Energy Supply Research Articles

Neuroprotective and neuroregenerative drugs after severe traumatic brain injury : Anarrative review from aclinical perspective.

Traumatic brain injuries cause enormous individual and socioeconomic burdens. Survivors frequently struggle with motor handicaps as well as impaired cognition and emotion. In addition to the primary mechanical brain damage, complex secondary mechanisms are the main drivers of functional impairment. Many of these pathophysiological mechanisms are now well known: excitotoxic amino acids, breakdown of the blood-brain barrier, neuroinflammation with subsequent damage to cell organelles and membranes, cerebral edema, and apoptotic processes triggering neuronal death; however, paracrine resilience factors may counteract these processes. Specific neuroprotective and neuroregenerative intensive care therapies are few. This review highlights medical approaches aimed at mitigating secondary damage and promoting neurotrophic processes in severe traumatic brain injury. Some pharmacologic attempts that appeared very promising in experimental settings have had disappointing clinical results (progesterone, cyclosporineA, ronopterin, erythropoietin, dexanabinol). Thus, the search for drugs that can effectively limit ongoing posttraumatic neurological damage is ongoing. Some medications appear to be beneficial: N‑methyl-D-aspartate receptor (NMDA) antagonists (esketamine, amantadine, Mg++) reduce excitotoxicity and statins and cerebrolysin are known to counteract neuroinflammation. By supporting the impaired mitochondrial energy supply, oxidative processes are inhibited and neuroregenerative processes, such as neurogenesis, angiogenesis and synaptogenesis are promoted by citicoline and cerebrolysin. First clinical evidence shows an improvement in cognitive and thymopsychic outcomes, underlined by own clinical experience combining different therapeutic approaches. Accordingly, adjuvant treatment with neuroprotective substances appears to be apromising option, although more randomized prospective studies are still needed.

Mitochondrial respiration is lower in the intrauterine growth-restricted fetal sheep heart.

Fetuses affected by intrauterine growth restriction have an increased risk of developing heart disease and failure in adulthood. Compared with controls, late gestation intrauterine growth-restricted (IUGR) fetal sheep have fewer binucleated cardiomyocytes, reflecting a more immature heart, which may reduce mitochondrial capacity to oxidize substrates. We hypothesized that the late gestation IUGR fetal heart has a lower capacity for mitochondrial oxidative phosphorylation. Left (LV) and right (RV) ventricles from IUGR and control (CON) fetal sheep at 90% gestation were harvested. Mitochondrial respiration (states 1-3, LeakOmy, and maximal respiration) in response to carbohydrates and lipids, citrate synthase (CS) activity, protein expression levels of mitochondrial oxidative phosphorylation complexes (CI-CV), and mRNA expression levels of mitochondrial biosynthesis regulators were measured. The carbohydrate and lipid state 3 respiration rates were lower in IUGR than CON, and CS activity was lower in IUGR LV than CON LV. However, relative CII and CV protein levels were higher in IUGR than CON; CV expression level was higher in IUGR than CON. Genes involved in lipid metabolism had lower expression in IUGR than CON. In addition, the LV and RV demonstrated distinct differences in oxygen flux and gene expression levels, which were independent from CON and IUGR status. Low mitochondrial respiration and CS activity in the IUGR heart compared with CON are consistent with delayed cardiomyocyte maturation, and CII and CV protein expression levels may be upregulated to support ATP production. These insights will provide a better understanding of fetal heart development in an adverse in utero environment. KEY POINTS: Growth-restricted fetuses have a higher risk of developing and dying from cardiovascular diseases in adulthood. Mitochondria are the main supplier of energy for the heart. As the heart matures, the substrate preference of the mitochondria switches from carbohydrates to lipids. We used a sheep model of intrauterine growth restriction to study the capacity of the mitochondria in the heart to produce energy using either carbohydrate or lipid substrates by measuring how much oxygen was consumed. Our data show that the mitochondria respiration levels in the growth-restricted fetal heart were lower than in the normally growing fetuses, and the expression levels of genes involved in lipid metabolism were also lower. Differences between the right and left ventricles that are independent of the fetal growth restriction condition were identified. These results indicate an impaired metabolic maturation of the growth-restricted fetal heart associated with a decreased capacity to oxidize lipids postnatally.

Fast switching hydrogen gas leakage identification using FPGA

Hydrogen will play a key part in the decarbonization of the global energy supply as the wealth of hydrogen continues to rise. However, applying hydrogen knowledge necessitates the installation of strong safety protocols, including the use of reliable hydrogen gas-detecting devices. Gas detection and monitoring at a large scale in industries need programmable electronic systems that can provide fast response and switching. The objective of the research work is to analyze the hydrogen gas detection system using a field programmable gate array (FPGA). The system is integrated with the gas sensor module, temperature control module, buzzer for alarm, liquid crystal display (LCD) and light-emitting diode (LED) for information display, and global system for mobile communication (GSM) for wireless message communication to the mobile. The temperatures module works on the sampling of the heater resistance using a voltage divider and inbuilt analog-to-digital converter (ADC), which depend on the output voltage of the digital-to-analog converter (DAC). The comparator module compares the gas concentration with the pre-decided threshold limit and acknowledges the buzzer accordingly. The system behavior for monitoring and gas concentration detection is simulated in Xilinx Vivado design suite 2020.1. The functionality of the chip is also verified in the Modelsim 10.0 waveform simulator. for the testbench tata sand samples used. LCD (16x2) displays the messages whether hydrogen gas is detected or not and the same message is delivered on mobile. The novelty of the work is that the system is applicable for faster switching in industrial applications and the designed system supported 550 MHz frequency in our simulation and synthesis.

Optimizing solar panel orientation for enhanced power output in cloudy conditions: a case study on solar energy supply in a water treatment plant

Optimizing solar panel orientation for enhanced power output in cloudy conditions: a case study on solar energy supply in a water treatment plant

Thermal Cycling Test of Solar Salt in Contact with Sustainable Solid Particles for Concentrating Solar Power (CSP) Plants

Thermal energy storage (TES) is crucial in bridging the gap between energy demand and supply globally. Concentrated Solar Power (CSP) plants, employing molten salts for thermal storage, stand as an advanced TES technology. However, molten salts have drawbacks like corrosion, solidification at lower temperatures, and high costs. To overcome these limitations, research is focusing on alternative TES materials such as ceramic particles. These solids match molten salts in energy density and can withstand higher temperatures, making them well-suited for CSP systems. This study revolves around subjecting Solar Salt alone and Solar Salt alongside Volcanic Ash (VA) and Electric Arc Furnace Slag (EAFS) to a comprehensive thermal cycling test. This test is designed to assess the compatibility over the thermal cycles of the Solar Salt and the Solar Salt in contact with these solids in a CSP plant with a thermocline configuration. With a final thermal and chemical evaluation, our observations indicate that EAFS and VA demonstrate promising compatibility but an increase in the reduction rate of the Solar Salt due to a catalyst effect from EAFS in contact with the salt. No discernible alterations were detected in the properties of either the solid materials or solar salt when combined.

Global Development and Readiness of Nuclear Fusion Technology as the Alternative Source for Clean Energy Supply

Global Development and Readiness of Nuclear Fusion Technology as the Alternative Source for Clean Energy Supply

Quantifying the resilience of coal energy supply in China towards carbon neutrality

Quantifying the resilience of coal energy supply in China towards carbon neutrality

Analysis of Energy Consumption and Economy of Regional Gas Tri-Supply Composite System

With the development of Chinese society, there is an increasing demand for emissions reduction and the stable operation of the power grid. Regional comprehensive energy supply systems have entered the public’s view owing to their advantages of reducing capacity, unified dispatch, improving efficiency, and reducing energy consumption. This paper focuses on a system under construction in Chongqing, which adopts a combined gas tri-supply (combined cooling, heat, and power, CCHP) and dynamic ice storage cooling system as the research object. By establishing a mathematical model for the simulation research, this study examines the start–stop priority sequence of the gas tri-supply subsystem and the heat pump subsystem under the ice storage priority strategy in winter and summer and proposes corresponding optimization solutions. By comparing the annual operating energy consumption of the system, we conclude that the gas tri-supply composite system has good economic efficiency and peak-shaving capability, indicating that regional gas tri-supply composite systems have great application potential in the future. The proposed optimized operation strategy and simulated energy consumption calculation provide theoretical guidance for the construction and operation of both this project and similar projects.

Анаеробні можливості дівчат постпубертатного періоду онтогенезу мешканок гірських районів Закарпаття

Anaerobic productivity is characterized by the body’s ability to perform muscle work due to anaerobic processes of energy supply and is characterized by their power and capacity. Data on the age-related dynamics of the body’s anaerobic productivity are contradictory. Studying the anaerobic capabilities of girls in the mountainous regions of Zakarpattia Oblast, we understand that the somatotype and functional capabilities of the body are formed in this category of persons in conditions of relative hypoxia. Szymczak R.K. et al researching the effect on the physiology of the body of a long stay at a very high altitude (over 3500 m), male climbers note that in conditions of hypoxia, the anaerobic productivity of the body decreases, the maximum respiratory volume increases, and the relative content of the fat component of the body weight decreases. Therefore, the study of indicators of anaerobic productivity of the body’s energy supply in healthy girls may allow developing individual and population medical prognosis in the future. The level of physical health was assessed by indicators of anaerobic productivity of the body, assessing the ability of women living in mountainous regions to adapt to performing physical work in an anaerobic mode. For this purpose, we used the Wingat anaerobic test method described by Yu.M. Furman et al, the method of Shogy A., Cherebetin G. and somatotype was determined according to Heath-Carter. Having studied the anaerobic capabilities of 102 girls aged 16 to 20, residents of the mountainous regions of the Transcarpathian region. we noted that the performance of physical work in the anaerobic regime of the body’s energy supply depends on the somatotype. The power of anaerobic alactate and lactate processes turned out to be the highest in girls with an endomesomorphic somatotype. The power of anaerobic alactate and lactate processes turned out to be the lowest in girls with an ectomorphic somatotype. Studying the level of anaerobic productivity of females based on the relative indicator of the capacity of anaerobic lactate processes of energy supply showed that girls with a balanced somatotype have the lowest level of anaerobic capacity compared to girls of other somatotypes.

Influence of morbidity, early nutritional intake, and total energy: protein ratio on longitudinal extrauterine growth restriction of very preterm newborns at term-equivalent age: an observational study.

To evaluate the influence of early nutritional intake on the growth pattern of very preterm infants. This was an observational study including 109 newborns (< 32weeks gestational age). Perinatal morbidities, nutritional therapy (first four weeks of life), and weight, length, and head circumference (HC) growth at term-equivalent age were evaluated. Growth restriction was defined as a difference > 1.2 SD between the birth and term age measurements. Growth restriction at term-equivalent age: 52.3% (weight), 42.9% (length), and 22% (HC). Morbidities were positively correlated with nutrition therapy and negatively correlated with the total energy provision: protein ratio. The duration of parenteral nutrition, the time to reach full enteral feedings, and the total energy provision: protein ratio were significantly correlated. Nutrient intake influenced weight, length, and HC growth, and cumulative energy deficit was significantly associated with HC growth restriction. Conclusion: Perinatal morbidities interfere with nutritional therapy and early nutrient intake, leading to insufficient energy and energy provision: protein ratio for growth. What is Known: • The intake of macronutrients early in life, mainly protein, is important for the optimal growth of pretem infants. • The severity of morbidities and low gestational ages impact the nutritional management of preterm infants. What is New: • The number of morbidities, reflecting the severity of the neonatal clinical course, had a detrimental effect on the nutritional therapy and nutrients intake. • The inadequate energy provision per gram of protein ratio was significantly associated with growth restriction in all growth measures at the second week of life, persisting for head circumference up to the fourth week, highlighting the importance of its measurement, as it could be a precocious sign of development risk.

Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms

In the pursuit of sustainable development, accurate renewable energy demand forecasting holds great significance for climate change mitigation and promoting sustainability. However, renewable energy forecasting has been consistently challenged by seasonality and nonlinearity. Identifying the periodic and nonlinear characteristics concealed within renewable energy sources accurately is still an unexplored problem. Consequently, an innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting, which incorporates seasonal dummy variables and a power exponent term for handling the seasonality and nonlinear patterns in time series. Furthermore, an intelligent algorithm matching framework is proposed to augment the flexibility of the newly developed model. For practical purposes, the new methodology is contrasted against a range of benchmarks encompassing statistical, machine-learning, and traditional grey models in forecasting the quarterly total renewable energy consumption in the United States. The proposed model exhibits over 27% improvement rates over its counterparts, achieving the most superior predictive accuracies of 1.45%, 39.27, and 0.79 in MAPEP, RMSEP, and MASEP metrics, respectively. Furthermore, the probability density and sample size analyses are conducted to validate the robustness of the new model, confirming its adaptability and stability towards algorithm randomness and historical information volume. Consequently, the novel model is employed to forecast the short-to-long-terms renewable energy consumption in the U.S., showcasing an upward trend and seasonal fluctuations of the consumption for the forthcoming 24 quarters from 2023Q4 to 2029Q3. These insights can offer valuable implications to the stakeholders such as energy suppliers, utility managers, and policy advocates, highlighting actionable strategies for optimizing renewable energy consumption forecasting and aiding sustainable development initiatives.

Features of the Contract for Supply of Energy and Other Resources through an Attached Network

Features of the Contract for Supply of Energy and Other Resources through an Attached Network

The concept, legal features and meaning of the energy supply contract

The concept, legal features and meaning of the energy supply contract

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

Biodiesel supply chain network design: a comprehensive review with qualitative and quantitative insights.

The global community is actively pursuing alternative energy sources to mitigate environmental concerns and decrease dependence on fossil fuels. Biodiesel, recognized as a clean and eco-friendly fuel with advantages over petroleum-based alternatives, has been identified as a viable substitute. However, its commercialization encounters challenges due to costly production processes. Establishing a more efficient supply chain for mass production and distribution could surmount these obstacles, rendering biodiesel a cost-effective solution. Despite numerous review articles across various renewable energy supply chain domains, there remains a gap in the literature specifically addressing the biodiesel supply chain network design. This research entails a comprehensive systematic literature review (SLR) focusing on the design of biodiesel supply chain networks. The primary objective is to formulate an economically, environmentally, and socially optimized supply chain framework. The review also seeks to offer a holistic overview of pertinent technical terms and key activities involved in these supply chains. Through this SLR, a thorough examination and synthesis of existing literature will yield valuable insights into the design and optimization of biodiesel supply chains. Additionally, it will identify critical research gaps in the field, proposing the exploration of fourth-generation feedstocks, integration of multi-channel chains, and the incorporation of sustainability and resilience aspects into the supply chain network design. These proposed areas aim to address existing knowledge gaps and enhance the overall effectiveness of biodiesel supply chain networks.

Extracting building outlines based on convolutional neural networks using the property of linear connectivity

Abstract. For years, researchers have been developing an automated method that can replace humans by drawing the outlines of individual buildings in a vector format, which plays an important role in GIS creation, environmental monitoring, urban planning, population density estimation, and energy supply. There is no doubt that this is an extremely difficult task, not only because of the labor required to develop such a highly intelligent algorithm, but also because of the challenges posed by imperfect imaging conditions, different building structures, and the complexity of the background. One of the current challenges in extracting building outlines is to accurately recreate the polygonal boundaries of the building while extracting vectorized building masks as output for direct use in various applications. This work provides a comprehensive workflow for building extraction and improves the predicted area of buildings through boundary regularization. First, a convolutional neural network is used to train instance segmentation model, then regularization and vectorization processes are performed. The main difference from existing methods is a new regularization method based on the concepts of linear connectivity and convexity of a set of points. This approach can effectively identify and remove points that do not belong to the detected building but were incorrectly segmented by the algorithm. Based on the results of experiments, the algorithm showed a high level of efficiency, comparable to leading methods for extracting building boundaries as PolyWorld.

Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data

In the context of carbon neutrality target, renewable energy sources have been transforming from “supplementary energy” to “main energy”, which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the “resource-environment-demand” comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry.

OPERATE–H2: A tool for optimizing underground hydrogen storage

Underground hydrogen storage (UHS) is crucial in facilitating the transition to clean energy by reducing reliance on fossil fuels and ensuring a stable supply of emission-free energy. However, safe and efficient storage conditions require careful planning. While reservoir simulations could determine optimal storage conditions, exploring all possible scenarios can be computationally prohibitive and time-consuming. Reduced-order models (ROMs) can efficiently simulate reservoir performance and analyze multiple strategies with reduced computational cost, providing valuable insights for UHS decision-making. This paper introduces an innovative integrated assessment framework with a user-friendly graphical user interface (GUI), designed to optimize UHS operations. The GUI features several advanced functionalities, including (1) temporal evolution for visualizing and analyzing UHS performance over withdrawal cycles, (2) local sensitivity analysis to determine the impact of individual parameters on UHS sites while keeping other inputs constants, (3) uncertainty quantification to identify the key factors for effective decision-making via tornado plots, (4) global sensitivity analysis to comprehensively quantify the interactions between geologic and operational parameters and their effect on UHS performance, and (5) sites screening feature to identify the most prominent potential sites for UHS installations. The GUI is built based on ROMs and the PyQt5 packages in Python. With user-friendly design and advanced features, Optimization, Evaluation, and Risk-Assessment Techniques for Hydrogen Energy tool (OPERATE–H2) enables users to evaluate different UHS scenarios quickly and effectively, helping to accelerate the transition to a cleaner and more sustainable energy future.

Dynamics in Redox-Active Molecules Following Ischemic Preconditioning in the Brain

It is well known that the brain is quite vulnerable to oxidative stress, initiating neuronal loss after ischemia-reperfusion (IR) injury. A potent protective mechanism is ischemic preconditioning (IPC), where proteins are among the primary targets. This study explores redox-active proteins’ role in preserving energy supply. Adult rats were divided into the control, IR, and IPC groups. Protein profiling was conducted to identify modified proteins and then verified through activity assays, immunoblot, and immunohistochemical analyses. IPC protected cortex mitochondria, as evidenced by a 2.26-fold increase in superoxide dismutase (SOD) activity. Additionally, stable core subunits of respiratory chain complexes ensured sufficient energy production, supported by a 16.6% increase in ATP synthase activity. In hippocampal cells, IPC led to the downregulation of energy-related dehydrogenases, while a significantly higher level of peroxiredoxin 6 (PRX6) was observed. Notably, IPC significantly enhanced glutathione reductase activity to provide sufficient glutathione to maintain PRX6 function. Astrocytes may mobilize PRX6 to protect neurons during initial ischemic events, by decreased PRX6 positivity in astrocytes, accompanied by an increase in neurons following both IR injury and IPC. Maintained redox signaling via astrocyte-neuron communication triggers IPC’s protective state. The partnership among PRX6, SOD, and glutathione reductase appears essential in safeguarding and stabilizing the hippocampus.

A Multifunctional Nanoreactor‐Induced Dual Inhibition of HSP70 Strategy for Enhancing Mild Photothermal/Chemodynamic Synergistic Tumor Therapy

AbstractMild photothermal therapy (PTT) is a spatiotemporally controllable method that utilizes the photothermal effect at relatively low temperatures (40‐45 °C) to especially eliminate tumor tissues with negligible side effects on the surrounding normal tissues. However, the overexpression of heat shock protein 70 (HSP70) and limited effect of single treatment drastically impeded the therapeutic efficacy. Herein, the constructed multifunctional core‐shell structured Ag‐Cu@SiO2‐PDA/GOx nanoreactors (APG NRs) that provide a dual inhibition of HSP70 strategy for the second near‐infrared photoacoustic (NIR‐II PA) imaging‐guided combined mild PTT/chemodynamic therapy (CDT). The Ag‐Cu cores can convert endogenous H2O2 to hydroxyl radical (•OH), which could induce lipid peroxidation (LPO) and further degrade HSP70. The polydopamine (PDA)/glucose oxidase (GOx) shells were utilized as the NIR‐II photothermal agent to generate low temperature, and the GOx could reduce the energy supplies and inhibit energy‐dependent HSP70 expression. Furthermore, both the generation of •OH and GOx‐mediated energy shortage can reduce HSP70 expression to sensitize mild PTT under 1064 nm laser, and in turn, GOx and laser self‐amplify the catalytic reactions of APG NRs for more production of •OH. The multifunctional nanoreactors will provide more potential possibilities for the clinical employment of mild PTT and the advancement of tumor combination therapies.This article is protected by copyright. All rights reserved