Search Results (9,725)

Aging is an important risk factor for tumorigenesis. Metabolic reprogramming is a hallmark of both aging and tumor initiation. However, the manner in which the crosstalk between aging and metabolic reprogramming affects the tumor microenvironment (TME) to promote tumorigenesis was poorly explored. We utilized a computational approach proposed by our previous work, MMP³C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to characterize aging-related metabolic plasticity events using pan-cancer bulk RNA-seq data. Our analysis revealed a high degree of metabolically organized heterogeneity across 17 aging-related cancer types. In particular, a higher degree of several energy generation pathways, i.e., glycolysis and impaired oxidative phosphorylation, was observed in older patients. Similar phenomena were also found via single-cell RNA-seq analysis. Furthermore, those energy generation pathways were found to be weakened in activated T cells and macrophages, whereas they increased in exhausted T cells, immunosuppressive macrophages, and Tregs in older patients. It was suggested that aging-induced metabolic switches alter glucose utilization, thereby influencing immune function and resulting in the remodeling of the TME. This work offers new insights into the associations between tumor metabolism and the TME mediated by aging, linking with novel strategies for cancer therapy. Full article

In addition to bubble–particle interaction, particle–particle interaction also has a significant influence on mineral flotation. Fine particles that coat the mineral surface prevent direct contact with collectors and/or air bubbles, thereby lowering flotation recovery. Calculating the particle interaction energy can help in evaluating the interaction behavior of particles. In this study, the floatability of coarse ilmenite (−151+74 μm) and different particle sizes (−45+25, −25+19, −19 μm) of forsterite with NaOL as a collector was investigated. The results showed that forsterite sizes of −45+25 and −25+19 μm had no effect on the ilmenite floatability, whereas −19 μm forsterite significantly reduced ilmenite floatability. A particle size analysis of artificially mixed minerals and a scanning electron microscopy (SEM) analysis of the flotation products showed that heterogeneous aggregation occurred between ilmenite and −19 μm forsterite particles. The extended DLVO (Derjaguin–Landau–Verwey–Overbeek) theory was applied to calculate the interaction energy between mineral particles using data from zeta potential and contact angle measurements. The results showed that the interaction barriers between ilmenite (−151+74 μm) and forsterite (−45+25, −25+19, and −19 μm) were 11.94 × 10³ kT, 8.23 × 10³ kT and 4.09 × 10³ kT, respectively. Additionally, the interaction barrier between forsterite particles smaller than 19 μm was 0.51 × 10³ kT. The strength of the barrier decreased as the size of the forsterite decreased. Therefore, fine forsterite particles and aggregated forsterite can easily overcome the energy barrier, coating the ilmenite particle surface. This explains the effect of different forsterite sizes on the floatability of ilmenite and the underlying mechanism of particle interaction. Full article

Assessing market power in the presence of different production technologies such as renewable energies, including wind and solar power, is crucial for electric market analysis and operation. This paper investigates structural market power by incorporating wind farms and solar generation over a short-term period. The study examines the issue of market concentration boundaries to assess structural market power by calculating the minimum and maximum market concentration index values in the day-ahead market. It models the technical specifications of power plants, such as the maximum and minimum production limits, ramp-up and ramp-down rates, and minimum required up and down times. By extracting the spatiotemporal correlation of wind power generation from real data, the uncertainty of renewable power generation is represented through a set of scenarios. The analysis explores the correlation effects of wind farms, solar generation, and wind penetration levels under different ownership structures. Simulation results using a modified PJM five-bus system illustrate the effectiveness of the developed method. Our results indicate that integrating renewable energy can reduce the Herfindahl–Hirschman Index (HHI) by up to 30% as wind penetration levels rise from 0% to 40%, fostering a more competitive market structure. However, the correlation between wind farms also increases market volatility, with the standard deviation of the HHI rising by about 25% during peak load periods. These findings demonstrate the practical applicability of the developed methodology for assessing market dynamics in the presence of renewable energy sources. Full article

This study presents a comparative analysis of the accuracy of different methodologies for the design and performance evaluation of thermoelectric generators (TEGs), using analytical, computational numerical, and experimental approaches. TEGs are promising devices for capturing waste energy in industrial processes, converting waste heat into electrical energy and contributing to energy sustainability. However, the efficiency of TEGs is a significant challenge due to their low conversion rates. To address this challenge, three different methodologies were developed and systematically compared. Analytical Model: Developed for the electrical design of thermoelectric micro generators, using theoretical performance data and industrial temperature gradients. This method offers a robust theoretical view but may not capture all practical variables. Computational model in Simulink/MATLAB: Created and validated to consider the variation of the Seebeck coefficient and the internal resistance of thermoelectric modules with temperature. This model provides an accurate simulation of operating conditions but depends on the accuracy of the input parameters. Experimental Multi-string Electrical Arrangement Prototype: This involved the design and construction of a prototype followed by experimental tests to validate its performance. This method provides valuable empirical data but can be limited by the complexity and cost of the experiments. The results show that each methodology has specific advantages and limitations, offering valuable insights for the development of more efficient TEG systems. The comparison of analytical, numerical, and experimental methods revealed differences in accuracy and efficiency, highlighting the importance of an integrated approach to TEG design. This study lays a solid foundation for future research and practical applications in the field of industrial residual energy harvesting. Full article

The growing integration of renewable energy sources, such as photovoltaic and wind systems, into energy grids has underscored the need for reliable control mechanisms to mitigate the inherent intermittency of these sources. According to the Brazilian grid operator (ONS), there have been cascading disconnections in renewable energy distributed systems (REDs) in recent years, highlighting the need for robust control models. This article addresses this issue by presenting the validation of an active power ramp rate control (PRRC) function for a PV plant coupled with a Battery Energy Storage System (BESS) using WECC generic models. The proposed model underwent rigorous validation over an extended analysis period, demonstrating good accuracy using the Root Mean Squared Error (RMSE) and an R-squared (R²) metrics for the active power injected at the Point of Connection (POI), PV active power, and BESS State of Charge (SOC), providing valuable insights for medium and long-term analyses. The ramp rate control module was implemented within the plant power controller (PPC), leveraging second-generation Renewable Energy Systems (RES) models developed by the Western Electricity Coordination Council (WECC) as a foundational framework. We conducted simulations using the Anatem software, comparing the results with real-world data collected at 100 ms to 1000 ms intervals from a PV plant equipped with a BESS in Brazil. The proposed model underwent rigorous validation over an extended analysis period, with the presented results based on two days of measurements. The positive sequence model used to represent this control demonstrated good accuracy, as confirmed by metrics such as the Root Mean Squared Error (RMSE) and R-squared (R²). Furthermore, the article underscores the critical role of accurately accounting for the power sampling rate when calculating the ramp rate. Full article

In light of challenges like climate change, pollution, and depletion of fossil fuel reserves, governments and businesses prioritize renewable energy sources such as solar, wind, and hydroelectric power. Renewable energy forecasting models play a crucial role for energy market operators and prosumers, aiding in planning, decision-making, optimization of energy sales, and evaluation of investments. This study aimed to develop machine learning models for hydropower forecasting in plants integrated into Water Distribution Systems, where energy is generated from water flow used for municipal water supply. The study involved developing and comparing monthly and two-week forecasting models, utilizing both one-step-ahead and two-step-ahead forecasting methodologies, along with different missing data imputation techniques. The tested algorithms—Seasonal Autoregressive Integrated Moving Average, Random Forest, Temporal Convolutional Network, and Neural Basis Expansion Analysis for Time Series—produced varying levels of performance. The Random Forest model proved to be the most effective for monthly forecasting, while the Temporal Convolutional Network delivered the best results for two-week forecasting. Across all scenarios, the seasonal–trend decomposition using the LOESS technique emerged as the most successful for missing data imputation. The accurate predictions obtained demonstrate the effectiveness of using these models for energy planning and decision-making. Full article

As power system reforms deepen, direct trading with large power consumers has emerged as a crucial aspect of opening up the power sales market. In light of this trend, power sales enterprises should accelerate their digital transformation in response to the growing demand for personalized services from large consumers and continuous advancements in energy digitalization and smart technologies. In particular, big data technology is critical for power enterprises to satisfy users and increase profitability as it can help enterprises gain deeper insights into user needs and behavioral characteristics. The application of big data to provide customized value-added services for large power consumers has become a key development focus. In this paper, we develop a two-party evolutionary game model involving power sellers using big data technology to profile large consumers and offer them customized value-added power packages. We conduct a detailed analysis of the local stability of equilibrium points and employ MATLAB.R2021a to examine the impact of changes in the benefits of value-added services for large consumers and the cost coefficients associated with big data on the system’s evolutionary outcomes. The study results indicate that big data technology can enhance the competitiveness of power sellers in the market. Value-added services based on user-profiling using big data have become a crucial factor in influencing the decision-making behavior of large consumers. Additionally, the investment cost in big data infrastructure by power sellers impacts system evolution, with the cost coefficient being inversely proportional to their willingness to offer customized services. Full article

The saturation of natural gas hydrates is a key parameter for characterizing hydrate reservoirs, estimating hydrate reserves, and developing hydrate as an energy resource. Several methods have been proposed to estimate hydrate saturation, although most of these studies rely on logging and seismic data. However, the methods for estimating hydrate saturation from recovered core sediments have not been thoroughly reviewed, which hinders a deeper understanding, proper application, and the use of these experimental data to integrate geophysical and numerical model results with the actual geological conditions. In this paper, the methods widely used for estimating natural gas hydrate saturation from core sediments, including those based on pore water chemistry (Cl⁻ concentration, δD, and δ¹⁸O values), gas volumetric analysis, and temperature anomaly, have been summarized in terms of the principle, estimation strategy, and issues to be considered of each method. The applicability, advantages and disadvantages, and scope of application of each method are also compared and discussed. All methods for estimating gas hydrate saturation have certain limitations. A comprehensive application of results from multiple methods could lead to a better understanding of the amount of gas hydrate in sediments, although the chlorinity of pore water is the most commonly used method of estimation. Full article

Background/Objectives: Body composition (BC) affects the risk of developing metabolic dysfunction-associated steatotic liver disease (MASLD) and hypertension (HTN). Currently, dual-energy X-ray absorptiometry (DEXA) is considered the gold standard for assessing BC, even though it has some limitations, including immobility, ionizing radiation, and patient weight restrictions. The aim of the study was to evaluate the correlations of BC parameters measured by bioelectrical impedance analysis (BIA) with those measured by DEXA in patients with MASLD and HTN. Methods: Overall, 78 patients with MASLD and HTN underwent the following study procedures: compilation of an anamnesis, physical examination of a patient, laboratory tests, abdominal ultrasound, BIA, DEXA, and anthropometric measurements. Results: The agreement between BIA and DEXA in diagnosing reduced skeletal muscle mass (SMM) in patients with MASLD and HTN was moderate (kappa values were 0.440 and 0.404 in males and females, respectively). Significant strong direct correlations were found between fat mass (FM) and body fat percentage measured by BIA with corresponding measurements by DEXA (p < 0.001 for both). The area under the receiver operating characteristic curves (AUC) of SMM to body weight ratios calculated using BIA data were 0.834 and 0.929 for reduced appendicular SMM determined by DEXA in males and females with MASLD and HTN, respectively. Conclusions: In conclusion, BIA is an easy-to-use and widely available tool for assessing SMM and FM in patients with MASLD and HTN, demonstrating reliable agreement with DEXA measurement results and completely free of its limitations. Full article

Due to its simple structure and stable operation, the Organic Rankine Cycle (ORC) has gained significant attention as a primary solution for low-grade thermal power generation. However, the economic challenges associated with development difficulties in hot dry rock (HDR) geothermal power systems have necessitated a better balance between performance and cost effectiveness within ORC systems. This paper establishes a game pattern of the Organic Rankine Cycle with performance as the master layer and economy as the slave layer, based on the Stackelberg game theory. The optimal working fluid for the ORC is identified as R600. At the R600 mass flow rate of 50 kg/s, the net system cycle work is 4186 kW, the generation efficiency is 14.52%, and the levelized cost of energy is 0.0176 USD/kWh. The research establishes an optimization method for the Organic Rankine Cycle based on the Stackelberg game framework, where the network of the system is the primary optimization objective, and the heat transfer areas of the evaporator and condenser serve as the secondary optimization objective. An iterative solving method is utilized to achieve equilibrium between the performance and economy of the ORC system. The proposed method is validated through a case study utilizing hot dry rock data from Qinghai Gonghe, allowing for a thorough analysis of the working fluid and system parameters. The findings indicate that the proposed approach effectively balances ORC performance with economic considerations, thereby enhancing the overall revenue of the HDR power system. Full article

This study explores the long-term interplay between trade policy, energy efficiency, and carbon dioxide (CO₂) emissions in South Korea, using data spanning from 1985 to 2023. By applying the Fourier autoregressive distributed lag (FARDL) model, the analysis reveals that while trade liberalization initially leads to a 0.23% increase in CO₂ emissions for each 1% rise in trade openness—driven by the energy demands of industrial expansion—integrating energy efficiency standards within trade agreements helps mitigate these effects over time; this results in a 0.26% reduction in emissions for every 1% improvement in energy efficiency. The study also highlights the dual role of foreign direct investment (FDI), which contributes to a short-term 0.08% rise in emissions but significantly reduces carbon intensity in the long term by facilitating the adoption of cleaner technologies. These findings underscore the importance of innovation and FDI in decoupling economic growth from environmental degradation. The study advocates for the incorporation of energy efficiency measures into trade agreements and the prioritization of green technologies, recommending strategies that could enable South Korea to reduce its CO₂ emissions by up to 40% by 2030. This research positions South Korea as a key actor in achieving global climate goals while maintaining economic competitiveness, offering valuable insights into the balance between sustainable development and industrial growth. Full article

Background/Objectives: Energy availability (EA) is essential for maintaining physiological functions, significantly influencing athletes’ health and performance. Nutritional behaviors, however, vary across sports. This study aims to assess EA levels in athletes from different disciplines, focusing on the relationship between EA and body composition in endurance athletes compared to rugby players. Methods: This study involved 18 endurance athletes (15 men, 3 women) and 36 rugby players (all men). Data were gathered through interviews, questionnaires, and bioimpedance analysis. Energy intake (EI) was measured with a 24 h dietary recall, and exercise energy expenditure (EEE) was calculated using the IPAQ questionnaire. EA was calculated as EA = (EI − EEE)/fat-free mass (FFM), with results categorized into clinical, subclinical, and optimal ranges. Results: The endurance group had a lower average FFM (57.81 kg) compared to the rugby players (67.61 kg). EA was also significantly lower in endurance athletes (11.72 kcal/kg FFM) than in rugby players (35.44 kcal/kg FFM). Endurance athletes showed more restrictive nutritional behavior with lower EI and higher EEE, but both groups maintained body composition within normal ranges. Conclusions: Endurance athletes exhibit greater nutritional restrictions compared to rugby players, though their body composition remains healthy. Further research is required to investigate the long-term effects of low EA on performance, injury risk, and potential impairment when EA falls below the optimal threshold of 45 kcal/kg FFM/day. Full article

With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency. Full article

RNA-sequencing enables the comprehensive detection of gene expression levels at specific time points and facilitates the identification of stress-related genes through co-expression network analysis. Understanding the molecular mechanisms and identifying key genes associated with salt tolerance is crucial for developing rice varieties that can thrive in saline environments, particularly in regions affected by soil salinization. In this study, we conducted an RNA-sequencing-based time-course transcriptome analysis of ‘Jao Khao’, a salt-tolerant Thai rice variety, grown under normal or saline (160 mM NaCl) soil conditions. Leaf samples were collected at 0, 3, 6, 12, 24, and 48 h. In total, 36 RNA libraries were sequenced. ‘Jao Khao’ was found to be highly salt-tolerant, as indicated by the non-significant differences in relative water content, cell membrane stability, leaf greenness, and chlorophyll fluorescence over a 9-day period under saline conditions. Plant growth was slightly retarded during days 3–6 but recovered by day 9. Based on time-series transcriptome data, we conducted differential gene expression and weighted gene co-expression network analyses. Through centrality change from normal to salinity network, 111 key hub genes were identified among 1,950 highly variable genes. Enriched genes were involved in ATP-driven transport, light reactions and response to light, ATP synthesis and carbon fixation, disease resistance and proteinase inhibitor activity. These genes were upregulated early during salt stress and RT-qPCR showed that ‘Jao Khao’ exhibited an early upregulation trend of two important genes in energy metabolism: RuBisCo (LOC_Os10g21268) and ATP synthase (LOC_Os10g21264). Our findings highlight the importance of managing energy requirements in the initial phase of the plant salt-stress response. Therefore, manipulation of the energy metabolism should be the focus in plant resistance breeding and the genes identified in this work can serve as potentially effective candidates. Full article

This study explores the integration of a human kinetic energy-harvesting mechanism into lifejackets to address the energy needs of aid search and rescue operations in aquatic environments. Due to the limited data on the movement patterns of drowning individuals, a human motion model has been developed to identify optimal design parameters for energy harvesting. This model is developed from computer vision analysis of underwater footage and motion capture laboratory experiments and is used to quantify the potential for power generation. The field testing experiment is conducted underwater, replicating the environment used for footage collection and analysis for the modelling. During the field testing, the participant wears a lifejacket integrated with the energy-harvesting device. Field testing data are then collected to verify the model. The efficacy of this approach is demonstrated with observed power outputs ranging from 0 mW to 754 mW in simulations and experiments. Despite challenges such as the “dead zone” in a drowning person’s motion, the success of the experiments underscores the potential of the proposed energy-harvesting mechanism to efficiently harness the kinetic energy generated by a drowning person’s movements. This study contributes to the development of sustainable, energy-efficient solutions for search and rescue operations, particularly in remote and challenging aquatic environments. Full article