A 1 km daily high-accuracy meteorological dataset of air temperature, atmospheric pressure, relative humidity, and sunshine duration across China (1961–2021)

Water use efficiency in agriculture can be improved by implementing advisory systems that support on-farm irrigation scheduling, with reliable forecasts of the actual crop water requirements, where crop evapotranspiration (ETc) is the main component. The development of such advisory systems is highly dependent upon the availability of timely updated crop canopy parameters and weather forecasts several days in advance, at low operational costs. This study presents a methodology for forecasting ETc, based on crop parameters retrieved from multispectral images, data from ground weather sensors, and air temperature forecasts. Crop multispectral images are freely provided by recent satellite missions, with high spatial and temporal resolutions. Meteorological services broadcast air temperature forecasts with lead times of several days, at no subscription costs, and with high accuracy. The performance of the proposed methodology was applied at 18 sites of the Campania region in Italy, by exploiting the data of intensive field campaigns in the years 2014–2015. ETc measurements were forecast with a median bias of 0.2 mm, and a median root mean square error (RMSE) of 0.75 mm at the first day of forecast. At the 5th day of accumulated forecast, the median bias and RMSE become 1 mm and 2.75 mm, respectively. The forecast performances were proved to be as accurate and as precise as those provided with a complete set of forecasted weather variables.

Probabilistic yield forecasting of robusta coffee at the farm scale using agroclimatic and remote sensing derived indices

Daily water temperature was simulated at a regional scale during the summer period using a simplified model based on the equilibrium temperature concept. The factors considered were heat exchanges at the water/atmosphere interface and groundwater inputs. The selected study area was the Loire River basin (110 000 km2), which displays contrasted meteorological, hydrological and geomorphological features. To capture the intra-basin variability of relevant physical factors driving the hydrological and thermal response of the system, the modelling approach combined a semi-distributed hydrological model, simulating the daily discharge at the outlet of 68 subwatersheds (drainage area between 100 and 3700 km2), and a thermal model, simulating the average daily water temperature for each Strahler order in each subwatershed. Simulations at 67 measurement stations revealed a median root mean square error (RMSE) of 1.9°C in summer between 2000 and 2006. Water temperature at stations located more than 100 km from their headwater was adequately simulated (median RMSE < 1.5°C; −0.5°C < median biases < 0.5°C). However, performance for rivers closer to their source varied because of the averaging of geomorphological and hydrological features across all the tributaries with the same Strahler order in a subwatershed, which tended to mask the specific features of the tributaries. In particular, this increased the difficulty of simulating the thermal response of groundwater-fed rivers during the hot spells of 2003. This modelling by coupling subwatershed and Strahler order for temperature simulations is less time-consuming and has proven to be extremely consistent for large rivers, where the addition of streambed inputs is adequate to describe the effect of groundwater inputs on their thermal regime. Copyright © 2015 John Wiley & Sons, Ltd.

Temporally and spatially continuous meteorological variables are increasingly in demand to support many different types of applications related to climate studies. Using measurements from 600 climate stations, a thin-plate spline method was applied to generate daily gridded climate datasets for mean air temperature, maximum temperature, minimum temperature, relative humidity, sunshine duration, wind speed, atmospheric pressure, and precipitation over China for the period 1961–2011. A comprehensive evaluation of interpolated climate was conducted at 150 independent validation sites. The results showed superior performance for most of the estimated variables. Except for wind speed, determination coefficients (R 2) varied from 0.65 to 0.90, and interpolations showed high consistency with observations. Most of the estimated climate variables showed relatively consistent accuracy among all seasons according to the root mean square error, R 2, and relative predictive error. The interpolated data correctly predicted the occurrence of daily precipitation at validation sites with an accuracy of 83 %. Moreover, the interpolation data successfully explained the interannual variability trend for the eight meteorological variables at most validation sites. Consistent interannual variability trends were observed at 66–95 % of the sites for the eight meteorological variables. Accuracy in distinguishing extreme weather events differed substantially among the meteorological variables. The interpolated data identified extreme events for the three temperature variables, relative humidity, and sunshine duration with an accuracy ranging from 63 to 77 %. However, for wind speed, air pressure, and precipitation, the interpolation model correctly identified only 41, 48, and 58 % of extreme events, respectively. The validation indicates that the interpolations can be applied with high confidence for the three temperatures variables, as well as relative humidity and sunshine duration based on the performance of these variables in estimating daily variations, interannual variability, and extreme events. Although longitude, latitude, and elevation data are included in the model, additional information, such as topography and cloud cover, should be integrated into the interpolation algorithm to improve performance in estimating wind speed, atmospheric pressure, and precipitation.

When joint kinematics is analyzed using noninvasive stereophotogrammetry, movements of the skin markers relative to the underlying bone are regarded as artefacts (soft tissue artefact (STA)). Recent literature suggests that an appropriate estimation of joint kinematics may be obtained by compensating for only a portion of the STA, but no evidence for this case has been reported, and which portion of the STA should be selected remains an issue. The aim of this study was to fill this gap. A modal approach was used to represent the STA. This resulted in a series of additive components (modes) and in the possibility to select a subset of them. The following STA definitions were used: individual skin marker displacement (MD), marker-cluster geometrical transformation (GT), and skin envelope shape variation (SV). An STA approximation for each of the three definitions was obtained by ordering modes on the basis of their contribution to the total STA energy and truncating the relevant series at 90% of it. A fourth approximation was obtained when the GT definition was used, by selecting the modes that represented the marker-cluster rigid transformation (i.e., three translation and three rotation modes). The different STA approximations were compared using data obtained during the stance phase of running of three volunteers carrying both pin and skin markers. The STA was measured and knee joint kinematics estimated using four skin marker datasets compensated for the above-mentioned STA approximations. Accuracy was assessed by comparing results to the reference kinematics obtained using pin markers. The different approximations resulted in different numbers of modes. For joint angles, the compensation efficiency across the STA approximations based on an energy threshold was almost equivalent. The median root mean square errors (RMSEs) were below 1 deg for flexion/extension and 2 deg for both abduction/adduction and internal/external rotation. For the joint displacements, the compensation efficiency depended on the STA approximation. Median RMSEs for anterior/posterior displacement ranged from 1 to 4 mm using either MD, GT, or SV truncated series. The RMSEs were virtually null when the STA was approximated using only the GT rigid modes. This result, together with the limited number of modes used by this approximation (i.e., three translations and three rotations of the marker-cluster), makes the STA rigid component and a good candidate for designing an STA model to be incorporated in an enhanced bone pose estimator.

AimPhenological models have become a vital tool for predicting future phenological responses to global climate change. Recently, machine learning (ML) has been used successfully to develop phenological models based on ground observations. However, fitting an observation series that has been observed for only a few years (or even a decade) can easily lead to overfitting, and it is still a great challenge to predict future phenology accurately based on a short observation series. Here, based on historical ground phenological observations, we construct an ML‐based binary classification phenological model that can be applied to temperate trees.InnovationWe thoroughly describe the construction process of a species‐specific ML‐based binary classification phenological model that is suitable for phenological predictions in both spring and autumn. Through experiments, we evaluate 18 commonly used ML classification algorithms and the effects of two parameters on the prediction performance of the model. Finally, we compare the performance between the binary classification model and six widely used ecophysiological models for accuracy of spring phenological prediction.Main conclusionsThe median root mean square error (RMSE) of the binary classification model for the first flowering date (93 observation series) was only 2.99 days, which proves that it is a good method of phenological prediction for temperate trees. This model can effectively overcome the insufficient sample size of ground observations for specific species and provide new insights of phenological predictions for tropical and subtropical trees. The comparison of different ML algorithms showed that the median root mean square errors of the six algorithms were &lt;3.5 days, and lower than those of six ecophysiological models (&gt;3.7 days) in prediction of spring phenology. In addition, this model can help us to infer plant physiological mechanisms and drivers of phenological change and provide more accurate predictions of plant phenological responses to climate change.

Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling

Knee osteotomy is a surgical procedure that realigns the lower limb, aiming to relieve pressure on the overloaded cartilage and improve knee joint function. While this concept relies on load redistribution, direct in vivo measurement of knee joint contact forces remains challenging. To address this limitation, computational musculoskeletal modeling offers a non-invasive solution for estimating joint kinetics. This study introduces a personalized discrete element analysis (DEA) framework to calculate and compare pre- and postoperative intra-articular pressure distributions.Lower limb weight-bearing computed tomography (WBCT) were obtained from 5 patients (mean age = 39.4 years, SD = 14.36), scheduled for high tibial osteotomy. 3D osseous models were created using semi-automated statistical shape model-based segmentation. The cartilage layers were defined based on point-specific thickness maps, derived from Magnetic Resonance Imaging (MRI) of 53 healthy individuals. The menisci were modeled as triangular shapes with variable height and width, conforming to the femoral condyles. Soft tissue inclusion accuracy was validated using a leave-one-out cross validation on the MRI dataset. Personalized anatomical models were used for tibiofemoral distance analysis and discrete element analysis (DEA). The point-specific joint width was mapped as the shortest, Euclidean distance between the tibial and femoral bones. The DEA model utilized linear springs to represent cartilage-bone and meniscus-cartilage-bone interactions. A joint reaction force equal to half the body weight was applied to simulate weight-bearing conditions.Cartilage predictions showed a maximal error for patellar cartilage prediction with a median Root Mean Square Error (RMSE) of 0.75 mm. With the exception of the anterior cruciate ligament, presenting a median RMSE of 1.16 mm, all other structures exhibited a sub-millimetric accuracy. In figure 1, the pre- and postoperative configurations of a single subject were compared using a tibiofemoral distance map.We developed and validated a methodological workflow for patient-specific, morphological knee joint modeling, serving as an input for both tibiofemoral distance analysis and DEA. This methodology shows potential as an innovative tool to guide and improve high tibial osteotomy surgery. Further investigation with larger patient cohorts is warranted to validate the clinical applicability of this approach in routine surgical practice.For any figures or tables, please contact the authors directly.

Prediction of daily global solar radiation (GSR) plays an important role in design of renewable energy systems. Artificial Neural Networks (ANNs) are powerful tools for modeling and estimating GSR even though using few inputs. In order to train the networks, a dataset of meteorological daily time series for 15 years (1993–2008) collected in Tehran by Iran Meteorological Office were used. The meteorological parameters used to estimate GSR were daily values of maximum, minimum, and mean temperatures; relative humidity; sunshine duration; and precipitation as inputs and the daily GSR in MJ m−2 day−1 as output. Various ANN models were designed and implemented by combining different meteorological data. The optimum model for estimating GSR had one hidden layer multilayer perceptron (MLP) with 37 neurons in it when the inputs were number of the maximum and minimum temperature, sunshine duration, daylight hours, extraterrestrial radiation, and number of day in the year. The empirical Hargreaves and Samani equation (HS) was also considered for the comparison. To estimate the difference between measured and estimated values of ANN and empirical models, mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (r) were determined. For 6-37-1 topology, r, RMSE, and MAE values were found to be 0.968, 3.09, and 2.57, respectively. Obtained results showed that ANN model outperformed HS model and can be successfully used for estimating the daily GSR for Tehran province and any other location.

Augmented Reality Using Transurethral Ultrasound for Laparoscopic Radical Prostatectomy: Preclinical Evaluation

The photoplethysmogram (PPG) obtained from pulse oximetry shows the local changes of blood volume in tissues. Respiration induces variation in the PPG baseline due to the variation in venous blood return during each breathing cycle. We have proposed an algorithm based on the synchrosqueezing transform (SST) to estimate instantaneous respiratory rate (IRR) from the PPG. The SST is a combination of wavelet analysis and a reallocation method which aims to sharpen the time-frequency representation of the signal and can provide an accurate estimation of instantaneous frequency. In this application, the SST was applied to the PPG and IRR was detected as the predominant ridge in the respiratory band (0.1 Hz - 1 Hz) in the SST plane. The algorithm was tested against the Capnobase benchmark dataset that contains PPG, capnography, and expert labelled reference respiratory rate from 42 subjects. The IRR estimation accuracy was assessed using the root mean square (RMS) error and Bland-Altman plot. The median RMS error was 0.39 breaths/min for all subjects which ranged from the lowest error of 0.18 breaths/min to the highest error of 13.86 breaths/min. A Bland-Altman plot showed an agreement between the IRR obtained from PPG and reference respiratory rate with a bias of -0.32 and limits agreement of -7.72 to 7.07. Extracting IRR from PPG expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.

The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min) and cardiac (30 to 180 beats/min) frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. We investigated two window sizes (60 and 120 s) on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles) obtained for RR was 0.95 (0.27, 6.20) breaths/min and for HR was 0.76 (0.34, 1.45) beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.

The pulse oximeter's photoplethysmographic (PPG) signals, measure the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), respiratory rate (RR) and respiratory activity (RA) and this will reduce the number of sensors connected to the patient's body for recording vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR, RR and RA simultaneously from PPG signal. To examine the performance of the proposed algorithm, we used 45 epochs of PPG, electrocardiogram (ECG) and respiratory signal extracted from the MIMIC database (Physionet ATM data bank). The ECG and capnograph based respiratory signal were used as the ground truth and several metrics such as magnitude squared coherence (MSC), correlation coefficients (CC) and root mean square (RMS) error were used to compare the performance of EEMD-PCA algorithm with most of the existing methods in the literature. Results of EEMD-PCA based extraction of HR, RR and RA from PPG signal showed that the median RMS error (quartiles) obtained for RR was 0 (0, 0.89) breaths/min, for HR was 0.62 (0.56, 0.66) beats/min and for RA the average value of MSC and CC was 0.95 and 0.89 respectively. These results illustrated that the proposed EEMD-PCA approach is more accurate in estimating HR, RR and RA than other existing methods.

Automatic Segmentation and Online virtualCT in Head-and-Neck Adaptive Radiation Therapy

Objective: Functional electrical stimulation (FES) is conventionally performed by the stimulation of motor axons causing the muscle fibers innervated by these axons to contract. An alternative strategy that may evoke contractions with more natural motor unit behavior is to stimulate afferent fibers (primarily type Ia) to excite the motor neurons at the spinal level. The aim of the study was to investigate the range of forces that can be evoked in this way and the degree to which the torque can be controlled. Methods: We stimulated the tibial nerve of ten healthy participants at amplitudes at which the highest H-reflex with minimal M-wave was present. The evoked plantar flexion torque was recorded following short stimulation pulses (0.4 ms) with frequencies ranging from 20 to 200 Hz. Results: Across all subjects, the median highest evocable torque was 38.3% (quartiles: 16.9-51.0) of the maximum voluntary contraction torque (MVC). The average torque variability (standard deviation) was 1.7 +/- 0.7% MVC. For most subjects, the relation between stimulation frequency and evoked torque was well characterized by sigmoidal curves (median root mean square error: 6.4% MVC). The plateau of this sigmoid curve (indicating the range of frequencies over which torque amplitude could be modulated) was reached at 56.0 (quartiles: 29.4-81.9) Hz. Conclusion: Using the proposed method for FES, substantial evoked torques that could be controlled by stimulation frequency were achieved. Significance: Stimulation of afferent fibers could be a useful and fatigue-resistant strategy for several applications of FES.Objective: Functional electrical stimulation (FES) is conventionally performed by the stimulation of motor axons causing the muscle fibers innervated by these axons to contract. An alternative strategy that may evoke contractions with more natural motor unit behavior is to stimulate afferent fibers (primarily type Ia) to excite the motor neurons at the spinal level. The aim of the study was to investigate the range of forces that can be evoked in this way and the degree to which the torque can be controlled. Methods: We stimulated the tibial nerve of ten healthy participants at amplitudes at which the highest H-reflex with minimal M-wave was present. The evoked plantar flexion torque was recorded following short stimulation pulses (0.4 ms) with frequencies ranging from 20 to 200 Hz. Results: Across all subjects, the median highest evocable torque was 38.3% (quartiles: 16.9-51.0) of the maximum voluntary contraction torque (MVC). The average torque variability (standard deviation) was 1.7 +/- 0.7% MVC. For most subjects, the relation between stimulation frequency and evoked torque was well characterized by sigmoidal curves (median root mean square error: 6.4% MVC). The plateau of this sigmoid curve (indicating the range of frequencies over which torque amplitude could be modulated) was reached at 56.0 (quartiles: 29.4-81.9) Hz. Conclusion: Using the proposed method for FES, substantial evoked torques that could be controlled by stimulation frequency were achieved. Significance: Stimulation of afferent fibers could be a useful and fatigue-resistant strategy for several applications of FES.