Search Results (6,435)

This study focuses on the optimization of modern extraction techniques for selected by-product materials, including apple, lemon, and tangerine peels, and onion skins, using artificial neural network (ANN) models. The extraction methods included ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE) with water as the extractant, as well as maceration (MAC) with natural deep eutectic solvents (NADES). Key parameters, such as total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activities, including reducing power (EC₅₀) and free radical scavenging capacity (IC₅₀), were evaluated to compare the efficiency of each method. Among the techniques, UAE outperformed both MAE and MAC in extracting bioactive compounds, especially from onion skins and tangerine peels, as reflected in the highest TPC, TFC, and antioxidant activity. UAE of onion skins showed the best performance, yielding the highest TPC (5.735 ± 0.558 mg CAE/g) and TFC (1.973 ± 0.112 mg RE/g), along with the strongest antioxidant activity (EC₅₀ = 0.549 ± 0.076 mg/mL; IC₅₀ = 0.108 ± 0.049 mg/mL). Tangerine peel extracts obtained by UAE also exhibited high phenolic content (TPC up to 5.399 ± 0.325 mg CAE/g) and strong radical scavenging activity (IC₅₀ 0.118 ± 0.099 mg/mL). ANN models using multilayer perceptron architectures with high coefficients of determination (r² > 0.96) were developed to predict and optimize the extraction results. Sensitivity and error analyses confirmed the robustness of the models and emphasized the influence of the extraction technique and by-product type on the antioxidant parameters. Principal component and cluster analyses showed clear grouping patterns by extraction method, with UAE and MAE showing similar performance profiles. Overall, these results underline the potential of UAE- and ANN-based modeling for the optimal utilization of agricultural by-products. Full article

The present article relates to the description of phenomenological relations of amorphous material behavior within the framework of viscoelasticity and elastic-viscoplasticity theory, as well as to the creation of its digital analog. Ultra-high-molecular-weight polyethylene (UHMWPE) is considered in the study. The model is based on the results of a series of experimental studies. Free compression of cylindrical specimens in a wide range of temperatures [−40; +80] °C and strain rates [0.1; 4] mm/min was performed. Cylindrical specimens were also used to determine the thermal expansion coefficient of the material. Dynamic mechanical analysis (DMA) was performed on rectangular specimens using a three-point bending configuration. Maxwell and Anand models were used to describe the material behavior. In the framework of the study, the temperature dependence of a number of parameters was established. This influenced the mathematical formulation of the Anand model, which was adapted by introducing the temperature dependence of the activation energy, the initial deformation resistance, and the strain rate sensitivity coefficient. Testing of the material models was carried out in the process of analyzing the deformation of a spherical bridge bearing with a multi-cycle periodic load. The load corresponded to the movement of a train on a bridge structure, without taking into account vibrations. It is shown that the viscoelastic model does not describe the behavior of the material accurately enough for a quantitative analysis of the stress–strain state of the structure. It is necessary to move on to more complex models of material behavior to minimize the discrepancy between the digital analog and the real structure; it has been established that taking into account plastic deformation while describing UHMWPE would allow this to be performed. Full article

In intensive breeding systems, goats reared for meat production are often housed in group pens at high stocking densities. This study aimed to investigate the correlation between expressed behaviours and stocking density, and to compare the time budget of these confined goats with that of pasture-based goats. A detailed ethogram of 19 mutually exclusive behavioural activities was developed. Behavioural observations were conducted continuously over 72 h on group pens selected for their variation in stocking density and homogeneity in breed, age, body condition and acclimation period since arrival. Using the scan-sampling method (96 scans per goat daily), data were collected from 42 goats. The time budget, expressed as the mean frequency (%) ± standard deviation for each behavioural activity, was calculated. The associations between time budget and stocking density were assessed via bivariate analysis, with the strength and direction of relationships quantified using Pearson’s correlation coefficient (r). Results indicated that self-grooming and Bipedal stance/Climbing were positively correlated with increased space allowance (i.e., lower stocking density), suggesting their potential utility as positive welfare indicators for housed fattening goats in group pens. Furthermore, the time budget differed notably from pasture-based patterns, primarily characterized by resting (53.09% ± 2.72%), eating (16.05% ± 2.88%), and moving (2.30% ± 0.75%). Full article

The long-term monoculture of Eucalyptus plantations in southern China has raised ecological concerns, prompting a shift towards mixed-species plantations as a sustainable alternative. This study investigates the mechanisms by which companion tree species enhance soil functionality in subtropical red soil regions. A field experiment compared a pure Eucalyptus (CK) plantation with three mixed-species plantations: Eucalyptus × Mytilaria laosensis (A × M), Eucalyptus × Magnolia hypolampra (A × H), and Eucalyptus × Michelia gioii (A × X). Comprehensive soil analyses were conducted at three soil depths (0–20 cm, 20–40 cm, and 40–60 cm) to assess chemical properties, enzyme activities, and humus components, and soil organic carbon (SOC) molecular structure was characterized by Fourier-Transform Infrared Spectroscopy (FTIR), with the relationships quantified using structural equation modeling (SEM) to test predefined causal hypotheses. The results showed that A × H significantly boosted topsoil fertility (e.g., OM: 46.61 g/kg), while A × M enhanced the recalcitrant organic carbon (ROC: 35.29 g/kg), indicating superior carbon sequestration potential. The FTIR analysis revealed species-specific alterations in SOC chemistry, such as increased aromatic compounds in A × H/A × X. The SEM analysis demonstrated that the latent variable “Humus” (reflected by LOC and ROC) directly and positively influenced the latent variable “Soil Fertility” (reflected by pH, OM, and AP; path coefficient: 0.62). In contrast, the latent variable “Organic Components” (reflected by specific FTIR functional groups) exhibited a significant direct negative effect on “Soil Fertility” (−0.41). The significant pathway from “Organic Components” to “Enzymatic Activity” (0.55*) underscored the role of microbial mediation. The study concludes that mixed plantations, particularly with Mytilaria laosensis (A × M), improve soil health through an “organic input–microbial enzyme response–humus formation” pathway, offering a scientific basis for sustainable forestry practices that balance productivity and ecological resilience. Full article

The ab initio calculations of the electronic structure of the low-lying electronic states of the CdBr molecule are characterized in the ^2S+1Λ^(+/−) and Ω^(+/−) representations using the complete active-space self-consistent field (CASSCF) method, followed by the multireference configuration interaction (MRCI) method with Davidson correction (+Q). The potential energy curves are investigated, and spectroscopic parameters (T_e, R_e, ω_e, B_e, α_e, μ_e, and D_e) of the bound states are determined and analyzed. In addition, the rovibrational constants (E_v, B_v, D_v, R_min, and R_max) are reported for the investigated states with and without spin–orbit coupling. The electronic transition dipole moment curve (TDMC) is obtained for the C²Π_1/2 − X²Σ⁺_1/2 transition. Based on these data, Franck–Condon factors (FCFs), Einstein coefficient of spontaneous emission A_ν’ν, radiative lifetime τ, vibrational branching ratios, and the associated slowing distance are evaluated. The results indicated that CdBr is a promising candidate for direct laser cooling, and a feasible cooling scheme employing four pumping and repumping lasers in the ultraviolet region with suitable experimentally accessible parameters is presented. These findings provide practical guidance for experimental spectroscopists exploring ultracold diatomic molecules and their applications. Full article

To address the insufficient adaptability of virtual synchronous generators (VSGs) under traditional fixed-value damping control in multiple application scenarios and the lack of regulatory flexibility in transient damping control with a fixed cutoff frequency, a transient damping-type VSG control strategy with flexibly adjustable cutoff frequency is proposed. The aim is to break through the regulatory limitations of the fixed cutoff frequency, quantify the inverse coordination relationship between the cutoff frequency and the equivalent damping coefficient, establish a dynamic adjustment mechanism of the cutoff frequency based on the system natural oscillation frequency, damping ratio, and power grid parameters, and clarify the value range from 0 to ω_cmax as well as the real-time adaptation algorithm. First, the influence of damping on active power and frequency is analyzed through the VSG model. Second, combined with the characteristic analysis of different damping types, the advantages of transient damping in transient response capability under various operating conditions are derived. Furthermore, the role of the cutoff frequency in transient damping on output characteristics is specifically analyzed, a transient damping design method with flexibly adjustable cutoff frequency is proposed, and the value range of the cutoff frequency is calibrated. Finally, a hardware-in-the-loop experimental platform is established for experimental testing. The strategy effectively eliminates the output power deviation when the system frequency deviates, enhances the transient response capability of the VSG under different operating conditions, and exhibits superior output characteristics. Full article

Wireless power transfer is gaining attention in medium-to-short-range applications such as 1–3 kW-class UAVs and AGVs due to its safety, reliability, and adaptability to complex environments. The LCC-S topology is widely adopted due to its favorable output characteristics and device voltage-stress distribution. However, under fixed coil parameters and operating frequencies, conventional LCC-S achieves high efficiency only near the optimal equivalent load. When the actual load deviates from this value—especially in heavy-load regions—resonant cavity current increases sharply, voltage gain drops significantly, and overall efficiency deteriorates. To overcome this structural limitation without increasing control complexity or adding active regulation stages, this paper proposes a transformer-assisted LCC-S wireless charging topology based on “equivalent load reconstruction.” First, a unified equivalent circuit is constructed to derive analytical expressions for voltage gain, input impedance, and efficiency under arbitrary coupling coefficients and loads for both the traditional LCC-S and the proposed topology, revealing the mechanism behind efficiency degradation under heavy loads. Building upon this foundation, a high-frequency transformer is introduced, with an efficiency-oriented collaborative design method for its turns ratio and excitation inductance. Furthermore, by integrating simplified copper and iron-loss models, the losses in the resonant cavity and the transformer are decomposed and evaluated. Results demonstrate that when transformer parameters are appropriately selected, the newly introduced transformer losses are significantly smaller than the resonant cavity losses reduced through load reconstruction. The constructed 1 kW, 85 kHz prototype demonstrates that within the 0.5–2.5 Ω load range, the proposed topology achieves efficiency exceeding 88%. Under typical heavy-load conditions, its peak efficiency surpasses that of the conventional LCC-S by approximately 20%. The theoretical analysis, simulation, and experimental results are highly consistent, verifying that the transformer-assisted LCC-S topology and its efficiency-oriented design method can effectively expand the high-efficiency operating range across a wide load spectrum without altering the control strategy. This provides a concise and feasible structural optimization solution for wireless charging systems. Full article

This study examines the activity coefficients of benzene, toluene, and di-(2-ethylhexyl)phosphoric acid (D2EHPA) in binary benzene–D2EHPA and toluene–D2EHPA systems, as well as the ternary n-hexane–toluene–D2EHPA system, using gas chromatography at 293.0 K. The primary objective was to determine UNIFAC model interaction parameters and validate their accuracy for predicting thermodynamic behavior in these systems. Experimental measurements revealed activity coefficient maxima for benzene and toluene at mole fractions of 0.8–0.9, decreasing to 0.46–0.67 in dilute solutions. The UNIFAC interaction parameters were calculated as follows: ACH–HPO₄ (−334, 4605), ACCH₃–HPO₄ (680, 467), and refined CH₂–HPO₄ (54, 1199). The UNIFAC model achieved deviations of less than 2% from experimental data in both binary and ternary systems. A novel methodology incorporating intermediate standards for gas chromatography was developed to overcome challenges in measuring volatile solvent concentrations, enhancing measurement precision. These findings enable accurate prediction of activity coefficients in mixtures of alkanes, cycloalkanes, and monoaromatic hydrocarbons with D2EHPA, offering significant implications for optimizing metal liquid–liquid extraction processes. Full article

The study investigates the influence of atmospheric pressure fluctuations on methane emissions in a decommissioned coal mine in Poland (SRK S.A., KWK “Krupiński”). Continuous measurements of methane concentrations and atmospheric pressure were analyzed to identify periods of dynamic pressure drops, which were then correlated with recorded methane levels. Strong linear relationships were observed, with correlation coefficients ranging from 0.88 to 0.97 and determination coefficients exceeding 0.85, indicating that pressure changes are a primary factor influencing methane release. Individual regression models for each identified case showed the lowest mean absolute errors compared to generalized models, highlighting the impact of atypical cases on predictive performance. Key findings align with previous studies, confirming that both the magnitude and the gradient of pressure decline directly affect the rate and scale of methane release and that threshold effects may limit further concentration increases despite continued pressure drops. The results suggest the potential to develop a predictive model linking atmospheric pressure variations to methane emissions, which could support forecasting of methane capture in decommissioned mines or ventilation methane levels in active mines. Understanding these mechanisms is crucial for both occupational safety and for effective methane emission reduction strategies in the mining sector. Full article

To address the issue that distributed renewable energy grid-connected Virtual Synchronous Generator (VSG) systems are prone to significant power and frequency fluctuations under changing operating conditions, this paper proposes a multi-parameter coordinated control strategy for VSGs based on a fusion framework of fuzzy logic and the Soft Actor–Critic (SAC) algorithm, termed Improved SAC-based Virtual Synchronous Generator control (ISAC-VSG). First, the method uses fuzzy logic to map the frequency deviation and its rate of change into a five-dimensional membership vector, which characterizes the uncertainty and nonlinear features during the transient process, enabling segmented policy optimization for different transient regions. Second, a stage-based guidance mechanism is introduced into the reward function to balance the agent’s exploration and stability, thereby improving the reliability of the policy. Finally, the action space is expanded from inertia–damping to the coordinated regulation of inertia, damping, and active power droop coefficient, achieving multi-parameter dynamic optimization. MATLAB/Simulink R2022b simulation results indicate that, compared with the traditional SAC-VSG and DDPG-VSG method, the proposed strategy can reduce the maximum frequency overshoot by up to 29.6% and shorten the settling time by approximately 15.6% under typical operating conditions such as load step changes and grid phase disturbances. It demonstrates superior frequency oscillation suppression capability and system robustness, verifying the effectiveness and application potential of the proposed method in high-penetration renewable energy power systems. Full article

Accurate forecasting of groundwater level dynamics poses a critical challenge for sustainable water management in arid regions. However, the strong spatiotemporal heterogeneity inherent in groundwater systems and their complex interactions between natural processes and human activities often limit the effectiveness of conventional prediction methods. To address this, a hybrid CNN-LSTM deep learning model is constructed. This model is designed to extract multivariate coupled features and capture temporal dependencies from multi-variable time series data, while simultaneously simulating the nonlinear and delayed responses of aquifers to groundwater abstraction. Specifically, the convolutional neural network (CNN) component extracts the multivariate coupled features of hydro-meteorological driving factors, and the long short-term memory (LSTM) network component models the temporal dependencies in groundwater level fluctuations. This integrated architecture comprehensively represents the combined effects of natural recharge–discharge processes and anthropogenic pumping on the groundwater system. Utilizing monitoring data from 2021 to 2024, the model was trained and tested using a rolling time-series validation strategy. Its performance was benchmarked against traditional models, including the autoregressive integrated moving average (ARIMA) model, recurrent neural network (RNN), and standalone LSTM. The results show that the CNN-LSTM model delivers superior performance across diverse hydrogeological conditions: at the upstream well AJC-7, which is dominated by natural recharge and discharge, the Nash–Sutcliffe efficiency (NSE) coefficient reached 0.922; at the downstream well AJC-21, which is subject to intensive pumping, the model maintained a robust NSE of 0.787, significantly outperforming the benchmark models. Further sensitivity analysis reveals an asymmetric response of the model’s predictions to uncertainties in pumping data, highlighting the role of key hydrogeological processes such as delayed drainage from the vadose zone. This study not only confirms the strong applicability of the hybrid deep learning model for groundwater level prediction in data-scarce arid regions but also provides a novel analytical pathway and mechanistic insight into the nonlinear behavior of aquifer systems under significant human influence. Full article

Introduction: Postpartum hemorrhage remains a leading cause of maternal morbidity and mortality worldwide. While antifibrinolytic agents such as tranexamic acid are effective in treating established postpartum hemorrhage, the benefit of prophylactic tranexamic acid remains debated. The presence and frequency of early postpartum hyperfibrinolysis during routine childbirth have not been thoroughly investigated. Material & Methods: This prospective observational study was registered on ClinicalTrials.gov (NCT05975112) and conducted at the Medical University Hospital Graz between June 2023 and June 2024. Blood samples were collected from 413 women immediately after umbilical cord clamping; 379 were included in the analysis—291 undergoing Caesarean section and 88 vaginal delivery. Hyperfibrinolysis was assessed using thromboelastography and defined as an LY30 value > 8%. Additional coagulation parameters—including fibrinogen, D-dimer, activated partial thromboplastin time, and prothrombin time—were measured. Correlation analyses between viscoelastic and conventional parameters were performed using Pearson’s correlation coefficients. Results: No cases of clinically significant hyperfibrinolysis (LY30 > 8%) were observed. However, 15.5% of women showed elevated LY30 values (>0%). LY30 values were significantly higher in vaginal deliveries compared to Caesarean sections (p = 0.003). A moderate correlation between maximum amplitude (MA) and fibrinogen was observed (r = 0.52), strongest in vaginal deliveries (r = 0.65). Other correlations were weak or negligible. Conclusions: Clinically relevant hyperfibrinolysis was not observed immediately postpartum in women without hemorrhage. These findings are consistent with current guidelines recommending tranexamic acid for therapeutic rather than routine prophylactic use. Viscoelastic testing may be useful for rapid assessment in early-stage bleeding. Further research should explore fibrinolytic activity later in the postpartum period. Full article

Affordable high-frame-rate cameras and open-source software, such as Kinovea (ver. 2025.1.0), have expanded the potential for conducting kinematic assessments outside laboratory settings. This study examined the reliability and validity of Kinovea’s semi-automated linear kinematics tracking tool by comparing its outputs with those from a 3D marker-based motion capture system (Qualisys). Ten recreationally active male basketball players (x̄ ± SD: age 23.7 ± 1.7 years; height 183 ± 5 cm; body mass 76.8 ± 9.8 kg) performed three CMJ trials, simultaneously recorded using both systems. Reflective markers placed on the shoulder, hip, and knee were tracked in Kinovea by two raters with different levels of experience to extract core CMJ variables (total take-off time and maximum vertical displacement) and complementary variables (eccentric and propulsion duration, and minimum vertical displacement). Inter-rater reliability and concurrent validity were evaluated using intraclass correlation coefficients (ICCs), coefficients of variation (CV%), standard error of measurement (SEM), and Bland–Altman analysis. Results showed excellent inter-rater reliability (ICC = 0.73–0.99) across all markers, with the hip and knee demonstrating the highest consistency. Strong validity relative to Qualisys was observed for both raters (ICC = 0.68–0.99; r > 0.80), with small systematic biases primarily in temporal variables. Collectively, these findings demonstrate that Kinovea’s semi-automated 2D analysis yields reliable and valid CMJ measurements comparable to 3D motion capture, even for less experienced users. As a free and easily deployable tool, it offers a widely accessible alternative for field-based performance monitoring and applied biomechanics research where laboratory-grade equipment is not available. Full article

Monitoring surface water quality is essential for assessing water resources and identifying their quality patterns. Traditional monitoring methods, based on conventional point-sampling stations, are reliable but costly and limited in frequency and spatial coverage. These constraints hinder the ability to evaluate water quality parameters at the temporal and spatial scales required to detect the effects of extreme events on aquatic systems. Satellite imagery offers a viable complementary alternative to enhance the temporal and spatial monitoring scales of traditional assessment methods. However, limitations related to spectral, spatial, temporal, and/or radiometric resolution still pose significant challenges to prediction accuracy. This study aimed to propose a methodology for predicting optically active and inactive water quality parameters in lotic and lentic environments using remote-sensing data and machine-learning techniques. Three remote-sensing datasets were organized and evaluated: (i) data extracted from Sentinel-2 imagery; (ii) data obtained from raw PlanetScope (PS) imagery; and (iii) data from PS imagery normalized using the methodology developed by Dias. Data on water quality parameters were collected from 24 monitoring stations located along the Paraopeba River channel and the Três Marias Reservoir, covering the period from 2016 to 2023. Four machine-learning algorithms were applied to predict water quality parameters: Random Forest, k-Nearest Neighbors, Support Vector Machines with Radial Basis Function Kernel, and Cubist. Model performance was evaluated using four statistical metrics: root-mean-square error, mean absolute error, Lin′s concordance correlation coefficient, and the coefficient of determination. Models based on normalized PS data achieved the best performance in parameter estimation. Additionally, decision-tree-based algorithms showed superior generalization capability, outperforming the other models tested. The proposed methodology proved suitable for this type of analysis, confirming not only the applicability of PS data but also providing relevant insights for its use in diverse environmental-monitoring applications. Full article

This study develops a numerical model based on a multi-physics coupled finite element method to predict the carbonation depth of hardened alkali-activated slag under accelerated carbonation curing conditions. Drawing on existing literature data, the chemical composition and porosity of alkali-activated slag at different ages were determined under non-carbonation conditions, supported by thermodynamic and kinetic analyses of alkali activation reactions. A differential equation governing CO₂ diffusion—incorporating diffusion rate, diffusion coefficient, carbonation reaction rate, and related parameters—was established using Fick’s second law. The influence of humidity and carbonation degree on the reaction rate was quantified, and a correlation between carbonation degree and porosity was derived through thermodynamic analysis. These equations were solved numerically in a two-dimensional domain to predict carbonation depth over time. The results demonstrate that the proposed model, using only raw material composition and curing conditions, achieves reasonable accuracy in predicting carbonation depth. Full article