Ratio Of Standard Deviation Research Articles

Estimating the Confidence Intervals for the Coefficients of Variation Delta-Gamma Distributions

A common application of the coefficient of variation (CV), which is the ratio of the population standard deviation to the population mean, is frequently used to assess quality control and economic processes, among others. The fiducial quantity approach, Bayesian confidence intervals (CIs) using the Jeffreys, uniform, or normal-gamma-beta (NGB) priors, and highest posterior density (HPD) intervals using the Jeffreys, uniform, or NGB priors were used to provide estimators for the CI for the ratio of CV of two delta-gamma distributions. An evaluation of their performance in terms of average length and coverage probability was carried out using Monte Carlo simulations. The results of this study indicate that the HPD using the Jeffreys prior and fiducial quantity methods are the best for estimating the CI for the ratio of the CV of two delta-gamma distributions. Rainfall data from Mae Hong Son province in Thailand was used to illustrate their practicability when analyzing real-life processes.

Predicting Subsurface Drainage Requirement for Different Soil Desalinization Goals in Coastal Reclamation Areas

ABSTRACTBuilding subsurface drainage systems for more efficient salt leaching with the natural rainfall or artificial irrigation is a widely accepted practice for saline soils reclamation, but the high initial cost of the system construction often limits its adoption, and the uncertainty in drainage intensity requirement to meet desired desalinization goals for field crop production challenges the system design. In this paper, we proposed a soil desalinization model based on the field hydrology model‐DRAINMOD, predicted number of years required to lower soil salinity to a threshold level under different subsurface drainage conditions, and estimated the net return from different subsurface drainage system layout based on a case study in a coastal reclamation area in eastern China. The results showed that, DRAINMOD accurately predicted water table fluctuations in artificially drained fields: the root mean square error to standard deviation ratio (RSR) was 0.4 and 0.54, and the Nash Sutcliffe efficiency (NSE) was 0.84 and 0.68, respectively, during the model calibration and validation periods. The DRAINMOD based soil desalinization model predicted a negative relationship between subsurface drainage intensity and the soil desalinization period, that is, improving drainage condition shortened the time requirement for soil desalinization. For the study area, the model's predictions showed that installing subsurface drainage at 20 m spacing and 1.2 m deep lowered soil salinity to the slightly saline level in less than 7 years under the rainfall leaching only, and such system layout produced the maximum economic return based on the local agricultural practice. The results indicate that, early investment in adequate subsurface drainage systems is beneficial for field crop production in the salt impacted farmland areas. Findings from this research may provide technical references for saline soil reclamation or land improvement in both rain fed and irrigated agricultural areas.

The impact of spinal versus general anesthesia on the variability of surgical times: a systematic review and meta-analysis.

With spinal anesthesia, when cases are taking longer than usual, there may be behavioural tendencies for surgical teams to work more quickly. We conducted a systematic review with meta-analysis to examine standard deviations of surgical times for single-dose spinal anesthetics versus general anesthesia. We compared ratios of mean surgical times as a secondary endpoint. We included randomized trials of humans where general or spinal anesthesia was used for one category of surgical procedure (e.g., hip arthroplasty) and the article reported the means and standard deviations of operative durations. We used statistical methods suitable for surgical times following log-normal distributions. We used generalized confidence intervals to calculate point estimates of ratios and standard errors for each study, followed by pooling among studies using DerSimonian and Laird random-effects meta-analysis with Knapp-Hartung adjustment. Among the 77 included studies, 96% were of high quality for our endpoint (i.e., had a low risk of bias), as no (0%) study focused on comparing variability of surgical times and none had surgical time as the primary endpoint. Spinal anesthesia was associated with 6.6% smaller standard deviations than general anesthesia (95% confidence interval, 15.8% smaller to 1.9% larger, P = 0.13). By meta-regression, there was no significant association of the ratios of standard deviations with study quality (P = 0.39), year of publication (P = 0.76), or categories of procedures (all five P ≥ 0.28). Spinal anesthesia was associated with 1.1% smaller means than general anesthesia (95% confidence interval, 3.7% smaller to 1.5% larger, P = 0.42). There were no significant associations between the ratios of means and study quality (P = 0.47), year of publication (P = 0.95), or categories of procedures (all five, P ≥ 0.63). The results of this systematic review and meta-analysis show with high confidence that the effect of choosing spinal anesthesia on variability in surgical time, if present, is sufficiently small to have no substantive direct economic effect. The same conclusion applies to mean surgical time. Therefore, although anesthetic choice has a clinical (biological) impact and affects anesthesia times, the direct effects on surgical times and workflow are minimal at most. Anesthetic choice does not influence operating theatre productivity via changes to surgical times. The impact of spinal anesthetic effects is limited to nonoperative times (e.g., reducing anesthesia-controlled times by using a block room before the patient enters the operating room). PROSPERO ( CRD42023461952 ); first submitted 8 September 2023.

Stochastic Analysis of Epithelial/Absorbed Power Density in Multilayered Planar Skin Model With Uncertain Tissue Electric Properties

AbstractStochastic analysis of the absorbed power density (APD) on skin surface exposed to radiation of halfwave dipole at 10, 30 and 90 GHz and for different antenna‐body distances is presented. Skin tissue is modeled as a half‐space consisting of one layer (skin) or three layers (skin, fat and muscle) whose permittivities and conductivities are uncertain. Deterministic part is based on numerical solution of Pocklington equation via Galerkin Bubnov Indirect Boundary Element method (GB‐IBEM) and numerical integration of the corresponding field integrals. Uncertainty from tissue electric parameters is propagated to APD via non‐intrusive Stochastic Collocation method (SCM) in order to compute stochastic moments of APD. For 1‐layered model APD stochastic moments are computed with 3 deterministic simulations. On the other hand APD mean and variance for 3 layered model are successfully computed with 13 deterministic simulations while skewness and kurtosis require 85 deterministic simulations. The ratio of APD standard deviation and mean decreases with frequency thus indicating that the uncertainty in the tissue electric properties has smaller effect on APD uncertainty at higher frequencies. Finally, sensitivity analysis carried out for both 1‐layered and 3‐layered models indicates the same conclusions. At 10 GHz skin permittivity and conductivity are the two most important parameters. However, as frequency increases the impact of skin conductivity prevails. This indicates that in frequency range 10–90 GHz the APD uncertainty quantification and sensitivity analysis can be carried out by using only 2‐dimensional stochastic model.

Explainable artificial intelligence for the interpretation of ensemble learning performance in algal bloom estimation.

Chlorophyll-a (Chl-a) concentrations, a key indicator of algal blooms, were estimated using the XGBoost machine learning model with 23 variables, including water quality and meteorological factors. The model performance was evaluated using three indices: root mean square error (RMSE), RMSE-observation standard deviation ratio (RSR), and Nash-Sutcliffe efficiency. Nine datasets were created by averaging 1hour data to cover time frequencies ranging from 1hour to 1month. The dataset with relatively high observation frequencies (1-24 h) maintained stability, with an RSR ranging between 0.61 and 0.65. However, the model's performance declined significantly for datasets with weekly and monthly intervals. The Shapley value (SHAP) analysis, an explainable artificial intelligence method, was further applied to provide a quantitative understanding of how environmental factors in the watershed impact the model's performance and is also utilized to enhance the practical applicability of the model in the field. The number of input variables for model construction increased sequentially from 1 to 23, starting from the variable with the highest SHAP value to that with the lowest. The model's performance plateaued after considering five or more variables, demonstrating that stable performance could be achieved using only a small number of variables, including relatively easily measured data collected by real-time sensors, such as pH, dissolved oxygen, and turbidity. This result highlights the practicality of employing machine learning models and real-time sensor-based measurements for effective on-site water quality management. PRACTITIONER POINTS: XAI quantifies the effects of environmental factors on algal bloom prediction models The effects of input variable frequency and seasonality were analyzed using XAI XAI analysis on key variables ensures cost-effective model development.

Prediction of fresh herbage yield using data mining techniques with limited plant quality parameters

The purpose of this study was to ascertain the fresh herbage yield, fertilizer dosage, and plant characteristics of the Sorghum-Sudangrass hybrid grown in arid and semi-arid regions, as well as their interrelationships. For this reason, data from the Sorghum-Sudangrass hybrid were used to assess the predictive performance of several data mining techniques, including CHAID, CART, MARS, and Bagging MARS. Plant traits were measured in Konya and Sanliurfa during 2021 and 2022. The descriptive statistical values were calculated as follows: plant height 306.27 cm, stem diameter 9.47 mm, fresh herbage yield 10852.51 kg/da, crude protein ratio 9.66%, acid detergent fiber 33.39%, neutral detergent fiber 51.85%, acid detergent lignin 9.76%, dry matter digestibility 62.88%, dry matter intake 2.34%, and relative feed value 114.68 (average values). The predictive capacities of the fitted models were assessed using model fit statistics such as the coefficient of determination (R²), adjusted R², root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), and Akaike Information Criterion (AIC). With the lowest values for RMSE, MAPE, SD ratio, and AIC (246, 1.926, 0.085, and 845, respectively), and the highest R² value (0.993) and adjusted R² value (0.989), the MARS algorithm was determined to be the best model for characterizing fresh herbage yield. As a solid alternative to other data mining techniques, the MARS algorithm was shown to be the most appropriate model for forecasting fresh herbage production.

Impact of ESG Risk on Portfolio Optimization and Returns: An Analysis Using the Markowitz Model

This paper explores the integration of Environmental, Social, and Governance (ESG) factors into portfolio optimization using the Markowitz Model, focusing on two groups of ten stocks each from the Technology, Media, and Telecom (TMT) sectors. These stocks are categorized into low and high ESG risk groups according to established ESG rating agencies. Employing historical data, the study computes key financial metrics such as annualized returns, standard deviation, and Sharpe ratios to conduct various portfolio optimizations including the minimal risk frontier, efficient frontier, and inefficient frontier, while also applying five specific operational constraints reflective of real-world investment limits. The analysis distinctly highlights the superior performance of the low ESG risk group, which demonstrated higher returns and better risk-adjusted return profiles, as evidenced by elevated Sharpe ratios. These findings suggest that ESG integration not only aligns with broader societal goals but also enhances financial performance, especially under diverse regulatory and strategic constraints. This research enriches the discourse on sustainable investing by providing empirical evidence on how ESG factors influence traditional portfolio management strategies, potentially guiding future investor decisions and promoting a shift towards more sustainable investment practices.

Evaluating hydrological responses of satellite precipitation products over an Indian tropical catchment through a distributed physical model

AbstractSignificant advancements in satellite‐based precipitation retrieval algorithms have led to the development of continuous, quasi‐global precipitation products, offering unique opportunities for hydrometeorological and climate research. In this context, six satellite‐based precipitation products (SPPs), including CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were thoroughly investigated for their application in hydrological simulations over the Wardha River basin in India. The observed gridded precipitation product developed by India Meteorological Department (IMD) has been used as reference data to evaluate the performance of SPPs. Hydrological variables such as runoff, soil moisture (SM) and evapotranspiration (ET) were simulated using the Variable Infiltration Capacity model. The performances of SPPs are critically assessed using various statistical metrics, including Pearson's correlation coefficient (R), Klinga‐Gupta Efficiency (KGE), Percent Bias (PBIAS), root mean square error (RMSE), and the RMSE to standard deviation ratio (RSR). The model‐simulated discharge was compared with streamflow observations at a single gauging site, while a spatially distributed comparison was conducted between simulated SM and ET and satellite‐based SM and ET. The IMD dataset consistently shows superior performance for discharge simulation at both daily and monthly scales, with KGE values of 0.85 and 0.91, respectively. Among SPPs, MSWEP and CHIRPS excel in simulating daily and monthly discharge, respectively. For ET simulation, CHIRPS outperforms IMD and other SPPs, achieving an overall KGE value of 0.37. In contrast, PERSIANN is the most effective for simulating SM compared to other precipitation products.

Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling

In this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultural fields. To build the dataset, 401 samples were collected from two fields in Prince Edward Island (PEI) and 122 samples from the New Brunswick (NB), Canada. In addition, soil moisture (SM), temperature (ST), and electrical conductivity (EC), alongside eight climatic variables including wind speed (WS), solar radiation (SR), relative humidity (RH), precipitation (PCP), air temperature (AT), dew point (DP), vapour pressure difference (VPD) and reference evapotranspiration (ETo) were also collected. Greedy stepwise (GS) approach was implemented for feature selection. Finally, different qualitative (scatter plot, line graph, Taylor diagram, box plot, and Rug plot), and quantitative (uncertainty analysis, root mean square error (RMSE), percent of BIAS (PBIAS), Nash Sutcliff efficiency (NSE) and RMSE-observations standard deviation ratio (RSR)) techniques were used for model evaluation and comparison. Results of feature selection approaches revealed that DP, AT, SM, and ST are the four most effective variables at CO2 prediction in PEI, while AT, RH, DP, and ST are the most effective in the NB study area. For optimum input scenario, the GS algorithm was applied, and results showed that a combination of DP, AT, ST, SM, and ETo was the best for the PEI study area, while for NB, all input variables should be involved. Our analysis, for prediction of CO2 fluxes, confirmed that the ICO-AR-RF model performed the best at both PEI (RMSE=0.70, NSE=0.76, PBIAS=-5.11, RSR=0.48) and NB (RMSE=0.74, NSE=0.75, PBIAS=3.23, RSR=0.50), followed by MS-RF and AR-RF. Uncertainty analysis showed that CO2 prediction is more sensitive to input scenario selection than models in both study areas. Results revealed that climatic variables are more effective in CO2 prediction than soil characteristics and the developed hybrid model ICO-AR-RF can be a promising tool for decision-makers and beneficial for stakeholders.

Comparison of Nonlinear Functions to Define the Growth in Intensive Feedlot System with XGBoost Algorithm

The aim of this study was to define the growth by using nonlinear functions in intensive feedlot system with XGBoost algorithm. To achieve this aim, five nonlinear functions were implemented. To implementation of the study, Brown Swiss (n=41) and Simental (n=95) breed were used. Each nonlinear functions were examined for each breed. According to the results of the nonlinear functions, logistic model was the best prediction model for defining the growth of each breed. In this study, the parameters in the best prediction model were calculated individually and the relationship of these parameters with body weight was evaluated with the XGBoost algorithm. Model comparison criteria such as standard deviation ratio (SDratio), Pearson’s correlation coefficient (PC), determination of coefficient (R2) and Akaike’s information criteria (AIC) were used to evaluate the XGBoost algorithm. In conclusion, the XGBoost algorithm can be an effective and optional approach that allows breeders to estimate live weight from growth parameters. This algorithm can operate on large data sets with high accuracy and speed, leading to significant improvements in agricultural productivity and animal health management. XGBoost enables more accurate predictions by analyzing the effects of various characteristics (e.g., nutritional level, breed, age). Therefore, this method can be used to determine critical parameters such as body weight in animal breeding practices, serving as a powerful support tool for operational decisions.

Recruitment-to-inflation ratio to assess response to PEEP during laparoscopic surgery: A physiologic study

Study objectiveDuring laparoscopic surgery, the role of PEEP to improve outcome is controversial. Mechanistically, PEEP benefits depend on the extent of alveolar recruitment, which prevents ventilator-induced lung injury by reducing lung dynamic strain. The hypotheses of this study were that pneumoperitoneum-induced aeration loss and PEEP-induced recruitment are inter-individually variable, and that the recruitment-to-inflation ratio (R/I) can identify patients who benefit from PEEP in terms of strain reduction. DesignSequential study. SettingOperating room. PatientsSeventeen ASA I-III patients receiving robot-assisted prostatectomy during Trendelenburg pneumoperitoneum. Interventions and measurementsPatients underwent end-expiratory lung volume (EELV) and respiratory/lung/chest wall mechanics (esophageal manometry and inspiratory/expiratory occlusions) assessment at PEEP = 0 cmH2O before and after pneumoperitoneum, at PEEP = 4 and 12 cmH2O during pneumoperitoneum. Pneumoperitoneum-induced derecruitment and PEEP-induced recruitment were assessed through a simplified method based on multiple pressure-volume curve. Dynamic and static strain changes were evaluated. R/I between 12 and 4 cmH2O was assessed from EELV. Inter-individual variability was rated with the ratio of standard deviation to mean (CoV). Main resultsPneumoperitoneum reduced EELV by (median [IqR]) 410 mL [80–770] (p < 0.001) and increased dynamic strain by 0.04 [0.01–0.07] (p < 0.001), with high inter-individual variability (CoV = 70% and 88%, respectively). Compared to PEEP = 4 cmH2O, PEEP = 12 cmH2O yielded variable amount of recruitment (139 mL [96–366] CoV = 101%), causing different extent of dynamic strain reduction (median decrease 0.02 [0.01–0.04], p = 0.002; CoV = 86%) and static strain increases (median increase 0.05 [0.04–0.07], p = 0.01, CoV = 33%). R/I (1.73 [0.58–3.35]) estimated the decrease in dynamic strain (p ≤0.001, r = −0.90) and the increase in static strain (p = 0.009, r = −0.73) induced by PEEP, while PEEP-induced changes in respiratory and lung mechanics did not. ConclusionsTrendelenburg pneumoperitoneum yields variable derecruitment: PEEP capability to revert these phenomena varies significantly among individuals. High R/I identifies patients in whom higher PEEP mostly reduces dynamic strain with limited static strain increases, potentially allowing individualized settings.

QUICK ROBUST CLUSTERING USING LINKS (QROCK) UNTUK PENGELOMPOKAN DESA KABUPATEN BANJAR

AbstractAccurate village profile planning needs to be done with mapping based on its characteristics. Village grouping based on the characteristics of village facilities and potential or its characteristics based on the Building Village Index indicator can help determine priorities in village development. In this study, mixed data was used, with numerical data grouping using Hierarchical Agglomerative Nesting (AGNES) algorithm and categorical data with Quick Robust Clustering Using Links (QROCK). The resulting clusters are then combined using the QROCK Ensemble algorithm (algCEBMDC). The data is sourced from the 2021 Village Potential Data Collection (PODES) by the Central Statistics Agency in 277 villages in Banjar Regency, including 18 numerical variables and 29 categorical variables. The results of the study obtained optimal clusters based on the ratio of within-group standard deviation (SW) to between-group standard deviation (SB) resulting in a ratio of 4.82.10-9 with a threshold of 0.4 to 0.9 resulting in 6 clusters. The best cluster results are cluster 4 (4 villages) and cluster 3 (14 villages), then cluster 2 (villages) and cluster 1 (186 villages), and clusters that need development priorities are cluser 5 (2 villages) and cluster 6 (1 village) which are outliers based on processing results. Keywords: Village Grouping, Cluster, AGNES Algorithm, Quick Robust Clustering Using Links (QROCK), AlgCEBMDC

Creating a Lactation Model for 305-Day Milk Yield with Different Resampling Techniques (Bagging Mars) in Mars Modeling

The main purpose of this research is to obtain a prediction model for milk yield by using Multivariate Adaptive Regression Splines (MARS) and Bagging MARS algorithms as a non-parametric regression technique. For this purpose, the effects on milk yield of 305 days were investigated by using lactation parameters in dairy cattle. In the study, 9337 lactation milk yield records belonging to 37 animals belonging to the 2022-2023 period were used and the data set was created by randomly ordering the animals. Data on milk yield results were analyzed with MARS and Bagging MARS algorithms. For dairy cattle; it was modeled with explanatory variables such as lactation month (month), service period (SP), last 7 days average milk yield (L7DMMY), animal's first birth age (FP), animal's age (Age), number of lactations (LN).Correlation coefficient (r), coefficient of determination (R2), Adjusted R2, Root of Square Mean Error (RMSE), standard deviation ratio (SD ratio), mean absolute percent error (MAPE), mean absolute for MARS algorithm estimating total average milk yield deviation (MAD) and Akaike Information Criteria (AIC) values are 0.9986, 0.997, 0.977, 0.142, 0.052, 0.2389, 0.086 and -88, respectively. Similar statistics for the Bagging MARS algorithm are 0.754, 0.556, 0.453, 1.8, 0.666, 3.96, 1.47, and 115, respectively. It has been observed that MARS and Bagging MARS algorithms provide correct results according to the goodness of fit statistics. In this study, it was revealed that MARS algorithm gave better results in milk yield modeling of 305-day lactation.

A hybrid CNN–RNN model for rainfall–runoff modeling in the Potteruvagu watershed of India

AbstractAccurate rainfall‐runoff analysis is essential for water resource management, with artificial intelligence (AI) increasingly used in this and other hydrological areas. The need for precise modelling has driven substantial advancements in recent decades. This study employed six AI models. These were the support vector regression model (SVR), the multilinear regression model (MLR), the extreme gradient boosting model (XGBoost), the long‐short‐term memory (LSTM) model, the convolutional neural network (CNN) model, and the convolutional recurrent neural network (CNN‐RNN) hybrid model. It covered 1998–2006, with 1998–2004 for calibration/training and 2005–2006 for validation/testing. Five metrics were used to measure model performance: coefficient of determination (R2), Nash‐Sutcliffe efficiency (NSE), mean absolute error (MAE), root‐mean square error (RMSE), and RMSE‐observations standard deviation ratio (RSR). The hybrid CNN‐RNN model performed best in both training and testing periods (training: R2 is 0.92, NSE is 0.91, MAE is 10.37 m3s−1, RMSE is 13.13 m3s−1, and RSR is 0.30; testing: R2 is 0.95, NSE is 0.94, MAE is 12.18 m3s−1, RMSE is 15.86 m3s−1, and RSR is 0.25). These results suggest the hybrid CNN‐RNN model is highly effective for rainfall‐runoff analysis in the Potteruvagu watershed.

Investigation on particle slug flow using large eddy simulation combined a particle kinetic energy model

In this study, an investigation on particle hydrodynamic behaviors of gas-particle slug flow is modeled and numerically simulated. A novelty particle kinetic energy model at subgrid-scale level is developed to consider the effect of gas turbulence on particle movement and a four-way coupling approach is combined. Anisotropic particle dispersions and the multiphase interactions is revealed by using a second-order moment turbulence model and large eddy simulation is performed to solve the Euler-Euler two-fluid transport equations, Predictions are well agreed with the experimental data and reported results by discrete particle model built-in the multiphase flow model with interphase exchanges code. Bubble motions have great effects on the particle dynamics due to bubble entrainment. Flow patterns of particle circulation are generated between higher and lower concentrations. The largest frequency of power spectrum density at center of top surface is approximately 4.5 times larger than that of wall region. Bubblelike granular temperatures are much larger than smaller-scale particle granular temperature. At the top surface, maximum value of vertical bubblelike Reynolds stress is found at x/W = 0.28, it is 4.0 times larger than that of the lateral x-direction. In addition, the mean values, and the standard deviation of SGS particle kinetic energy at center are both approximately 3.5 times larger than those of z = 10.0 cm. For particle shear stress at middle section, maximum ratios of mean value and standard deviation at central regions to those of walls reach up to 8.0 and 11.0.

Generalized Confidence Intervals for Ratios of Standard Deviations Based on Log-Normal Distribution when Times Follow Weibull Distributions.

Modern anesthetic drugs ensure the efficacy of general anesthesia. Goals include reducing variability in surgical, tracheal extubation, post-anesthesia care unit, or intraoperative response recovery times. Generalized confidence intervals based on the log-normal distribution compare variability between groups, specifically ratios of standard deviations. The alternative statistical approaches, performing robust variance comparison tests, give P-values, not point estimates nor confidence intervals for the ratios of the standard deviations. We performed Monte-Carlo simulations to learn what happens to confidence intervals for ratios of standard deviations of anesthesia-associated times when analyses are based on the log-normal, but the true distributions are Weibull. We used simulation conditions comparable to meta-analyses of most randomized trials in anesthesia, and coefficients ofvariation . The estimates of the ratios of standard deviations were positively biased, but slightly, the ratios being 0.11% to0.33% greater than nominal. In contrast, the 95% confidence intervals were very wide (i.e., > 95% of P ≥ 0.05). Although substantive inferentially, the differences inthe confidence limits were small from a clinical or managerial perspective, with amaximum absolute difference inratios of 0.016. Thus, P < 0.05 isreliable, but investigators should plan for TypeII errors atgreater than nominal rates.

Statistical Performance of Gridded Rainfall Datasets Over Ungauged Jalaur River Basin, Philippines

The study presented aims to find the most appropriate climate dataset for the data-scarce Jalaur River Basin (JRB), Iloilo, Philippines, by evaluating the statistical performance of five rainfall datasets (APHRODITE, CPC NOAA, ERA5, SA-OBS, and PGF-V3) with resolutions of 0.25° and 0.5° having a time domain of 1981 to 2005. Bilinear interpolation implemented through Climate Data Operator (CDO) was used to extract and process grid climate datasets with Linear scaling as bias correction to minimize product simulation uncertainties. The datasets were compared to the lone meteorological station nearest to JRB investigated at monthly and annual timescales using six statistical metrics, namely, Pearson’s correlation coefficient (r), coefficient of determination (R2), modified index of agreement (d1), Kling-Gupta efficiency, Nash-Sutcliffe efficiency (NSE), and RMSE-observations standard deviation ratio (RSR). The results indicate a strong positive correlation with the observed data for both rainfall and temperature (r &gt; 0.8; R2, d1 &gt; 0.80). Although graphical observation shows an underestimation of rainfall, goodness-of-fit values indicate very good model performance (NSE, KGE &gt; 0.75; RSR &lt; 0.50). In terms of temperature, variable responses are observed with significant overestimation for maximum temperature and underestimation for minimum temperature. SA-OBS proved to be the best-performing dataset, followed by ERA5 and PGF-V3. These key findings supply useful information in deciding the most appropriate gridded climate dataset for hydrometeorological investigation in the JRB and could enhance the regional representation of global datasets.

Comparative analysis of HEC-HMS and SWAT hydrological models for simulating the streamflow in sub-humid tropical region in India.

Assessment of water availability in sub-humid regions is important due to distinct climatic and environmental conditions. In this study, Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) models have been assessed in simulating streamflows in the sub-humid tropical Kabini basin in Kerala, India, spanning 1260 km2. Calibration and validation utilized daily weather data from 1997 to 2015 from the Muthankera gauging station. The study investigated the impact of routing methods on runoff simulation in the ArcSWAT, exploring Muskingum and Variable Storage methods. Evaluation metrics encompassed Nash-Sutcliffe Efïciency (NSE), Coefficient of Determination (R2), Percent bias (PBIAS), RMSE-observations standard deviation ratio (RSR), and Peak Percent Threshold Statistics (PPTS) approach for high-flow values. The result indicates that HEC-HMS outperforms SWAT concerning R2 and NSE values during daily calibration and validation. Monthly simulations showed HEC-HMS closely aligning with SWAT (Variable storage), outperforming SWAT (Muskingum). The PPTS approach proved effective in simulating high-flow values. Both models exhibited proficiency in streamflow analysis within the study area, promising predictive potential for future hydrological studies in sub-humid regions.

Improved Land Use Efficiency Through Spectral Beam Splitting in Agrivoltaic Farms

Installing photovoltaic (PV) collectors above arable land (Agrivoltaics) can aid with the shortage of available land area for solar power generation and food production. Most open field agrivoltaics are based on opaque PV devices which absorb photosynthetically active radiation (PAR, 400-700 nm), reducing crop yield and increasing variability in light distribution across the field. This research evaluates the performance of spectral beam splitter integrated photovoltaic (BSIPV) modules using a PV performance model. A high percentage (66 %) of PAR incident on the spectral beam splitter is transmitted effectively to the plants, while the near infrared radiation (NIR, &gt; 700 nm) is reflected to the adjacent bifacial opaque photovoltaic module to generate power. In the model, seven rows of modules were placed uniformly across the field at a height of four meters from the ground. Considering a cool season (November – March) in Yuma, Arizona, in a conventional opaque PV agrivoltaic farm received 43 % lower total daylight integral (TDLI) across the season in comparison to open field with a coefficient of variation (ratio of standard deviation to mean expressed in percentage) of 56 % in TDLI across the field. On the other hand, the BSIPV agrivoltaic farm limited the drop in TDLI to 7 % in comparison to open field and the coefficient of variation to 14 % across the field. Thus, BSIPV showed a 36 % improvement in TDLI relative to the conventional opaque PV agrivoltaic farm. The results of the current study justify further research on the proposed collector concept.

Reference intervals for knee functions specific to outpatients with knee osteoarthritis: a cross-sectional study

BackgroundReference values (RVs) for knee function tests have been reported in perioperative patients with knee osteoarthritis (KOA); however, such values for practical use in outpatient setting has yet to be determined. Therefore, we aimed to establish the reference intervals (RIs) for outpatients with mild to moderate KOA.MethodsThis cross-sectional study enrolled 202 outpatients with KOA from 8 Japanese orthopedic clinics and measured knee extensor/flexor muscle strength (MS) and knee extension/flexion range of motion (ROM). We used multiple regression analysis to evaluate the sources of variation, including sex, age, body mass index, Kellgren–Lawrence (K-L) classification, bilateral KOA, and exercise habits. Magnitude of between-subgroup differences is expressed as standard deviation ratio (SDR) based on a three-level nested analysis of variance, with SDR ≥ 0.4 as the threshold for requiring RIs specific for subgroups. RIs were calculated parametrically using two-parameter Box-Cox formula if Gaussian transformation of RVs was successful, otherwise calculated nonparametrically.ResultsPartitioning was required by sex for extensor and flexor MS (SDR = 0.65, 0.57, respectively) and by K-L classification for flexion ROM (SDR = 0.54). RIs were determined parametrically for extensor MS as 0.27–2.09 (male) and 0.27–1.54 (female) Nm/kg and for flexor MS 0.18–1.20 (male) and 0.13–0.79 (female) Nm/kg. On the other hand, RIs for extension and flexion ROM were determined nonparametrically due to discrete nature of their RVs. The RIs determined for extension ROM were -15°–0° and for flexion ROM were 105°–150° (for K-L grade I/II) and 95°–140° (for K-L grade III/IV).ConclusionsThe ranges of RIs determined specifically for patients with mild to moderate KOA were in-between those of age-matched healthy controls and pre-surgical KOA patients, both of which we had reported for use in physiotherapeutic management of KOA patients undergone total knee arthroplasty. The newly derived RIs will provide an objective benchmark for physiotherapy targeting outpatients with mild to moderate KOA.