Bias Values Research Articles

Characteristics of attentional bias in adolescents with major depressive disorders: differentiating the impact of anxious distress specifier.

No consistent conclusion has been reached regarding the attentional bias characteristics of adolescents with major depressive disorders (MDD), and unexamined co-occurring anxiety distress may contribute to this inconsistency. We enrolled 50 MDD adolescents with anxiety distress, 47 MDD adolescents without anxiety distress and 48 healthy adolescents. We measured attentional bias using a point-probe paradigm during a negative-neutral emotional face task. Reaction time, correct response rate and attentional bias value were measured. MDD adolescents did not show a negative attentional bias; MDD adolescents with anxiety distress exhibited longer reaction time for negative and neutral stimuli, lower correct response rate for negative stimuli. Hamilton Anxiety Scale scores were positively correlated with reaction time, negatively correlated with correct response rate, and not significantly correlated with attentional bias value. The cross-sectional design hinders causal attribution, and positive emotional faces were not included in our paradigm. Negative attentional bias is not a stable cognitive trait in adolescents with MDD, and avoidance or difficulty in disengaging attention from negative emotional stimuli may be the attentional bias characteristic of MDD adolescents with anxiety distress.

Nlmixr2 Versus NONMEM: An Evaluation of Maximum A Posteriori Bayesian Estimates Following External Evaluation of Gentamicin and Tobramycin Population Pharmacokinetic Models.

The objective of this project is to compare the results of the same study carried out on NONMEM and nlmixr2. This analysis consists of evaluating previously published population pharmacokinetic models of gentamicin and tobramycin in our population of interest with sparse concentrations. A literature review was performed to determine the gentamicin and tobramycin models in critically ill adult patients. In parallel, gentamicin and tobramycin dosing data, information on the treatment, the patient, and the bacteria were collected retrospectively in 2 Quebec establishments. The external evaluations were previously performed using NONMEM Version 7.5. Model equations were rewritten with R, and external evaluations were performed using nlmixr2. Predictive performance was assessed based on the estimation of bias and imprecision of the prediction error for maximum a posteriori (MAP) Bayesian PK parameter and observed concentrations. Comparison between nlmixr2 and NONMEM was performed on 4 gentamicin and 3 tobramycin population pharmacokinetic models. Compared to NONMEM, for gentamicin and tobramycin clearance and central volume of distribution, nlmixr2 produced individual pharmacokinetic parameters with bias values ranging from -32.5% to 5.67% and imprecision values ranging from 6.33% to 32.5%. Despite these differences, population bias and imprecision for sparse concentrations were low and ranged from 0% to 5.3% and 0.2% to 6.5%, respectively. The external evaluations performed with both software packages resulted in the same interpretation in terms of population predictive performance for all 7 models. Nlmxir2 showed comparable predictive performance with NONMEM with sparse concentrations that are, at most, sampled twice within a single dose administration (peak and trough).

Robust Gaussian process regression based on bias trimming

This paper presents a new robust Gaussian process regression (GPR) algorithm based on identifying and trimming outliers, and it can reduce the computation time and improve the accuracy compared with other robust algorithms. Currently, most existing robust GPR approaches model noise with heavy-tailed distributions that are less sensitive to outliers, such as the Laplace and Student-t distributions. However, a model that is designed this way will be analytically intractable, and the computationally expensive Bayesian approximation method needs to be adopted to obtain the posterior distribution. In our method, the deviation between normal points and outliers is modeled as a bias value, so the non-Gaussian likelihood approach, which consumes substantial time, is not necessary. Nevertheless, the decrease in calculation time is accompanied by a decrease in accuracy. To solve this problem, the trimming algorithm with an adaptive threshold is adopted to improve the prediction precision. Therefore, the proposed approach is simple but very computationally attractive. We conducted various simulation experiments to numerically evaluate the model with different outlier ratios and noise levels. We compared the approach with existing robust Gaussian process methods and other kinds of anomaly detection algorithms to demonstrate the excellent performance of the proposed method. Finally, industrial datasets are used to verify the method, and the results illustrate that it can significantly improve the practicality of the measurement system.

Comparison of the application of HBV and HEC-HMS hydrology models for accessing climate change in the upper Citarum Watershed, Indonesia

Hydrologically, the upper Citarum watershed was critically damaged. This study propose to compare HBV and HEC-HMS model performance for estimating discharge in upper Citarum watershed. Besides, this study wants to know changes in discharge in the future as a result of climate changes. Discharge simulation was carried out with the HBV and HEC HMS models in the period 2006-2007 as calibration and 2008 as validation. Meanwhile, future discharge changes are calculated in 2045 based on climate model projections output from CORDEX namely MPI, CNRM, EcEarth, and CSIRO. Model performance is calculated based on the value of statistical bias, NSE, and correlation. The results showed that HEC-HMS model has bias, NSE, and corellation value of 6.33, 0.57, and 0.8. Whereas, the HBV values are 3.67, 0.61, and 0.76. Therefore, based on NSE and bias value, the HBV model performs slightly better than the HEC-HMS in upper Citarum watershed. There are an increase in the daily discharge by RCP4.5 scenario. In contrast, the daily discharge by RCP8.5 decreased in upper Citarum.

Evaluation of two commercial broth microdilution systems for dalbavancin susceptibility testing of methicillin-resistant Staphylococcus aureus and other resistant Gram-positive cocci

ObjectivesThis study aims to evaluate two commercial broth microdilution (BMD) systems, E1-185-100 (Merlin) and FDANDPF (ThermoFisher), for dalbavancin susceptibility testing in comparison with reference BMD assay. MethodsStudy collection was composed of 200 non-replicate multidrug-resistant Gram-positive cocci of clinical origin, including 180 methicillin-resistant Staphylococcus aureus, 10 vancomycin-resistant enterococci, seven linezolid-resistant Staphylococcus epidermidis, and three methicillin-resistant coagulase-negative staphylococci. S. aureus ATCC 29213 and Enterococcus faecalis ATCC 29212 reference strains were also included as controls. Testing was performed according to the ISO 20776-1 standard, starting from the same bacterial inoculum, and results were compared according to the ISO 20776-2 standard. ResultsReference BMD showed that 92.6% (187/202) of the strains were susceptible to dalbavancin, whereas few staphylococci and all VanA-producing enterococci showed a resistant phenotype. In comparison with the reference method, Category Agreement and Essential Agreement were 98% (198/202; 95% CI, 95.4–99.3%) and 98% (198/202; 95% CI, 95.4–99.3%) for both Merlin and ThermoFisher panels. A few false susceptibilities were observed, for both commercial systems, with dalbavancin-resistant staphylococci. BIAS values of 11% and 3% were calculated for the Merlin and ThermoFisher systems, respectively. DiscussionThis study, reporting the first evaluation of the two commercially available BMD assays for dalbavancin susceptibility testing, revealed an overall good correlation with reference BMD, although with some underestimation tendency of MIC values by both commercial systems. Further studies involving a higher number of resistant isolates will be necessary to better evaluate this issue.

Quantitative measurement of mammographic density in breast-tissue explants using portable NMR: Precision and accuracy.

Single-sided portable NMR (pNMR) has previously been demonstrated to be suitable for quantification of mammographic density (MD) in excised breast tissue samples. Here we investigate the precision and accuracy of pNMR measurements of MD ex vivo as compared with the gold standards. Forty-five breast-tissue explants from 9 prophylactic mastectomy patients were measured. The relative tissue water content was taken as the MD-equivalent quantity. In each sample, the water content was measured using some combination of three pNMR techniques (apparent T2, diffusion, and T1 measurements) and two gold-standard techniques (computed microtomography [μCT] and hematoxylin and eosin [H&E] histology). Pairwise correlation plots and Bland-Altman analysis were used to quantify the degree of agreement between pNMR techniques and the gold standards. Relative water content measured from both apparent T2 relaxation spectra, and diffusion decays exhibited strong correlation with the H&E and μCT results. Bland-Altman analysis yielded average bias values of -0.4, -2.6, 2.6, and 2.8 water percentage points (pp) and 95% confidence intervals of 13.1, 7.5, 11.2, and 11.8 pp for the H&E - T2, μCT - T2, H&E - diffusion, and μCT - diffusion comparison pairs, respectively. T1-based measurements were found to be less reliable, with the Bland-Altman confidence intervals of 27.7 and 33.0 pp when compared with H&E and μCT, respectively. Apparent T2-based and diffusion-based pNMR measurements enable quantification of MD in breast-tissue explants with the precision of approximately 10 pp and accuracy of approximately 3 pp or better, making pNMR a promising measurement modality for radiation-free quantification of MD.

Novel adaptive quantization methodology for 8-bit floating-point DNN training

There is a high energy cost associated with training Deep Neural Networks (DNNs). Off-chip memory access contributes a major portion to the overall energy consumption. Reduction in the number of off-chip memory transactions can be achieved by quantizing the data words to low data bit-width (E.g., 8-bit). However, low-bit-width data formats suffer from a limited dynamic range, resulting in reduced accuracy. In this paper, a novel 8-bit Floating Point (FP8) data format quantized DNN training methodology is presented, which adapts to the required dynamic range on-the-fly. Our methodology relies on varying the bias values of FP8 format to fit the dynamic range to the required range of DNN parameters and input feature maps. The range fitting during the training is adaptively performed by an online statistical analysis hardware unit without stalling the computation units or its data accesses. Our approach is compatible with any DNN compute cores without any major modifications to the architecture. We propose to integrate the new FP8 quantization unit in the memory controller. The FP32 data from the compute core are converted to FP8 in the memory controller before writing to the DRAM and converted back after reading the data from DRAM. Our results show that the DRAM access energy is reduced by 3.07×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} while using an 8-bit data format instead of using 32-bit. The accuracy loss of the proposed methodology with 8-bit quantized training is ≈1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 1\\%$$\\end{document} for various networks with image and natural language processing datasets.

Transmitter design and AE–AR region characterization for NOMA-SLIPT UWOC systems with uniformly distributed message and Imp-SIC

This paper investigates simultaneous lightwave and information transfer (SLIPT) based non-orthogonal multiple access (NOMA) in underwater optical wireless communication (UWOC) systems. At the user signal separation (SS) based SLIPT scheme is employed. Imperfect successive interference cancellation (SIC) at the near user is considered. Firstly, we derive the maximum allowable electrical power for message symbols at the transmitter, ensuring input to the laser diode lies within the linear region and. A novel linearity-constrained transmitter design is proposed for the uniformly distributed message, followed by calculations of instantaneous data rate and instantaneous harvested energy for both users. Then, closed-form expressions for average data rates (AR) and average energy harvested (AE) under weak turbulence-induced fading channels using Holtzman’s approximation are derived. The tightness of the approximation is confirmed by Monte-Carlo simulations. AE–AR boundaries for both users show an initial rise and subsequent decline in data rates with increased harvested energy, owing to higher bias values restricting transmitter power. The study also examines trade-offs between AE and AR across various system parameters including beam divergence angles, imperfection of SIC, power allocation coefficients, and water types, is conducted. We observe shifting and shrinking/expansion of the AE–AR region depending upon the variation of the various system parameters. We also show that the discussed SS scheme is general and encompasses time-switching (TS), and power-splitting (PS) schemes as special cases. We also briefly discuss as to how the developed framework could be extended to more than two users. In summary, the overall effective energy consumption deduces owing to the adoption of SLIPT at the users, making the considered setup more energy-efficient.

A Preliminary Investigation about the Influence of WIMU PROTM Location on Heart Rate Accuracy: A Comparative Study in Cycle Ergometer.

Technological development has boosted the use of multi-sensor devices to monitor athletes' performance, but the location and connectivity between devices have been shown to affect data reliability. This preliminary study aimed to determine whether the placement of a multi-sensor device (WIMU PROTM) could affect the heart rate signal reception (GARMINTM chest strap) and, therefore, data accuracy. Thirty-two physical education students (20 men and 12 women) performed 20 min of exercise in a cycle ergometer based on the warm-up of the Function Threshold Power 20 test in laboratory conditions, carrying two WIMU PROTM devices (Back: inter-scapula; Bicycle: bicycle's handlebar-20 cm from the chest) and two GARMINTM chest straps. A one-dimensional statistical parametric mapping test found full agreement between the two situations (inter-scapula vs. bicycle's handlebar). Excellent intra-class correlation values were obtained during the warm-up (ICC = 0.99, [1.00-1.00], p < 0.001), the time trial test (ICC = 0.99, [1.00-1.00], p < 0.001) and the cool-down (ICC = 0.99, [1.00-1.00], p < 0.001). The Bland-Altman plots confirmed the total agreement with a bias value of 0.00 ± 0.1 bpm. The interscapular back placement of the WIMU PROTM device does not affect heart rate measurement accuracy with a GARMINTM chest strap during cycling exercise in laboratory conditions.

Diversity and genetic structure of freshwater shark Wallago attu: an emerging species of commercial interest.

Pakistan has natural freshwater resources acting as a hotspot for diverse fish fauna. However, this aquatic fauna is declining at an alarming rate due to over-exploitation, habitat degradation, water pollution, climate change, and certain anthropogenic activities. The freshwater shark, Wallago attu, is a popular edible catfish inhabiting these freshwater ecosystems. Habitat degradation, overfishing, and human activities are heavily impacting the natural population of this species. So, sound knowledge about its population structure is necessary for its proper management in natural waters. The current study involves utilizing two mtDNA markers (COI, Cytb) to assess the genetic structure and differentiation among W. attu populations of Pakistani Rivers. Genetic variability analysis indicated a high haplotype (0.343 ± 0.046-0.870 ± 0.023) and low nucleotide diversity (0.0024 ± 0.012-0.0038 ± 0.018) among single and combined gene sequences, respectively. Overall, River Indus was populated with more diverse fauna of Wallago attu as compared to River Chenab and River Ravi. Population pairwise, Fst values (0.40-0.61) were found to be significantly different (p < 0.01) among three Riverine populations based upon combined gene sequences. The gene flow for the combined gene (COI + Cytb) dataset among three populations was less than 1.0. The transition/transversion bias value R (0.58) was calculated for testing of neutral evolution, and it declared low genetic polymorphism among natural riverine populations of Wallago attu. The current study's findings would be meaningful in planning the management and conservation of this economically important catfish in future.

Implementasi Penggunaan Generalisasi Thinning Process pada Penduga Fungsi Ragam Proses Poisson Periodik Majemuk

This article implements the thinning process algorithm, which has been generalized for estimators of compound periodic Poisson processes. The use of generalizations in the algorithm has been prepared with a linear trend in the periodic elements. This research aims to discuss estimators of the variance function. The method used in this research is the simulation method. Simulation results using a generalized algorithm thinning process show that in the case of a limited observation time interval, some estimators are good enough to approach the actual value. As the value of n increases, the simulated value of the estimator moves towards the predicted value. This is following the lemmas, theorems, and consequences that have been discussed. It was also found that several estimators were quite slow. This results in the movement of the bias, variance, and MSE values of the estimators being slow, even though they are moving towards 0. So that further modifications can be made to the model being studied.

Saltwater Intrusion at Kapuas Besar Estuary Using 1-Dimensional Analytical Method

The Kapuas Besar Estuary in West Kalimantan is vital for both the local community’s ecological and economic well-being. One major issue faced in this estuary is saltwater intrusion. This study aims to investigate saltwater intrusion variation using a one-dimensional (1D) analytical model. The model input data includes estuary geometry, tides, salinity, and river discharge. Simulation results demonstrate the effectiveness of the 1D analytical model in predicting saltwater distance based on the Root Mean Square Error (RMSE), Percent Bias (PBIAS), and Nash-Sutcliffe Efficiency (NSE) values between the analytical model results and observed data ranging from 0.82-1.70 psu, 1.64-2.85%, and 0.96-0.99 respectively. The model proves applicable during the May-October period, subject to the condition that the river discharge remains below 3,200 m3/s. However, during November-April the model tends to underestimate compared to actual distance. Model results show saltwater intrusion increasing from December to June by 49 km, then decreasing from July to November by 38 km. The minimum distance of saltwater intrusion occurred in May (1 km from the estuary mouth) when river discharge of 8,853.82 m3/s. Conversely, the maximum saltwater intrusion distance was observed in June (71 km from the estuary mouth) with a river discharge of 1,034.55 m3/s.

The contribution of residential wood combustion to the PM2.5 concentrations in the Helsinki metropolitan area

Abstract. This article has investigated the contribution of residential wood combustion (RWC) to the fine particulate matter (PM2.5) concentrations in the Helsinki metropolitan area (HMA) for 6 years, from 2009 to 2014. We have used the PM2.5 concentrations measured at eight air quality measurement stations. The dispersion of emissions on an urban scale was evaluated with multiple-source Gaussian dispersion models, UDM-FMI (urban dispersion model of the Finnish Meteorological Institute) and CAR-FMI (Contaminants in the Air from a Road model of the Finnish Meteorological Institute), and on a regional scale using the chemical transport model SILAM (System for Integrated modeLling of Atmospheric coMposition). The overall agreement of the predicted concentrations with measurements of PM2.5 was good or fairly good for all stations and years; e.g. at the permanent residential station the daily average values of the index of agreement ranged from 0.69 to 0.81, and the fractional bias values ranged from −0.08 to 0.11, for the considered 6 years. Both the measured and predicted daily averaged concentrations showed increasing trends towards the lower-temperature values. The highest predicted annual averaged concentrations in the region occurred in the vicinity of major roads and streets and in the suburban residential areas to the northwest, north, and northeast of the city centre. The average concentrations of PM2.5 attributed to RWC in winter were up to 10- or 15-fold, compared to the corresponding concentrations in summer. During the considered 6-yearly period, the spatially highest predicted fractions of RWC of the annual PM2.5 concentrations ranged from 12 % to 14 %. In winter, the corresponding contributions ranged from 16 % to 21 %. The RWC contribution was higher than the corresponding urban vehicular traffic contribution at all the residential stations during all years. The study has highlighted new research needs for the future, in particular (i) the modelling of the RWC emissions that would be explicitly based on the actual ambient temperatures and (ii) the modelling of the impacts of the most important holiday periods on the emissions from RWC.

Identifying the Learning Style of Students Using Machine Learning Techniques: An Approach of Felder Silverman Learning Style Model (FSLSM)

Identification of the learning style of the students in the teaching and learning environment plays a significant importance in improving both teaching and learning perspectives. The intension of the research was to investigate about applying the Machine Learning Techniques for identification of the Learning style of the students in online learning environment based on the Felder Silverman Learning Style (FSLSM) identification model. The significance of this experiment is that the proposed methodology considers the combination of access frequency (f) of course materials and total time (T) students spent on each course activity. For data collection process, it was designed reusable plugin for the Moodle for time tracking. Real-time dataset was prepared using three course modules designed according to the features of FSLSM model and the features for analyzing the students according to the FSLSM, it was selected seven criteria, and the features were validated using Pearson Correlation Coefficient method. These course modules were enrolled 150 students per each module. Once the data set was prepared, the data set was preprocessed and applied Five Supervised Classification Machine learning algorithms as Decision Tree, Logistic Regression, Random Forest, Support Vector Machine and K-Nearest Neighbors algorithm. The models were evaluated using Accuracy, Precision, Recall and F1 values. Over the five algorithms the Decision Tree classifier algorithm performed with best average accuracy with 93.5% for Input, 86% for Perception, 89.5 for Processing and 94% for Understanding dimension. The models were validated using the K-fold Cross validation and Standard Deviation values. Mean Squared Error, Bias and Variance values were considered the evaluation of underfitting or overfitting context of the model. To parameter optimization, the Grid Search Methodology was applied to find the best combination of criterion for the model. Finally, an application was developed for Identifying the Learning Style of the Students using the designed Machine learning model. The Consistency of the ML Model based on the Decision Tree classifier algorithm were evaluated using the results generated through developed application and the results suggested that consistency for taught machine learning algorithms is often between 85% to 95%, which is an acceptable range. The results generated by the application for identification of the learning style suggested the combination of learning style for particular students sample as Global-Mild, Visual- Strong, Sensing- Moderate and Reflective-Strong. Identification of these combination of learning style assist for teachers by giving an insight which components of the learning contents should be improved in course designing process.

High-Resolution Gravity Measurements on Board an Autonomous Underwater Vehicle: Data Reduction and Accuracy Assessment

Gravity on Earth is of great interest in geodesy, geophysics, and natural resource exploration. Ship-based gravimeters are a widely used instrument for the collection of surface gravity field data in marine regions. However, due to the considerable distance from the sea surface to the seafloor, the spatial resolution of surface gravity data collected from ships is often insufficient to image the detail of seafloor geological structures and to explore offshore natural minerals. Therefore, the development of a mobile underwater gravimetry system is necessary. The GraviMob gravimeter, developed for a moving underwater platform by Geo-Ocean (UMR 6538 CNRS-Ifremer-UBO-UBS), GeF (UR4630, Cnam) and MAPPEM Geophysics, has been tested over the last few years. In this study, we report on the high-resolution gravity measurements from the GraviMob system mounted on an Autonomous Underwater Vehicle, which can measure at depths of up to several kilometres. The dedicated GraviMob underwater gravity measurements were conducted in the Mediterranean Sea in March 2016, with a total of 26 underwater measurement profiles. All these measurement profiles were processed and validated. In a first step, the GraviMob gravity measurements were corrected for temperature based on a linear relationship between temperature and gravity differences. Through repeated profiles, we acquired GraviMob gravity measurements with an estimated error varying from 0.8 to 2.6 mGal with standard deviation after applying the proposed temperature correction. In a second step, the shipborne gravity data were downward continued to the measurement depth to validate the GraviMob measurements. Comparisons between the corrected GraviMob gravity anomalies and downward continued surface shipborne gravity data revealed a standard deviation varying from 0.8 to 3.2 mGal and a mean bias value varying from −0.6 to 0.6 mGal. These results highlight the great potential of the GraviMob system in measuring underwater gravity.

Retrieval of Tree Height Percentiles over Rugged Mountain Areas via Target Response Waveform of Satellite Lidar

The retrieval of tree height percentiles from satellite lidar waveforms observed over mountainous areas is greatly challenging due to the broadening and overlapping of the ground return and vegetation return. To accurately represent the shape distributions of the vegetation and ground returns, the target response waveform (TRW) is resolved using a Richardson–Lucy deconvolution algorithm with adaptive iteration. Meanwhile, the ground return is identified as the TRW component within a 4.6 m ground signal extent above the end point of the TRW. Based on the cumulative TRW distribution, the height metrics of the energy percentiles of 25%, 50%, 75%, and 95% are determined using their vertical distances relative to the ground elevation in this study. To validate the proposed algorithm, we select the received waveforms of the Global Ecosystem Dynamics Investigation (GEDI) lidar over the Pahvant Mountains of central Utah, USA. The results reveal that the resolved TRWs closely resemble the actual target response waveforms from the coincident airborne lidar data, with the mean values of the coefficient of correlation, total bias, and root-mean-square error (RMSE) taking values of 0.92, 0.0813, and 0.0016, respectively. In addition, the accuracies of the derived height percentiles from the proposed algorithm are greatly improved compared with the conventional Gaussian decomposition method and the slope-adaptive waveform metrics method. The mean bias and RMSE values decrease by the mean values of 1.68 m and 2.32 m and 1.96 m and 2.72 m, respectively. This demonstrates that the proposed algorithm can eliminate the broadening and overlapping of the ground return and vegetation return and presents good potential in the extraction of forest structure parameters over rugged mountainous areas.

Assessment of snow simulation using Noah-MP land surface model forced by various precipitation sources in the Central Tianshan Mountains, Central Asia

Accurate mountainous snow estimation is paramount for hydrological processes and water resources estimation in arid regions. Using Land surface models (LSMs) is a practical numerical approach for snow estimation in a complex orography region such as the Tianshan Mountains. However, the bias of precipitation forcing is a major source of uncertainty for snow simulation over scarce-data regions. This study evaluated the performance of 4 km snow simulations using the Noah-MP LSM driven by eight precipitation sources. They are retrieved from non-model products and climate model simulations during the 2018-2019 cold season in the Central Tianshan Mountains (CTS) region. Multi-source validation datasets (in-situ observation, field snow pits, and remote sensing products) have been used for evaluation and uncertainty estimation. The results showed that the difference in precipitation amount significantly affected the snowpack simulation performance. Compared with non-model products (Global Land Data Assimilation System (GLDAS), Global Precipitation Measurement (GPM), and Gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP)), the cold season precipitation from climate model simulations exhibited a better performance overall in the high-elevation regions (elevation > 1000 m) evaluated by in-situ observations. In addition, the 4 km convection-permitting modeling (CPM) precipitation (WRF-Morrison and WRF-WSM6) showed higher accuracy (RMSE: 23.48 mm/season and 18.94 mm/season, respectively) than gray-zone resolution simulations and ERA5-land driven runs in the high-elevation regions. The snow depth simulation driven by WRF-Morrison precipitation had the second smallest RMSE (4.67 cm/day) and lowest bias (-0.74 cm/day) value in the high-elevation regions compared with in-situ observation. Meanwhile, CPM precipitation-driven runs exhibited the smallest RMSE value based on the assessment of snow field pits. In addition, the CPM-driven simulation achieved the closest match to the elevation-based distribution of snow cover days in regions with elevation over 1000 m and duration over 60 days compared to the MODIS product. The findings of this study highlight that CPM with proper parameterization configurations has added values in producing realistic topographic precipitation for snow modeling using LSMs over data-scarcity mountainous areas.

Racial effects on masimo pulse oximetry: impact of low perfusion index

Purpose: Evaluate the SpO2-SaO2 difference between Black and White volunteer subjects having a low perfusion index (Pi) compared to those having a normal Pi. Methods: The Pi data were abstracted from electronic files collected on 7183 paired SpO2-SaO2 samples (3201 Black and 3982 White) from a recently reported desaturation study of 75 subjects (39 Black and 36 White) where SaO2 values were sequentially decreased from 100 to 70%. The Pi values from that dataset were divided into two groups (Pi ≤ 1 or Pi > 1) for analysis. A Pi value ≤ 1 was considered “low perfusion” and a Pi value > 1 was considered “normal perfusion”. Statistical calculations included values of bias (mean difference of SpO2-SaO2), precision (standard deviation of the difference), and accuracy (root-mean-square error [ARMS]). During conditions of low perfusion (Pi ≤ 1, range [0.1 to 1]), overall bias and precision were + 0.48% ± 1.59%, while bias and precision were + 0.19 ± 1.53%, and + 0.91 ± 1.57%, for Black and White subjects, respectively. Results: During normal perfusion (Pi > 1, range [1 to 12]), overall bias and precision were + 0.18% ± 1.34%, while bias and precision were -0.26 ± 1.37%, and − 0.12 ± 1.31%, for Black and White subjects, respectively. ARMS was 1.37% in all subjects with normal perfusion and 1.64% in all subjects with low perfusion. Conclusion: Masimo SET® pulse oximeters with RD SET® sensors are accurate for individuals of both Black and White races when Pi is normal, as well as during conditions when Pi is low. The ARMS for all conditions studied is well within FDA standards. This study was conducted in healthy volunteers during well-controlled laboratory desaturations, and results could vary under certain challenging clinical conditions.

Magnetic Properties and THz Emission from Co/CoO/Pt and Ni/NiO/Pt Trilayers.

THz radiation emitted by ferromagnetic/non-magnetic bilayers is a new emergent field in ultra-fast spin physics phenomena with a lot of potential for technological applications in the terahertz (THz) region of the electromagnetic spectrum. The role of antiferromagnetic layers in the THz emission process is being heavily investigated at the moment. In this work, we fabricate trilayers in the form of Co/CoO/Pt and Ni/NiO/Pt with the aim of studying the magnetic properties and probing the role of very thin antiferromagnetic interlayers like NiO and CoO in transporting ultrafast spin current. First, we reveal the static magnetic properties of the samples by using temperature-dependent Squid magnetometry and then we quantify the dynamic properties with the help of ferromagnetic resonance spectroscopy. We show magnetization reversal that has large exchange bias values and we extract enhanced damping values for the trilayers. THz time-domain spectroscopy examines the influence of the antiferromagnetic interlayer in the THz emission, showing that the NiO interlayer in particular is able to transport spin current.

NEURAL NETWORK BASED ESTIMATION OF ECOLOGICAL FOOTPRINT IN TURKEY WITH ECONOMIC AND ENVIRONMENTAL FACTORS

The globe is currently confronted with escalating ecological challenges as a result of the exponential growth of the population, the expansion of industrial activities, the rapid urbanization process, and the heightened levels of consumption. Due to the prevailing ecological issues, there has been an unbridled surge in the demand for natural resources. The emergence of the Ecological Footprint was driven by the observable escalation of environmental degradation, with the primary objective of promoting sustainability. In this study, an Artificial Neural Network (ANN) model was developed for the estimation of ecological footprint. In the first stage, the variables affecting the ecological footprint were determined. It was concluded that there are strong relationships between the independent variables of Gross Domestic Product (GDP), KOF Globalization Index (KOFGI), and Natural Resource Rent (NRR) and the dependent variable of ecological footprint. In the light of this information, the data of GDP (% annual increase), NRR (% GDP) and KOF Globalization Index and ecological footprint data of Turkey between 1970 and 2016 were used to present the ANN model. Feed-Forward Backprop Method, which is one of the multi-layer network models, was applied in the modeling of the ANN. Levenberg-Marquardt optimization, which updates the weight and bias values of the network, was used as the network training function. The study's results suggest that there was a strong correlation between the whole dataset and the model's accuracy, with a close match of 99.316%. Based on the findings, it can be deduced that the developed artificial neural network (ANN) model has a significant level of precision in forecasting the Ecological Footprint.