Search Results (29,008)

Under the global decarbonization trend, combined cooling, heating, and power (CCHP) systems are critical for improving regional energy efficiency. However, the integration of high-penetration variable renewable energy (RE) sources introduces significant volatility and multi-dimensional uncertainties, challenging conventional operation strategies designed for stable energy inputs. This review systematically examines recent advances in CCHP optimization under high-RE scenarios, with a focus on flexibility-enabled operation mechanisms and uncertainty-aware optimization strategies. It first analyzes the evolving architecture of variable RE-driven CCHP systems and core challenges arising from RE intermittency, demand volatility, and multi-energy coupling. Subsequently, it categorizes key flexibility resources and clarifies their roles in mitigating uncertainties. The review further elaborates on optimization methodologies tailored to high-RE contexts, along with their comparative analysis and selection criteria. Additionally, it details the formulation of optimization models, model formulation, and solution techniques. Key findings include the following: Generalized energy storage, which integrates physical and virtual storage, increases renewable energy utilization by 12–18% and reduces costs by 45%. Hybrid optimization strategies that combine robust optimization and deep reinforcement learning lower operational costs by 15–20% while strengthening system robustness against renewable energy volatility by 30–40%. Multi-energy synergy and exergy-efficient flexibility resources collectively improve system efficiency by 8–15% and reduce carbon emissions by 12–18%. Overall, this work provides a comprehensive technical pathway for enhancing the efficiency, stability, and low-carbon performance of CCHP systems in high-RE environments, supporting their scalable contribution to global decarbonization efforts. Full article

The tumor microenvironment (TME) has a substantial impact on the progression of gastric cancer. Collagen, the most abundant protein in the extracellular matrix (ECM), forms a dense physical barrier that regulates anti-tumor immunity in the TME. It is a significant regulator of the signaling pathways of cancer cells, which are responsible for migration, proliferation, and metabolism. ECM proteins, particularly remodeling enzymes and collagens, can be modified to increase stiffness and alter the mechanical properties of the stroma. This, in turn, increases the invasive potential of tumor cells and resistance to immunotherapy. Given the dynamic nature of collagen, novel therapeutic strategies have emerged that target both collagen biosynthesis and degradation, processes that are essential for addressing ECM stiffening. This review delineates the upregulation of the expression and deposition of collagen, as well as the biological functions, assembly, and reorganization that contribute to the dissemination of this aggressive malignancy. Furthermore, the review emphasizes the importance of creating 3D in vitro models that incorporate innovative biomaterials that avoid the difficulties of traditional 2D culture in accurately simulating real-world conditions that effectively replicate the distinctive collagen microenvironment. Ultimately, it investigates the use of decellularized ECM-derived biomaterials as tumor models that are designed to precisely replicate the mechanisms associated with the progression of stomach cancer. Full article

Accurate parametric modeling of lithium-ion batteries is essential for battery management system (BMS) design in electric vehicles and broader energy storage applications, enabling reliable state estimation and effective thermal control under diverse operating conditions. This study presents a detailed characterization of lithium-ion cells to support advanced BMS in electric vehicles and stationary storage. A second-order equivalent circuit model is developed to capture instantaneous and dynamic voltage behavior, with parameters extracted through Hybrid Pulse Power Characterization over a broad range of temperatures (−10 °C to 45 °C) and state-of-charge levels. The method includes multi-duration pulse testing and separates ohmic and transient responses using two resistor–capacitor branches, with parameters tied to physical processes like charge transfer and diffusion. A weakly coupled electro-thermal model is presented to support real-time BMS applications, enabling accurate voltage, temperature, and heat generation prediction. This study also evaluates open-circuit voltage and direct current internal resistance across pulse durations, leading to power capability maps (“fish charts”) that capture discharge and regenerative performance across SOC and temperature. The analysis highlights performance asymmetries between charging and discharging and confirms model accuracy through curve fitting across test conditions. These contributions enhance model realism, thermal control, and power estimation for real-world lithium-ion battery applications. Full article

The dynamic behavior of quartz flexure accelerometers remains a subject of ongoing investigation, particularly in areas such as theoretical modeling, standardization, calibration methodology, and performance evaluation. To address the limitation of conventional static calibration models in accurately representing accelerometer responses under dynamic acceleration excitation, a dynamic calibration model is proposed. A mathematical model is first developed based on the physical mechanism of the accelerometer, characterizing its intrinsic dynamic response. Simulation-based analysis demonstrates that the proposed dynamic model offers significantly improved accuracy compared to traditional static approaches. Furthermore, a dynamic calibration method leveraging a dual-axis precision centrifuge is designed and validated. The results confirm that the proposed approach enables the precise calibration of quartz flexure accelerometers in accordance with the dynamic model. The calibration of the dynamic parameter yields a relative standard deviation of −0.048%. Full article

The evolution of mining-induced overburden fractures (MIOFs) and their dynamic monitoring are critical for preventing roof water hazards and gas disasters in coal mines. Conventional methods often fail to provide sufficient accuracy under the thin soft–hard interbedded roof strata, necessitating advanced alternatives. Here, we address this challenge by proposing a borehole resistivity method (BRM) based on Back-Propagation Neural Network full-space inversion (BPNN-FSI). Based on the Carboniferous Taiyuan Formation in the North China Coalfield, geoelectric models of MIOFs were established for different mining stages. Finite element simulations generated apparent resistivity responses to train and validate the BPNN-FSI model. At the 9-204 working face of Dianping Coal Mine (Shanxi Province), we compared the proposed BRM based on BPNN-FSI with an empirical formula, numerical simulation, similarity physical simulation, and underground inclined drilling water-loss observations (UIDWLOs). Results demonstrate that the BRM based on BPNN-FSI achieves sub-1% error in height of MIOF (HMIOF) monitoring, with a maximum detected fracture height of 52 m—significantly outperforming conventional methods. This study validates the accuracy and robustness of BRM based on BPNN-FSI for MIOF monitoring in thin soft–hard interbedded roof strata, offering a reliable tool for roof hazard prevention and sustainable mining practices. Full article

Traditional hydraulic-fracturing models are restricted by low computational efficiency, insufficient field data, and complex physical mechanisms, causing evaluation delays and failing to meet practical engineering needs. To address these challenges, this study innovatively develops a dynamic hydraulic-fracturing monitoring method that integrates machine learning with numerical simulation. Firstly, this study uses GOHFER 9.5.6 software to generate 12,000 sets of fracture geometry data and constructs a big dataset for hydraulic fracturing. In order to improve the efficiency of the simulation, a macro command is used in combination with a Python 3.11 code to achieve the automation of the simulation process, thereby expanding the data samples for the surrogate model. On this basis, a parameter sensitivity analysis is carried out to identify key input parameters, such as reservoir parameters and fracturing fluid properties, that significantly affect fracture geometry. Next, a neural-network surrogate model is established, which takes fracturing geological parameters and pumping parameters as inputs and fracture geometric parameters as outputs. Data are preprocessed using the min–max normalization method. A neural-network structure with two hidden layers is chosen, and the model is trained with the Adam optimizer to improve its predictive accuracy. The experimental results show that the efficiency of automated numerical simulation for hydraulic fracturing is significantly improved. The surrogate model achieved a prediction accuracy of over 90% and a response time of less than 10 s, representing a substantial efficiency improvement compared to traditional fracturing models. Through these technical approaches, this study not only enhances the effectiveness of fracturing but also provides a new, efficient, and accurate solution for oilfield fracturing operations. Full article

γ-TiAl alloy is a lightweight high-temperature structural material, featuring low density, excellent high-temperature strength, creep resistance, etc. It is a key material in the aerospace field. However, the essential defects of γ-TiAl alloys, such as poor room-temperature plasticity and low fracture toughness, have become the biggest obstacles to their practical application. Therefore, in this paper, the physical mechanism of modification of the mechanical properties and electronic structure of γ-TiAl alloys by doping with Sc, V, and Si was investigated by using the first-principles pseudopotential plane wave method. This paper specifically calculates the geometric structure, phonon spectrum, mechanical properties, electron density of states, Mulliken population analysis, and differential charge density of γ-TiAl alloys before and after doping. The results show that after doping, the structural parameters of γ-TiAl have changed significantly, and the doping models all have thermodynamic stability. The B, G, and E values of the doped system are, respectively, within the range of 94–112, 57–69, and 143–170 GPa, indicating that the material’s ability to resist compressive deformation is weakened. Moreover, the B/G values change from 1.5287 to 1.6350, 1.7279, and 1.6327, respectively, and a transformation from brittleness to plasticity occurs. However, it is still lower than the critical value of 1.75, indicating that the doped γ-TiAl alloy material retains its high-strength characteristics while also exhibiting a certain degree of toughness. The total elastic anisotropy index of the doped system increases, and the degree of anisotropy of mechanical behavior significantly increases. The total electron density of states diagram indicates that γ-TiAl alloys possess conductive properties. The covalent interactions between doped atoms and adjacent atoms have been weakened to varying degrees, which is manifested as a significant change in the charge distribution around each atom. The above results indicate that the doping of Sc, V, and Si can effectively tune the mechanical properties and electronic structure of γ-TiAl alloys. Full article

Backgrounds: Adverse Childhood Experiences (ACEs) are linked to early and long-lasting mental health issues and somatic multimorbidity. Emerging evidence suggests ACEs may also accelerate physical frailty in old age. This study examines the association between ACEs and sarcopenia, an ageing-related disease and core component of frailty. Methods: Baseline data from the Canadian Longitudinal Study on Aging (CLSA), including 25,327 participants aged 45–85 years (50.3% female sex) were analyzed. Sarcopenia was defined using the revised European Working Group of Sarcopenia in Older People (EWGSOP2) guidelines. ACE were assessed via the Childhood Experiences of Violence Questionnaire and the National Longitudinal Study of Adolescent to Adult Health Wave III questionnaire, covering eight ACE categories. Multiple logistic regression models examined the association between the number of ACE count and sarcopenia, which were adjusted for age, sex, education, income, and ethnicity. Results: Given a significant interaction between age and ACE (p < 0.01), analyses were stratified into four age groups (45–54, 55–64, 65–74, and 75–85 years). A significant association only emerged in the oldest group (75–85 years; OR = 0.93 [95% CI: 0.86–1.00], p = 0.043), but this result was in the opposite direction we hypothesized. Sensitivity analyses confirmed findings across different operationalisations of ACE and sarcopenia. Conclusions: Higher ACE exposure was not associated with sarcopenia in middle aged and older adults. The unexpected protective association in the oldest-old subgroup may reflect survival bias. Age-stratified longitudinal studies are needed to clarify this relationship. Full article

The interaction between tires and snow layer is fundamental for vehicle safety on snowy roads. Due to the instantaneous high torque output characteristics of electric vehicles, they are more prone to slipping when driving in snow, which exacerbates the complexity of tire–snow interaction. In order to construct a more accurate tire–snow interaction model in Northern China, the Bekker formula is introduced to establish the snow pressure–sinkage relationship formula, and the parameters are calibrated by disk experiments. Then the improved tire–snow interaction model is proposed by combining the use of the brush model on the rigid road surface and the dynamic discussion of the tire’s motion behavior on the snow. A coupled finite element (FE) tire model and discrete element (DE) snow terrain model are established, with interactions governed by snow–rubber contact mechanics. The simulation tests the sinking depth of tires on snowy road surface under different slip rates and different loads, as well as the force on tires. The model provides high-precision input to the EV snow traction control algorithm to optimize motor torque distribution to improve energy efficiency. By comparing and analyzing with theoretical values, the traditional empirical model, and the modified physical model, it is finally concluded that the modified model has better reliability than the original model. Compared with the empirical model, the improved model reduces the vertical stress prediction error from 5% to less than 1%, and the motion resistance error from 6% to approximately 2%, providing high-precision input for the snow traction control of electric vehicles. Full article

Despite the widespread deployment of inclinometers and GPS, an engineering gap remains for a low-cost, seepage-sensitive landslide early-warning technique. To explore the application of self-potential (SP) in landslide monitoring and early warning, a series of physical simulations were conducted, focusing on slope rainfall and slope cracking conditions. The self-potential signals were monitored using a custom-built STM32-based acquisition system, which provided continuous, real-time data with minimal noise. The relationship between self-potential signals and internal changes within the landslide body was analyzed, revealing that SP signals are highly sensitive to seepage, saturation, and structural changes within the slope. During slope rainfall simulations, the self-potential signals responded rapidly to changes in rainfall intensity, capturing the dynamic nature of seepage and saturation changes. A dynamic early-warning model was developed based on statistical methods, including sliding t-tests/Pettitt mutation tests and Mahalanobis distance test, to detect early signs of landslide instability. The model successfully identified significant changes in SP signals that corresponded to the onset of landslide movement, demonstrating the potential of self-potential for real-time landslide monitoring and early warning. This study highlights the effectiveness of self-potential monitoring in detecting early signs of landslide instability and suggests that SP signals can be a valuable addition to existing landslide monitoring systems. Full article

Background and objectives: Overweight and obesity are global public health problems, as they increase the risk of chronic diseases, reduce quality of life, and generate a significant economic and healthcare burden. This study evaluates the capacity of nutritional patterns and psychological status to predict the presence of cardiometabolic risk among overweight and obese young adults, from a neural network approach. Method: The study included N = 188 overweight or obese students, who provided measures on their dietary intake, physical and psychological state, and sociodemographic profile. Neural networks were used to predict their metabolic status, classified into two categories based on anthropometric, biochemical, and cardiometabolic risk factors: metabolically unhealthy obesity (MUO) versus metabolically healthy obesity (MHO). Results: The predictive models demonstrated differences in specificity and sensitivity capacity depending on the criteria employed for the classification of MUO/MHO and gender. Among the female subsample, MUO was predicted by poor diet (low consumption of mineral and vitamins, and high consumption of fats and sodium) and high levels of depression and stress, while among the male subsample high body mass index (BMI), depression, and anxiety were the key factors. Protective factors associated to MHO were lower BMI, lower psychopathology distress and more balanced diets. Predictive models based on the HOMA-IR criterion yielded very high specificity and low sensibility (high capacity to identify MHO but low accuracy to identify MUO). The models based on the IDF criterion achieved excellent discriminative capacity for men (specificity and sensitivity around 92.5%), while the model for women obtained excellent sensitivity and low specificity. Conclusions: The results provide empirical support for personalized prevention and treatment programs, accounting for individual differences with the aim of promoting healthy habits among young adults, especially during university education. Full article

The study aimed to evaluate the influence of ink composition, a blend of blueberry and banana purée with hydrocolloids such as xanthan gum and carrageenan in concentrations ranging from 1 to 4%, on various physical properties. These parameters included dry matter, water activity, density, syneresis index, and rheological and textural attributes of fruit inks. Additionally, the stability of the inks post-printing and after 60 min was examined using image analysis method. Increased hydrocolloid additives from 1 to 4% caused the increase of the viscoelastic modulus G′ and G″, force and extrusion work values extrudability of inks. The stability and fidelity of the inks were enhanced, resulting in a notable reduction in syneresis during storage. The modulus of elasticity exceeded the modulus of viscosity for all ink formulations evaluated, thereby ensuring structural stability. Notably, the formulation comprising 4% xanthan gum and 4% carrageenan exhibited the highest values in both viscoelasticity and extrudability indices, indicating superior performance characteristics within the studied parameters. The shape of the printed objects remained comparable to the designed model over time. Considering the constraints associated with the use of carrageenan, it is possible to attain a comparable effect by utilising reduced concentrations of hydrocolloids. For instance, formulations incorporating 3% xanthan gum in tandem with either 3% carrageenan or 2% carrageenan can achieve similar functionalities. The 3D printing of fruit purées, including blueberries and bananas, represents a significant innovation in personalising food products in terms of consistency. This is particularly relevant for individuals with dysphagia, children, and the elderly. Full article

Global climate change has intensified the frequency and severity of drought events, posing significant threats to agricultural sustainability, particularly for water-sensitive crops such as tea. In northern China, where precipitation is unevenly distributed and evapotranspiration rates are high, tea plantations frequently experience water stress, leading to reduced yields and declining quality. Therefore, accurately simulating soil water content (SWC) is essential for drought forecasting, soil moisture management, and the development of precision irrigation strategies. However, due to the high complexity of soil–vegetation–atmosphere interactions in field conditions, the practical application of the HYDRUS-1D model in northern China remains relatively limited. To address this issue, a three-year continuous monitoring campaign (2021–2023) was conducted in a coastal area of northern China, covering both young tea plantations and adjacent grasslands. Based on the measured meteorological and soil data, the HYDRUS-1D model was used to simulate SWC dynamics across 10 soil layers (0–100 cm). The model was calibrated and validated against observed SWC data to evaluate its accuracy and applicability. The simulation results showed that the model performed reasonably well, achieving an R² of 0.739 for the tea plantation and 0.878 for the grassland, indicating good agreement with the measured values. These findings demonstrate the potential of physics-based modeling for understanding vertical soil water processes under different land cover types and provide a scientific basis for improving irrigation strategies and water use efficiency in tea-growing regions. Full article

Engineered wetland has been used as a Best Management Practice (BMP) to remove pollutants and maintain water quality in watersheds. This study is focused on developing models to analyze the impacts of discharges on the efficiency of wetlands to improve water quality downstream. The watershed hydrological Soil & Water Assessment Tool (SWAT) and wetland (Personal Computer Storm Water Management Model—PCSWMM) models were developed to analyze the efficiency of engineered wetlands to remove the pollutants for different basins under three different climatic conditions (i.e., dry, average and wet year). The SWAT was calibrated and validated to simulate discharge and water quality parameters. The wetland model was developed using inflow hydrographs and concentrations of the water quality parameters biochemical oxygen demand (BOD), total suspended solids (TSSs), total nitrogen (TN) and total phosphorous (TP), simulated from a Soil & Water Assessment Tool (SWAT) model. A PCSWMM (wetland) was developed based on the physical and first order decay process within the wetland system for three basins in Prairie du Pont watershed in Illinois, USA. The results showed that pollutant removal efficiencies decreased from low to high discharges (dry to wet climatic conditions) for all watersheds and pollutants (except for BOD) based on trendline analysis. Nevertheless, the efficiencies were highly variable, specifically during low discharges. Furthermore, the sensitivity of the k-parameter (areal rate constant) was pollutant dependent. Overall, this study is helpful to understand the efficacy of wetlands’ pollutant removal as a function of discharge. The approach can be used in watersheds located in other geographic regions for the preliminary design of engineered wetlands to remove non-point source pollution and treat stormwater runoff. Full article

To address the difficulty in the characterization of the geomechanical properties of reservoirs in petroleum engineering using the traditional formula, due to the complexity of the reservoir, this study proposes a framework of inverse analysis to characterize the geomechanical properties of reservoirs formed through hydraulic fracturing by combining the XGBoost, multi-objective particle swarm optimization (MOPSO), and numerical models. XGBoost was used to generate a surrogate model to approximate the physical model, and the numerical model was used to generate a dataset for XGBoost. MOPSO is regarded as an optimal technology to deal with the conflict between multi-objective functions in inverse analysis. On comparing the results between the actual geomechanical properties and those obtained by using traditional inverse analysis, the proposed framework accurately characterizes the geomechanical parameters of reservoirs obtained through hydraulic fracturing. This provides a feasible, scientific, and promising way to characterize reservoir formation in petroleum engineering, as well as a reference for other fields of engineering. Full article