Estimation of potential evapotranspiration using a single weather element -The evaporation

Knowledge of potential evapotranspiration is a basic requirement in any study related to crop water management Observing the conceptual similarity between potential evapotranspiration and evaporation an attempt has been made to establish a linear relationship between the two. Using 10 years potential evapotranspiration and evaporation data, linear regression analysis was carried out. Three stations, namely, Bangalore, Pune and Hissar in different latitude belts were selected for the present study. It was observed that partitioning of the annual period into dry and wet periods gives better results. Analysis of l0 years data for dry as well as wet period shows that correlation coefficient is more than 0.95 and variance of residual is very small for each data set. Using the linear regression equation, potential evapotranspiration values were predicted for independent data set. It was found that correlation coefficient between estimated and observed potential evapotranspiration exceeds 0.90, implying that more than 80% of the variation in potential evapotranspiration can be explained by this simple method. Error analysis and also Chi-square test show that predicted values are quite close to observed values.

Wave-packet continuum discretisation for nucleon-nucleon scattering predictions

In this paper we analyse the efficiency, precision, and accuracy of computing elastic nucleon-nucleon (\NN) scattering amplitudes with the wave-packet continuum discretisation method (\wpcd). This method provides approximate scattering solutions at multiple scattering energies simultaneously. We therefore utilise a graphics processing unit (GPU) to explore the benefits of this inherent parallelism. From a theoretical perspective, the \wpcd{} method promises a speedup compared to a standard matrix-inversion method. We use the chiral NNLO$_{\rm opt}$ interaction to demonstrate that \wpcd{} enables efficient computation of \NN{} scattering amplitudes provided one can tolerate an averaged method error of $~1-5$ mb in the total cross section at scattering energies $0-350$ MeV in the laboratory frame of reference. Considering only scattering energies $\sim40-350$ MeV, we find a smaller method error of $\lesssim 1-2$ mb. By increasing the number of wave-packets we can further reduce the overall method error. However, the parallel leverage of the \wpcd{} method will be offset by the increased size of the resulting discretisation mesh. In practice, a GPU-implementation is mainly advantageous for matrices that fit in the fast on-chip shared memory. We find that \wpcd{} is a promising method for computationally efficient, statistical analyses of nuclear interactions from effective field theory, where we can utilise Bayesian inference methods to incorporate relevant uncertainties.

Facial and Cephalometric Features of Individuals With Mucopolysaccharidosis: A Cross-Sectional Study

Objective The aim was to assess craniofacial features through facial anthropometric and lateral cephalometry measurements of individuals with mucopolysaccharidosis (MPS) and compare them with individuals without MPS. Design Cross-sectional study. Patients A total of 14 individuals with MPS and 28 non-MPS age- and sex-matched were enrolled in this study. Methods A clinical facial analysis to evaluate the soft tissues and cephalometric analysis that comprised linear and angular measurements were performed. The calculation of the method error suggested no systematic errors ( p > .05). Random errors for linear and angular measurements were low (less than 0.5° and 1.6 mm). Chi-square test and independent t-test were performed. Results Most individuals with MPS were dolichofacial, presented altered facial proportions with an increased anterior lower facial height (ALFH) and lip incompetence (all p < .05), when compared with non-MPS individuals. Six angular measurements (1s.Na, 1s.NB, FMA, IMPA, AFI, and Po.Or_Go.Me; all p < .05) were significantly increased among individuals with MPS, and two (1s.1i and Ba.N-Ptm.Gn, all p < .05) were significantly decreased among them. Four linear measurements were significantly increased among individuals with MPS (1s-NA, 1i-NB, S-UL, and S-LL; all p < .05) and five (PogN-Perp, Co-A, Co-Gn, Nfa-Nfp, and overbite; all p < .05) were significantly decreased among them. Conclusion In summary, most individuals with MPS were dolichofacial with increased ALFH. Proclined upper and lower incisors, reduced nasopharyngeal space, and reduced overbite was also noted.

A semi-automatic approach for longitudinal 3D upper airway analysis using voxel based registration

Objectives: To propose and validate a reliable semi-automatic approach for three-dimensional (3D) analysis of the upper airway (UA) based on voxel-based registration (VBR). Methods: Post-operative cone beam computed tomography (CBCT) scans of ten orthognathic surgery patients were superimposed to the pre-operative CBCT scans by VBR using the anterior cranial base as reference. Anatomic landmarks were used to automatically cut the UA and calculate volumes and cross-sectional areas (CSA). The 3D analysis was performed by two observers twice, at an interval of two weeks. Intraclass correlations and Bland-Altman plots were used to quantify the measurement error and reliability of the method. The relative Dahlberg error was calculated and compared with a similar method based on landmark re-identification and manual measurements. Results: Intraclass correlation coefficient (ICC) showed excellent intra- and inter observer reliability (ICC ≥0.995). Bland-Altman plots showed good observer agreement, low bias and no systematic errors. The relative Dahlberg error ranged between 0.51–4.30% for volume and 0.24–2.90% for CSA. This was lower when compared with a similar, manual method. Voxel-based registration introduced 0.05–1.44% method error. Conclusions: The proposed method is shown to have excellent reliability and high observer agreement. The method is feasible for longitudinal clinical trials on large cohorts due to being semi-automatic.

Two Comprehensive and Practical Methods for Simulating Pan Evaporation Under Different Climatic Conditions in Iran

Evaporation from surface water plays a crucial role in water accounting of basins, water resource management, and irrigation systems management. As such, the simulation of evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran’s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. The best overall relationship obtained in the first method was used as the basic relationship in the second method, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation, were drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.

Prediction of Cerebral Aneurysm Hemodynamics With Porous-Medium Models of Flow-Diverting Stents via Deep Learning

The interventional treatment of cerebral aneurysm requires hemodynamics to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in calculating cerebral aneurysm hemodynamics before and after flow-diverting (FD) stent placement. However, the complex operation (such as the construction and placement simulation of fully resolved or porous-medium FD stent) and high computational cost of CFD hinder its application. To solve these problems, we applied aneurysm hemodynamics point cloud data sets and a deep learning network with double input and sampling channels. The flexible point cloud format can represent the geometry and flow distribution of different aneurysms before and after FD stent (represented by porous medium layer) placement with high resolution. The proposed network can directly analyze the relationship between aneurysm geometry and internal hemodynamics, to further realize the flow field prediction and avoid the complex operation of CFD. Statistical analysis shows that the prediction results of hemodynamics by our deep learning method are consistent with the CFD method (error function &lt;13%), but the calculation time is significantly reduced 1,800 times. This study develops a novel deep learning method that can accurately predict the hemodynamics of different cerebral aneurysms before and after FD stent placement with low computational cost and simple operation processes.

Mobile augmented reality based indoor map for improving geo-visualization

Unlike traditional visualization methods, augmented reality (AR) inserts virtual objects and information directly into digital representations of the real world, which makes these objects and data more easily understood and interactive. The integration of AR and GIS is a promising way to display spatial information in context. However, most existing AR-GIS applications only provide local spatial information in a fixed location, which is exposed to a set of problems, limited legibility, information clutter and the incomplete spatial relationships. In addition, the indoor space structure is complex and GPS is unavailable, so that indoor AR systems are further impeded by the limited capacity of these systems to detect and display location and semantic information. To address this problem, the localization technique for tracking the camera positions was fused by Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR). The multi-sensor fusion-based algorithm employs a particle filter. Based on the direction and position of the phone, the spatial information is automatically registered onto a live camera view. The proposed algorithm extracts and matches a bounding box of the indoor map to a real world scene. Finally, the indoor map and semantic information were rendered into the real world, based on the real-time computed spatial relationship between the indoor map and live camera view. Experimental results demonstrate that the average positioning error of our approach is 1.47 m, and 80% of proposed method error is within approximately 1.8 m. The positioning result can effectively support that AR and indoor map fusion technique links rich indoor spatial information to real world scenes. The method is not only suitable for traditional tasks related to indoor navigation, but it is also promising method for crowdsourcing data collection and indoor map reconstruction.

Two Comprehensive and Practical Methods for Simulating Pan Evaporation Under Different Climatic Conditions in Iran

Evaporation from surface water plays a key role in water accounting of basins, water resources management, and irrigation systems management, so simulating evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran&rsquo;s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. In the second method, the best overall relationship obtained in the first method was used as the basic relationship, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation was drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.

Individual Tree Crown Delineation for the Species Classification and Assessment of Vital Status of Forest Stands from UAV Images

Monitoring the structure parameters and damage to trees plays an important role in forest management. Remote-sensing data collected by an unmanned aerial vehicle (UAV) provides valuable resources to improve the efficiency of decision making. In this work, we propose an approach to enhance algorithms for species classification and assessment of the vital status of forest stands by using automated individual tree crowns delineation (ITCD). The approach can be potentially used for inventory and identifying the health status of trees in regional-scale forest areas. The proposed ITCD algorithm goes through three stages: preprocessing (contrast enhancement), crown segmentation based on wavelet transformation and morphological operations, and boundaries detection. The performance of the ITCD algorithm was demonstrated for different test plots containing homogeneous and complex structured forest stands. For typical scenes, the crown contouring accuracy is about 95%. The pixel-by-pixel classification is based on the ensemble supervised classification method error correcting output codes with the Gaussian kernel support vector machine chosen as a binary learner. We demonstrated that pixel-by-pixel species classification of multi-spectral images can be performed with a total error of about 1%, which is significantly less than by processing RGB images. The advantage of the proposed approach lies in the combined processing of multispectral and RGB photo images.

Volumetric data of normal nucleus accumbens from magnetic resonance imaging scans

Backgrounds and aims. The nucleus accumbens (AcN) belongs to the ventral striatum and it is involved in several neuropsychiatric disorders. In contrast to other subcortical structures, the number of morphometric studies that concern the healthy nucleus is limited. This study aims to investigate the normal volumetric data of the AcN as derived from a large number of manually segmented magnetic resonance imaging (MRI) scans. Methods. The measurements were performed in 106 MRI scans of healthy adults. The resulting volumes have been analyzed for differences related to hemisphere, sex and age. Results. The mean AcN volume was estimated at 473.3 mm3 (SD=±106.8). A slight interhemispheric difference in favor of the left side was found, the value of which was, however, within the limits of the method error. There were no sexual dimorphism signs concerning both the raw and the normalized volumes. A negative correlation between volumes and age was observed only in males. Conclusions. The study provides normal volumetric data of the AcN, useful in the conduct of comparative imaging and post-mortem studies in pathological conditions