it decreased to 40% for denser network architecture. Optimal choice of the number and position of the cluster heads in certain network architecture has proved beneficial for designing an energy efficient wireless network with improved network lifetime.

Long-Term Thermo-Hydraulic Numerical Assessment of Thermo-Active Piles—A Case of Tropical Soils

Thermo-active piles are an upcoming technology for the utilization of subsurface geothermal energy in urban areas. This environmentally friendly technology has already been widespread for the heating and cooling of buildings in temperate regions, whereas in tropical regions it is still limited due to their unbalanced energy demands. This paper presents 3D thermo-hydraulic coupled numerical simulations to assess the long-term performance of thermo-active pile systems in tropical environments for different energy demands. The simulations are based on real data (in situ tests and field investigations) considering three typical thermal solicitations, thereby maintaining their practical relevance. Moreover, the energy exchange within soil control volumes is quantified based on an approach that allows calculating conductive and advective divergence. Parametric analyses regarding thermal solicitation, pile diameter, and groundwater flow are also performed. The results indicate that groundwater flow plays the most important role in improving the thermal balance of thermo-active piles.

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm.

Leveraging Energy, Latency, and Robustness for Routing Path Selection in Internet of Battlefield Things

Internet of Battlefield Things (IoBT) connects massive tactical devices to collect battlefield situations and share perceived information. The IoBT can enhance the intelligent battlefield command, collaborative attack, and other applications, such as landmine trigger and post-war clearance. Existing routing path selection methods designed for wireless sensor networks (WSNs) are effective but still face challenges in IoBT scenarios. First, tactical devices follow nonuniform distributions with high density on boundaries in IoBT to prevent the location of devices from being speculated and protect strategic positions, which results in unbalanced energy consumption. Second, increasing latency in IoBT is caused by various data generation probabilities of tactical devices. Third, the military task features, such as landmine explosion, disconnection, and failure of tactical devices, may put forward special requirements on network robustness. To this end, we propose a routing path selection method with joint optimization in IoBT based on nonuniform node distributions and location-related data generation probabilities. Specifically, we first investigate and formulate the distribution and data generation probability of tactical devices. Based on the special features, energy consumption, latency, and network robustness are analyzed during multihop communications in IoBT. Then, a joint optimization problem is formulated to minimize energy consumption and latency, while maximizing the network robustness simultaneously. Furthermore, two path assignment algorithms are developed to solve this optimization problem. Finally, our simulation results show that the proposed routing path selection method can reduce energy consumption and latency with the guaranteed robustness of IoBT.

Local dielectric tunnel junction to manage the current distribution for AlGaN-based deep-ultraviolet light-emitting diodes with a thin p-GaN layer.

In this report, a p+-GaN/SiO2/Ni tunnel junction with a local SiO2 insulation layer is designed to manage the current distribution for commercially structured AlGaN-based deep-ultraviolet light-emitting diodes (DUV LEDs) with a thin p-GaN layer. The experimental and calculated results prove that, besides the increased hole injection at the p+-GaN/SiO2/Ni tunnel junction, the local SiO2 layer produces an in-plane unbalanced energy band in the p-GaN layer for the proposed DUV LEDs, thus modulating the carrier transport paths and increasing the spread of holes. Enhanced optical power is obtained when compared to conventional DUV LEDs. In addition, the influence of the position of the SiO2 insulation layer on the current distribution is also investigated in this work. Placing the SiO2 insulation layer in the middle position of the p+-GaN layer is most helpful for increasing the hole injection efficiency for commercially structured DUV LEDs.

Metrics of the Hadley circulation strength and associated circulation trends

Abstract. This study compares trends in the Hadley cell (HC) strength using different metrics applied to the ECMWF ERA5 and ERA-Interim reanalyses for the period 1979–2018. The HC strength is commonly evaluated by metrics derived from the mass-weighted zonal-mean stream function in isobaric coordinates. Other metrics include the upper tropospheric velocity potential, the vertical velocity in the mid-troposphere, and the water vapour transport in the lower troposphere. Seven known metrics of HC strength are complemented here by a metric of the spatially averaged HC strength, obtained by averaging the stream function in the latitude–pressure (φ–p) plane, and by the total energy of zonal-mean unbalanced circulation in the normal-mode function decomposition. It is shown that metrics, which rely on single-point values in the φ–p plane, produce unreliable 40-year trends in both the northern and southern HCs, especially in ERA-Interim; magnitudes and even the signs of the trends depend on the choice of the HC strength metric. The two new metrics alleviate the vertical and meridional inhomogeneities of the trends in HC strength. The unbalanced energy metric suggests a positive HC trend in both reanalyses, whereas the metric based on averaging the stream function finds a significant positive trend only in ERA5.

Identifying Key Descriptors for the Single-Atom Catalyzed CO Oxidation

Identifying Key Descriptors for the Single-Atom Catalyzed CO Oxidation

A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm

For the sensing layer of the Internet of Things, the mobile wireless sensor network has problems such as limited energy of the sensor nodes, unbalanced energy consumption, unreliability, and long transmission delay in the data collection process. It is proved by mathematical derivation and theory that this is a typical multiobjective optimization problem. In this paper, the optimization goal is to minimize the energy consumption and improve the reliability under time-delay constraints and propose a path optimization mechanism to optimize the mobile Sink of mobile wireless sensor networks based on the improved dragonfly optimization algorithm. The algorithm takes full advantage of the abundant storage space, sufficient energy, and strong computing power of the mobile Sink to ensure network connectivity and improve network communication efficiency. Through simulation comparison and analysis, compared with random movement method, artificial bee colony algorithm, and basic dragonfly optimization algorithm, the energy consumption of the network is reduced, the lifespan of the network is increased, and the connectivity and transmission delay of the network are improved. The proposed algorithm balances the energy consumption of the sensors nodes to meet the network service quality and improve the reliability of the network.

Recent Techniques Used in Home Energy Management Systems: A Review

Power systems are going through a transition period. Consumers want more active participation in electric system management, namely assuming the role of producers–consumers, prosumers in short. The prosumers’ energy production is heavily based on renewable energy sources, which, besides recognized environmental benefits, entails energy management challenges. For instance, energy consumption of appliances in a home can lead to misleading patterns. Another challenge is related to energy costs since inefficient systems or unbalanced energy control may represent economic loss to the prosumer. The so-called home energy management systems (HEMS) emerge as a solution. When well-designed HEMS allow prosumers to reach higher levels of energy management, this ensures optimal management of assets and appliances. This paper aims to present a comprehensive systematic review of the literature on optimization techniques recently used in the development of HEMS, also taking into account the key factors that can influence the development of HEMS at a technical and computational level. The systematic review covers the period 2018–2021. As a result of the review, the major developments in the field of HEMS in recent years are presented in an integrated manner. In addition, the techniques are divided into four broad categories: traditional techniques, model predictive control, heuristics and metaheuristics, and other techniques.

Utility-driven renewable energy sharing systems for community microgrid

Community microgrid has become a popular platform for managing renewable energy in power distribution networks. However, due to the intermittency of renewable energy, the unbalanced energy supply and demand in community microgrid has become an urgent problem. To solve the problem, this paper develops a grid and renewable energy sharing system, which consists of pricing and allocation stages. Specifically, we first propose a game theoretic pricing approach to explore an optimal energy price. With the obtained optimal price, the utility of community microgrid users is maximized. We then transform the energy allocation problem into a convex optimization by derivative analysis, and design an energy allocation method based on a water filling algorithm to solve the convex optimization problem. Experimental results show that as compare to benchmarking methods, our proposed electric energy sharing system can reduce the buyers’ energy cost by up to 46.2% and increase the sellers’ energy profit by up to 62.5%.

Effects of Mitochondrial Dynamics in the Pathophysiology of Obesity.

Obesity has become an urgent and serious public health challenge with an overwhelming increase over the decades worldwide. The rate of obese children and adolescents has recently accelerated, especially in China. Obesity is closely related to unbalanced cellular energy metabolism. Mitochondria, as the main organelles of energy metabolism, play an important role in the pathophysiology of obesity. Recent researches have revealed that mitochondrial dynamics with constant fission and fusion, can alter mitochondrial structure, organelle connections, ROS production, neuronal activity, and OXPHOS system as well as adipose tissue thermogenesis, which ultimately lead to obesity. In this review, we will update the latest findings about mitochondrial fission/fusion related GTPase proteins and discuss the effects of mitochondrial dynamics in the pathophysiology of obesity.

A risk-based energy-structure balance development model for restoring the sustainability of energy-economy-ecological complex system

Faced with the degraded ecological environment, it is necessary to optimize the energy structure and inter-regional energy trade framework to achieve sustainable and balanced development. A RIMSP-based energy-structure balance development model is proposed to solve the contradictions among energy consumption, economic system cost, and ecological emissions. In this study, the systemic risks and uncertainties are considered through integrating the Conditional Value at Risk module, Interval Programming, Multi-Stage stochastic Programming, and Integer Programming. A typical region with unbalanced energy endowments and economic development, Shanxi Province, China was investigated to illustrate the model applicability and superiority. It is found that the probability of system failures can be controlled within 1% and the most stable energy system will be found when risk weighted coefficient at 20. The optimal power generation schemes can be obtained for target power generation stage with compensation mechanism and for recourse power generation stage with substitution ratio of 20%, 15%, and 10% at three planning periods, respectively. The total installed capacity of clean energy is expected to reach approximately 41.4% by 2035. Wind power and solar power have more advantages than natural gas power in responding to flexible power demand and long-term sustainable requirements. In addition, the emissions of Particulate Matter, Sulfur Dioxide, Nitrogen Oxide, and Carbon Dioxide would be reduced to varying degrees. The results can provide a solid scientific basis for formulating balanced development policies to restore the ecological environment related to energy consumption.

Splitting–merging-based automatic scheduling scheme for balancing energy consumption and task allocation in WSANs

In wireless sensor and actuator networks (WSANs), sensing region partition, task allocation, and collector schedule greatly affect the performance of WSANs. Specifically, nonuniform data aggregation causes unbalanced energy consumption, thereby reducing the network lifetime. Additionally, unbalanced allocation of data collection tasks of collectors increases the gathering delay. Thus, it is critical to balance the energy consumption of sensor nodes, as well as task allocation and scheduling with the limited number of collectors. This study proposes a novel splitting–merging-based automatic scheduling scheme to balance energy consumption and task allocation for WSANs. First, a sensing region splitting–merging scheme is proposed to make the data load uniform, in which the sensing region is self-organized into many subregions taking into account the maximum number of sensor nodes and the maximum distance between sensor nodes in each subregion. Second, a task allocation strategy based on genetic algorithm is proposed to optimize the number of collectors and uniformly assign tasks to collectors. Finally, the trajectory of each collector is optimized by applying ant colony optimization algorithm. Numerical experiments show that the proposed method outperforms well in terms of prolonging the network lifetime and reducing the gathering delay when compared with several related methods.

Polymorphism of Uncoupling Protein 2 (UCP2) Gene in Obes People with the Family History of Type 2 Diabetes Mellitus

History with type 2 diabetes mellitus (T2DM) is manifested by the presence of insulin resistance and β-cell dysfunction which prerequisites for T2DM development involving unbalanced energy intake and expenditure. Uncoupling gene protein 2 is the main regulator of energy balance. This study aims to determine the UCP2 -866G/A genetic variation in obese individuals with a family history of T2DM and without a family history of T2DM. The study was a case control design in which the case subjects (n = 60) were obese individuals with a family history of T2DM and control subjects (n = 60) without a family history of T2DM. Polymorphism was analyzed with PCR-RFLP. The value of HOMA-IR and HOMA-β was calculated by the HOMA formula. Data was analyzed by Independent Sample t-test, Mann Whitney U-test, Chi-Square test and Kruskal-Wallis test with a significance level of p < 0.05. The frequency of genotype and alleles in obese individuals with a family history of T2DM and without a history of T2DM did not differ significantly, GA+AA (56.7 %) and A (32.5 %) allele was higher in individuals with a family history of T2DM. The GG+AA genotype in the male group with a family history of T2DM could significantly increase the risk of UCP2 gene polymorphism in T2DM by 2.23 times (CI 95 % 0.64 - 8.14) whereas in the female group there was no risk of T2DM. HOMA-IR and HOMA-β value in both family background did not differ significantly. The value of HOMA-β in the female gender group had significant relationship between obese individuals with a family history of T2DM (p = 0.04). Conclusion: The result suggests obese individuals with a family history of T2DM have a higher risk of getting GA+AA genotypes and A allele than individuals without a family history of T2DM.
 HIGHLIGHTS
 
 Energy intake that exceeds energy expenditure will cause body fat mass to increase when a state of positive energy balance occurs
 The genetic component for obesity is associated with relative risks between family relationships and body composition relationships
 The obese individuals with a family history of T2DM have a higher risk of GA + AA genotype and A allele than individuals without a family history of T2DM
 
 GRAPHICAL ABSTRACT

Mitigating Hotspot Issues in Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of a spatially distributed set of autonomous connected sensor nodes. The deployed sensor nodes are extensively used for sensing and monitoring for environmental surveillance, military operations, transportation monitoring, and healthcare monitoring. The sensor nodes in these networks have limited resources in terms of battery, storage, and processing. In some scenarios, the sensor nodes are deployed closer to the base station and responsible to forward their own and neighbor nodes’ data towards the base station and depleted energy. This issue is called a hotspot in the network. Hotspot issues mainly appear in those locations where traffic load is more on the sensor nodes. The dynamic and unequal clustering techniques have been used and mitigate the hotspot issues. However, with few benefits, these solutions have suffered from coverage overhead, network connection issues, unbalanced energy utilization among the sink nodes, and network stability issues. In this paper, a comprehensive review of various equal clustering, unequal clustering, and hybrid clustering approaches with their clustering attributes is presented to mitigate hotspot issues in heterogeneous WSNs by using various parameters such as cluster head selection, number of clusters, zone formation, transmission, and routing parameters. This review provides a detailed platform for new researchers to explore the new and novel solutions to solve the hotspot issues in these networks.