Similar R2 Values Research Articles

Handheld Near-Infrared Spectroscopy for Undried Forage Quality Estimation.

This study investigates the efficacy of handheld Near-Infrared Spectroscopy (NIRS) devices for in-field estimation of forage quality using undried samples. The objective is to assess the precision and accuracy of multiple handheld NIRS instruments-NeoSpectra, TrinamiX, and AgroCares-when evaluating key forage quality metrics such as Crude Protein (CP), Neutral Detergent Fiber (aNDF), Acid Detergent Fiber (ADF), Acid Detergent Lignin (ADL), in vitro Total Digestibility (IVTD)and Neutral Detergent Fiber Digestibility (NDFD). Samples were collected from silage bunkers across 111 farms in New York State and scanned using different methods (static, moving, and turntable). The results demonstrate that dynamic scanning patterns (moving and turntable) enhance the predictive accuracy of the models compared to static scans. Fiber constituents (ADF, aNDF) and Crude Protein (CP) show higher robustness and minimal impact from water interference, maintaining similar R2 values as dried samples. Conversely, IVTD, NDFD, and ADL are adversely affected by water content, resulting in lower R2 values. This study underscores the importance of understanding the water effects on undried forage, as water's high absorption bands at 1400 and 1900 nm introduce significant spectral interference. Further investigation into the PLSR loading factors is necessary to mitigate these effects. The findings suggest that, while handheld NIRS devices hold promise for rapid, on-site forage quality assessment, careful consideration of scanning methodology is crucial for accurate prediction models. This research contributes valuable insights for optimizing the use of portable NIRS technology in forage analysis, enhancing feed utilization efficiency, and supporting sustainable dairy farming practices.

Sun-induced fluorescence spectrum as a tool for assessing peatland vegetation productivity in the framework of warming and reduced precipitation experiment

Northern peatlands store a large amount of carbon in the form of partially decomposed organic matter. Because the majority of northern peatlands are located in remote areas, remote sensing serves as a suitable alternative to traditional surveys, enabling to enhance our understanding of peatland vegetation. Among various optical remote sensing signals, sun-induced fluorescence (SIF) is the most directly connected to carbon assimilation by plants, making it a promising early-response indicator for assessing the impact of climate change on natural ecosystems. However, the behavior of SIF throughout the season and the strength of the relationship between SIF and carbon assimilation for peatland vegetation, which consists of peat mosses and vascular plants with diverse anatomy, morphology, physiology, and phenology, have not yet been studied. Therefore, we conducted the first comprehensive assessment of the full spectrum SIF, reflectance, and gross primary production (GPP) for two distinct peatland vegetation communities under control (C), warming (W), and warming with reduced precipitation (WP) conditions throughout an entire season. While we could detect clear differences in SIF and reflectance between the two vegetation communities for original, C, vegetation during the main growing season, these differences diminished when W and WP were applied. The W and WP conditions caused a more pronounced change in plant biomass for vegetation characterized by a higher proportion of peat mosses and low creeping shrubs, which resulted in significant changes in the SIF and reflectance spectrum. Our findings demonstrate that the domination of peatland by vascular plants that is expected due to future warmer conditions causes stronger seasonal variation of SIF, reflectance, and GPP. We observed that far-red SIF and the spectrally integrated full SIF spectrum strongly correlate (r2 > 0.85) with GPP regardless of vegetation community, temperature, and precipitation regime. However, the use of a novel multiple wavelength regression model using ten bands from the full SIF spectrum allowed for higher accuracy in the estimation of GPP compared to the use of single bands or integrated total SIF value. Moreover, such a model has a more stable performance when transferring from one vegetation community to another. Conversely, the correlation strength of traditional vegetation indices like the Normalized Difference Vegetation Index or MERIS Terrestrial Chlorophyll Index depends on the peatland vegetation community. While the SIF:GPP relationship exhibits similar r2 values for vegetation communities with different ratios of planophile and electrophile leaves, the slope of the linear model depends on this ratio. This study performed at the ground level shows for the first time the importance of full spectrum SIF for the monitoring of a heterogeneous ecosystem like peatland, which will help to better utilize the SIF products obtained through current and future satellite missions.

98 Characterization of Factors Affecting the Fecal Microbiome in Young Canadian Pigs

Abstract Although established as an important contributor to pig health and performance, high diversity and variability of the microbiome has challenged identification of a beneficial community composition that could inform best management practice and gut modifier development. To address this challenge, rectal swabs were collected at intervals from near birth to one-week postweaning from 4 piglets in 10 litters from 13 conventional and 9 RWA barns across Canada. The 16S rRNA gene was sequenced in 1,997 fecal samples collected at ~4 days of age (4d), 1 day before weaning (W-1), and 7 days after weaning (W+7), piglets were also weighed at the time of sampling. Raw sequences were denoised and assigned to genera using DADA2. After quality control and filtering there was an average of 32,710 reads per sample, with 649 genera identified in the samples. Principal Coordinate Analysis (PCoA) plots were created using the vegan and phyloseq packages (cao model and MDS settings). Relationships between metadata factors and the microbiome were investigated using PERMANOVA analysis in the adonis2 package. In PCoA plots samples clustered by production stage, with an expected marked change of the bacterial community following weaning. Because production stage had a large effect on the microbiome the effects of metadata factors were investigated within sampling visit. Metadata factors investigated included farrowing pen (FP) location at the 4 d-old sampling, barn and rearing system [conventional versus raised without antibiotics (RWA)] and the lifetime health (LH) status including always healthy, received a health score 1 or greater at any one visit or did not complete the study. At ~4 d of age, PERMANOVA indicated that all these factors were significantly linked to the microbiome composition (P = 0.001), FP explained the most microbiome variation (R2 = 0.338), followed by barn (R2 = 0.217). Rearing system and LH explained much less of the variation, R2 = 0.009 and R2 = 0.005, respectively. At W-1, the microbiome was not significantly correlated to LH (P = 0.053); however, the other metadata factors were significant (P = 0.001) with similar R2 values to the previous timepoint (FP; R2 = 0.317, barn R2 = 0.210, and system R2 = 0.008). At W+7 all metadata factors were once again significant (P < 0.005). The correlation to FP had reduced to R2 = 0.245, cohort increased to R2 = 0.266, system effects doubled to R2 = 0.21, and LH remained very low at R2 = 0.006. However, interactions between LH and FP (R2 = 0.059) and system and LH (R2=0.003) were also observed. These results suggest that farrowing pen has a significant and lasting effect on the microbiome. The effect of being raised RWA or conventionally was very small in preweaning piglets, but that affect increased post weaning. The immediate environment early in life appears to have a large effect on piglet microbiomes and may present an opportunity for beneficial intervention.

Deep predictions and transfer learning for simulation-driven structural health monitoring based on guided waves

Simulation-driven structural health monitoring (SHM) is crucial for enhancing the safety and reliability of structures. Its primary aim is to utilize numerical simulations to design efficient SHM systems, reducing costs while maintaining effectiveness. However, challenges arise due to disparities between simulations and physical measurements, stemming from uncertainties, noise, and simplifications. These challenges also extend to SHM methods using machine learning, particularly deep neural networks (DNNs).This paper bridges the gap between DNNs trained on simulated data and real-world experiments by investigating three transfer learning (TL) techniques: feature augmentation, direct fine-tuning, and boosting-based fine-tuning. The focus is on guided wave damage interaction coefficients (WDICs) as damage-sensitive features, modeled by DNNs for damage identification in thin plates, commonly found in aerospace structures like aircraft fuselages. This classification task compares measured and predicted WDICs in a common guided wave SHM configuration with one actuator and three sensors. Training and extensive testing datasets are generated using advanced finite element simulations and laser Doppler vibrometer experiments.The study demonstrates that TL techniques significantly enhance DNN prediction accuracy and improve damage identification performance for untrained scenarios compared to purely simulation-based DNNs. Among the TL approaches explored, feature augmentation with appropriate activation functions and boosting-based fine-tuning yield the most promising results. Using four out of twelve TL datasets, feature augmentation reduces the simulation-based mean absolute percentage error from 46.80% to 35.98% and increases coefficient of determination R2 values from 0.02 to 0.72. With six TL datasets and a boosting-based fine tuning, the mean absolute percentage error decreases from 47.42% to 35.20%, maintaining similar R2 values. While TL substantially enhances prediction accuracy, its impact on damage identification is slightly less pronounced, with improvements in relative classification accuracy by approximately 5% compared to simulation-based DNNs.These findings highlight the efficacy of TL techniques for damage identification in thin plates using WDICs and DNNs, emphasizing the influence of TL training and DNN model variations. Furthermore, they showcase the potential of TL methods to enhance DNN performance in future SHM applications, particularly emphasizing the promise of feature augmentation and boosting.

Prediction of primal and retail cut weights, tissue composition and yields of youthful cattle carcasses using computer vision systems; whole carcass camera and/or ribeye camera

The application of two computer vision systems (CVS) was evaluated to predict primal and retail cut composition in youthful beef carcasses. Left carcass sides from a total of 634 animals were broken down into primal cuts, scanned using dual-energy x-ray absorptiometry for the prediction of tissue composition and fabricated into retail cuts. Cold carcass camera (CCC) images led to higher R2 values than hot carcass camera (HCC) images. The CVS coefficients of prediction for the primal cut weights ranged from 0.61 to 0.97. For the primal cut tissue composition predictions, R2 values ranged from 0.09 for Brisket HCC bone prediction to 0.82 for Chuck CCC fat prediction. Retail cut weight estimations had similar R2 values, ranging from 0.10 for IMPS 112 (Ribeye roll–denuded ribeye) to 0.99 for IMPS 113C (semi-boneless chuck) both using CCC. The results suggest the feasibility of CVS technologies to predict beef primal and retail cuts weights together with tissue composition, and yield percentages.

CollagenTransformer: End-to-End Transformer Model to Predict Thermal Stability of Collagen Triple Helices Using an NLP Approach.

Collagen is one of the most important structural proteins in biology, and its structural hierarchy plays a crucial role in many mechanically important biomaterials. Here, we demonstrate how transformer models can be used to predict, directly from the primary amino acid sequence, the thermal stability of collagen triple helices, measured via the melting temperature Tm. We report two distinct transformer architectures to compare performance. First, we train a small transformer model from scratch, using our collagen data set featuring only 633 sequence-to-Tm pairings. Second, we use a large pretrained transformer model, ProtBERT, and fine-tune it for a particular downstream task by utilizing sequence-to-Tm pairings, using a deep convolutional network to translate natural language processing BERT embeddings into required features. Both the small transformer model and the fine-tuned ProtBERT model have similar R2 values of test data (R2 = 0.84 vs 0.79, respectively), but the ProtBERT is a much larger pretrained model that may not always be applicable for other biological or biomaterials questions. Specifically, we show that the small transformer model requires only 0.026% of the number of parameters compared to the much larger model but reaches almost the same accuracy for the test set. We compare the performance of both models against 71 newly published sequences for which Tm has been obtained as a validation set and find reasonable agreement, with ProtBERT outperforming the small transformer model. The results presented here are, to our best knowledge, the first demonstration of the use of transformer models for relatively small data sets and for the prediction of specific biophysical properties of interest. We anticipate that the work presented here serves as a starting point for transformer models to be applied to other biophysical problems.

Camera-Derived Photoplethysmography (rPPG) and Speckle Plethysmography (rSPG): Comparing Reflective and Transmissive Mode at Various Integration Times Using LEDs and Lasers.

Background: Although both speckle plethysmography (SPG) and photoplethysmography (PPG) examine pulsatile changes in the vasculature using opto-electronics, PPG has a long history, whereas SPG is relatively new and less explored. The aim of this study was to compare the effects of integration time and light-source coherence on signal quality and waveform morphology for reflective and transmissive rSPG and rPPG. Methods: (A) Using time-domain multiplexing, we illuminated 10 human index fingers with pulsed lasers versus LEDs (both at 639 and 850 nm), in transmissive versus reflective mode. A synchronized camera (Basler acA2000-340 km, 25 cm distance, 200 fps) captured and demultiplexed four video channels (50 fps/channel) in four stages defined by illumination mode. From all video channels, we derived rPPG and rSPG, and applied a signal quality index (SQI, scale: Good > 0.95; Medium 0.95–0.85; Low 0.85–0.8; Negligible < 0.8); (B) For transmission videos only, we additionally calculated the intensity threshold area (ITA), as the area of the imaging exceeding a certain intensity value and used linear regression analysis to understand unexpected similarities between rPPG and rSPG. Results: All mean SQI-values. Reflective mode: Laser-rSPG > 0.965, LED-rSPG < 0.78, rPPG < 0.845. Transmissive mode: 0.853–0.989 for rSPG and rPPG at all illumination settings. Coherent mode: Reflective rSPG > 0.951, reflective rPPG < 0.740, transmissive rSPG and rPPG 0.990–0.898. Incoherent mode: Reflective all <0.798 and transmissive all 0.92–0.987. Linear regressions revealed similar R2 values of rPPG with rSPG (R2 = 0.99) and ITA (R2 = 0.98); Discussion: Laser-rSPG and LED-rPPG produced different waveforms in reflection, but not in transmission. We created the concept of ITA to investigate this behavior. Conclusions: Reflective Laser-SPG truly originated from coherence. Transmissive Laser-rSPG showed a loss of speckles, accompanied by waveform changes towards rPPG. Diffuse spatial intensity modulation polluted spatial-mode SPG.

Un nuevo algoritmo para la evaluación de los parámetros cinéticos R∞ y k, en el proceso de flotación de carbon

Floating models have been a topic of investigation for more than 90 years, which has allowed their introduction in many industrial processes. In this paper is presented, an algorithm to determine the kinetic parameters for the coal flotation process in a fast and practical way, using a spreadsheet. A 22 factorial design was run with three replicates. The selected factors were: concentration of methyl isobutyl carbinol as foaming agent, and diesel as a collector. For both foaming agent and collector, low and high levels were 25 g/ton, and 100 g/ton respectively. To verify the model’s curvature, an intermediate point was included. The statistical analysis of the fuel recovery and kinetic flotation is presented. Three kinetic models were used for the evaluation of the proposed algorithm, García-Zuñiga’s algorithm, and both of the algorithms proposed by Klimpel. With the analysis of variance, it is concluded that both factors influence significantly the efficiency of coal flotation. However, the factor that has the most significant effect is the foaming agent. It is also concluded that the proposed algorithm is functional, presenting correlation coefficients higher than 0.9948. The utilized kinetic models present similar average R2 values, for the first order R2=0.9984, and for the second-order R2=0.9975.

Estimation of direct-seeded guayule cover, crop coefficient, and yield using UAS-based multispectral and RGB data

Guayule (Parthenium argentatum, A. Gray), a perennial desert shrub, produces high-quality natural rubber and is targeted as a domestic natural rubber source in the U.S. While commercialization efforts for guayule are on-going, crop management requires plant growth monitoring, irrigation requirement assessment, and final yield estimation. Such assistance for guayule management could be provided with remote sensing (RS) data. In this study, field and RS data, collected via drones, from a 2-year guayule irrigation experiment conducted at Maricopa, Arizona were evaluated. In-season field measurements included fractional canopy cover (fc), basal (Kcb) and single (Kc) crop coefficients, and final yields of dry biomass (DB), rubber (RY), and resin (ReY). The objectives of this paper were to compare vegetations indices from MS data (NDVI) and RGB data (triangular greenness index, TGI); and derive linear prediction models for estimating fc, Kcb, Kc, and yield as functions of the MS and RGB indices. The NDVI and TGI showed similar seasonal trends and were correlated at a coefficient of determination (r2) of 0.52 and a root mean square error (RMSE) of 0.11. The prediction of measured fc as a linear function of NDVI (r2 = 0.90) was better than by TGI (r2 = 0.50). In contrast to TGI, the measured fc was highly correlated with estimated fc based on RGB image evaluation (r2 = 0.96). Linear models of Kcb and Kc, developed over the two years of guayule growth, had similar r2 values vs NDVI (r2 = 0.46 and 0.41, respectively) and vs TGI (r2 = 0.48 and 0.40, respectively). Final DB, RY, and ReY were predicted by both NDVI (r2 = 0.75, 0.53, and 0.70, respectively) and TGI (r2 = 0.72, 0.48, and 0.65, respectively). The RS-based models enable estimation of irrigation requirements and yields in guayule production fields in the U.S.

Development of a Filtered CFD-DEM Drag Model with Multiscale Markers Using an Artificial Neural Network and Nonlinear Regression

The accuracy of coarse-grained Euler–Lagrangian simulations of fluidized beds heavily depends on the mesoscale drag models to account for the influences of the unresolved subgrid structures. Traditional filtered drag models are regressed with mesoscale markers such as voidage and slip velocities. In this research, a filtered drag was regressed with both mesoscale and macroscale markers using fine-grid Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) simulations. The traditional nonlinear regression method was compared with machine learning regression using an Artificial Neural Network (ANN) implemented in PyTorch and coupled with MFiX. The new drag showed higher accuracy than the Wen–Yu drag and another filtered drag derived from the two-fluid model. The nonlinear regression shows slightly better results than ANN regression in cases with similar R2 values. The utilization of the gas inlet velocity as an additional macroscale marker reduced the errors by up to 55.3% in the tested cases.

Evaluation of using digital photography as a cost-effective tool for the rapid assessment of soil organic carbon at a regional scale

A rapid and cost-effective soil organic carbon (SOC) monitoring is vital for soil management. While SOC laboratory analysis is expensive and time-consuming, spectroscopic sensor technology allows for much faster and inexpensive acquisition of data. Still, the initial cost of spectroscopic sensors is high for farmers. With soil color as a proxy, digital cameras could be used as SOC sensors. We compared the performance of SOC prediction based on soil color on a regional scale. Soil color measurements were made on samples covering the German state of North Rhine-Westphalia. SOC ranged between 0.03 and 4.74%. Images of the samples were taken under standardized conditions with four different sensors. Various soil color space models and indices were derived for SOC prediction. Modeling was performed using multiple linear regression (MLR) and random forest (RF). Best SOC prediction results were achieved using color of wet soils. Best MLR and RF models gave similar validation R2 values for the prediction of SOC at 0.66 and 0.63 with RMSE values of 0.57 and 0.61%, respectively. The MLR models using one color space showed the same performance as the RF models using the full vis spectrum. This suggested that the vis spectrum does not contain more information than the three variables of a common color space. A smartphone had the same accuracy as a more professional camera, making it a potential soil sensor. With increasing scale, soil mineralogy influences soil color weakening the performance.

Simulating Canopy Temperature Using a Random Forest Model to Calculate the Crop Water Stress Index of Chinese Brassica

The determination of crop water status has positive effects on the Chinese Brassica industry and irrigation decisions. Drought can decrease the production of Chinese Brassica, whereas over-irrigation can waste water. It is desirable to schedule irrigation when the crop suffers from water stress. In this study, a random forest model was developed using sample data derived from meteorological measurements including air temperature (Ta), relative humidity (RH), wind speed (WS), and photosynthetic active radiation (Par) to predict the lower baseline (Twet) and upper baseline (Tdry) canopy temperatures for Chinese Brassica from 27 November to 31 December 2020 (E1) and from 25 May to 20 June 2021 (E2). Crop water stress index (CWSI) values were determined based on the predicted canopy temperature and used to assess the crop water status. The study demonstrated the viability of using a random forest model to forecast Twet and Tdry. The coefficients of determination (R2) in E1 were 0.90 and 0.88 for development and 0.80 and 0.77 for validation, respectively. The R2 values in E2 were 0.91 and 0.89 for development and 0.83 and 0.80 for validation, respectively. Our results reveal that the measured and predicted CWSI values had similar R2 values related to stomatal conductance (~0.5 in E1, ~0.6 in E2), whereas the CWSI showed a poor correlation with transpiration rate (~0.25 in E1, ~0.2 in E2). Finally, the methodology used to calculate the daily CWSI for Chinese Brassica in this study showed that both Twet and Tdry, which require frequent measuring and design experiment due to the trial site and condition changes, have the potential to simulate environmental parameters and can therefore be applied to conveniently calculate the CWSI.

The use of Colilert-18, Colilert and Enterolert for the detection of faecal coliform, Escherichia coli and Enterococci in tropical marine waters, Trinidad and Tobago

Membrane Filtration (MF) is still widely used in many Caribbean countries for bathing beach water quality analysis. MF is an excellent technique but has a few limitations that restrict the number of samples processed due to the time required and the need for confirmation. Another method Defined Substrate Technology (DST) has overcome these limitations and was investigated as an alternative for faecal coliform, Escherichia coli and enterococci detection.A total of 110 samples were collected from two popular bathing areas in Trinidad, during both wet and dry seasons. IDEXX Colilert-18, Colilert and Enterolert were tested against MF and the data was separated into three water impact groups based on the level of bacteria pollution: low, medium and high. Statistical analysis was performed using ISO 17994:2004 standard as well as Paired t-test to determine whether there were any differences between the DST and MF methodsOverall, the DST was more sensitive with higher values obtained compared to the MF. The recovery of enterococci had low relative mean differences for all three water groups 10.3%, 7.6% and 14.3% (low, medium and high). The recovery of E. coli was similar to the recovery of enterococci with 9.3%, 8.8% and 12.6% and the recovery of faecal coliform had the largest percentage difference for all three water groups with 16.7%, 18.9% and 26.1%. The analysis showed the DST recovery had no significant difference (P>0.05) to MF with positive correlation and similar r2 values obtained. Differences in both methods were also small enough that there was more than 85% agreement to whether the Trinidad and Tobago Water Pollution Rules water quality standards were met or exceeded. In conclusion, the DST will be suitable for water quality monitoring of recreational waters in the Caribbean due to its similarity to MF and its simpler and a faster methodology.

Partitioning evapotranspiration using stable isotopes and Lagrangian dispersion analysis in a small agricultural catchment

Abstract Measuring evaporation and transpiration at the field scale is complicated due to the heterogeneity of the environment, with point measurements requiring upscaling and field measurements such as eddy covariance measuring only the evapotranspiration. During the summer of 2014 an eddy covariance device was used to measure the evapotranspiration of a growing maize field at the HOAL catchment. The stable isotope technique and a Lagrangian near field theory (LNF) were then utilized to partition the evapotranspiration into evaporation and transpiration, using the concentration and isotopic ratio of water vapour within the canopy. The stable isotope estimates of the daily averages of the fraction of evapotranspiration (Ft) ranged from 43.0–88.5%, with an average value of 67.5%, while with the LNF method, Ft was found to range from 52.3–91.5% with an average value of 73.5%. Two different parameterizations for the turbulent statistics were used, with both giving similar R 2 values, 0.65 and 0.63 for the Raupach and Leuning parameterizations, with the Raupach version performing slightly better. The stable isotope method demonstrated itself to be a more robust method, returning larger amounts of useable data, however this is limited by the requirement of much more additional data.

Estimation of carcass weight of Hanwoo (Korean native cattle) as a function of body measurements using statistical models and a neural network.

ObjectiveThe objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network.MethodsData from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation.ResultsThe R2 values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R2 values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy.ConclusionThe developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

Comparison of the decision tree, artificial neural network and multiple regression methods for prediction of carcass tissues composition of goat kids

The aim of this study was to predict carcass tissue composition of goat kids using the decision tree with CHAID algorithm (DT) and artificial neural network (ANN) method in comparison with classical step-wise regression (SWR) analyse. Data were obtained from 57 goat kids of Gokceada breed. Predictor variables were pre-slaughter weight, several carcass measurements and indices, weights of different carcass joints and dressing percentage. R2 values ranging from 0.212 to 0.371 indicating low to moderate accuracy were obtained for predicting muscle proportion. DT and ANN yielded similar R2 values for predicting bone proportion. DT was the best prediction method for estimating proportions of subcutaneous fat (R2 = 0.828) and intermuscular fat (R2 = 0.789). According to DT analyses, cold carcass weight was the most important factor influencing bone proportion, while kidney knob and channel fat weight was the predominant factor influencing subcutaneous, intermuscular and total fat proportions. Consequently, the use of DT method can be considered to predict carcass fat proportions.

Modeling of NF/RO membrane fouling and flux decline using real-time observations

Many numerical models for membrane filtration have been developed to explain and predict fouling mechanisms. The models can simulate flux decline, transmembrane pressure increase, and fouling thickness based on theoretical equations. However, the simulated fouling layer thicknesses have not been validated by in-situ observations on membrane surfaces because the membrane system is operated under a sealed and pressurized condition. In this study, humic acid fouling layers on nanofiltration and reverse osmosis membranes were monitored in-situ and in real-time using optical coherence tomography (OCT). The OCT system detected fouling layer growth over time, and showed that the compact and thick fouling layer had an estimated thickness of 80 µm. When comparing the thickness of the fouling layer between OCT and scanning electron microscopy (SEM) images, the OCT images showed values that were approximately 8 times higher than those of the SEM images. By comparing the obtained fouling thickness values with the estimated results, two existing models (Faridirad model and pore blockage-cake filtration model) were validated in terms of root mean square error (RMSE), Akaike Information Criteria (AIC), and coefficient of determination (R2). Both models showed similar high R2 (≥0.97) and low RMSE (<10-5) values, but the pore blockage-cake filtration model had lower AIC values than the Faridirad model. This study highlighted that the in-situ and real-time monitoring of fouling thickness can provide significant information for developing an accurate and precise membrane model.

Accuracy assessment of NLCD 2011 impervious cover data for the Chesapeake Bay region, USA

The National Land Cover Database (NLCD) contains three eras (2001, 2006, 2011) of percentage urban impervious cover (%IC) at the native pixel size (30 m-×-30 m) of the Landsat Thematic Mapper satellite. These data are potentially valuable to environmental managers and stakeholders because of the utility of %IC as an indicator of watershed and aquatic condition, but lack an accuracy assessment because of the absence of suitable reference data. Recently developed 1 m2 land cover data for the Chesapeake Bay region makes it possible to assess NLCD %IC accuracy for a 262,000 km2 region based on a census rather than a sample of reference data. We report agreement between the two %IC datasets for watersheds and the riparian zones within watersheds and four additional square units. The areas of the six assessment units were 40 ha cell, 433 ha (riparian mean), 2756 ha cell, 5626 ha cell, 8569 ha (watershed mean) and 22,500 ha cell. Mean Absolute Deviation (MAD) and Mean Deviation (MD) were about 1.5% and -1.5%, respectively, for each of the assessment units except for the riparian unit, for which MAD and MD were 0.88 and 0.62, respectively. NLCD reliably reproduced %IC from the 1 m2 data with a small, consistent tendency for underestimation. Results were sensitive to assessment unit choice. The results for the four largest assessment units had very similar regression parameters, R2 values, and bias patterns. Results for the riparian assessment were different from those for the watershed unit and the other three larger units. MAD was about 50% less for the riparian zones than it was for the watersheds, the direction of bias was less consistent, and NLCD %IC was uniformly higher than 1 m2 %IC in urbanized riparian zones. For the smallest unit, bias patterns were more similar to the riparian unit and regression results were more similar to the four larger units. MAD and MD were also sensitive to the amount of urbanization, increasing as NLCD %IC increased. The low overall bias and positive relationship between bias and urbanization suggest that the benefits of obtaining 1 m2 IC data outside of urban areas may not outweigh the costs of obtaining such data.

Estimation of sex and stature using anthropometry of the upper extremity in an Australian population.

Stature and a further 8 anthropometric dimensions were recorded from the arms and hands of a sample of 96 staff and students from the Australian National University and The University of Newcastle, Australia. These dimensions were used to create simple and multiple logistic regression models for sex estimation and simple and multiple linear regression equations for stature estimation of a contemporary Australian population. Overall sex classification accuracies using the models created were comparable to similar studies. The stature estimation models achieved standard errors of estimates (SEE) which were comparable to and in many cases lower than those achieved in similar research. Generic, non sex-specific models achieved similar SEEs and R2 values to the sex-specific models indicating stature may be accurately estimated when sex is unknown.

Spatial downscaling of climate variables using three statistical methods in Central Iran

Spatial downscaling methods are widely used for the production of bioclimatic variables (e.g. temperature and precipitation) in studies related to species ecological niche and drainage basin management and planning. This study applied three different statistical methods, i.e. the moving window regression (MWR), nonparametric multiplicative regression (NPMR), and generalized linear model (GLM), to downscale the annual mean temperature (Bio1) and annual precipitation (Bio12) in central Iran from coarse scale (1 km × 1 km) to fine scale (250 m ×250 m). Elevation, aspect, distance from sea and normalized difference vegetation index (NDVI) were used as covariates to create downscaled bioclimatic variables. Model assessment was performed by comparing model outcomes with observational data from weather stations. Coefficients of determination (R2), bias, and root-mean-square error (RMSE) were used to evaluate models and covariates. The elevation could effectively justify the changes in bioclimatic factors related to temperature and precipitation. All three models could downscale the mean annual temperature data with similar R2, RMSE, and bias values. The MWR had the best performance and highest accuracy in downscaling annual precipitation (R2=0.70; RMSE=123.44). In general, the two nonparametric models, i.e. MWR and NPMR, can be reliably used for the downscaling of bioclimatic variables which have wide applications in species distribution modeling.