Search Results (68)

Snow water equivalent (SWE) can be estimated using Differential SAR Interferometry (DInSAR), which captures changes in snow depth and density between two SAR acquisitions. However, challenges arise due to SAR signal penetration into the snowpack and the intrinsic limitations of DInSAR measurements. This study addresses these issues and explores the use of multi-band SAR data to derive SWE maps in alpine regions characterized by steep terrain, small spatial extent, and a potentially heterogeneous snowpack. We first conducted a performance analysis to assess SWE estimation precision and the maximum unambiguous SWE variation, considering incidence angle, wavelength, and coherence. Based on these results, we selected C-band Sentinel-1 and L-band SAOCOM data acquired over alpine areas and applied tailored DInSAR processing. Atmospheric artifacts were corrected using zenith total delay maps from the GACOS service. Additionally, sensitivity maps were generated for each interferometric pair to identify pixels suitable for reliable SWE estimation. A comparative analysis of the C- and L-band results revealed several critical issues, including significant atmospheric artifacts, phase decorrelation, and phase unwrapping errors, which impact SWE retrieval accuracy. A comparison between our Sentinel-1-based SWE estimations and independent measurements over an instrumented site shows results fairly in line with previous works exploiting C-band data, with an RSME in the order of a few tens of mm. Full article

Background/Objectives: Computational models are increasingly used in orthopedic research, such as in the context of total knee arthroplasty (TKA). However, the models’ actual integration in clinical practice is far from routine. Major limitations include the amount of input data, effort, and time required for personalization and simulation. In this paper, we present and validate a patient-specific multi-body musculoskeletal TKA model based on sparse input data to address these limitations. Methods: The simulation model was individualized based on the patients’ bone and knee implant 3D geometries, predicted bony landmarks, and soft tissue attachments using annotated statistical shape models, a statistical squat motion pattern, and a statistically based load case. For the validation, we used publicly accessible in vivo knee contact forces during squatting from four patients of the Grand Challenge Competitions (GCCs). Results: The prediction accuracy was quantified using several error metrics, including the root mean square error (RSME). For GCC3 and GCC5, both the range and trend of the mean in vivo contact forces were well matched by the simulation (RMSE lateral: 8.2–26.1% of body weight (BW); RMSE medial: 15.9–42.7 %BW). In contrast, there were relevant deviations between the experiment and simulation in the trend of contact forces for patient GCC2, as well as in the range of medial contact forces for patient GCC6 (RMSE medial: 52.6 %BW). The model setup time was at the magnitude of 15 min per patient, and the simulation was completed in less than 4 min. Conclusions: When comparing our results with the literature, we found similar accuracy to state-of-the-art models in predicting knee contact forces. While remaining deviations between in vivo and simulation data still warrant investigation and evaluation for clinical significance, the model has already successfully addressed important limitations of these previous models, which represent significant barriers to clinical application. Full article

In recent years, the number of safety accidents in new-energy electric vehicles due to lithium-ion battery failures has been increasing, and the lithium-ion battery fault diagnosis technology is particularly important to ensure the safe operation of electric vehicles. This paper proposes a method for lithium-ion battery fault diagnosis based on the historical trajectory of lithium-ion battery remaining discharge capacity in medium and long time scales. The method first utilizes the sparrow search algorithm (SSA) to identify the parameters of the second-order equivalent circuit model of the lithium-ion battery, and then estimates the state of charge (SOC) of the lithium-ion battery using the extended Kalman filter (EKF). The remaining discharge capacity is estimated according to the SOC, and finally the feature vectors are used to diagnose the faults using box plots on the medium and long time scales. Experimental results verify that the root mean squared error (RSME) and mean absolute error (MAE) of the proposed SOC estimation method are 0.0049 and 0.0034, respectively. This method can accurately identify the faulty single cell in a battery pack with low-capacity single cells and promptly detect any abnormalities in the single cell when a micro-short circuit fault occurs. Full article

Background: Mouth rinsing (MR) with a carbohydrate solution is one of the most popular methods athletes use to improve their game-based performance due to its acute ergogenic effect. Objectives: This study aimed to evaluate the effects of the carbohydrate MR intervention on psychophysiological responses and kinematic profiles during intermittent (_INT) and continuous (_CON) 4-a-side small-sided soccer games (SSGs). Methods: Thirty-two adolescent soccer players (age: 16.5 ± 0.5 years) played six bouts of 4-a-side SSGs with MR_INT or MR_CON at 3-day intervals in a randomized, double-blinded, placebo-controlled, and crossover study design. Psychophysiological responses and kinematic profiles were continuously recorded during all games. The rating of perceived exertion (RPE), the rating scale of mental effort (RSME), and the physical enjoyment scores (PES) were also determined at the end of each game. Results: The MR_CON induced higher psychophysiological responses such as RPE, internal training load (ITL), and RSME (p ≤ 0.05, d values ranging from 0.50 to 1.04 [small to moderate effect]). Conversely, the MR_INT induced higher PES (p ≤ 0.05, d values = 1.44 [large effect]) compared to MR_CON. Although the MR intervention led to similar improvements in the performance of 4-a-side MR_INT and MR_CON, there was no significant difference between the groups. Conclusions: Our results suggest that the MR intervention can be used as an effective ergogenic supplement for acute game performance enhancement, regardless of the game’s structure. Full article

The aboveground biomass (AGB) of summer maize is an important indicator for assessing crop growth status and predicting yield, playing a significant role in agricultural management and decision-making. Traditional on-site measurements of AGB are limited, due to low efficiency and a lack of spatial information. The development of unmanned aerial vehicle (UAV) technology in agriculture offers a rapid and cost-effective method for obtaining crop growth information, but currently, the prediction accuracy of summer maize AGB based on UAVs is limited. This study focuses on the entire growth period of summer maize. Multispectral images of six key growth stages of maize were captured using a DJI Phantom 4 Pro, and color indices and elevation data (DEM) were extracted from these growth stage images. Combining measured data such as summer maize AGB and plant height, which were collected on the ground, and based on the three machine learning algorithms of partial least squares regression (PLSR), random forest (RF), and long short-term memory (LSTM), an input feature analysis of PH was carried out, and a prediction model of summer maize AGB was constructed. The results show that: (1) using unmanned aerial vehicle spectral data (CIS) alone to predict the biomass of summer maize has relatively poor prediction accuracy. Among the three models, the LSTM (CIS) model has the best simulation effect, with a coefficient of determination (R²) ranging from 0.516 to 0.649. The R² of the RF (CIS) model is 0.446–0.537. The R² of the PLSR (CIS) model is 0.323–0.401. (2) After adding plant height (PH) data, the accuracy and stability of model prediction significantly improved. R² increased by about 25%, and both RMSE and NRSME decreased by about 20%. Among the three prediction models, the LSTM (PH + CIS) model had the best performance, with R² = 0.744, root mean square error (RSME) = 4.833 g, and normalized root mean square error (NRSME) = 0.107. Compared to using only color indices (CIS) as the model input, adding plant height (PH) significantly enhances the prediction effect of AGB (aboveground biomass) prediction in key growth periods of summer maize. This method can serve as a reference for the precise monitoring of crop biomass status through remote sensing with unmanned aerial vehicles. Full article

Soil thermal conductivity in the near-phase-transition zone is a key parameter affecting the thermal stability of permafrost engineering and its catastrophic thermal processes. Therefore, accurately determining the soil thermal conductivity in this specific temperature zone has important theoretical and engineering significance. In the present work, a method for testing the thermal conductivity of fine sandy soil in the near-phase-transition zone was proposed by measuring thermal conductivity with the transient plane heat source method and determining the volumetric specific heat capacity by weighing unfrozen water contents. The unfrozen water content of sand specimens in the near-phase-transition zone was tested, and a corresponding empirical fitting formula was established. Finally, based on the testing results, temperature variation trends and parameter influence laws of thermal conductivity in the near-phase-transition zone were analyzed, and thermal conductivity prediction models based on multiple regression (MR) and a radial basis function neural network (RBFNN) were also established. The results show the following: (1) The average error of the proposed test method in this work and the reference steady-state heat flow method is only 7.25%, which validates the reliability of the proposed test method. (2) The variation in unfrozen water contents in fine sandy soil in the range of 0~−3 °C accounts for over 80% of the variation in the entire negative temperature range. The unfrozen water content and thermal conductivity curves exhibit a similar trend, and the near-phase-transition zone can be divided into a drastic phase transition zone and a stable phase transition zone. (3) Increases in the thermal conductivity of fine sandy soil mainly occur the drastic phase transition zone, where these increases account for about 60% of the total increase in thermal conductivity in the entire negative temperature region. With the increase in density and total water content, the rate of increase in thermal conductivity in the drastic phase transition zone gradually decreases. (4) The R², MAE, and RSME of the RBFNN model in the drastic phase transition zone are 0.991, 0.011, and 0.021, respectively, which are better than those of the MR prediction model. Full article

Machine learning and response surface methods for predicting the compressive strength of high-strength concrete have not been adequately compared. Therefore, this research aimed to predict the compressive strength of high-strength concrete (HSC) using different methods. To achieve this purpose, neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), and response surface methodology (RSM) were used as ensemble methods. Using an ANN and ANFIS, high-strength concrete (HSC) output was modeled and optimized as a function of five independent variables. The RSM was designed with three input variables: cement, and fine and coarse aggregate. To facilitate data entry into Design Expert, the RSM model was divided into six groups, with p-values of responses 1 to 6 of 0.027, 0.010, 0.003, 0.023, 0.002, and 0.026. The following metrics were used to evaluate model compressive strength projection: R, R², and MSE for ANN and ANFIS modeling; R², Adj. R², and Pred. R² for RSM modeling. Based on the data, it can be concluded that the ANN model (R = 0.999, R² = 0.998, and MSE = 0.417), RSM model (R = 0.981 and R² = 0.963), and ANFIS model (R = 0.962, R² = 0.926, and MSE = 0.655) have a good chance of accurately predicting the compressive strength of high-strength concrete (HSC). Furthermore, there is a strong correlation between the ANN, RSM, and ANFIS models and the experimental data. Nevertheless, the artificial neural network model demonstrates exceptional accuracy. The sensitivity analysis of the ANN model shows that cement and fine aggregate have the most significant effect on predicting compressive strength (45.29% and 35.87%, respectively), while superplasticizer has the least effect (0.227%). RSME values for cement and fine aggregate in the ANFIS model were 0.313 and 0.453 during the test process and 0.733 and 0.563 during the training process. Thus, it was found that both ANN and RSM models presented better results with higher accuracy and can be used for predicting the compressive strength of construction materials. Full article

This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2), using in situ, and meteorological and satellite data from Landsat and Sentinel missions (Case 3). In all cases, each machine learning model shows robust performance, with promising results in predicting chlorophyll-a concentrations. Among these models, LSTM stands out as the most effective, with the best metrics in the estimation, the best performance was Case 1, with R² = 0.89, an RSME of 0.32 µg/L, an MAE 1.25 µg/L and an MSE 0.25 (µg/L)², consistently outperforming the others according to the static metrics used for validation. This finding underscores the effectiveness of LSTM in capturing the complex temporal relationships inherent in the dataset. However, increasing the dataset in Case 3 shows a better performance of TCNs (R² = 0.96; MSE = 0.33 (µg/L)²; RMSE = 0.13 µg/L; and MAE = 0.06 µg/L). The successful application of machine learning algorithms emphasizes their potential to elucidate the dynamics of algal biomass in Lake Ranco, located in the southern region of Chile. These results not only contribute to a deeper understanding of the lake ecosystem but also highlight the utility of advanced computational techniques in environmental research and management. Full article

This study aimed to streamline the determination of chlorophyll content in Cunninghamia lanceolate while achieving precise measurements of canopy chlorophyll content. Relative chlorophyll content (SPAD) in the Cunninghamia lanceolate canopy were assessed in the study area using the SPAD-502 portable chlorophyll meter, alongside spectral data collected via onboard multispectral imaging. And based on the unmanned aerial vehicle (UAV) multispectral collection of spectral values in the study area, 21 vegetation indices with significant correlation with Cunninghamia lanceolata canopy SPAD (CCS) were constructed as independent variables of the model’s various regression techniques, including partial least squares regression (PLSR), random forests (RF), and backpropagation neural networks (BPNN), which were employed to develop a SPAD inversion model. The BPNN-based model emerged as the best choice, exhibiting test dataset coefficients of determination (R²) at 0.812, root mean square error (RSME) at 2.607, and relative percent difference (RPD) at 1.942. While the model demonstrated consistent accuracy across different slope locations, generalization was lower for varying slope directions. By creating separate models for different slope directions, R² went up to about 0.8, showcasing favorable terrain applicability. Therefore, constructing inverse models with different slope directions samples separately can estimate CCS more accurately. Full article

Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse arrival time (PAT), the time difference between the proximal and distal signal peaks. The most widely employed pairing involves electrocardiography (ECG) and photoplethysmography (PPG). Possessing similar characteristics in terms of measuring blood flow changes, a recently investigated optical signal known as speckleplethysmography (SPG) showed its stability and high signal-to-noise ratio compared with PPG. Thus, SPG is a potential surrogate to pair with ECG for CNBP estimation. The present study aims to unlock the untapped potential of SPG as a signal for non-invasive blood pressure monitoring based on PAT. To ascertain SPG’s capabilities, eight subjects were enrolled in multiple recording sessions. A third-party device was employed for ECG and PPG measurements, while a commercial device served as the reference for arterial blood pressure (ABP). SPG measurements were obtained using a prototype smartphone-based system. Following the completion of three scenarios—sitting, walking, and running—the subjects’ signals and ABP were recorded to investigate the predictive capacity of systolic blood pressure. The collected data were processed and prepared for machine learning models, including support vector regression and decision tree regression. The models’ effectiveness was evaluated using root-mean-square error and mean absolute percentage error. In most instances, predictions utilizing ${PAT}_{SPG}$ exhibited comparable or superior performance to ${PAT}_{PPG}$ (i.e., SPG Rest ± 12.4 mmHg vs. PPG Rest ± 13.7 mmHg for RSME, and SPG 8% vs. PPG 9% for MAPE). Furthermore, incorporating an additional feature, namely the previous SBP value, resulted in reduced prediction errors for both signals in multiple model configurations (i.e., SPG Rest ± 12.4 mmHg to ±3.7 mmHg for RSME, and SPG Rest 8% to 3% for MAPE). These preliminary tests of SPG underscore the remarkable potential of this novel signal in PAT-based blood pressure predictions. Subsequent studies involving a larger cohort of test subjects and advancements in the SPG acquisition system hold promise for further improving the effectiveness of this newly explored signal in blood pressure monitoring. Full article

Perishable commodities like cassava necessitate effective postharvest preservation for various industrial applications. Hence, optimizing pretreatment processes and modeling drying kinetics hold paramount importance. This study aimed to optimize cassava pretreatment using the central composite design of a response surface methodology while also assessing microstructure and dehydration kinetics. Diverse chemical and thermal pretreatments were explored, encompassing sodium metabisulfite concentrations (0–4% w/w), citric acid concentrations (0–4% w/w), and blanching time (0–4 min). The four investigated responses were moisture content, whiteness index, activation energy (E_a), and effective moisture diffusivity (D_eff). Employing five established drying models, suitability was appraised after optimal pretreatment conditions were determined. The findings revealed that moisture content ranged from 5.82 to 9.42% db, whereas the whiteness index ranged from 87.16 to 94.23. D_eff and E_a ranged from 5.06 × 10⁻⁹ to 6.71 × 10⁻⁹ m²/s and 29.65–33.28 kJ/mol, respectively. The optimal pretreatment conditions for dried cassava were identified by optimizing the use of 1.31% citric acid, 1.03% sodium metabisulfite, and blanching time for 1.01 min. The microstructure indicated that particular chemical and thermal pretreatment configurations yielded particles in the shape of circular and elliptical granules. The logarithmic model provided the most accurate description of the dehydration kinetics, with the highest R² value (0.9859) and the lowest χ², RSME, and SSE values of 0.0351, 0.0015, and 0.0123, respectively. Full article

In this study, molecular dynamics (MD) and docking simulations were carried out on the crystal structure of Neisseria Gonorrhoeae RsmE aiming at free energy of binding estimation (ΔG_binding) of the methyl transfer substrate S-adenosylmethionine (SAM), as well as its homocysteine precursor S-adenosylhomocysteine (SAH). The mechanistic insight gained was generalized in view of existing homology to two other crystal structures of RsmE from Escherichia coli and Aquifex aeolicus. As a proof of concept, the crystal poses of SAM and SAH were reproduced reflecting a more general pattern of molecular interaction for bacterial RsmEs. Our results suggest that a distinct set of conserved residues on loop segments between β12, α6, and Met169 are interacting with SAM and SAH across these bacterial methyltransferases. Comparing molecular movements over time (MD trajectories) between Neisseria gonorrhoeae RsmE alone or in the presence of SAH revealed a hitherto unknown gatekeeper mechanism by two isoleucine residues, Ile171 and Ile219. The proposed gating allows switching from an open to a closed state, mimicking a double latch lock. Additionally, two key residues, Arg221 and Thr222, were identified to assist the exit mechanism of SAH, which could not be observed in the crystal structures. To the best of our knowledge, this study describes for the first time a general catalytic mechanism of bacterial RsmE on theoretical ground. Full article

The application of data science (DS) techniques has become increasingly essential in various fields, including epidemiology and climatology in agricultural production systems. In this sector, traditionally large amounts of data are acquired, but not well-managed and -analyzed as a basis for evidence-based decision-making processes. Here, we present a comprehensive step-by-step guide that explores the use of DS in managing epidemiological and climatological data within rice production systems under tropical conditions. Our work focuses on using the multi-temporal dataset associated with the monitoring of diseases and climate variables in rice in Colombia during eight years (2012–2019). The study comprises four main phases: (I) data cleaning and organization to ensure the integrity and consistency of the dataset; (II) data management involving web-scraping techniques to acquire climate information from free databases, like WordClim and Chelsa, validation against in situ weather stations, and bias removal to enrich the dataset; (III) data visualization techniques to effectively represent the gathered information, and (IV) a basic analysis related to the clustering and climatic characterization of rice-producing areas in Colombia. In our work, a process of evaluation and the validation of climate data are conducted based on errors (r, R^2, MAE, RSME) and bias evaluation metrics. In addition, in phase II, climate clustering was conducted based on a PCA and K-means algorithm. Understanding the association of climatic and epidemiological data is pivotal in predicting and mitigating disease outbreaks in rice production areas. Our research underscores the significance of DS in managing epidemiological and climatological data for rice production systems. By applying a protocol responsible for DS tools, our study provides a solid foundation for further research into disease dynamics and climate interactions in rice-producing regions and other crops, ultimately contributing to more informed decision-making processes in agriculture. Full article

Maize (Zea mays) is a high-nitrogen (N)-demanding crop potentially contributing to nitrate contamination and emissions of nitrous oxide. The N fertilization is generally split between sowing time and the V6 stage. The right split N rate to apply at V6 and minimize environmental damage is challenging. Our objectives were to (1) predict maize response to added N at V6 using machine learning (ML) models; and (2) cross-check model outcomes by independent on-farm trials. We assembled 461 N trials conducted in Eastern Canada between 1992 and 2022. The dataset to predict grain yield comprised N dosage, weekly precipitations and corn heat units, seeding date, previous crop, tillage practice, soil series, soil texture, organic matter content, and pH. Random forest and XGBoost predicted grain yield accurately at the V6 stage (R² = 0.78–0.80; RSME and MAE = 1.22–1.29 and 0.96–0.98 Mg ha⁻¹, respectively). Model accuracy up to the V6 stage was comparable to that of the full-season prediction. The response patterns simulated by varying the N doses showed that grain yield started to plateau at 125–150 kg total N ha⁻¹ in eight out of ten on-farm trials conducted independently. There was great potential for economic and environmental gains from ML-assisted N fertilization. Full article

In banana cultivation, a considerable amount of the production is wasted every year because of various constraints present in the post-harvest management chain. Converting green banana pulp and peels into flour could help to reduce losses and enable the food sector to keep the product for an entire year or more. In order to use green banana fruit and peel flour in the food industry as a raw ingredient such as in bakery and confectionery items—namely biscuits, cookies, noodles, nutritious powder, etc.—it is essential to standardize the process for the production of the flour. As a result, the purpose of this study was to investigate the influence of pretreatment and temperature on the drying capabilities and quality of dried green banana peel. The green banana peel pieces were pretreated with 0.5 and 1.0% KMS (potassium metabisulfite), and untreated samples were taken as control, and dried at 40°, 50°, and 60 °C in a tray dryer. To reduce the initial moisture content of 90–91.58% (wb) to 6.25–9.73% (wb), a drying time of 510–360 min was required in all treatments. The moisture diffusivity (D_eff) increased with temperature, i.e., D_eff increased from 5.069–6.659 × 10⁻⁸, 6.013–7.653 × 10⁻⁸, and 4.969–6.510 × 10⁻⁸ m²/s for the control sample, 0.5% KMS, and 1.0% KMS, respectively. The Page model was determined to be the best suited for the drying data with the greatest R² and the least χ² and RSME values in comparison with the other two models. When 0.5% KMS-pretreated materials were dried at 60 °C, the water activity and drying time were minimal. Hue angle, chroma, and rehydration ratio were satisfactory and within the acceptable limits for 0.5% KMS-pretreated dried banana peel at 60 °C. Full article