High Energy Demand Research Articles

Predicting wind turbine energy production with deep learning methods in GIS: A study on HAWTs and VAWTs

The increasing global demand for renewable energy necessitates accurate forecasting methods to optimize wind energy production, particularly in regions with varying climatic conditions. This study addresses this need by utilizing advanced deep learning techniques and Geographical Information Systems (GIS) to estimate the energy output of wind turbines. Specifically, it focuses on predicting the energy production of both horizontal axis wind turbines (HAWTs) and vertical axis wind turbines (VAWTs) using a combination of Markov and Cellular Automata-Markov (CA-Markov) models, alongside deep learning methods such as long short-term memory (LSTM), LSTM-Wavelet, and Support Vector Regression (SVR). Additionally, the study evaluates the energy output of each turbine type, factoring in their construction costs within the study area. The analysis reveals significant variations in energy output over time, with maximum values increasing from 85,017 Wh in 2000 to 166,050 Wh in 2020 in the northern region, while minimum outputs also rose significantly. Projections for 2030 suggest that approximately 17% of the northern region experience a substantial increase in wind power potential. Among the forecasting methods, the LSTM-Wavelet hybrid model demonstrated superior accuracy, surpassing the 90% threshold, primarily due to its effective handling of data instability and noise reduction. This study underscores the potential of using sophisticated modeling techniques to enhance wind energy forecasting, contributing to more efficient energy management in regions with high energy demand and limited resources.

Cobalt Ferrite@Barium titanate core-shell nanoparticles empowered triboelectric electromagnetic coupled nanogenerator for self-powered electronics

In pursuing sustainable energy solutions for wearable technology and Internet of Things (IoT) devices, achieving high power in self-powered electronics is a significant challenge. This study introduces a novel hybrid nanogenerator by integrating a triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG) to achieve both high voltage and high current outputs simultaneously. The cobalt ferrite barium titanate (CoFe2O4@ BaTiO3) core–shell nanoparticles (NPs) were chosen for their unique ability to enhance the system’s performance by combining triboelectric, piezoelectric, and electromagnetic properties. The BaTiO3 shell exhibited robust triboelectric and piezoelectric properties essential for TENG operation, while the CoFe2O4 core, influenced by the EMG’s magnetic field, induces additional static charges, significantly boosting overall energy conversion efficiency. The TENG and EMG in the combined system operate together in a contact-separation mode, where their in-phase output signals are seamlessly combined via a double bridge rectifier, resulting in a robust power output of 37.7 mW and a power density of 1.86 mW/cm2, with cycling stability of over 10,000 cycles. The uniquely designed TENG-EMG combined nanogenerator (C-TENG) exhibited striking adaptability, effectively operating across different low-to-high frequencies and load situations while constantly powering portable electronic devices. This innovative approach meets the high energy demands of modern self-powered electronics with exceptional efficiency, durability, and high-power density, positioning it as a versatile solution for wearable biomechanical energy harvesting.

Identification of Cause–Effect Relationships between Process Parameters and the Film Formation in the Semidry Electrode Production for Lithium‐Ion Batteries

Conventional electrode production for lithium‐ion batteries has high energy and plant space demand, due to the high solvent content of the slurry to be processed by slot die or doctor blade coating. By using the semidry electrode production, the solvent content is reduced by more than 50% compared to the conventional electrode production, decreasing energy demand and drying length. Regarding technology readiness level, the semidry electrode production is on the pilot scale, as the basic principles have been shown. However, many unknown cause–effect correlations exist between the process parameters and the product properties. This study aims to analyze process parameter variations and their influence on the geometric, electrochemical, and mechanical properties of the electrode. An experimental design is utilized to obtain statistically relevant conclusions. It is found that the first calender gap and the roller speed influence the mass loading and the porosity of the electrode. The roller speed significantly influences the ionic resistance within the electrode, which may be attributed to the used release foil.

ANÁLISE DE RENTABILIDADE DE INVESTIMENTOS EM SISTEMAS DE REFRIGERAÇÃO COM ALTA EFICIÊNCIA ENERGÉTICA

This study analyzes the profitability of investments in high-energy-efficiency refrigeration systems, addressing the economic and environmental impacts of these systems compared to conventional technologies. Energy efficiency is essential for reducing operational costs and promoting sustainability, especially in sectors with high continuous refrigeration demand. The objective is to evaluate the economic feasibility of efficient systems, highlighting variables that influence the return on investment. A descriptive and qualitative literature review was conducted, analyzing studies on energy efficiency, environmental impact, and cost-benefit in commercial and industrial sectors. The findings indicate that, although the initial investment in high-efficiency technologies is high, these systems significantly reduce energy consumption and operational costs over time. It concludes that adopting efficient refrigeration systems is economically viable across various sectors, especially those with high energy demand, and an effective strategy for meeting environmental requirements and reducing emissions.

High mobility group A1 (HMGA1) promotes the tumorigenesis of colorectal cancer by increasing lipid synthesis.

Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to meet the high energy and biosynthetic demands required for their proliferation. High mobility group A1 (HMGA1) is a structural transcription factor and frequently overexpressed in human colorectal cancer (CRC). Here, we show that HMGA1 promotes CRC progression by driving lipid synthesis in a AOM/DSS-induced CRC mouse model. Using conditional knockout (Hmga1△IEC) and knock-in (Hmga1IEC-OE/+) mouse models, we demonstrate that HMGA1 enhances CRC cell proliferation and accelerates tumor development by upregulating fatty acid synthase (FASN). Mechanistically, HMGA1 increases the transcriptional activity of sterol regulatory element-binding protein 1 (SREBP1) on the FASN promoter, leading to increased lipid accumulation in intestinal epithelial cells. Moreover, a high-fat diet exacerbates CRC progression in Hmga1△IEC mice, while pharmacological inhibition of FASN by orlistat reduces tumor growth in Hmga1IEC-OE/+ mice. Our findings suggest that targeting lipid metabolism could offer a promising therapeutic strategy for CRC.

Targeting Glucose Metabolism: A Novel Therapeutic Approach for Parkinson’s Disease

Glucose metabolism is essential for the maintenance and function of the central nervous system. Although the brain constitutes only 2% of the body weight, it consumes approximately 20% of the body’s total energy, predominantly derived from glucose. This high energy demand of the brain underscores its reliance on glucose to fuel various functions, including neuronal activity, synaptic transmission, and the maintenance of ion gradients necessary for nerve impulse transmission. Increasing evidence shows that many neurodegenerative diseases, including Parkinson’s disease (PD), are associated with abnormalities in glucose metabolism. PD is characterized by the progressive loss of dopaminergic neurons in the substantia nigra, accompanied by the accumulation of α-synuclein protein aggregates. These pathological features are exacerbated by mitochondrial dysfunction, oxidative stress, and neuroinflammation, all of which are influenced by glucose metabolism disruptions. Emerging evidence suggests that targeting glucose metabolism could offer therapeutic benefits for PD. Several antidiabetic drugs have shown promise in animal models and clinical trials for mitigating the symptoms and progression of PD. This review explores the current understanding of the association between PD and glucose metabolism, emphasizing the potential of antidiabetic medications as a novel therapeutic approach. By improving glucose uptake and utilization, enhancing mitochondrial function, and reducing neuroinflammation, these drugs could address key pathophysiological mechanisms in PD, offering hope for more effective management of this debilitating disease.

Enhanced energy storage performance of lead-free Sr0.7Bi0.2TiO3 ceramics via tape casting

Abstract The development of pulsed power technology demands high energy storage density dielectric materials. In this study, Sr0.7Bi0.2TiO3 relaxor ferroelectric (rFE) ceramic thick films were prepared using a tape-casting process. The ceramics exhibit a dense structure and typical rFE hysteresis curves, achieving an energy storage density of 4.77 J cm−3 and efficiency of 94.4%, attributed to the high polarization intensity, low remnant polarization, and low hysteresis loss of rFE. Additionally, the material demonstrates good temperature stability. Moreover, the Sr0.7Bi0.2TiO3 ceramic thick films show advantages in charge-discharge performance, capable of discharging within hundreds of nanoseconds and achieving a power density of 137 MW cm−3. Overall, the Sr0.7Bi0.2TiO3 ceramic thick film exhibits a comprehensive performance advantage in terms of energy storage density, efficiency, and power density. Additionally, the material was fabricated using the tape-casting process, which is compatible with subsequent multilayer ceramic capacitor production, indicating potential for further performance enhancement.

Techno-Economic Analysis of BAU-STR Dryer for Rice Drying: An Approach to Accelerate Adoption

Postharvest food loss and waste offset worldwide agricultural productivity and food security. Insufficient drying and storage are the prominent drivers of food loss and waste in underdeveloped countries. Mechanical grain drying systems have distinct benefits over sun drying but are inaccessible to underserved communities due to high capital costs and energy demand. This study evaluated the techno-economic and financial performance of a half-ton-capacity BAU-STR dryer. The moisture extraction rate, drying rate, drying efficiency, and energy consumption were used as technical performance indicators. In contrast, the net present value (NPV), internal rate of return (IRR), benefit–cost ratio (BCR), and payback period were considered economic performance indicators. The technical performance analysis results revealed that the moisture content of rice was reduced from 19.5% to 13.5 ± 0.15% in 4.0 h with an average drying rate of 1.5%/h and a drying efficiency of 75.1%. The financial performance analysis resulted in a drying cost, NPV, IRR, BCR, and PBP of USD 0.96 per 100 kg of grain, USD 3018, 135%, 3.0, and 0.73 yr., respectively, when the annual use was 240 h. If the yearly use of the dryer increased from 240 to 720 h, a higher NPV, IRR, and BCR, as well as a lower payback period and drying cost, could be achieved. Adopting a BAU-STR dryer for drying grain (rice and corn) among underserved communities could play a key role in postharvest food loss and waste.

A VOx-based optoelectronic memristor for application in visual perception

Abstract While biological vision systems excel at in-memory processing with low power consumption, traditional silicon-based vision chips struggle with high energy demands. This gap motivates the exploration of alternative materials for artificial intelligence applications. This paper presents a VOx-based optoelectronic synaptic memristive device. The proposed artificial synaptic device ITO/VO x /Pt mimics biological functions such as potentiation (P), depression (D), long-term memory, short-term memory (STM), and paired-pulse facilitation (PPF). The PPF index, standing at 105%, suggests a favorable pattern in STM function. The device served as synapses within a spiking neural network showing an achievable pattern classification accuracy of 88.68%, highlighting the potential of the VO x synaptic device for pattern classification tasks. The suggested VO x -based synaptic devices could represent an efficient pattern recognition and visual perception application.

Using micro-CT to explore bone density variations in the skulls of the vulnerable Opsariichthys uncirostris uncirostris (Three-lips fish) during reproductive migration to a Lake Biwa tributary.

Not much is known about the changes in bone density due to fish reproductive migration. We used micro-CT and inferential statistics to determine whether the relative bone density in the skulls of adult Three-lips fish, that seasonally upstream migrated to a Lake Biwa tributary, changed across their known reproductive season. The relative bone density significantly decreased as standard length and condition factor (K) increased in both sexes. This negative relationship is likely due to age and hormonal effects in the fish. Results from the bone density analysis also revealed that male Three-lips fish had potentially lower relative bone density (although not significantly different) than females during peak reproductive migration, i.e., July to August. On average, male Three-lips fish are larger in length and weight than females, and in many species, females prefer larger males to smaller males, viewing their size as an indicator of genetic fitness and their ability to provide protection. Resources in the skulls of Three-lips males may be distributed in such a way that increases reproductive success, i.e., size at the expense of quality. In addition, individuals with slightly less dense bones, particularly males, appeared later than those with denser bones during the peak of the reproductive season. The high energy demands involved with aggression in males, often requires resource mobilization from various tissue compartments and could explain the slightly lower density in the latter half of the peak migration. Furthermore, Three-lips individuals that migrated earlier and later during the reproductive season may have more energy reserves than those that had been in the river for some time, hence the variable bone density between individuals. This study serves as a foundation for future studies on the effects of migration, changes in physiology and age on bone density analysis of Three-lips fish and other species in various ecosystems.

Optimal power dispatch in microgrids using mixed-integer linear programming

Abstract As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.

Urine luck: Environmental assessment of yellow water management in buildings for urban agriculture

The increasing global demand for agricultural production poses challenges to maintain the needs for critical fertilizers such as nitrogen. This study explores the potential of human urine as a source of renewable nitrogen for fertilizer production. Through a life cycle assessment, three different urine management strategies were compared: (S1) an artificial wetland, (S2) an on-site lab-scale aerobic reactor for nitrogen recovery, and (S3) a centralized wastewater treatment plant. While scenario S2 had the highest impacts in 6 out of 8 categories, an advantage in marine eutrophication was identified. S2 showed high energy demand (750 kg MJ-eq) and ecotoxicity (602 kg 1.4-DCB-eq.) mainly due to energy requirements. Nitrogen production exceeded 2.3 times the yearly nitrogen demands of the building tomato production. Upscaling S2 reduces impacts up to 2 times, lowering the payback time from 29 to 13 years. Therefore, implementing large-scale nitrogen recovery systems in cities is encouraged.

Process Simulation and Technical Evaluation Using Water-Energy-Product (WEP) Analysis of an Extractive-Based Biorefinery of Creole-Antillean Avocado Produced in the Montes De María

The annual increase in the world’s population significantly contributes to recent climate change and variability. Therefore, researchers, engineers, and professionals in all fields must integrate sustainability criteria into their decision-making. These criteria aim to minimize the environmental, social, economic, and energy impacts of human activities and industrial processes, helping mitigate climate change. This research focuses on developing scalable technology for the comprehensive use of avocados, adhering to sustainability principles. This work presents the modeling, simulation, and the WEP (Water-Energy-Product) technical evaluation of the process for obtaining bio-oil, chlorophyll, and biopesticide from the Creole-Antillean avocado. For this, the extractive-based biorefinery data related to water, energy, and products are taken from the material balance based on experimental results and process simulation. Then, eight process parameters are calculated, and eleven technical indicators are determined. Later, the extreme technical limitations for every indicator are demarcated, and an evaluation of the performance of the indicators is carried out. Results showed that the process has a high execution in aspects such as fractional water cost (TCF) and energy cost (TCE), as well as solvent reuse during extraction processes (SRI) and production yield, noting that the mentioned indicators are above 80%. In contrast, the metrics related to water management (FWC) and specific energy (ESI) showed the lowest performance. These discoveries support the use of optimization techniques like mass process integration. The energy-related indicators reveal that the process presents both benefits and drawbacks. One of the drawbacks is the energy source due to the high demand for electrical energy in the process, compared to natural gas. The specific energy intensity indicator (ESI) showed an intermediate performance (74%), indicating that the process consumes high energy. This indicator enables us to highlight that we can find energy aspects that require further study; for this reason, it is suitable to say that there is potential to enhance the energy efficiency of the process by applying energy integration methods.

Sustainability challenges and solutions in medical laboratory equipment manufacturing

Background: Ultra-low temperature (ULT) freezers are vital for storing biological samples in medical labs, but they have high energy demands and significant environmental impacts. This study investigates the energy efficiency, environmental footprint, and performance of different ULT freezer models. Methodology: A lifecycle assessment (LCA) was performed on five ULT freezers to evaluate global warming potential (GWP), ozone depletion potential (ODP), and energy consumption. Key performance metrics, including compressor efficiency, thermal stability, and material recyclability, were assessed under various operating conditions. Real-time data was collected over 30 days. Results: Freezers with variable-speed compressors exhibited up to 25% lower energy consumption than those with fixed-speed systems. Freezers using low-GWP refrigerants, such as CO₂, reduced total CO₂ emissions by up to 30% over a 10-year lifespan. Models incorporating advanced insulation materials, like vacuum-insulated panels (VIPs), showed improved thermal stability and reduced heat rejection, leading to lower cooling loads. Conclusion: Adopting advanced compressor technologies, low-GWP refrigerants, and high-performance insulation materials in ULT freezers can significantly reduce energy use and environmental impact. These innovations are crucial for sustainable medical laboratory operations, balancing performance with environmental responsibility.

Climate policy uncertainty and its impact on real estate market dynamics: A sectoral and regional analysis.

This study explores the impact of Climate Policy Uncertainty (CPU) on real estate market volatility, utilizing the CPU index to assess how climate policy affects various real estate segments. It highlights the significant impact of CPU on sectors with high energy demand and emissions, such as industrial and residential. A multi-horizon analysis reveals the long-term sensitivity of CPU's influence, with significant sensitivity noted in coastal regions prone to climate risks. The findings provide crucial insights for investors and policymakers, emphasizing the importance of integrating CPU considerations into strategic decision-making for real estate investment.

Advances in myocardial energy metabolism: metabolic remodeling in heart failure and beyond.

The very high energy demand of the heart is primarily met by ATP production from mitochondrial oxidative phosphorylation, with glycolysis providing a smaller amount of ATP production. This ATP production is markedly altered in heart failure, primarily due to a decrease in mitochondrial oxidative metabolism. Although an increase in glycolytic ATP production partly compensates for the decrease in mitochondrial ATP production, the failing heart faces an energy deficit, that contributes to the severity of contractile dysfunction. The relative contribution of the different fuels for mitochondrial ATP production dramatically changes in the failing heart, which depends to a large extent on the type of heart failure. A common metabolic defect in all forms of heart failure (including HFrEF, HFpEF, and diabetic cardiomyopathies) is a decrease in mitochondrial oxidation of pyruvate originating from glucose (i.e. glucose oxidation). This decrease in glucose oxidation occurs regardless of whether glycolysis is increased, resulting in an uncoupling of glycolysis from glucose oxidation that can decrease cardiac efficiency. The mitochondrial oxidation of fatty acids by the heart increases or decreases, depending on the type of heart failure. For instance, in HFpEF and diabetic cardiomyopathies myocardial fatty acid oxidation increases, while in HFrEF myocardial fatty acid oxidation either decreases or remains unchanged. The oxidation of ketones (which provides the failing heart with an important energy source) also differs depending on the type of heart failure, being increased in HFrEF, and decreased in HFpEF and diabetic cardiomyopathies. The alterations in mitochondrial oxidative metabolism and glycolysis in the failing heart are due to transcriptional changes in key enzymes involved in the metabolic pathways, as well as alterations in redox state, metabolic signaling, and posttranslational epigenetic changes in energy metabolic enzymes. Of importance, targeting the mitochondrial energy metabolic pathways has emerged as a novel therapeutic approach to improving cardiac function and cardiac efficiency in the failing heart.

3DP for sustainable built environment – Synthesis and thermomechanical characterization of composite materials based on local soil and date palm fiber waste

There is an increasing demand for sustainable construction materials to address the challenges posed by the environmental impact of the built environment (BE), which is driven by climate change, population growth, and urbanization. Conventional construction materials like cement contribute significantly to carbon emissions during production, transport, use, and disposal phases, and their poor thermal conductivity hinders efforts to maintain comfortable indoor environments. To address these challenges, there is an urgent need for innovative, locally sourced thermal insulation materials specifically designed for regions with extreme climates and high energy demands to maintain building comfort. This research explores synthesizing novel, environmentally friendly building materials using locally sourced date palm fiber waste and clay. The study also investigates the developed material's 3D printing (3DP) capabilities and optimizes its printing parameters with lab-scale prototypes. Additionally, thermo-mechanical characterization is conducted to assess its suitability for built environment applications. Different concentrations, from 1 to 5 wt% of date palm fiber to clay ratio (Dpf/C), were studied regarding microstructural, thermal, and mechanical properties, dimensional accuracy, and feasibility for 3DP of BE structures. Results demonstrated a significant reduction in thermal conductivity by 73%, achieving 0.244 W/mK, and an increase in compressive strength by 106%, reaching 10.9 MPa at 5 wt% Dpf/C. The promising thermomechanical properties of these composites and their suitability for 3DP support their use in real-world applications in the future's sustainable built environment. This scalable methodology can be adapted to regions with similar sustainable local and waste resources, advancing the circular economy.

Mapping Natural Sugars Metabolism in Acute Myeloid Leukaemia Using 2D Nuclear Magnetic Resonance Spectroscopy.

Metabolism plays a central role in cancer progression. Rewiring glucose metabolism is essential for fulfilling the high energy and biosynthetic demands as well as for the development of drug resistance. Nevertheless, the role of other diet-abundant natural sugars is not fully understood. In this study, we performed a comprehensive 2D NMR spectroscopy tracer-based assay with a panel of 13C-labelled sugars (glucose, fructose, galactose, mannose and xylose). We assigned over 100 NMR signals from metabolites derived from each sugar and mapped them to metabolic pathways, uncovering two novel findings. First, we demonstrated that mannose has a semi-identical metabolic profile to that of glucose with similar label incorporation patterns. Second, next to the known role of fructose in driving one-carbon metabolism, we explained the equally important contribution of galactose to this pathway. Interestingly, we demonstrated that cells growing with either fructose or galactose became less sensitive to certain one-carbon metabolism inhibitors such as 5-Flurouracil and SHIN1. In summary, this study presents the differential metabolism of natural sugars, demonstrating that mannose has a comparable profile to that of glucose. Conversely, galactose and fructose contribute to a greater extent to one-carbon metabolism, which makes them important modulators for inhibitors targeting this pathway. To our knowledge, this is the first NMR study to comprehensively investigate the metabolism of key natural sugars in AML and cancer.

Energy Consumption Trends and Determinants in Polish Hospitals: Implications for Energy Efficiency Strategies

In the construction sector, hospitals are the buildings with the highest energy consumption. Due to the high demand for energy, hospitals’ energy efficiency is becoming very important. This study aims to examine the trends and factors that determine energy consumption in Polish hospitals from 2010 to 2019, highlighting the impact of hospital size and medical activities on energy efficiency. The analysis was carried out using data from 3061 hospital reports obtained from the e-Health Center, a state budgetary unit established by the Minister of Health. To measure and compare the efficiency of energy usage in hospitals, we developed eight energy usage efficiency indexes based on hospital size and medical activity. The size of the hospitals was described by the number of beds, operation rooms, doctors, nurses, and fixed assets value. Hospital activity was measured by the number of person-days, patients, and operations. Statistical analysis was carried out using StatSoft Statistica software version 13.3. The results show that larger hospitals are more energy efficient across various measures of energy use than smaller hospitals. The findings revealed also several important relationships between energy usage and factors connected with size and hospital activity, such as the number of beds, patients and person-days, medical staff, operations, and fixed asset values, underscoring the necessity for customizing energy efficiency strategies. This research contributes empirical insights that can guide policymakers and hospital administrators in their endeavors to improve energy efficiency and promote sustainability within healthcare facilities.

Classification of Faults in Power System Transmission Lines Using Deep Learning Methods with Real, Synthetic, and Public Datasets

Every part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines are a crucial component of the electrical power system. Therefore, in order to determine the power system’s protection plan and increase its reliability, it is critical to foresee and classify fault types. With this motivation, the main goal of this paper is to design a deep network model to classify faults in transmission lines based on real, generated, and publicly available datasets. A deep learning architecture that was based on a one-dimensional convolutional neural network (CNN) was utilized in this study. Accuracy, specificity, recall, precision, F1 score, ROC curves, and AUC were employed as performance criteria for the suggested model. Not only synthetic but also real data were used in this study. It has been seen that the created model can be used successfully for both real data and synthetic data. In order to measure the robustness of the network, it was tested with three different datasets consisting of real, generated, and publicly available datasets. In the paper, 1D CNN, one of the machine learning methods, was used on three different power systems, and it was observed that the machine learning method was successful in all three power systems.