Search Results (1,473)

The study of tropical landscape dynamics is of critical importance, particularly within protected areas, for evaluating ecosystem functioning and the effectiveness of natural conservation efforts. This study aims to identify landscape dynamics within the Dong Nai Biosphere Reserve (including Cat Tien National Park and the Ma Da Forest) using remote sensing (Landsat and others) and geographic information system methods. The analysis is based on changes in the Enhanced Vegetation Index (EVI), land cover transformations, landscape metrics (Class area, Percentage of Landscape and others), and natural landscape fragmentation, as well as a spatio-temporal assessment of anthropogenic impacts on the area. The results revealed structural changes in the landscapes of the Dong Nai Biosphere Reserve between 2000 and 2024. According to Sen’s slope estimates, a generally EVI growth was observed in both the core and buffer zones of the reserve. This trend was evident in forested areas as well as in regions of the buffer zone that were previously occupied by highly productive agricultural land. An analysis of Environmental Systems Research Institute (ESRI) Land Cover and Land Cover Climate Change Initiative (CCI) data confirms the relative stability of land cover in the core zone, while anthropogenic pressure has increased due to the expansion of agricultural lands, mosaic landscapes, and urban development. The calculation of landscape metrics revealed the growing isolation of natural forests and the dominance of artificial plantations, forming transitional zones between natural and anthropogenically modified landscapes. The human disturbance index, calculated for the years 2000 and 2024, shows only a slight change in the average value across the territory. However, the coefficient of variation increased significantly by 2024, indicating a localized rise in anthropogenic pressure within the buffer zone, while a reduction was observed in the core zone. The practical significance of the results obtained lies in the possibility of their use for the management of the Dongnai biosphere Reserve based on a differentiated approach: for the core and the buffer zone. There should be a ban on agriculture and development in the core zone, and restrictions on urbanized areas in the buffer zone. Full article

Microviscosity and lipid order are the main parameters characterizing the phase states of the membrane. Variations in microviscosity and lipid composition in a living cell may indicate serious disturbances, including various kinds of stress. In this work, the effect of hyperosmotic stress on the microviscosity of mitochondrial membranes was investigated, using potato (Solanum tuberosum L.) tuber mitochondria. The microviscosity of mitochondrial membranes isolated from check and stressed (9 days at 34–36 °C) tubers was estimated by determining the generalized polarization (GP) values using a Laurdan fluorescent probe in confocal microscopy studies. It was revealed that the GP distribution in mitochondria isolated from stressed tubers contained new component-characterizing membrane domains with an increased lipid order compared to the rest of the membrane. We have mapped the microviscosity of mitochondrial membranes for the first time and observed the dynamics of the membrane microviscosity of an individual mitochondrion. The hyperosmotic stress significantly influences the functional state of potato mitochondria, decreasing the substrate oxidation rate and respiratory control coefficient but increasing MitoTracker Orange fluorescence. Under hyperosmotic stress, the microviscosity of mitochondrial membranes changes, and membrane domains with increased lipid order are formed. The revealed changes open up prospects for further research on the participation of raft-like microdomains of mitochondria in plant resistance to stress factors. Full article

Atmospheric turbulence severely limits the stability and reliability of free-space optical (FSO) uplinks by inducing wavefront distortions and random intensity fluctuations. This study investigates the use of reinforcement learning (RL) with beacon-based feedback for adaptive beam shaping in a spatial light modulator (SLM)-controlled FSO link. The RL agent dynamically adjusts phase patterns to maximize received signal strength, while the beacon channel provides turbulence estimates that guide the optimization process. Experiments under low, moderate, and high turbulence levels demonstrate that incorporating beacon feedback can enhance link stability in severe conditions, reducing signal variability and suppressing extreme fluctuations. In low-turbulence scenarios, the performance is comparable to non-feedback operation, whereas under high turbulence, beacon-assisted control consistently achieves lower coefficients of variation and improved bit error rate (BER) performance. Under high turbulence replay experiments—where the best-performing RL-learned phase patterns are reapplied without learning—further show that configurations trained with feedback retain robustness, even without real-time turbulence measurements under high turbulence. These results highlight the potential of integrating contextual feedback with RL to achieve turbulence-resilient and stable optical uplinks in dynamic atmospheric environments. Full article

Land Surface Temperature (LST) is a critical parameter for analyzing urban heat islands, surface–atmosphere interactions, and environmental management. This study enhances the temporal resolution of LST data by leveraging machine learning and deep learning models. A novel methodology was developed using Landsat 8 thermal data and Sentinel-2 multispectral imagery to predict LST at finer temporal intervals in an urban setting. Although Sentinel-2 lacks a thermal band, its high-resolution multispectral data, when integrated with Landsat 8 thermal observations, provide valuable complementary information for LST estimation. Several models were employed for LST prediction, including Random Forest Regression (RFR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Gated Recurrent Unit (GRU). Model performance was assessed using the coefficient of determination (R²) and Mean Absolute Error (MAE). The CNN model demonstrated the highest predictive capability, achieving an R² of 74.81% and an MAE of 1.588 °C. Feature importance analysis highlighted the role of spectral bands, spectral indices, topographic parameters, and land cover data in capturing the dynamic complexity of LST variations and directional patterns. A refined CNN model, trained with the features exhibiting the highest correlation with the reference LST, achieved an improved R² of 84.48% and an MAE of 1.19 °C. These results underscore the importance of a comprehensive analysis of the factors influencing LST, as well as the need to consider the specific characteristics of the study area. Additionally, a modified TsHARP approach was applied to enhance spatial resolution, though its accuracy remained lower than that of the CNN model. The study was conducted in Tehran, a rapidly urbanizing metropolis facing rising temperatures, heavy traffic congestion, rapid horizontal expansion, and low energy efficiency. The findings contribute to urban environmental management by providing high-temporal-resolution LST data, essential for mitigating urban heat islands and improving climate resilience. Full article

To reveal the spatiotemporal variations in near-surface oxygen content during the growing season across different altitudinal gradients in Qinghai Province and to deepen the understanding of oxygen cycling in plateau ecosystems, this study analyzed daily observations from 12 monitoring stations spanning three elevation ranges (1500–2500 m, 2500–3500 m, and 3500–4500 m) during the 2022–2023 growing seasons (March–July). The Mann–Kendall test was employed to detect temporal trends, variability indices such as standard deviation and coefficient of variation were used to quantify fluctuation intensity, Kernel density estimation (KDE) was applied to characterize distributional features, and the Kruskal–Wallis test was conducted to assess statistical significance. The results indicate that: (1) oxygen content showed a significant increasing trend at all three altitudinal gradients, with the strongest rise at low elevations and the weakest at high elevations; (2) fluctuation intensity exhibited clear spatial heterogeneity, with the most pronounced variability in summer at low elevations, a distinct peak in June at mid-elevations, and overall stability at high elevations; and (3) KDE analysis revealed a broader distribution and higher frequency of extreme oxygen values at low elevations, while mid- and high-elevations displayed more concentrated distributions. Both the Kruskal–Wallis test and post hoc comparisons confirmed highly significant differences among the three elevation ranges. These findings demonstrate that elevation is a key factor influencing the spatiotemporal distribution of near-surface oxygen content during the growing season in Qinghai Province. Differences are not only evident in absolute oxygen levels but also in fluctuation intensity and distributional characteristics. This study provides empirical evidence for understanding oxygen variability mechanisms on the plateau and offers theoretical and practical references for ecological management and health risk prevention in high-altitude regions. Full article

Advancements in unmanned aerial vehicle (UAV) multispectral systems offer robust technical support for the precise and efficient estimation of japonica rice yield in cold regions within the framework of precision agriculture. These innovations also present a viable alternative to conventional yield estimation methods. However, recent research suggests that reliance solely on vegetation indices (VIs) may result in inaccurate yield estimations due to variations in crop cultivars, growth stages, and environmental conditions. This study investigated six fertilization gradient experiments involving two conventional japonica rice varieties (KY131, SJ22) and two hybrid japonica rice varieties (CY31, TLY619) at Yanjiagang Farm in Heilongjiang Province during 2023. By integrating UAV multispectral data with machine learning techniques, this research aimed to derive critical phenotypic parameters of rice and estimate yield. This study was conducted in two phases: In the first phase, models for assessing phenotypic traits such as leaf area index (LAI), canopy cover (CC), plant height (PH), and above-ground biomass (AGB) were developed using remote sensing spectral indices and machine learning algorithms, including Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). In the second phase, plot yields for hybrid rice and conventional rice were predicted using key phenotypic parameters at critical growth stages through linear (Multiple Linear Regression, MLR) and nonlinear regression models (RF). The findings revealed that (1) Phenotypic traits at critical growth stages exhibited a strong correlation with rice yield, with correlation coefficients for LAI and CC exceeding 0.85 and (2) the accuracy of phenotypic trait evaluation using multispectral data was high, demonstrating practical applicability in production settings. Remarkably, the R² for CC based on the RF algorithm exceeded 0.9, while R² values for PH and AGB using the RF algorithm and for LAI using the XGBoost algorithm all surpassed 0.8. (3) Yield estimation performance was optimal at the heading (HD) stage, with the RF model achieving superior accuracy (R² = 0.86, RMSE = 0.59 t/ha) compared to other growth stages. These results underscore the immense potential of combining UAV multispectral data with machine learning techniques to enhance the accuracy of yield estimation for cold-region japonica rice. This innovative approach significantly supports optimized decision-making for farmers in precision agriculture and holds substantial practical value for rice yield estimation and the sustainable advancement of rice production. Full article

Pulse wave velocity (PWV) is a key marker of aortic stiffness and cardiovascular risk, yet current methods typically offer only global or regional estimates and lack the possibility to measure local variations along the thoracic aorta. This study aimed to develop and evaluate a pipeline for assessing local aortic PWV using the flow–area (QA) method (PWV_QA) by combining high-resolution 4D MRI techniques. A 3D cine balanced steady-state free precession (bSSFP) sequence was used to capture dynamic changes in aortic geometry, while 4D flow MRI measured time-resolved blood flow. The QA method was applied during the reflection-free early systolic phase. Scan–rescan reproducibility was assessed in six healthy volunteers, and feasibility was additionally explored in Marfan syndrome patients. The mean ± SD values of the Pearson correlation coefficients for per-slice maximum area, velocity, flow, and PWV_QA were 0.99 ± 0.00, 0.82 ± 0.11, 0.96 ± 0.01, and 0.20 ± 0.35, respectively. The median (Q1–Q3) average PWV_QA was 6.6 (5.4–9.4) m/s for scan 1 and 9.1 (6.7–11.3) m/s for scan 2 (p = 0.16) in healthy volunteers and 7.1 (6.9–8.0) m/s in Marfan patients. Combining 4D bSSFP and 4D flow MRI is technically feasible, but the derived PWV_QA maps show high variability, particularly in the aortic root and descending aorta, requiring further optimization. Full article

This study presents a comprehensive experimental investigation into the frictional pressure drop of two-phase flows—boiling and condensation—in horizontal minichannels, emphasizing its impact on the energy efficiency of vapor compression systems. A total of 3553 data points were obtained using six low-GWP refrigerants (R32, R134a, R290, R410A, R513A, and R1234yf) across a wide range of operating conditions in multiport aluminum tubes with hydraulic diameters of 0.715 mm and 1.16 mm. The dataset covers mass fluxes from 200 to 1230 $\mathrm{kg}{\mathrm{m}}^{-2}{\mathrm{s}}^{-1}$, saturation temperatures between 5 °C and 55 °C, and vapor qualities from 0.05 to 0.95. Results showed a strong dependence of frictional pressure gradient on vapor quality, mass flux, and channel size. Boiling flows generated higher frictional losses than condensation, and high-density refrigerants such as R32 exhibited the largest pressure penalties, which can directly translate into increased compressor power demand. Conversely, higher saturation temperatures were associated with lower frictional losses, highlighting the role of thermophysical properties in improving energy performance. Additionally, an inverse correlation between saturation temperature and frictional pressure gradient was observed, attributed to variations in thermophysical properties such as viscosity and surface tension. Existing correlations from the literature were assessed against the experimental dataset, with notable deviations observed in several cases, particularly for R134a under high-quality conditions. Consequently, a new empirical correlation was developed for predicting the frictional pressure drop in two-phase flow through minichannels. The proposed model, formulated using a power-law regression approach and incorporating dimensionless parameters, achieved better agreement with the experimental data, reducing prediction error to within ±20%, improving the accuracy for the majority of cases. This work provides a robust and validated dataset for the development and benchmarking of predictive models in compact heat exchanger design. By enabling the more precise estimation of two-phase pressure drops in compact heat exchangers, the findings support the design of refrigeration, air-conditioning, and heat pump systems with minimized flow resistance and reduced auxiliary energy consumption. This contributes to lowering compressor workload, improving coefficient of performance (COP), and it ultimately advances the development of next-generation cooling technologies with enhanced energy efficiency. Full article

The dynamic shear modulus ratios and dynamic damping ratios of soil are critical parameters for soil seismic response analyses and seismic safety evaluation of engineering sites. This study utilized dynamic triaxial test and resonant column test data of 5208 soil samples collected from more than 2500 boreholes across the Beijing-Tianjin-Hebei (BTH) region. Statistical analyses were conducted for five typical soil types (silty clay, clay, silt, silty sand, and fine sand), focusing on their dynamic shear modulus ratios and dynamic damping ratios. Key parameters representing the characteristics of soil dynamics, including the reference strain, the maximum damping ratio, and the damping ratio nonlinearity coefficient, were statistically evaluated. Median values, as well as the values corresponding to 84% and 16% exceedance probabilities, were provided. The median values of the reference strain, the maximum damping ratio, and the damping ratio nonlinearity coefficient were 13.43 × 10⁻⁴, 0.2155, and 0.7799 for silty clay; 16.47 × 10⁻⁴, 0.2266, and 0.7722 for clay; 10.64 × 10⁻⁴, 0.2012, and 0.7856 for silt; 11.98 × 10⁻⁴, 0.1842, and 0.7911 for silty sand; and 12.73 × 10⁻⁴, 0.1803, and 0.8064 for fine sand. Based on these statistics, the influence of various factors on the reference shear strain, maximum damping ratio, and damping ratio nonlinearity coefficient were investigated. The results showed considerable variability, and weak correlations were observed between these parameters and site-related factors such as sampling depth, shear wave velocity at sampling depth, overburden thickness, 30 m average shear wave velocity (V_S30), and 20 m equivalent shear wave velocity (V_se). The coefficients of determination for the linear regressions considering each factor were between 0.001 and 0.274, which were sufficiently close to 0 and indicated a weak predictive ability of the model considering only one factor. Furthermore, multivariate linear regression models incorporating all five influencing factors also achieved a slight reduction in standard deviation compared with directly adopting the mean values—by <5.5% for the reference shear strain, <3.9% for the maximum damping ratio, and <7.3% for the damping ratio nonlinearity coefficient. A case study was conducted to demonstrate the impact of the variability in soil dynamic parameters on both site seismic response and structural seismic response. For the selected ground motion inputs, site model, and structural model, differences in soil dynamic parameters led to variations in structural seismic response up to 54.5%. Comparative analyses with recommended values from existing studies indicate that the dynamic parameters of the five typical soil types in the BTH region investigated exhibited distinct regional characteristics: the dynamic shear modulus ratios were significantly lower, while the dynamic damping ratios were significantly higher. Comparisons with results from other studies on soil dynamic parameters in China showed that the dynamic shear modulus ratios derived from this study were noticeably smaller, while the dynamic damping ratios were significantly larger. At least one of the three soil dynamic parameters for each soil type failed to pass two-side t-tests, which indicated that the statistical data were from two distributions, that is, soil dynamic properties were intrinsically linked to sedimentary environments, exhibiting distinct regional specificity. Therefore, for boreholes lacking laboratory dynamic test data of soil in the BTH region, it was recommended to use the median values of reference shear strains, maximum damping ratios, and damping ratio nonlinearity coefficients provided in this study for the estimation of dynamic shear modulus ratios and dynamic damping ratios, while their variability must be taken into consideration. Full article

Background: Generic drugs account for approximately 40% of the Korean prescription drug market, despite limited generic substitution at the point of dispensing. This suggests that switching between originator and generic drugs often occurs at the point of prescription. Physicians, in fact, have opposed pharmacy-level substitution due to concerns about the clinical equivalence of generics, despite the regulatory confirmation of their bioequivalence. Importantly, multi-source prescription switching (MSPS) may reflect discretionary prescribing behavior, underscoring the need for targeted benefit policies to enhance substitutability and promote effective competition among multi-source drugs. This study aimed to quantify the extent of physician-initiated MSPS among adults with hypertension or diabetes and to identify factors associated with these switching behaviors. Methods: We conducted a retrospective cohort study using Korean National Health Insurance claims data. The studied cohort consisted of patients newly initiated, between January and June 2014, on a pharmaceutically equivalent and bioequivalent antihypertensive or antidiabetic drug. Patients were followed for up to 24 months to identify MSPS episodes occurring during drug therapy courses, which were defined as 12 ± 3 consecutive visits resulting in prescriptions for pharmaceutically equivalent, bioequivalent multi-source drugs. An MSPS episode was defined as a change in product code—uniquely identifying a multi-source drug—within the same pharmaceutically equivalent drug code between any two consecutive prescriptions within the course. We estimated the mean MSPS rate and assessed variation by patient characteristics, drug types, physician practices, and geographic regions. Results: Among 1,325,334 identified drug therapy courses, 17.06% involved at least one MSPS. Switching rates varied substantially (coefficient of variation = 227%) by physician practice setting (e.g., public health center branches: 26%; tertiary hospitals: 15%) and by drug market size (e.g., glimepiride: 29%; cilnidipine: 1%). In contrast, patient age and gender were not associated with switching behavior. Conclusions: In Korea, physicians frequently switch prescriptions between originator and generic drugs, even as generic substitution at the pharmacy level remains uncommon. The substantial variation in MSPS across provider settings and drug markets—but not by patient characteristics—underscores the need for targeted pharmacy benefit policies to promote effective substitutability and competition among multi-source drugs. Full article

The voltage model (VM) is widely used in sensorless induction motor drives owing to its structural simplicity and speed-independence. However, DC offsets in back electromotive force (BEMF) caused by measurement errors can degrade the accuracy of flux and speed estimation. To address this issue, this article proposes a second-order complex-coefficient flux observer (SCFO) that effectively eliminates DC offsets without introducing phase delay and amplitude attenuation, while maintaining excellent dynamic performance. Furthermore, to enhance the robustness of the flux observer against stator resistance variations, an improved stator resistance adaptive law based on the rotating reference frame is proposed. Ultimately, experimental validation on a 1.5 kW induction motor drive platform confirms the effectiveness of the proposed sensorless scheme. Full article

Conventional grid-forming (GFM) inverter control strategies often rely on fixed parameters and overlook the dynamic variation in energy stored in the DC link capacitor. This limitation can degrade transient performance and stability, particularly under power fluctuations and grid disturbances in renewable energy systems. To address this issue, this paper proposes an adaptive GFM control method that integrates real-time estimation of the DC link capacitor energy into the control loop. A Kalman filter-based observer is designed to estimate the capacitor energy state accurately and robustly using only local voltage and current measurements. The estimated energy deviation is then used to dynamically adjust key control parameters, including the virtual inertia and droop coefficients in the virtual synchronous generator (VSG) framework. These adaptive adjustments enhance the inverter’s damping and inertial behavior according to the internal energy buffer, improving performance under variable operating conditions. Simulation results in MATLAB/Simulink R2023b demonstrate that the proposed method significantly reduces power and voltage overshoots, shortens settling time, and improves DC link voltage regulation compared to conventional fixed-parameter control. Full article

Bifacial Photovoltaic (bPV) technology is rapidly becoming the standard in the solar photovoltaic (PV) industry due to its ability to capture reflected radiation and generate additional energy. This experimental study analyses the electrical performance of bPV modules under specific installation conditions, including varying heights, module tilt angles (MTA), and surface reflectivity. The methodology combines controlled indoor testing with outdoor experiments that replicate real-world operating environments. The outdoor test setup was carefully designed and included dual data acquisition systems: one with independent sensors and another with wireless telemetry for data transfer from the inverter. A thermal performance model was used to estimate energy output and was benchmarked against experimental measurements. All electrical parameters were obtained in accordance with international standards, including current-voltage characteristic (I–V curve) corrections, using calibrated instruments to monitor irradiance and temperature. Indoor measurements under Standard Test Conditions yielded at bifaciality coefficient $\phi =0.732$, a rear bifacial power gain $BiFi=0.285$, and a relative bifacial gain $BiF{i}_{rel}=9.4\%$. The outdoor configuration employed volcanic red stone (Tezontle) as a reflective surface, simulating a typical mid-latitude installation with modules mounted 1.5 m above ground, tilted from 0° to 90° regarding floor and oriented true south. The study was conducted at a site located at 18.8° N latitude during the early summer season. Results revealed significant non-uniformity in rear-side irradiance, with a 32% variation between the lower edge and the centre of the bPV module. The thermal model used to determine electrical performance provides power values higher than those measured in the time interval between 10 a.m. and 3 p.m. Maximum energy output was observed at a MTA of $0°$, which closely aligns with the optimal summer tilt angle for the site’s latitude. Bifacial energy gain decreased as the MTA increased from $0°$ to $90°$. These findings offer practical, data-driven insights for optimizing bPV installations, particularly in regions between $15°$ and $30°$ north latitude, and emphasize the importance of tailored surface designs to maximize performance. Full article

Tropical and subtropical forests account for approximately one-third of global terrestrial gross primary productivity (GPP), and the diurnal patterns of GPP strongly regulate the land–atmosphere CO₂ interactions and feedback to the climate. Combined with ground eddy-covariance (EC) flux towers, geostationary satellites offer significant advantages for continuously monitoring these diurnal variations in the “breathing of biosphere”. Here we utilized half-hourly optical signals from the Himawari-8 Advanced Himawari Imager (H8/AHI) geostationary satellite and tower-based EC flux data to investigate the diurnal variations in subtropical forest GPP and its drivers. Results showed that three machine learning models well estimated the diurnal patterns of subtropical forest GPP, with the determination coefficient (R²) ranging from 0.71 to 0.76. Photosynthetically active radiation (PAR) is the primary driver of the diurnal cycle of GPP, modulated by temperature, soil water content, and vapor pressure deficit. Moreover, the effect magnitude of PAR on GPP varies across three timescales. This study provides robust technical support for diurnal forest GPP estimations and the possibility for large-scale estimations of diurnal GPP over tropics in the future. Full article

Ionospheric scintillation represents a disturbance phenomenon induced by irregular electron density variations, predominantly occurring in equatorial, auroral, and polar regions, thereby posing significant threats to Global Navigation Satellite Systems (GNSS) performance. Polar regions in particular confront distinctive challenges, including the sparse deployment of dedicated ionospheric scintillation monitoring receiver (ISMR) equipment, the limited availability of strong scintillation samples, severely imbalanced training datasets, and the insufficient sensitivity of conventional Deep Neural Networks (DNNs) to intense scintillation events. To address these challenges, this study proposes a modeling framework that integrates residual neural networks (ResNet) with the Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN). The proposed model incorporates multi-source disturbance features to accurately estimate phase scintillation indices (${\sigma }_{\phi }$) in polar regions. The methodology was implemented and validated across multiple polar observation stations in Canada. Shapley Additive Explanations (SHAP) interpretability analysis reveals that the rate of total electron content index (ROTI) features contribute up to 64.09% of the predictive weight. The experimental results demonstrate a substantial performance enhancement compared with conventional DNN models, with root mean square error (RMSE) values ranging from 0.0078 to 0.038 for daytime samples in 2024, and an average coefficient of determination (R²) consistently exceeding 0.89. The coefficient of determination for the Pseudo-Random Noise (PRN) path estimation results can reach 0.91. The model has good estimation results at different latitudes and is able to accurately capture the distribution characteristics of the local strong scintillation structures and their evolution patterns. Full article