Search Results (5,872)

High-intensity interval training (HIIT) provides substantial cardiovascular benefits; however, precise monitoring typically requires expensive devices. These systems are feasible in research laboratories but are costly for schools and the fitness industry. Low-cost, validated devices are required to facilitate broader implementation. A cross-sectional study was conducted with 213 students (173 men and 40 women) from the Catholic University of Valencia, Spain. The participants completed an HIIT protocol consisting of five 3 min blocks. Heart rate (HR) was recorded using a Moofit HW401 armband (ANT+ technology). Ratings of perceived exertion (RPE, Omni-Res scale) and the Wint index were also obtained. Pearson correlations were computed between reserve heart rate (HRr), RPE, and Wint index during the warm-up phases (T1, T2) and HIIT, stratified by sex, age, and body mass index (BMI). HRr was strongly correlated with the Wint index (r = 0.95, p < 0.0001) and moderately correlated with RPE (r = 0.235, p = 0.001). No significant sex differences were observed (men 83.66 ± 8.18% vs. women 82.31 ± 10.89%; p > 0.05). Correlations were weaker in participants with extreme BMI values (n < 10, obese). The Moofit HW401 armband showed consistent agreement between HRr, RPE, and Wint index during HIIT, supporting its practical use for group monitoring in educational settings, pending formal validation against gold standards. Full article

Soil organic matter (SOM) is a key indicator of arable land quality and the global carbon cycle; accurate regional-scale SOM estimation is vitally significant for sustainable agricultural development and climate change research. This study evaluates a multisource data-fusion approach for improving cropland SOM prediction in Yucheng City, Shandong Province, China. We applied a Local Energy Weighted Discrete Wavelet Transform (LEW-DWT) to fuse multi-temporal Landsat 8 imagery (2014–2023). Quantitative analysis (e.g., Information Entropy and Average Gradient) demonstrated that LEW-DWT effectively preserved high-frequency spatial details and texture features of fragmented croplands better than traditional DWT and simple splicing methods. These were combined with 41 environmental predictors to construct composite Ev–Tn–Mm features (environmental variables, temporal NDVI features, and multi-temporal multispectral information). Random Forest (RF) and Convolutional Neural Network (CNN) models were trained and compared to assess the contribution of the fused data to SOM mapping. Key findings are: (1) Comparative analysis showed that the LEW-DWT fusion strategy achieved the lowest spectral distortion and highest spatial fidelity. Using the fused multitemporal dataset, the CNN attained the highest predictive performance for SOM (R² = 0.49). (2) Using the Ev–Tn–Mm features, the CNN achieved R² = 0.62, outperforming the RF model (R² = 0.53). Despite the limited sample size, the optimized shallow CNN architecture effectively extracted local spatial features while mitigating overfitting. (3) Variable importance analysis based on the RF model reveals that mean soil moisture is the primary single variable influencing the SOM, (relative importance 15.22%), with the NDVI phase among time-series features (1.80%) and the SWIR1 band among fused multispectral bands (1.38%). (4) By category, soil moisture-related variables contributed 45.84% of total importance, followed by climatic factors. The proposed multisource fusion framework offers a practical solution for regional SOM digital monitoring and can support precision agriculture and soil carbon management. Full article

The Discrete Element Method is widely used in applied mechanics, particularly in situations where material continuity breaks down (fracturing, crushing, friction, granular flow) and classical rheological models fail (phase transition between solid and granular). In this study, the Discrete Element Method was employed to simulate stick–slip cycles, i.e., numerical earthquakes. At 2000 selected, regularly spaced time checkpoints, parameters describing the average state of all particles forming the numerical fault were recorded. These parameters were related to the average velocity of the particles and were treated as the numerical equivalent of (pseudo) Acoustic Emission. The collected datasets were used to train the Random Forest and Deep Learning models that successfully predicted the time to failure. SHapley Additive exPlanations (SHAP) was used to quantify the contribution of individual physical parameters of the particles to the prediction results. The main novelty of this study was the prediction of time to failure for entire event sequences. Using only instantaneous particle velocity statistics and without using information about the history of previous events, coefficients of determination in the range R² = 0.81–0.96 were obtained. Full article

Increased global energy consumption contributes to higher operational costs in the energy sector and results in environmental deterioration. This study evaluates the effectiveness of integrating Internet of Things (IoT) sensors and machine learning techniques to predict adaptive thermostat setpoints to support behavior-aware Heating, Ventilation, and Air Conditioning (HVAC) operation in residential buildings. The dataset was collected over two years from 2080 IoT devices installed in 370 zones in two buildings in Halifax, Canada. Specific categories of real-time information, including indoor and outdoor temperature, humidity, thermostat setpoints, and window/door status, shaped the dataset of the study. Data preprocessing included retrieving data from the MySQL database and converting the data into an analytical format suitable for visualization and processing. In the machine learning phase, deep learning (DL) was employed to predict adaptive threshold settings (“from” and “to”) for the thermostats, and a gradient boosted trees (GBT) approach was used to predict heating and cooling thresholds. Standard metrics (RMSE, MAE, and R²) were used to evaluate effective prediction for adaptive thermostat setpoints. A comparative analysis between GBT ”from” and “to” models and the deep learning (DL) model was performed to assess the accuracy of prediction. Deep learning achieved the highest performance, reducing the MAE value by about 9% in comparison to the strongest GBT model (1.12 vs. 1.23) and reaching an R² value of up to 0.60, indicating improved predictive accuracy under real-world building conditions. The results indicate that IoT-driven setpoint prediction provides a practical foundation for behavior-aware thermostat modeling and future adaptive HVAC control strategies in smart buildings. This study focuses on setpoint prediction under real operational conditions and does not evaluate automated HVAC control or assess actual energy savings. Full article

The aim of this study was to explore the effectiveness of cold atmospheric plasma (CAP) treatments for reducing the deoxynivalenol (DON) content in spiked white wheat flour samples containing 750 μg kg⁻¹ DON. The flour samples were treated with plasma generated in air for durations of 30 s, 60 s, 90 s, 120 s, 150 s, and 180 s and at four distances from the cold plasma source: 6 mm, 21 mm, 36 mm, and 51 mm. An artificial neural network (ANN) model with three layers utilizing the Broyden–Fletcher–Goldfarb-Shanno (BFGS) iterative algorithm was developed to predict the reduction in deoxynivalenol (DON) content, moisture content, and temperature in wheat flour samples following cold atmospheric plasma (CAP) treatment. The model accounted for two key variables: the distance from the plasma source and the treatment duration. The ANN model exhibited excellent predictive performance, achieving coefficient of determination (r²) values of 0.999, 0.996, and 0.996 for DON reduction, moisture content, and temperature, respectively, during the training phase. The ANN model successfully identified the experimental optimal CAP conditions (51 mm distance and 150 s treatment), resulting in a 71% reduction in DON content. Multi-objective optimization (MOO) using the ANN further predicted the same level of reduction but at 168 s while maintaining acceptable moisture and temperature levels, representing the model-derived optimal treatment within the investigated design space. The study highlights the potential of ANNs to model complex relationships and optimize CAP treatment for efficient mycotoxin reduction in wheat flour. Full article

This study used a Systemic Modeling technique, based on the methodologies of Churchman and Ackoff, to integrate and assess the subsystems regulating the functionality of a Rocket Candy (R-Candy) motor. The nozzle and combustion chamber design was improved using a five-phase systemic architecture to assure the coherent interplay of essential factors, including pressure, temperature, and velocity fields. The principles of experimental rocketry are elucidated through the examination of impulse performance throughout class A to class C engines. A preliminary design was developed in SolidWorks 2024, incorporating the engine’s three main components: the igniter, the combustion chamber, and a convergent–divergent nozzle that enhances the acceleration of the exhaust gases. The system model was validated using simulations in FEATool and verified through experimentation. This allowed for the analysis of fluid behavior, as well as the geometry of the structures, initial parameters, and boundary conditions. The results demonstrate a strong correlation between the simulations and the experimental data, with discrepancies of less than 1.5%, confirming the reliability and feasibility of the nozzle design. The findings indicate that systemic modeling, in conjunction with CFD and experimentation, can provide a strategic framework for iterative refinement, optimization of key performance metrics, and the development of cost-effective, high-performance R-Candy engines for educational and experimental purposes. Full article

In today’s world, the demand for compact, high-efficiency, and low-cost motors plays a significant role in the design of low-power electric machines. In combi fan applications, single-phase brushless direct current (BLDC) motors are generally preferred. Although these motors offer efficient and compact solutions, the occurrence of dead points at certain rotor positions creates a serious disadvantage that may prevent the motor from initiating motion. In this study, an asymmetric air gap design is proposed for a single-phase BLDC motor to eliminate the dead point problem and increase starting torque. The motor’s performance has been evaluated through analytical calculations and two-dimensional finite element analysis (FEA) conducted using ANSYS Electronics Desktop 2020 R2 (Maxwell) software. The results show that the asymmetric air gap effectively eliminates the dead point and improves the motor’s starting performance. However, torque ripple is still identified as a design parameter that must be considered. The scope of this study is not limited to single-phase BLDC motors; it also provides analytical approaches that can be applied to different electric motor designs, contributing to engineering applications in this field. Full article

This study conducts a comprehensive geochemical analysis of natural gas, crude oil, and mudstone to investigate the origin and alteration of recently obtained deep heavy oil from the Dongying Depression, Bohai Bay Basin. High contents of β-carotene, gammacerane (gammacerane index = 4.41), and dibenzothiophene (71.22%) and the low value of pristane/phytane (0.32) suggest that the deep heavy oil is mainly generated from in situ source rocks that formed in saline and reducing environments. According to the molecular maturity indicators, the deep heavy oil is at a low maturity level (%Ro ≈ 0.58). The occurrence of complete series n-alkanes and the absence of 25-norhopanes, normal C₇ ratio K₁ (1.0), high ααα(20R)-C₂₉ sterane (8394.22 μg/g) and low (3- + 4-) methyldiamantane (41.81 μg/g) concentrations, along with the high values of toluene/nC₇ (2.73) and diamantine/adamantane (16.6) in the deep heavy oil, suggest that the reservoir did not undergo noticeable biodegradation, TSR, or thermal cracking, but suffered from phase fractionation, respectively. The formation of the deep heavy oil is associated with its low maturity, which is further thickened by phase fractionation due to the recharge of excessive gas. The moderate reservoir temperature (90–150 °C) after charging performs an essential function in the preservation of the deep heavy oil by inhibiting both biodegradation and thermal cracking. Full article

MicroRNA (miRNA) is a type of small non-coding RNA that influences various biological processes by targeting gene expression. However, the roles of microRNA in mediating ruminant mammary cell proliferation and survival remain poorly understood. This study aimed to elucidate how miR-215-5p regulates cell cycle and apoptosis-related genes in goat mammary epithelial cells (GMECs). The effects of miR-215-5p on cell cycle and apoptosis were assessed by flow cytometry. A combination of bioinformatics analysis was conducted to predict the target genes of miR-215-5p; this was followed by experimental validation using techniques such as luciferase reporter assays. The effects of CTCF, the targeting gene of miR-215-5p, on cell cycle and apoptosis were examined by qRT-PCR, Western blot and flow cytometry in GMECs. The study demonstrated that miR-215-5p induced cell-cycle arrest at the G0/G1 phase and promoted apoptosis in GMECs. Mechanistically, miR-215-5p downregulated CTCF expression by directly targeting its 3′-untranslated region (3′UTR). This miR-215-5p-mediated depletion of CTCF inhibits CDK2 and CDK6 activity, consequently downregulating genes involved in cell-cycle progression. Furthermore, the miR-215-5p/CTCF axis was found to promote apoptosis by downregulating the protein expression of Bcl-xL and upregulating the gene expression of Bax. In summary, miR-215-5p suppresses GMEC proliferation and survival through CTCF-dependent histone modifications. Full article

The widespread adoption of nonlinear loads in industry has introduced significant power quality issues in electric power distribution grids. The integration of these nonlinear loads has led to the proliferation of serious power quality problems such as the generation of harmonics and reactive power that negatively impact the quality and stability of the electrical grid. In addition to eliminating current harmonics, a shunt active power filter (APF) can also provide reactive power compensation. By dynamically adjusting the reactive power injection, these APFs can improve the power factor of the system and maintain the desired voltage regulation. The proposed control leverages the differential flatness property of the SAPF system, allowing for exact linearization and simplified tracking control without requiring complex modulation techniques. In this paper, a flatness-based control scheme is proposed for a three-phase three-level Neutral Point Clamped (NPC) APF. The main objectives of this work are twofold. The first objective is to mitigate current harmonics and compensate the reactive power drawn by nonlinear loads. The second objective focuses on maintaining a stable DC-link capacitor voltage of the active power filter (APF). To meet these requirements, a cascaded control structure is used, where the external loop regulates the DC-link voltage, while the inner loop is responsible for harmonic current compensation. The effectiveness of the proposed control strategy is validated through simulation results obtained using the MATLAB/Simulink R2024a environment. Full article

The aim of this study was to evaluate, on a preliminary basis, the ability of multivariate techniques to predict the antioxidant activity of selected Stachys and Betonica species, based on chromatographic data. The methanol extracts of six Stachys species and ten Betonica species were analyzed using reversed-phase high-performance liquid chromatography (RP-HPLC) to obtain their chromatographic profiles. The phytochemical similarity of the samples was assessed using a selected chemometric method (principal component analysis (PCA) and hierarchical cluster analysis (HCA)). The antioxidant activity of the studied extracts (DPPH with 2,2-diphenyl-1-picrylhydrazyl reagent and FRAP—ferric reducing antioxidant power) was determined using a spectrophotometric technique. A multivariate PLS model was then used to predict the antioxidant activity of the methanolic extracts of Stachys and Betonica species based on their RP-HPLC fingerprints. The two obtained PLS models proved useful for predicting the biological activity of the tested extracts. High correlation coefficients (DPPH: R² = 0.9963; FRAP: R² = 0.9895) confirmed the reliability of the PLS prediction model. The results confirmed the effectiveness of combining qualitative and quantitative chromatographic fingerprinting methods with antioxidant activity testing and chemometric analysis, demonstrating that extracts from Stachys and Betonica are a rich source of bioactive substances with antioxidant properties. Full article

The foliar pathogens of wheat, particularly Zymoseptoria tritici and Pyrenophora tritici-repentis, represent a significant threat to yield. We used a SEIR (Susceptible–Exposed–Infected–Removed) model to quantify epidemic dynamics based on different fungicide application strategies, focusing on the daily dynamic growth rate r(t) (net infection increase) and the removal rate γ(t) (loss infectious tissue) after BBCH 37. In Scenario A (treatment of seed with Systiva^®), the r(t) of Z. tritici was positive only during the early phase of the epidemic, followed by progressive suppression over time, while the r(t) for P. tritici-repentis remained negative throughout. Scenario B (seed treatment combined with foliar propiconazole) resulted in uniformly negative r(t) values for both pathogens, indicating stronger and sustained suppression. These findings highlight the practical utility of epidemic growth rate modeling for evaluating fungicide strategies and support integrated seed + foliar applications as a robust approach to disease management in wheat. Full article

The impact of climate change on marine bivalves, particularly on their reproductive processes, is a current issue of concern. The aim of this study was to investigate how seawater temperatures influenced the gonadal development and overall condition of the grooved carpet shell clam (Ruditapes decussatus, Linnaeus, 1758) population in the Baldaio lagoon (N.W. Spain) over the last 20 years. Adult clams were collected, and biometric, histological, and biochemical analyses were performed. Gonadal development phases were assessed, several condition indices were calculated, water temperatures were recorded, and statistical analyses were carried out. Results indicated variations in reproductive timing, including changes in gonadal maturation, an earlier spawning period, and prolonged maturation phases, which contrasted with previous reproductive patterns described for this species. These findings coincided with thermal changes in the lagoon, where mean minimum temperatures increased and maximum temperatures decreased, and the annual thermal range was reduced in comparison with historical data (1998–1999). Biochemical composition and condition indices also reflected variations linked to temperature fluctuations, suggesting that warmer water temperatures may alter energy storage and reproduction. This highlights the importance of continuous environmental monitoring to better understand the effects of climate change on clam populations and to improve management strategies that could help to restore natural R. decussatus populations. Full article

Essential oils from species of the genus Varronia (Boraginaceae) are recognized for their chemical diversity and biological potential; however, phytochemical information on Varronia crenata Ruiz & Pav. remains scarce, despite its wide distribution in the Andean region. The aim of this study was to provide the first chemical and enantioselective characterization of the essential oil obtained from the leaves of V. crenata growing in Ecuador. Qualitative and quantitative analyses were carried out by GC–MS and GC–FID, respectively, using two columns with stationary phases of contrasting polarity. Compounds were identified by matching linear retention indices and mass spectra to literature references and quantified by external calibration using relative response factors (RRFs) calculated for each compound based on its combustion enthalpy. The most abundant constituents (≥3.0% on average between the two columns) of the essential oil of V. crenata, both in the nonpolar and polar stationary phases, were germacrene D (18.4%), (E)-β-caryophyllene (13.3%), α-copaene (10.4%), tricyclene (9.3%), δ-cadinene (8.9%), and α-pinene (8.3%). The volatile fraction was dominated by sesquiterpenes (60.2%) and monoterpenes (22.1%), while other chemical families were present in minor proportions. The enantioselective analysis was performed on two different columns, coated with stationary phases based on β-cyclodextrins: 2,3-diacetyl-6-tert-butyl-dimethylsilyl-β-cyclodextrin and 2,3-diethyl-6-tert-butyl-dimethylsilyl-β-cyclodextrin. Nine chiral compounds were analyzed; among them, (1R,5R)-(+)-α-pinene, (1R,5R)-(+)-sabinene, and (S)-(+)-β-phellandrene were detected as enantiomerically pure, while the other metabolites presented scalemic mixtures. Overall, the high content of bioactive sesquiterpenes and the observed stereochemical complexity highlight the potential pharmaceutical and agricultural relevance of V. crenata essential oil, while also providing novel chemotaxonomic information for the genus. Full article

In this scientific article, a quantitative assessment is carried out of the influence of the ERTMS modernisation factor on the practical efficiency of operation and resilience of the Belgian railway lines 50A/51A with the application of methodological triangulation in the MATLAB R2025a Update 1 (25.1.0.2973910) software environment (discrete-event modelling, Petri nets, Markov reliability modelling, and correlation analysis). The modelling reveals that the scenario with an expanded level of automation increases the capacity from 18.3 to 26.0 trains over 2 h (+42.1%) and reduces the average waiting time from 1.53 min (baseline level) to 0.21 min—virtually the theoretical lower bound of zero under favourable conditions. The results of the block-occupancy analysis by means of Petri nets show that a more dynamic distribution of blocks provides higher capacity, and Markov chains reflect the reduction of the impact of control centre unavailability in developing communications and virtualisations. Spearman correlation analysis additionally shows coordinated improvement of the metrics of safety, digital protection, resilience, and performance. Relying on the modelling results, a phased roadmap is proposed, combining technical improvements (development of communication systems, readiness for automation, comparable management of rolling stock movement) with compliance with regulatory requirements and the goals of sustainable development, related to SDGs 9, 11, and 13. Full article