Unbalanced Energy Research Articles

Institutional unlocking or technological unlocking? The logic of carbon unlocking in the new energy vehicle industry in China

As a result of unbalanced energy structure, high carbon emissions, and global climate change, carbon lock-in has gained considerable attention. To achieve carbon unlocking, technological advancements and institutional innovation are essential. This paper uses the new energy vehicle (NEV) industry as its case study, identifying the differences and evolutionary trends between institutional unlocking and technological unlocking logics, and demonstrating how these logics affect industry and enterprise development and eventually advance the process of carbon unlocking. The findings are: (1) Institutional incentives play a critical role in the development of an industry at the beginning. As the industry matures, technological innovation plays a central role in driving industry development, while institutional logic gradually loses influence and technological logic gains influence. (2) Dual logics have undergone a complex transformation of compatibility, mutual exclusivity, partial confrontation, and cooperation during the process of carbon unlocking. It is possible for the complexity of the relationship to range from low to high, with both becoming less complex as the industry matures. (3) Through ongoing gaming and adjustment, the dual logics encourage the NEV industry to realize carbon unlocking and low-carbon transformation at the same time.

Is Zagreb Green Enough? Influence of Urban Green Spaces on Mitigation of Urban Heat Island: A Satellite-Based Study

The urban heat island phenomenon is a climatic condition in which urbanized areas exhibit higher temperature values than their natural surroundings. This occurs due to an unbalanced energy budget caused by the extensive use of synthetic materials. In such a scenario, urban green areas act as stressors to mitigate the intensity of the urban heat island and improve urban well-being. This study analyzes the spatial-temporal characteristics of the urban heat island in Zagreb, Croatia, aiming to examine the role of different types of green infrastructure in mitigating elevated temperature values and facilitating the definition of greener planning strategies. To achieve this, a multitemporal remote sensing- and NDVI-based analysis was conducted for the time series 1984–2014. An urban heat island intensity map was obtained for the selected 30-year period, along with thermal graphs registering land surface temperature values among different city districts. The results reveal significant heterogeneity, displaying variable behavior dependent on the city district. The role of Zagreb’s urban green areas in urban heat island mitigation is evident but largely dependent on urban morphology, construction types, and periods. Urban forests and urban parks play the most significant role in temperature reduction, followed by residential building neighborhoods and extensive neighborhoods consisting of familiar houses with gardens. Continuously built areas, such as the city center and industrial zones, are less prone to registering lower intensity values. Additionally, multitemporal intensity variations based on land use changes are registered in several districts.

Reliable and Energy-Efficient Communications in Mobile Robotic Networks by Collaborative Beamforming

For mobile robotic networks in industrial scenarios, reliable and energy-efficient communications are crucial yet challenging. Fortunately, collaborative beamforming (CB) emerges as a promising solution, which can increase the transmission gain and reduce the transmit power of robots by constructing a mobile robot-enabled virtual antenna array (MRVAA). The performance of CB is tightly related to robot positions, necessitating proper robot selection. However, robot selection may expose the network to the risk of unbalanced energy distribution, reducing network lifetime. Additionally, the mobility and variable numbers of robots require flexible and scalable robot selection algorithms. To tackle these challenges, we first formulate a multi-objective optimization problem to reduce the maximum sidelobe level (MSLL) of MRVAA while minimizing the standard deviation of the network energy distribution (SDNED) by selecting robots for CB. Then, based on distributed multi-agent learning (MARL), we propose an effective and scalable robot selection algorithm with energy considered (RoSE) to solve the problem, where difference-rewards function (DRF) and policy sharing are designed for enhancing convergence rate and policy stability. Simulation results show that the RoSE has the scalability to positions and numbers of robots. Furthermore, RoSE surpasses existing selection algorithms in network lifetime and time efficiency, while still maintaining comparable MSLL.

Hybrid sand cat‐galactic swarm optimization‐based adaptive maximum power point tracking and blade pitch controller for wind energy conversion system

SummaryAs wind energy is sustainable, pollution‐free, easily available, and free of cost, it has become an efficient source of renewable energy for electricity generation. But, the problem with wind energy is that it varies with time, seasons, and location. This makes the Wind Energy Conversion System (WECS) unstable as it frequently needs to match the load demands. The balance in power generation by wind energy is essential since it has to be connected to various grids. So, this unbalanced energy production can affect the stability of the associated power grids as well. It also results in expensive regulatory measures, storage options, and load shedding. So, the stable operation of the WECS is highly essential to adapt it as a trustable source of electricity production. The stable operation of the WECS requires a robust and advanced system for control. Better control of the wind power extracting model is achieved by controlling the Maximum Power Point Tracking (MPPT) and blade pitch. So, an Adaptive MPPT and Blade Pitch Controller (BPC) for the WECS have been developed in this article, with the support of a hybrid optimization algorithm. In order to enhance the working principles of this controller, two effective algorithms such as Sand Cat Swarm Optimization (SCSO) and Galactic Swarm Optimization (GSO) are integrated and named Hybrid Sand Cat Galactic Swarm Optimization (HSC‐GSO). With the help of the recommended HSC‐GSO, the functionality of the controller is enhanced and also at the same time this algorithm helps to optimize the three gains in the Proportional Integral Differential (PID) controller of both MPPT and BPC, respectively. Moreover, with the support of the proposed HSC‐GSO the damping oscillations in the WECS output power and voltage are minimized. In the end, the numerical analysis is conducted for the presented system by comparing it with the traditional techniques. From the overall result analysis, the stability of the recommended adaptive WECS is 97, which is higher than the conventional algorithms such as DHOA, SCSO, GSO, and DA. Thus, it has been proved that the proposed HSC‐GSO algorithm for the parameters optimization in the PID controller of MPPT and the PID controller of BPC attains high robustness, increased steady‐state stability, and efficient transient response than the traditional techniques.

Pharmacophore-Assisted Covalent Docking Identifies a Potential Covalent Inhibitor for Drug-Resistant Genotype 3 Variants of Hepatitis C Viral NS3/4A Serine Protease.

The emergence of drug-resistance-inducing mutations in Hepatitis C virus (HCV) coupled with genotypic heterogeneity has made targeting NS3/4A serine protease difficult. In this work, we investigated the mutagenic variations in the binding pocket of Genotype 3 (G3) HCV NS3/4A and evaluated ligands for efficacious inhibition. We report mutations at 14 positions within the ligand-binding residues of HCV NS3/4A, including H57R and S139P within the catalytic triad. We then modelled each mutational variant for pharmacophore-based virtual screening (PBVS) followed by covalent docking towards identifying a potential covalent inhibitor, i.e., cpd-217. The binding stability of cpd-217 was then supported by molecular dynamic simulation followed by MM/GBSA binding free energy calculation. The free energy decomposition analysis indicated that the resistant mutants alter the HCV NS3/4A-ligand interaction, resulting in unbalanced energy distribution within the binding site, leading to drug resistance. Cpd-217 was identified as interacting with all NS3/4A G3 variants with significant covalent docking scores. In conclusion, cpd-217 emerges as a potential inhibitor of HCV NS3/4A G3 variants that warrants further in vitro and in vivo studies. This study provides a theoretical foundation for drug design and development targeting HCV G3 NS3/4A.

Active Equalization for Lithium-Iron Battery Pack Based on Reduced-Order Solving Strategy for the Hanoi Tower Problem

In order to address the energy imbalance issue of a series-connected lithium-iron battery pack, this paper proposes an active equalization method based on a reduced-order solving strategy for the Hanoi Tower problem. The proposed scheme utilizes a combined structure of a switching-network circuit and a bidirectional Cuk converter and leverages an ultracapacitor cell as the energy-transfer carrier. Simulation and comparison demonstrate that the exchange of unbalanced energy within the battery pack can be achieved. The proposed approach can effectively achieve various balancing modes such as cell-to-cell, cell-to-string, string-to-cell, and string-to-string with a relatively fast balancing speed.

Optimized diagnosis of local anomalies in charge and discharge of solar cell capacitors

BackgroundWith the increasingly serious environmental pollution and natural environment damage, renewable energy such as solar cells have gradually become the key to change this situation. Therefore, the local abnormal diagnosis of the charge and discharge of solar cell capacitors is particularly important. ObjectiveTo extend the life of ultracapacitors by resolving the issue of their low detection rate and enhancing the capacity to recognize fault diagnosis factors. A novel approach to charging and discharging, as well as the diagnosis of local anomalies, is put forth, utilizing switching networks. MethodsBy controlling the capacitors of multiple solar cells and supercapacitors to work together, it is possible to compensate for the shortage of photovoltaic power. The performance of fault diagnosis is optimized by combining principal component analysis and binary K-means clustering, which completes the fault diagnosis of capacitors. ResultsThe experimental results show that the research method can increase the maximum output power of photovoltaic by 32.9% under multi-layer shadows. In the charging state of the training set, the number of abnormal capacitors is 6, and the number of normal capacitors is 12, and both of them are in accordance with the preset value. The number of abnormal capacitors and normal capacitors in the discharge state is the same as that in the charging state, which is also 6 and 12. ConclusionThe research method can effectively address the issue of unbalanced energy storage battery packs and minimize the impact of local shadows on photovoltaic systems. In comparison to fuzzy C-means clustering, this method requires fewer iterations, enables faster fault diagnosis, and produces more accurate clustering results. It can provide technical support for diagnosing local abnormalities in the charging and discharging of solar cell capacitors.

Energy import dependence, renewable energy deployment, and carbon emissions in G20 countries

AbstractAgainst the background of unbalanced global energy distribution and increased exogenous shocks to energy trade, major countries bear special responsibilities for energy security and the achievement of greenhouse gas emission reduction under the Paris Agreement. Stabilizing energy supply chains and deploying renewable energy on a large scale are seen as fundamental ways to address both issues. To this end, this paper uses a sample of G20 countries covering the period 1980–2020 to estimate the links between energy import dependence, renewable energy deployment, and carbon emissions per capita. The results indicate that the energy import dependence of G20 countries can act as a combined brake on carbon emissions in large developed countries and emerging market countries, suggesting that global energy trade also has an additional role in reducing carbon emissions. Oil import dependence as one of the robustness check strategies further confirms these findings. The impact mechanism estimates imply that the channel for reducing carbon emissions of energy import dependence was to prompt countries to accelerate renewable energy deployment. Within the established framework of energy demand and economic growth, the universal deployment of renewable energy is a fundamental way to achieve environmental friendliness. These findings provide insights into multinational coordinated policies that ensure open and cooperative international energy markets and a transnational energy trading system that puts humanity on a path to global carbon neutrality.

Energy-efficient Communication in an Underwater Acoustic Sensor Network

Energy efficiency is one of the major concerns in UWSNs due to limited energy budget of the underwater sensor nodes. In this paper, the solution of avoiding energy holes in UWSNs is discussed while taking in to consideration of the unique characteristics of the underwater channel.so here proposed Even Data Distribution scheme to tackle the energy holes created due to unbalanced energy consumption in UWSNs. Using this scheme, to balance the energy usage through the network provided that sensors can use multiple transmission range when they send or forward the periodically generated data. In this proposed scheme the sensor node can able to adjust their transmission range up to three possible levels. And also determine the set of possible next hops with the associated load weights that lead to a fair energy consumption among all under water sensor. Hence energy holes can be avoided and consequently the network lifetime is highly increased.

Design of PV, Battery, and Supercapacitor-Based Bidirectional DC-DC Converter Using Fuzzy Logic Controller for HESS in DC Microgrid

Renewable energy sources (RES) are becoming more popular globally as a reaction to critical energy concerns. Modern energy management technologies are used to maximize their efficiency while preserving the reliability of the grid. A hybrid energy storage system (HESS) connects to the DC microgrid through the bidirectional converter, allowing energy to be transferred among the battery and supercapacitor (SC). In this paper, a fuzzy logic control (FLC) technique is developed for PV-based DC microgrid systems that use both batteries and SCs. The proposed method uses the unbalanced energy from the battery pack to enhance the overall effectiveness of the HESS. The FLC approach is performed to validate under conditions of variable irradiance using MATLAB Simulink. When sudden changes in irradiance occur, the proposed FLC brings the voltage back to the desired level in terms of transient response like 33 ms settling times and 19% overshoot values. The results exhibit that the proposed method is more efficient in terms of time response, power output, increasing battery life, and ensuring a continuous supply of the PV system.

YOUTH NUTRITIONAL STATUS WITH ENERGY INTAKE AND CALORIE DRINK IN INDONESIA: A CROSS-SECTIONAL STUDY

Background: Adolescents often experience an imbalance between intake and energy expenditure, resulting in the problem of excess nutrition. Unbalanced energy intake and expenditure and lack of nutritional knowledge are the main factors causing over nutrition. The results of research on National Ministry data showed that the nutritional status of adolescents aged 12-18 years who were over-nourished was 19.1% and obesity was 31%. However, the nutritional status of over-nutrition at the provincial level in Banten was 20.9% and obesity was 30%. Purpose: The purpose of this study is to determine the relationship between energy intake, macronutrients, calorie drinks and nutritional knowledge on the nutritional status of adolescents aged 15-18 years in Sukabakti Sub district, Tangerang Regency. Methods: Sample was taken by using Proportional Random Sampling technique as many as 97 samples. The variables studied were energy intake, macronutrients, calorie drinks and nutritional knowledge on adolescent nutritional status. This type of research used quantitative research with Chi-Square analysis test. Results: In this study, there was no significant (P > 0.05) between energy intake (P = 0.827), protein intake (P = 468), fat intake (P = 0.849), carbohydrate intake (P = 0.449), calorie drinks (P = 0.227), and no relationship between nutritional knowledge and nutritional status (P = 0.231). Conclusion: Since adolescent nutritional status is influenced by several factors. not only intake and knowledge, but also influenced by physical activity, infectious diseases, attitudes, parents, environment and many more.

Microstructure and molten pool morphology of TiB2/ AlSi7Mg composites fabricated by infrared-blue hybrid laser powder bed fusion

A hybrid laser composed of infrared and blue laser is applied in fabricating TiB2/AlSi7Mg composites on AlSi7Mg substrate by LPBF. The effect on formability, molten pool morphology, molten pool size and microstructure under infrared, blue and hybrid laser were compared. It was confirmed that hybrid laser can make up for the unbalanced energy distribution of infrared laser and the low energy density of blue laser. The increased energy input improves the molten pool size and cellular dendrites size. Therefore, the hybrid laser can improve the formability and forming stability in the LPBF process of low absorption rate alloys.

Adaptive Time-Varying Routing for Energy Saving and Load Balancing in Wireless Body Area Networks

Routing plays an essential role in ensuring normal and lasting operation of wireless body area networks (WBANs). However, existing routing schemes cause inefficient and unbalanced energy dissipation, which contributes to premature death of some nodes and high temperature within a small area of the body. In this article, we propose an adaptive time-varying routing (ATVR) protocol to address these issues. Unlike in conventional routing solutions, in our protocol a node may act as different roles (source node or relay node) and select different paths in disparate periods. This dynamic routing pattern helps to achieve a globally optimal routing solution. In ATVR, a node evaluation function and a path evaluation function are designed to reflect the node state and the path state respectively. Then, the path selection problem is transformed into a Hitchcock transportation problem, in which the nodes with worse node state act as source nodes (i.e., producers) and the nodes with better node state act as relay nodes (i.e., consumers). Then, this Hitchcock transportation problem is addressed by the AlphaBeta algorithm, in which the paths with less energy consumption and less path loss are selected to forward data. The experimental results show that our protocol has better performance in terms of energy consumption, network lifetime, and node temperature.

A Genetic-Algorithm-Based Dynamic Transmission of Data for Communicable Disease in IoMT Environment

Recent advancements in the field of the Internet of medical things (IoMT) have enabled the real-time monitoring and treatment of patients with communicable infectious diseases while minimizing human intervention. However, IoMT devices face challenges such as unbalanced energy consumption, memory constraints, computation power, and low latency, which can deter the efficient transfer of patient monitoring data. Thus, there is an urgent need to establish an energy-efficient infrastructure for IoMT devices to remotely monitor and collect data on communicable diseases. For this, a genetic algorithm (GA) based dynamic transmission of data for communicable diseases in the IoMT environment is proposed in this paper. The energy utilization of the IoMT is enhanced by considering the GA evolutionary processing based on the dynamic sensor range. The proposed work incorporates a periphery of the fixed area for deploying the IoMT devices to settle the energy hole problem. Multiple sinks and direct information collection concepts are also introduced which further improve the performance and reduce the movement of data packets. The proposed protocols not only optimize energy usage but also provide a robust approach for massive data collection and communication.

A Novel Hybridization of ML Algorithms for Cluster Head Selection in WSN

Generally, Wireless Sensor Networks (WSNs) are infrastructure-less networks with thousands of sensor nodes that sense or monitor the physical and environmental changes and forward the collected data to a central node. Besides, WSN has become the most efficient technology for handling Internet of Things (IoT) devices. Still, challenges such as node failures, high traffic among the nodes, link failures, etc., limit the performance of WSNs. To solve the challenges in WSN, this paper aims to develop a novel non-uniform clustering model, where the Cluster Heads (CHs) are selected based on the candidate CH selection strategy that transfers the data. Moreover, unbalanced energy utilization and data redundancy are eliminated via multi-hop communication. For attaining the non-uniform clustering model, the routing among the data packets is done by the efficiency of the hybridization of the Machin Learning (ML) algorithms viz Genetic Algorithm (GA) and Lion Algorithm (LA) with the consideration of energy, cost, time, network lifetime, and data accuracy. Finally, the performance of the proposed model is verified and validated through a comparative study with the existing models.

HP-ECD: Heuristic Prophet protocol based on energy balance,cache optimization, and asynchronous dormancy

In opportunistic networks, opportunistic routing protocols are used for nodes to forward messages, which are the core technology to realize various opportunistic network applications. However, the existing opportunistic routing protocols generally have three problems: Key nodes forwarded too many messages, resulting in unbalanced energy consumption; Delivered message still occupies the cache space of nodes, resulting in the loss of undelivered message; Nodes constantly sense their surroundings and forward messages, resulting in rapid energy depletion. To better solve these problems, this paper proposes HP-ECD: heuristic Prophet routing protocol based on energy balance, cache optimization, and asynchronous dormancy. Based on Prophet, First, the HP-ECD defines the message forwarding benefits based on multiple node attributes and message importance, to realize the energy balance mechanism of nodes. Then, the HP-ECD designs the message delivery list based on the node history information, to realize the cache optimization mechanism of nodes. Finally, the HP-ECD determines whether a node is in dormancy based on the node running status, to realize the asynchronous dormancy mechanism of nodes. Simulation results show that, compared with Epidemic, Prophet, and EC-CW, HP-ECD greatly improves the message delivery rate and average remaining energy, and reduces the routing overhead rate and average storage time.

Performance of energy piles foundation in hot-dominated climate: A case study in Dubai

Energy piles represent an innovative technology that can help provide sustainable geothermal heating or cooling energy for thermal conditioning purposes. In hot-dominated climates, the interest is to inject heat in the ground and extract energy for space-cooling purposes. This study evaluates the feasibility of energy piles in these regions through three-dimensional numerical modelling. The modelling framework is validated against a published experiment and is able to sufficiently capture the development of outlet temperature over time. The numerical analysis is then used to evaluate a case study in Dubai where it is targeted to provide 40 % of the cooling demand of a typical building. The unbalanced energy demand causes an increase in the outlet temperature of the heat carrier fluid and the radial temperature over time. However, observation of long-term behaviour indicates that the temperature increase is most significant in the initial years and gradually stabilizes over time. This stabilization enables to respect the outlet temperature limitation of the heat pump over 50 years. A sensitivity analysis confirms these observations with respect to system dimensioning variables. The obtained results highlight the effectiveness of energy piles to decarbonize energy supply in buildings in hot-dominated climates via the use of renewable energy sources.

Spatial-successive transfer of virtual scarcity water along China's coal-based electric chain

Spatially unbalanced energy supply and demand drive energy transfer. However, the spatial impact of energy transfers on water resources throughout the energy chain remains unexplored. This gap is largely a result of the difficulty in modeling successive transfers of virtual scarcity water (VSW). Thus, a more detailed approach than single or discrete transfer modeling is required. Here, we integrated the water footprint, water stress index, and multi-flow and multi-node models into an analytical framework for a coal-based electric chain (CBEC) to link discrete VSW transfers owing to coal transport and electricity transmission. A case study in China was modeled to illustrate the building mechanism of successive VSW transfers along the CBEC. Our model shows that interprovincial coal transport leads to a VSW transfer of 280.95 Mm3, 8.8 % of which was inherited by a VSW transfer of 388.18 Mm3 for interprovincial electricity transmission. The two-stage transfer results in a 451.42 Mm3 of difference in provincial VSW and the water scarcity footprint. We propose to promote technical-level strategies of cleaner production and technical configuration adjustment, management-level patterns of energy supply chain optimization and power source diversification, and market-level incentives of interprovincial compensation and resource coordination to enhance energy-water co-benefits.

Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN

The Internet of Medical Things (IoMT) is the network of medical devices, hardware infrastructure, and software applications used to connect the healthcare information technology. Massive traffic growth and user expectations cause challenges in the current exhausting models of IoMT data. To reduce the IoMT traffic, Information Centric Network (ICN) is a suitable technique. ICN uses persistent naming multicast communication that reduces the response time. ICN in IoMT provides a promising feature to reduce the overhead due to the distribution of commonly accessed contents. Some parameters such as energy consumption, communication cost, etc., influence the performance of sensors in the IoMT network. Excessive and unbalanced energy consumption degrades the network performance and lifetime. This article presents a framework called Dynamic Cache Scheme (DCS) that implements energy-efficient cache scheduling in IoMT over ICN to reduce network traffic. The proposed framework establishes a balance between the multi-hop traffic and data item freshness. The technique improves the freshness of data; thus, updated data are provided to the end-users via the effective utilization of caching in IoMT. The proposed framework is tested on important parameters, i.e., cache-hit-ratio, stretch, and content retrieval latency. The results obtained are compared with the state-of-the-art models. Results’ analysis shows that the proposed framework outperforms the compared models in terms of cache-hit-ratio, stretch, and content retrieval latency by 59.42%, 32.66%, and 18.8%, respectively. In the future, it is intended to explore the applicability of DCS in more scenarios and optimize further.

Metabolic interactions between organs in overweight and obesity using total-body positron emission tomography.

Overweight and obesity is a complex condition resulting from unbalanced energy homeostasis among various organs. However, systemic abnormalities in overweight and obese people are seldom explored in vivo by metabolic imaging techniques. The aim of this study was to determine metabolic abnormities throughout the body in overweight and obese adults using total-body positron emission tomography (PET) glucose uptake imaging. Thirty normal weight subjects [body mass index (BMI) < 25 kg/m2, 55.47 ± 13.94 years, 16 men and 14 women], and 26 overweight and obese subjects [BMI ≥ 25 kg/m2, 52.38 ± 9.52 years, 21 men and 5 women] received whole-body 18F-fluorodeoxyglucose PET imaging using the uEXPLORER. Whole-body standardized uptake value normalized by lean body mass (SUL) images were calculated. Metabolic networks were constructed based on the whole-body SUL images using covariance network approach. Both group-level and individual-level network differences between normal weight and overweight/obese subjects were evaluated. Correlation analysis was conducted between network properties and BMI for the overweight/obese subjects. Compared with normal weight subjects, overweight/obese subjects exhibited altered network connectivity strength in four network nodes, namely the pancreas (p = 0.033), spleen (p = 0.021), visceral adipose tissue (VAT) (p = 1.12 × 10-5) and bone (p = 0.021). Network deviations of overweight/obese subjects from the normal weight were positively correlated with BMI (r = 0.718, p = 3.64 × 10-5). In addition, overweight/obese subjects experienced altered connections between organs, and some of the altered connections, including pancreas-right lung and VAT-bilateral lung connections were significantly correlated with BMI. Overweight/obese individuals exhibit metabolic alterations in organ level, and altered metabolic interactions at the systemic level. The proposed approach using total-body PET imaging can reveal individual metabolic variability and metabolic deviations between organs, which would open up a new path for understanding metabolic alterations in overweight and obesity.