Search Results (168)

Creating a comfortable microclimate in the premises of buildings is currently becoming one of the priorities in the field of architecture, construction and engineering systems. The increased attention from the scientific community to this topic is due not only to the desire to ensure healthy and favorable conditions for human life but also to the need for the rational use of energy resources. This area is becoming particularly relevant in the context of global challenges related to climate change, rising energy costs and increased environmental requirements. Practice shows that any technical solutions to ensure comfortable temperature, humidity and air exchange in rooms should be closely linked to the concept of energy efficiency. This allows one not only to reduce operating costs but also to significantly reduce greenhouse gas emissions, thereby contributing to sustainable development and environmental safety. In this connection, this study presents a parametric assessment of the influence of climatic and geometric factors on the aerodynamic characteristics of the air cavity, which affect the heat exchange process in the ventilated layer of curtain wall systems. The assessment was carried out using a combined analytical calculation method that provides averaged thermophysical parameters, such as mean air velocity ($V_s$), average internal surface temperature ($t_{in.s}^{av}$), and convective heat transfer coefficient (${\alpha }_s$) within the air cavity. This study resulted in empirical average values, demonstrating that the air velocity within the cavity significantly depends on atmospheric pressure and façade height difference. For instance, a 10-fold increase in façade height leads to a 4.4-fold increase in air velocity. Furthermore, a three-fold variation in local resistance coefficients results in up to a two-fold change in airflow velocity. The cavity thickness, depending on atmospheric pressure, was also found to affect airflow velocity by up to 25%. Similar patterns were observed under ambient temperatures of +20 °C, +30 °C, and +40 °C. The analysis confirmed that airflow velocity is directly affected by cavity height, while the impact of solar radiation is negligible. However, based on the outcomes of the analytical model, it was concluded that the method does not adequately account for the effects of solar radiation and vertical temperature gradients on airflow within ventilated façades. This highlights the need for further full-scale experimental investigations under hot climate conditions in South Kazakhstan. The findings are expected to be applicable internationally to regions with comparable climatic characteristics. Ultimately, a correct understanding of thermophysical processes in such structures will support the advancement of trends such as Lightweight Design, Functionally Graded Design, and Value Engineering in the development of curtain wall systems, through the optimized selection of façade configurations, accounting for temperature loads under specific climatic and design conditions. Full article

In this study, a numerical model of a microchannel gas cooler was developed using a segment-by-segment approach for thermohydraulic performance evaluation. State-of-the-art heat transfer and pressure drop correlations were used to determine the air and refrigerant side heat transfer coefficients and friction factors. The developed model was validated against a wide range of experimental data and was found to accurately predict the gas cooler capacity (Q) and pressure drop (ΔP) within an acceptable margin of error. Furthermore, advanced machine learning algorithms such as extreme gradient boosting (XGB), random forest (RF), support vector regression (SVR), k-nearest neighbors (KNNs), and artificial neural networks (ANNs) were employed to analyze their predictive capability. Over 11,000 data points from the numerical model were used, with 80% of the data for training and 20% for testing. The evaluation metrics, such as the coefficient of determination (R², 0.99841–0.99836) and mean squared error values (0.09918–0.10639), demonstrated high predictive efficacy and accuracy, with only slight variations among the models. All models accurately predict the Q, with the XGB and ANN models showing superior performance in ΔP prediction. Notably, the ANN model emerges as the most accurate method for refrigerant and air outlet temperatures predictions. These findings highlight the potential of machine learning as a robust tool for optimizing thermal system performance and guiding the design of energy-efficient heat exchange technologies. Full article

Particulate matter affects both the light environment and air quality in greenhouses, obstructing normal gas exchange and hindering efficient physiological activities such as photosynthesis. This study focused on young Chinese cabbage (Brassica rapa L. Chinensis Group) in a greenhouse at harvest time, monitoring and comparing hyperspectral information, net photosynthetic rate, and microscopic leaf structure under two conditions: a quantitative artificial particulate matter environment and a healthy environment. Based on microscopic results combined with spectral responses and changes in photosynthetic physiological information, it is believed that particulate matter enters plant cells through stomata. Through retention and transport pathways, it disrupts the membrane structure, organelles, and other components of plant cells, resulting in adverse effects on the plant’s physiological functions. The study analyzed the mechanisms by which particulate matter influences the photosynthesis, spectral characteristics, and physiological responses of young Chinese cabbage. Physiological Reflectance Index (PRI), Modified Chlorophyll Absorption Ratio Index (MCARI), spectral red-edge position (λr), and spectral sensitive bands were used as spectral feature variables. Through cubic polynomial and 24 combinations of spectral preprocessing and modeling methods, an inversion model of spectral features and net photosynthetic rate was established. The optimal combination of spectral preprocessing and modeling methods was finally selected as SG + SD + PLS + MSC, which consists of Savitzky-Golay smooth (SG), second derivative (SD), partial least squares (PLS), and multiplicative scatter correction (MSC). The coefficient of determination (R²) of the model is 0.9513. The results indicate that particulate matter affects plant photosynthesis. The SG + SD + PLS + MSC combination method is relatively advantageous for processing the photosynthetic spectral physiological information of plants under the influence of particulate matter. The results of this study will deepen the understanding of the mechanisms by which particulate matter affects plants and provide a reference for the physiological information inversion of greenhouse vegetables under particulate matter pollution. Full article

As a new form of economic activity driven by data resources and digital technologies, the digital economy underscores the strategic significance of data as a core production factor. This growing importance necessitates accurate and robust valuation methods. Data valuation poses core modeling challenges due to its nonlinear nature and the instability of neural networks, including gradient vanishing, parameter sensitivity, and slow convergence. To overcome these challenges, this study proposes a genetic algorithm-optimized BP (GA-BP) model, enhancing the efficiency and accuracy of data valuation. The BP neural network employs a symmetrical architecture, with neurons organized in layers and information transmitted symmetrically during both forward and backward propagation. Similarly, the genetic algorithm maintains a symmetric evolutionary process, featuring symmetric operations in both crossover and mutation. The empirical data used in this study are sourced from the Shanghai Data Exchange, comprising 519 data samples. Based on this dataset, the model incorporates 9 primary indicators and 21 secondary indicators to comprehensively assess data value, optimizing network weights and thresholds through the genetic algorithm. Experimental results show that the GA-BP model outperforms the traditional BP network in terms of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), achieving a 47.6% improvement in prediction accuracy. Furthermore, GA-BP exhibits faster convergence and greater stability. When compared to other models such as long short-term memory (LSTM), convolutional neural networks (CNNs), and optimization-based BP variants like particle swarm optimization BP (PSO-BP) and whale optimization algorithm BP (WOA-BP), GA-BP demonstrates superior generalization and robustness. This approach provides valuable insights into the commercialization of data assets. Full article

Accurate segmentation of soil pore structure is crucial for studying soil water migration, nutrient cycling, and gas exchange. However, the low-contrast and high-noise CT images in complex soil environments cause the traditional segmentation methods to have obvious deficiencies in accuracy and robustness. This paper proposes a hybrid model combining a Multi-Modal Low-Frequency Reconstruction algorithm (MMLFR) and UNet (MMLFR-UNet). MMLFR enhances the key feature expression by extracting the image low-frequency signals and suppressing the noise interference through the multi-scale spectral decomposition, whereas UNet excels in the segmentation detail restoration and complexity boundary processing by virtue of its coding-decoding structure and the hopping connection mechanism. In this paper, an undisturbed soil column was collected in Hainan Province, China, which was classified as Ferralsols (FAO/UNESCO), and CT scans were utilized to acquire high-resolution images and generate high-quality datasets suitable for deep learning through preprocessing operations such as fixed-layer sampling, cropping, and enhancement. The results show that MMLFR-UNet outperforms UNet and traditional methods (e.g., Otsu and Fuzzy C-Means (FCM)) in terms of Intersection over Union (IoU), Dice Similarity Coefficients (DSC), Pixel Accuracy (PA), and boundary similarity. Notably, this model exhibits exceptional robustness and precision in segmentation tasks involving complex pore structures and low-contrast images. Full article

The analysis of the influence of microhotplate size on the convective heat exchange of gas sensors is presented. Usually, the role of convection in the heat exchange of gas sensors is not considered in thermal simulation models because of the complexity of the convection process. As a result, the contribution of this process to the overall heat loss of sensors remains without detailed analysis. We analyzed convection issues in two groups of gas sensors: semiconductor and thermocatalytic (calorimetric) sensors and, on the other hand, in the oxygen sensors of the thermomagnetic type. It is demonstrated that there is a critical size leading to the formation of convective heat exchange flow. Below this critical value, only thermal conductivity of ambient air, IR (infrared) radiation from the heated microhotplate surface, and thermal conductivity of the microhotplate-supporting elements should be considered as channels for heat dissipation by the microhotplate, and the contribution of free convection can be neglected. The expression for the critical size contains only fundamental constants of air, d_cr~$4·\sqrt[3]{\raisebox{1ex}{$\nu ·D$}\!\left/ \!\raisebox{-1ex}{$g$}\right.}$, where ν is the kinematic viscosity of air, D is the diffusion coefficient, and g is the acceleration of free fall, d_cr~0.5 cm. Therefore, if the size of the microhotplate d <<d_cr, the influence of convection heat exchange can be neglected. Similar results were obtained in the analysis of the behavior of thermal magnetic sensors of oxygen, which use paramagnetic properties of molecular oxygen for the determination of O₂ concentration. In this case, the critical size of the sensor is also of significance; if the size of the magnetic sensor is much below this value, the oxygen concentration value measured with such a device is independent of the orientation of the sensor element. The results of the simulation were compared with the measurement of heat loss in micromachined gas sensors. The optimal dimensions of the sensor microhotplate are given as a result of these simulations and measurements. Full article

Based on the COMSOL simulation software (v.6.1), this paper systematically investigates the influence law of runner length on the velocity and pressure distribution of cathode and anode gas runners in proton exchange membrane fuel cells (PEMFCs), and experimentally verifies the measurement effect of thin-film thermocouples on the operating temperature of PEMFCs. The simulation results show that the maximum pressure of the cathode and anode increases nonlinearly with the increase in the runner length, while the velocity distribution remains stable; the shortening of the runners significantly reduces the friction loss along the flow path and optimizes the matching of the permeability of the porous medium. In addition, the NiCr/NiSi thin-film thermocouple prepared by magnetron sputtering exhibits high accuracy (Seebeck coefficient of 41.56 μV/°C) in static calibration and successfully captures the dynamic response characteristics of temperature in PEMFC operation. This study provides a theoretical basis and experimental support for the optimization of fuel cell flow channel design and temperature monitoring technology. Full article

Proton exchange membrane fuel cells (PEMFCs), recognized as promising sources of waste heat for space heating, domestic hot water supply, and industrial thermal applications, have garnered substantial interest owing to their environmentally benign operation and high energy conversion efficiency. Since the uniformity of oxygen diffusion toward catalytic layers critically governs electrochemical performance, this study establishes a three-dimensional, non-isothermal computational fluid dynamics (CFD) model to systematically optimize the cathode flow channel width distribution, targeting the maximization of power output through enhanced reactant homogeneity. Numerical results reveal that non-uniform flow channel geometries markedly improve oxygen distribution uniformity, reducing the flow inhomogeneity coefficient by 6.6% while elevating maximum power density and limiting current density by 9.1% and 7.8%, respectively, compared to conventional equal-width designs. There were improvements attributed to the establishment of longitudinal oxygen concentration gradients and we alleviated mass transfer limitations. Synergistic integration with gas diffusion layer (GDL) gradient porosity optimization further amplifies performance, yielding a 12.4% enhancement in maximum power density and a 10.4% increase in limiting current density. These findings validate the algorithm’s efficacy in resolving coupled transport constraints and underscore the necessity of multi-component optimization for advancing PEMFC design. Full article

The fluid disturbance effect is a significant challenge in CBM (CBM) co-production within superimposed pressure systems in China. To address the unique CBM reservoir of superimposed pressure systems, a CBM co-production experimental apparatus for multi-pressure systems has been independently developed. To comprehensively understand fluid behavior during the early stage of CBM co-production, two sets of experiments were conducted using the self-developed physical simulation test device: one in single-production mode and the other in co-production mode. The dynamic response of reservoir fluids and gas production characteristics were analyzed, and the fluid disturbance mechanism under wellbore fluid confluences was explored. The method adopted in this study addresses the issues of traditional co-production equipment, such as the use of series-parallel core holders, small dimensions, limited monitoring capabilities, single loading methods, and the lack of consideration for wellbore co-production flow disturbance and fluid redistribution in superimposed pressure systems. The following results were obtained: ① A flow disturbance effect emerges when fluids from coal reservoirs with different pressure properties converge and mix in a main wellbore. The pressure inside the four horizontal wells simultaneously reaches 1.45 MPa at t = 0.03 min. ② Based on the fluid disturbance effect, the evolution process of wellbore pressure is categorized into two stages: the confluence disturbance stage and the confluence pressure drop stage. ③ This fluid disturbance effect exacerbates the disparities among coal reservoirs, facilitating fluid exchange between the main wellbore and coal reservoirs through branch wellbores. Under the co-production mode, the instantaneous gas production of the No. 1 coal reservoir reaches its maximum negative value at the moment of production, amounting to −3.85 L/min, indicating that a portion of the fluid from high-pressure coal reservoirs flows back into low-pressure coal reservoirs. ④ A dynamic characterization compatibility method is proposed based on the differences in fluid flow between the single and co-production modes during the early stage of CBM production. For example, at t = 0.1 min, the pressure compatibility coefficients of the No. 1–4 coal reservoirs are 0.72, 0.45, 0.34, and 0.33, respectively. The pressure compatibility and production compatibility coefficients exhibit rapid growth during the early stages, followed by a slight decrease during the middle and later stages. ⑤ The worst compatibility performances are observed during the early stage of CBM co-production, but these performances improve as the co-production time extends. ⑥ Optimizing superimposed pressure systems involves progressive co-production: dynamically introducing coal reservoirs, balancing reservoir pressure, minimizing fluid disturbance, and enhancing recovery efficiency. Full article

A gas diffusion layer (GDL) is an essential component for the efficient operation of proton exchange membrane fuel cells, requiring stable mechanical strength and uniform stress distribution to achieve higher durability. The various microstructure characteristics of GDLs have coupled and complex effects on mechanical properties, which have not been fully considered in previous studies. In this study, we have combined stochastic reconstruction techniques, explicit dynamics compression simulation, and orthogonal design methods to evaluate and optimize the coupling effects of carbon fiber diameter, porosity, GDL thickness, and fiber orientation coefficient on the mechanical properties of pore-scale GDLs. Finally, mathematical expressions have been developed to predict stress distribution under compression. The results show that the impact of fiber diameter and porosity is greater than that of GDL thickness and fiber orientation coefficient. Average stress and stress uniformity increase with increases in fiber diameter, fiber orientation coefficient, and GDL thickness, but porosity shows an opposite trend. We achieved a remarkable reduction of 292% in optimal average stress and a significant enhancement of 278% in stress uniformity. The mathematical expressions have been validated for accuracy by considering the simultaneous coupled effects of various microstructural characteristics. This work provides valuable engineering tools for enhancing the performance and durability of GDLs and fuel cells. Full article

Stomatal pore area and density determine the capacity for gas exchange between the leaf interior and the atmosphere. Stomatal area is given by the profile formed by two guard cells, and the cumulative stomatal area characterizes the area of leaf surface occupied by stomata. The areas of all stomata captured in a micrograph are sorted in ascending order to form a sequence, which is referred to as a sequence of stomatal area here. In total, 360 leaves of 12 Magnoliaceae species with 30 leaves for each species were sampled. For each leaf, two 662 μm × 444 μm fields of view (micrographs) of stomata were captured on the right leaf width axis. In each micrograph, the length and width of each stoma were measured, and the area of the stoma was determined using the product of stomatal length and width multiplied by a proportionality coefficient. Stomatal area sequences of Magnoliaceae in the constant field of view were found to follow a geometric series (GS). Prior studies estimated the common ratio of the GS as the mean of the quotients of any two adjacent terms, and estimated the first term as the mean of the first terms (i.e., the smallest stomatal area) represented by the quotient of each term and the estimated common ratio to a power of the order of the term minus 1, which is referred to as Method-1. However, it produced large prediction errors for some stomatal area sequences. In the present study, the nonlinear regression was used to fit the stomatal area sequences using the common ratio and the first term as two model parameters (Method-2). We compared the two methods using the mean absolute percent error (MAPE, ≤5% considered as a good fit) values of the 720 stomatal micrographs from the 12 Magnoliaceae species. The goodness of fit of Method-2 was better than that of Method-1 (52.4% MAPE values were ≤5% for Method-1 and 99.6% for Method-2). There were significant variations in the estimated common ratios, as well as the estimated first terms and the MAPE values across the 12 Magnoliaceae species, but overall, the interspecific differences in the MAPE values were small. We conclude that the GS hypothesis for the stomatal area sequences of the 12 Magnoliaceae species was further strengthened by the new method. This method further provides a valuable approach for the calculation of total stomatal area per unit leaf area. Full article

The production of high-quality coffee seedlings is essential to meet the demands of the coffee sector, requiring more efficient and sustainable water management practices. In this context, the use of hydroretentive polymers, particularly biodegradable ones, emerges as a promising alternative to optimize water use, reduce the environmental impact associated with synthetic polymers, and improve the agronomic traits of seedlings. Therefore, this study aimed to evaluate the effects of different irrigation intervals and hydroretentive polymer doses on the water consumption and agronomic characteristics of Coffea arabica L. seedlings. This study was conducted in a protected environment using a randomized block design with split plots and four replicates. The plots consisted of two irrigation intervals (2 and 4 days), and the subplots included four doses of hydroretentive polymer (0%, 0.25%, 0.5%, and 1%), applied in 0.5 dm³ polypropylene bags. Results showed that the 0.5% polymer dose combined with a 2-day irrigation interval resulted in the highest water consumption, while the combination of 0% polymer and a 4-day irrigation interval led to the lowest water consumption. The 0.25% hydroretentive polymer dose with irrigation every 2 days showed the best performance in gas exchange, promoting photosynthesis without causing water saturation. This management also promoted better seedling growth, increasing biomass, height, leaf area, and root volume compared to longer irrigation intervals. The crop coefficients (Kc × Ks) were 0.20, 0.28, and 0.45 during the periods of 0–50, 51–80, and 81–150 days after sowing, respectively. A dose of 0.25% hydroretentive polymer with a 2-day irrigation interval is recommended for the production of Arabica coffee seedlings, contributing to agricultural practices aligned with environmental preservation and productive efficiency. Full article

Using the data of radon accumulation in a chamber with excess volume at one of the points of the Kamchatka subsurface gas-monitoring network, the change in radon flux density due to seismic waves and post-seismic relaxation of the medium is shown. A linear fractional equation is considered to be a model equation. The change of radon-transport intensity due to changes in the state of the geo-environment is described by a fractional Gerasimov–Caputo derivative of constant order. Presumably, the order of the fractional derivative is related to the radon-transport intensity in the geosphere. Using the Levenberg–Marquardt method, the optimal values of the model parameters were determined based on experimental data: air exchange coefficient and order of fractional derivative, which allowed the solving of the problems of radon flux density determination. Data in the temporal neighborhood of a strong earthquake with $M_w=7.0$, which occurred in the northern part of Avacha Bay on 17 August 2024, were used. As a result of the modeling, it is shown that the strong seismic impact and subsequent processes led to changes in the radon flux in the accumulation chamber. The obtained model curves agree well with the real data, and the obtained estimates of radon flux density agree with the theory. Full article

Brassinosteroids (BRs) are recognized for their ability to enhance plant salt tolerance. While considerable research has focused on their effects under neutral salt conditions, the mechanisms through which BRs regulate photosynthesis under alkaline salt stress are less well understood. This study investigates these mechanisms, examining plant growth, photosynthetic electron transport, gas exchange parameters, Calvin cycle dynamics, and the expression of key antioxidant and Calvin cycle genes under alkaline stress conditions induced by NaHCO₃. The findings indicate that NaHCO₃ stress substantially impairs cucumber growth and photosynthesis, significantly reducing chlorophyll content, net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (E), maximum photochemical efficiency (Fv/Fm), actual photochemical efficiency (ΦPSII), antenna conversion efficiency (Fv′/Fm′), and photochemical quenching coefficient (qP). This disruption suggests a severe dysregulation of the photosynthetic electron transport system, impairing electron transfer from photosystem II (PSII) to photosystem I (PSI) and subsequently the Calvin cycle. Application of exogenous 24-epibrassinolide (EBR) alleviated these effects, reducing leaf chlorosis and growth inhibition and significantly enhancing the expression of key genes within the antioxidant system (AsA-GSH cycle) and the Calvin cycle. This intervention also led to a reduction in reactive oxygen species (ROS) accumulation and improved photosynthetic performance, as evidenced by enhancements in Pn, Gs, E, Fv/Fm, ΦPSII, Fv′/Fm′, and qP. Moreover, NaHCO₃ stress hindered chlorophyll synthesis, primarily by blocking the conversion from porphobilinogen (PBG) to uroporphyrinogen III (UroIII) and by increasing chlorophyllase (Chlase) and decreasing porphobilinogen deaminase (PBGD) activity. Exogenous EBR countered these effects by enhancing PBGD activity and reducing Chlase activity, thereby increasing chlorophyll content under stress conditions. In summary, EBR markedly mitigated the adverse effects of alkaline stress on cucumber leaf photosynthesis by stabilizing the photosynthetic electron transport system, accelerating photosynthetic electron transport, and promoting the Calvin cycle. This study provides valuable insights into the regulatory roles of BRs in enhancing plant resilience to alkaline stress. Full article

Harsh operating conditions imposed by vehicular applications significantly limit the utilization of proton exchange membrane fuel cells (PEMFCs) in electric propulsion systems. Improper/poor management and supervision of rapidly varying current demands can lead to undesired electrochemical reactions and critical cell failures. Among other failures, flooding and catalytic degradation are failure mechanisms that directly impact the composition of the membrane electrode assembly and can cause irreversible cell performance deterioration. Due to the functional significance and high manufacturing costs of the catalyst layer, monitoring internal fuel cell states is crucial. For this purpose, a diagnostic-oriented multi-scale PEMFC catalytic degradation model is developed which incorporates the failure effects of catalytic degradation on cell dynamics and global stack performance. Embedded to the multi-scale model is a square root unscented Kalman filter (SRUKF)-based multiple-model fault diagnosis scheme. In this approach, multiple models are used to estimate specific internal PEMFC system parameters, such as the mass transfer coefficient of the gas diffusion layer or the exchange current density, which are treated as additional system states. Online state estimates are provided by the SRUKF, which additionally propagates model-conditioned statistical information to update a Bayesian framework for model selection. The Bayesian model selection method carries fault indication signals that are interpreted by a derived decision logic to obtain reliable information on the current-operating system regime. The proposed diagnosis scheme is evaluated through simulations using the LA 92 and NEDC driving cycles. Full article