Quality Estimation Research Articles

Dietary Diversity and Its Association with Diet Quality and Health Status of European Children, Adolescents, and Adults: Results from the I.Family Study.

Dietary diversity (DD) plays a crucial role in fostering high-quality diets, but its association with health outcomes, particularly body adiposity and non-communicable diseases (NCDs), is inconsistent. This may be due to a lack of a standardized method for estimating DD. Our study investigates the association between two DD indices, namely the dietary diversity score (DDS) and food variety score (FVS), and anthropometric measures, biochemical parameters, and diet quality in a large population sample from the I.Family study across research centers in eight European countries. In our cross-sectional analysis of 3035 participants, DDSs varied among countries, with a higher prevalence in the third DDS tertile among those with higher education. DDS showed a positive association with diet quality across all age groups. Higher DDS tertile individuals showed increased fiber, fruit, and vegetable intake, greater meal frequency, and lower ultra-processed food consumption. No relevant biochemical differences were observed across DDS tertiles, and a higher DDS was associated with lower overweight/obesity prevalence only in adults. No significant associations were found with FVS. Our findings emphasize the need to consider food groups for a more accurate estimation of diet quality. This aligns with studies suggesting DDS alone is not an independent risk factor for obesity in children and adolescents. Public health programs should prioritize food diversity to promote improved nutrition and overall well-being in communities.

Mercury-induced excitotoxicity in presynaptic brain nerve terminals: modulatory effects of carbonaceous airborne particulate simulants.

Multipollutant approach is a breakthrough in up-to-date environmental quality and health risk estimation. Both mercury and carbonaceous air particulate are hazardous neurotoxicants. Here, the ability of carbonaceous air particulate simulants, i.e. carbon dots obtained by heating of organics, and nanodiamonds, to influence Hg2+-induced neurotoxicity was monitored using biological system, i.e. presynaptic rat cortex nerve terminals. Using HgCl2 and classical reducing/chelating agents, an adequate synaptic parameter, i.e. the extracellular level of key excitatory neurotransmitter L-[14C]glutamate, was selected for further analysis. HgCl2 starting from 5µM caused an acute and concentration-dependent increase in the extracellular L-[14C]glutamate level in nerve terminals. Combined application of Hg2+ and carbon dots from heating of citric acid/urea showed that this simulant was able to mitigate in an acute manner excitotoxic Hg2+-induced increase in the extracellular L-[14C]glutamate level in nerve terminals by 37%. These carbon dots and Hg2+ acted as a complex in nerve terminals that was confirmed with fluorimetric data on Hg2+-induced changes in their spectroscopic features. Nanodiamonds and carbon dots from β-alanine were not able to mitigate a Hg2+-induced increase in the extracellular L-[14C]glutamate level in nerve terminals. Developed approach can be applicable for monitoring capability of different particles/compounds to have Hg2+-chelating signs in the biological systems. Therefore, among testing simulants, the only carbon dots from citric acid/urea were able to mitigate acute Hg2+-induced neurotoxicity in nerve terminals, thereby showing a variety of effects of carbonaceous airborne particulate in situ and its potential to interfere and modulate Hg2+-associated health hazard.

A Framework for the Estimation of Air Quality by Applying Meteorological Images: Colours-of-the-Wind (COLD)

This paper presents a new framework, “colours-of-the-wind” (COLD), which is designed to estimate air quality based on images from meteorological cameras, data analytics techniques, and the application of deep learning. Existing air quality estimation systems mainly rely on physical monitoring stations, which are limited by the monitoring stations’ physical constraints. Instead of collecting data from environmental monitoring stations, COLD collects air quality data from meteorological cameras. This approach can collect data from any location where a camera could capture a reliable image, which is otherwise not collectable by the physical environmental monitoring station(s). This approach can also avoid bias due to the location of data collection. The system is evaluated by building a prototype based on the Air Quality Health Index from the Hong Kong Observatory. This is one of the unique data-analytic frameworks based on such air quality measurement. The COLD’s air quality estimation is also based on AQHI, the first machine learning-based estimation framework that generates AQHI as the proposed output. Experimental results suggest that the approach adopted by the COLD prototype is feasible and has some promising outcomes. The results also suggest possible parameters for the CNN model used for the training and analyses of the images.

Denoising diffusion model as handwritten digit generator

The diffusion model is the process by which variables are introduced and propagated through a population. The equation can be applied to any physical system that exhibits such processes through time. Generally speaking, diffusion occurs when a physical system is connected to another by a small number of interconnected points; the interconnected points' overall connectivity can be represented by a network. The diffusion model is a mathematically defined process used to analyse the movement of particles from a region of high concentration to that of low concentration. Based on the diffusion coefficient and polarization, several studies have established that the equilibrium port-to-port distance can be calculated. The diffusion model is useful for solving the problem of noise in imaging systems, especially when an object has similar properties in all directions. When discussing diffusion, it is essential to refer to the diffusion coefficient. The literatures find denoising diffusion model to involves the process where a pixel value is estimated based on values at surrounding pixels. On the other hand, a forward process is passing through an image and replacing pixels based on their quality estimates. Reconstruction involves reconstructing an image from its components, including the subsamples and low-quality components. This model achieves satisfactory performance on digital number image generation.

MS in the UAE: Population Estimates of Anxiety, Depression and Health-Related Quality of Life

MS in the UAE: Population Estimates of Anxiety, Depression and Health-Related Quality of Life

Abstract PR019: Evaluating the role of ambient air pollution in racial disparities of colorectal cancer incidence and survival in metropolitan Detroit

Abstract Colorectal cancer (CRC) is the third most diagnosed cancer in the United States and the third leading cause of cancer-related deaths. Moreover, Non-Hispanic Black (NHB) individuals have the highest rates of CRC incidence and mortality in the United States. We have become increasingly aware of the potential impact of social determinants of health (SDOH) on colorectal physiology and carcinogenesis. Other than individual and neighborhood level socioeconomic status (SES), neighborhood quality can also affect an individual’s risk of exposure to harmful environmental contaminants including ambient air pollution. Moreover, as NHB individuals are more likely to live in areas of lower area-level SES they experience these effects even further. We sought to understand whether higher exposure rates to fine particulate matter (PM2.5) played a role in racial differences in CRC incidence and survival in metropolitan Detroit. To do this, we obtained data from the Environmental Protection Agency (EPA) Environmental Justice Mapping and Screening Tool through the Georgetown University Environmental Impact Data Collective (EIDC) and patient data from the Metropolitan Detroit Cancer Surveillance System (MDCSS) registry. Incidence rates were calculated using population data from the American Community Survey. Associations between PM2.5 exposure and CRC incidence were estimated using Poisson modeling. Data were obtained for NHB (n=3778) and Non-Hispanic White (NHW) (n=9727) individuals diagnosed with invasive CRC between 2010 and 2019 in Wayne, Oakland, and Macomb counties, including their geocoded census tract at diagnosis as well as demographic and clinical features. Vital status was reported as months from the date of diagnosis to either death or last contact. PM2.5 metrics were converted to the national percentile of PM2.5 in the air utilizing the Bayesian space-time downscaling fusion model for derived estimates of air quality at the census tract level. Survival was assessed using Cox Proportional Hazards modeling. We found that an increase in the PM2.5 percentile increased the risk for CRC overall for NHB and NHW patients (Relative Risk [RR] = 1.80, 95% Confidence Interval [CI] = 1.55 - 2.08, p-value <0.0001). This association was even stronger among NHB patients (RR = 7.94, 95% CI = 5.45 - 11.52, p-value < 0.0001), but was not statistically significant among NHW patients (RR = 1.12, 95% CI = 0.95 – 1.32, p-value = 0.16). PM2.5 exposure was not associated with survival overall or stratified by race. We conclude that ambient air pollution, particularly PM2.5, increases the risk of CRC cancer and disproportionately affects NHB patients but may not play a role in mortality risk. These results highlight the importance of environmental justice research in biomedical research and the need for a better understanding of the etiology of CRC within and among differing genetic ancestries and races by evaluating the genetic and epigenetic differences in patient tumor samples. Citation Format: Natalie G. Snider, Theresa Hastert, Ed Peters, Elena M. Stoffel, Laura Rozek, Ann Schwartz, Kristen Purrington. Evaluating the role of ambient air pollution in racial disparities of colorectal cancer incidence and survival in metropolitan Detroit [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr PR019.

Harnessing data augmentation to quantify uncertainty in the early estimation of single-photon source quality

Novel methods for rapidly estimating single-photon source (SPS) quality have been promoted in recent literature to address the expensive and time-consuming nature of experimental validation via intensity interferometry. However, the frequent lack of uncertainty discussions and reproducible details raises concerns about their reliability. This study investigates the use of data augmentation, a machine learning technique, to supplement experimental data with bootstrapped samples and quantify the uncertainty of such estimates. Eight datasets obtained from measurements involving a single InGaAs/GaAs epitaxial quantum dot serve as a proof-of-principle example. Analysis of one of the SPS quality metrics derived from efficient histogram fitting of the synthetic samples, i.e. the probability of multi-photon emission events, reveals significant uncertainty contributed by stochastic variability in the Poisson processes that describe detection rates. Ignoring this source of error risks severe overconfidence in both early quality estimates and claims for state-of-the-art SPS devices. Additionally, this study finds that standard least-squares fitting is comparable to using a Poisson likelihood, and expanding averages show some promise for early estimation. Also, reducing background counts improves fitting accuracy but does not address the Poisson-process variability. Ultimately, data augmentation demonstrates its value in supplementing physical experiments; its benefit here is to emphasise the need for a cautious assessment of SPS quality.

Relationship Between P‐tau217 and Subjective Sleep Quality in MCI and Mild AD. A Preliminary Study

AbstractBackgroundPlasma‐tau217 (ptau217) has recently emerged as a biomarker for Alzheimer’s disease (AD). With sleep changes as an established feature of AD, previous studies suggest a link between sleep and tau pathology. We investigated whether overall subjective estimation of sleep quality is associated with p‐tau217 concentration (pg/ml) in a small sample population of older adults with mild cognitive impairment (MCI) or mild Alzheimer’s disease (AD).MethodWe examined participants (mean age = 69) with MCI (n = 14) and mild AD (n = 2). MCI participants had CDR 0.5 and MoCA scores ranging 17‐28. Mild AD participants had CDR of 1.0 and MoCA scores of 17. pTau217 was measured with a novel validated SIMOA assay, developed by one of us (A.J.) for ALZPath, Inc. The Pittsburgh Sleep Quality Index (PSQI) was used as a subjective measure of sleep quality. Pearson correlation coefficient was used to test the association between ptau217 and the global score of PSQI, and spearman’s rho was used for each of the 7 subscales of the PSQI.ResultThe global score (total score) of the PSQI was found to be significantly correlated with ptau217 concentration, such that poorer sleep was associated with increased ptau217 (r = ‐.587, p<.05). Partial correlations revealed that this correlation remained after controlling for age, sex, and years of education (all p<.05).ConclusionGlobal scores on the PSQI were associated with ptau217 concentration in MCI and mild AD participants, such that poor reported sleep quality was associated with increased plasma tau. Future studies in larger samples and longitudinal investigations using objective sleep data (actigraphs) would serve to confirm these findings.

Ultrafast Cardiac Imaging Using Deep Learning for Speckle-Tracking Echocardiography.

High-quality ultrafast ultrasound imaging is based on coherent compounding from multiple transmissions of plane waves (PW) or diverging waves (DW). However, compounding results in reduced frame rate, as well as destructive interferences from high-velocity tissue motion if motion compensation (MoCo) is not considered. While many studies have recently shown the interest of deep learning for the reconstruction of high-quality static images from PW or DW, its ability to achieve such performance while maintaining the capability of tracking cardiac motion has yet to be assessed. In this article, we addressed such issue by deploying a complex-weighted convolutional neural network (CNN) for image reconstruction and a state-of-the-art speckle-tracking method. The evaluation of this approach was first performed by designing an adapted simulation framework, which provides specific reference data, i.e., high-quality, motion artifact-free cardiac images. The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts. The performance was then further evaluated on nonsimulated, experimental in vitro data, using a spinning disk phantom. This experiment demonstrated that our approach yielded high-quality image reconstruction and motion estimation, under a large range of velocities and outperforms a state-of-the-art MoCo-based approach at high velocities. Our method was finally assessed on in vivo datasets and showed consistent improvement in image quality and motion estimation compared to standard compounding. This demonstrates the feasibility and effectiveness of deep learning reconstruction for ultrafast speckle-tracking echocardiography.

Automatic chest computed tomography image noise quantification using deep learning

PurposeThis study aimed to develop a deep learning (DL) method for noise quantification for clinical chest computed tomography (CT) images without the need for repeated scanning or homogeneous tissue regions. MethodsA comprehensive phantom CT dataset (three dose levels, six reconstruction methods, amounting to 9240 slices) was acquired and used to train a convolutional neural network (CNN) to output an estimate of local image noise standard deviations (SD) from a single CT scan input. The CNN model consisting of seven convolutional layers was trained on the phantom image dataset representing a range of scan parameters and was tested with phantom images acquired in a variety of different scan conditions, as well as publicly available chest CT images to produce clinical noise SD maps. ResultsNoise SD maps predicted by the CNN agreed well with the ground truth both visually and numerically in the phantom dataset (errors of < 5 HU for most scan parameter combinations). In addition, the noise SD estimates obtained from clinical chest CT images were similar to running-average based reference estimates in areas without prominent tissue interfaces. ConclusionsPredicting local noise magnitudes without the need for repeated scans is feasible using DL. Our implementation trained with phantom data was successfully applied to open-source clinical data with heterogeneous tissue borders and textures. We suggest that automatic DL noise mapping from clinical patient images could be used as a tool for objective CT image quality estimation and protocol optimization.

Detect Arbitrary-Shaped Text via Adaptive Thresholding and Localization Quality Estimation

The two-stage scene text detection algorithms based on Mask R-CNN have achieved good performances on multiple challenging benchmarks. However, their effectiveness is degraded due to artificially setting constant thresholds and low localization quality of candidate boxes. In this paper, we present a novel scene text detection method based on Mask R-CNN and the proposed method, named LOAD, proposes adaptive threshold module and localization quality estimation module to address the above two problems. We propose two kinds of adaptive thresholds which are used for the filtering of candidate boxes and the binarization of pixels respectively. We introduce the self-attention mechanism to obtain the global information for generating the adaptive thresholds. Besides, we introduce the localization quality estimation into our model to obtain more accurate candidate boxes for subsequent segmentation. Comparative experiments are conducted on five benchmarks(ICDAR 2015, ICDAR 2017, MSRA-TD500, Total-Text and CTW1500), and the results demonstrate that the proposed method achieves the state-of-the-art performance with an F-measure of 91.0%, 78.7%, 87.4%, 90.6% and 86.0%. We also provide adequate ablation experiments to demonstrate the effectiveness of the proposed components.

АНАЛИЗ ЭПИДЕМИОЛОГИИ ОСНОВНЫХ СТОМАТОЛОГИЧЕСКИХ ЗАБОЛЕВАНИЙ У ЖИТЕЛЕЙ СЕЛЬСКИХ ТЕРРИТОРИЙ

The enlarged literature review concerning an important relevant dental problem related to caries spreading and its intensity, inflammatory periodontal diseases, non carious dental lesions, dentomaxillar abnormalities in the population living in the rural areas is presented.&#x0D; The aim of the study is to analyze the epidemiology of main dental diseases in the population living in the rural areas according to the review of the modern scientific literature for the period since 2013. &#x0D; Methodology. To write this article the analytical work on studying data taken from the special available literature was carried out. The scientific library resourses such as PubMed Central and Elibrary were used. The search of original publications was done using key words to get relevant results. 43 scientific sources were included in the review.&#x0D; Results and conclusion. The modern scientific data concerning incidence and degree of manifestation of key pathologies of oral organs and tissues such as carious process, gingivitis and perodontitis, malocclusion, non carious dental lesions (mainly-hypoplasia, fluorosis) were systematized as well as the estimation of prosthesis quality of teeth and dental sets of teeth in the rural population living both in Russia and abroad was given. The clinical significance of the work is to know a real incidence of dental diseases in the different regions of the country and develop complex preventive programs taking into consideration the territory peculiarities. The perspective way for studying is to reveal risk factors of the dental diseases development paying attention to the association with the oral health, social deprivation, diet, hygiene, bad habits and stress which have to improve dental health in the population living in the rural areas.

Sustainable healthy diet modeling for a plant-based dietary transitioning in the United States.

The potential environmental and nutritional benefits of plant-based dietary shifts require thorough investigation to outline suitable routes to achieve these benefits. Whereas dietary consumption is usually in composite forms, sustainable healthy diet assessments have not adequately addressed composite diets. In this study, we build on available data in the Food4HealthyLife calculator to develop 3 dietary concepts (M) containing 24 model composite diet scenarios (S) assessed for their environmental and nutritional performances. The Health Nutritional Index (HENI) and Food Compass scoring systems were used for nutritional quality profiling and estimates of environmental impact were derived from previously reported midpoint impact values for foods listed in the What We Eat in America database. The diets were ranked using the Kruskal‒Wallis nonparametric test, and a dual-scale data chart was employed for a trade-off analysis to identify the optimal composite diet scenario. The results showcased a distinct variation in ranks for each scenario on the environment and nutrition scales, describing an inherent nonlinear relationship between environmental and nutritional performances. However, trade-off analysis revealed a diet with 10% legumes, 0.11% red meat, 0.28% processed meat and 2.81% white meat could reduce global warming by 54.72% while yielding a diet quality of 74.13 on the Food Compass Scoring system. These observations provide an interesting forecast of the benefits of transitioning to an optimal plant- and animal-based dieting pattern, which advances global nutritional needs and environmental stewardship among consumers.

Exploring reliable infrared object tracking with spatio-temporal fusion transformer

Currently, most thermal infrared (TIR) trackers rely on feature matching between the search image and a fixed template cropped from the first frame. Some Siam-based TIR trackers with a template update mechanism introduce historical prediction information in the temporal dimension through correlation filters. However, their feature characterization capability is inadequate to resist target scale variations, appearance changes, and occlusion. To address this challenge, we explore a novel spatio-temporal fusion Transformer (STFT) model to realize robust TIR object tracking. Our approach involves a Transformer-based encoder–decoder that fuses spatio-temporal information. Specifically, we design a dynamic template update strategy based on salient points feature(SPF) representation, which allows the model to leverage the most powerful spatio-temporal information by retrieving multiple salient points on the target image. To further fortify the dynamic template update strategy, we propose an IoU-Aware target state estimation head that utilizes the joint representation of target classification and localization. An IoU-Aware criterion is developed for quality estimation of the dynamic template. The proposed STFT-Net approach has been put to the evaluation on three challenging benchmarks, with extensive experimental results showcasing its superior performance in contrast to acclaimed tracking algorithms. The code is available at https://github.com/qinxin-wh/STFT-Net.

In Vivo Assessment of Skin Surface Pattern: Exploring Its Potential as an Indicator of Bone Biomechanical Properties.

The mechanical properties of bone tissue are the result of a complex process involving collagen-crystal interactions. The mineral density of the bone tissue is correlated with bone strength, whereas the characteristics of collagen are often associated with the ductility and toughness of the bone. From a clinical perspective, bone mineral density alone does not satisfactorily explain skeletal fragility. However, reliable in vivo markers of collagen quality that can be easily used in clinical practice are not available. Hence, the objective of the present study is to examine the relationship between skin surface morphology and changes in the mechanical properties of the bone. An experimental study was conducted on healthy children (n = 11), children with osteogenesis imperfecta (n = 13), and women over 60 years of age (n = 22). For each patient, the skin characteristic length (SCL) of the forearm skin surface was measured. The SCL quantifies the geometric patterns formed by wrinkles on the skin's surface, both in terms of size and elongation. The greater the SCL, the more deficient was the organic collagen matrix. In addition, the bone volume fraction and mechanical properties of the explanted femoral head were determined for the elderly female group. The mean SCL values of the healthy children group were significantly lower than those of the elderly women and osteogenesis imperfecta groups. For the aged women group, no significant differences were indicated in the elastic mechanical parameters, whereas bone toughness and ductility decreased significantly as the SCL increased. In conclusion, in bone collagen pathology or bone aging, the SCL is significantly impaired. This in vivo skin surface parameter can be a non-invasive tool to improve the estimation of bone matrix quality and to identify subjects at high risk of bone fracture.

You should reject this paper: Dynamic agency, sequential evaluation, and learning in academic publishing

A variety of real-world situations take the form of repeated principal-agent problems with binary evaluation. I evaluate the principal's optimal evaluation effort and quality threshold for acceptance in the setting of dynamic agency with binary evaluation, focusing specifically on the evaluation by a top academic journal of papers submitted by economists. In the baseline model, the journal should statistically discriminate in favour of high-status economists, by setting an acceptance threshold that declines with their prior estimate of the economist's quality, while evaluation effort should likely increase with perceived author quality, as this provides incentives for authors to exert more effort, which is particularly valuable from the highest-quality authors. However, if a first good publication is more valuable than subsequent publications by the same economist, acceptance thresholds will tend to increase after publication success, relative to the threshold that would have followed a rejection.

Face Recognition Bias Assessment through Quality Estimation Models

Recent advances in facial recognition technology have achieved outstanding performance, but unconstrained face recognition remains an ongoing issue. Facial-image-quality-evaluation algorithms evaluate the quality of the input samples, providing crucial information about the accuracy of recognition decisions. By doing so, this can lead to improved results in challenging scenarios. In recent years, significant progress has been made in assessing the quality of facial images. The computation of quality scores has become highly precise and closely correlated with the model results. In this paper, we reviewed and analyzed the existing biases of cutting-edge quality-estimation techniques for face recognition. Our experimentation focused on the quality estimators developed by MagFace, FaceQNet, and SER-FIQ and were evaluated on the CelebA reference dataset. A study of bias in the face-recognition model was conducted by analyzing the quality scores presented in each article. This allowed for an examination of existing biases within both the quality estimators and the face-recognition models.

DISTILLER’S DRIED GRAINS SUPPLEMENTED Wz3ITH ENZYME COCKTAIL AND YEAST AFFECT EGG QUALITY, REPRODUCTIVE PERFORMANCE AND BLOOD PROFILE OF BREEDING HENS

This experiment aimed to examine the influences of distiller’s dried grains with solubles (DDGS) in breeding hen’s nutrition (0, 10, and 20%, respectively) with or without enzyme cocktail blend and yeast (Saccharomyces cerevisiae, SC) supplementation on the sustainability of eggs and semen quality, fertility and hatchability, and blood biochemistry. In total, 360, twenty weeks old indigenous Inshas breeding hens and 36 Inshas cocks (for natural mating) were used in the experiment. Inshas is a crossing breed that originated from mating Sinai and White Plymouth Rock breeds. Hens and cocks of all experimental groups had approximately similar initial body weights (1,560 ± 12.3 g) at the start of the experiment and were divided into 12 groups, each containing three replicates (10 hens + 1 cock). Moreover, 60 cocks were divided into 12 groups of five cocks each and housed individually in cages for semen quality estimation using the artificial collection technique. Increasing DDGS up to 20% in diets of breeding hens decreased (p≤0.05) eggshell thickness compared to 0 and 10% DDGS, but it elevated (p≤0.05) color of yolk and Haugh unit score during the storage period. Dietary treatments did not significantly affect the percentage fertility, hatchability, and abnormality of embryos and body weight of day-old chicks. Semen quality was not significantly affected by dietary DDGS. Yeast supplementation increased (p≤0.05) sperm concentration per ejaculate by 5.21% and total normal sperm/ejaculate and sperm concentration by 5.87%. Increasing DDGS levels up to 10% and 20% decreased blood plasma total cholesterol by about 4.1%. Enzyme cocktail supplementation increased (p≤0.05) blood plasma Ca by 5.4% of 20% DDGS diets, but other blood plasma parameters were unaffected. Enzyme cocktail addition to DDGS diets also decreased plasma cholesterol by 3.63%. Feeding a 10% and 20% DDGS diet increased (p≤0.05) plasma creatinine by 9.75% and decreased the uric acid/creatinine ratio by about 11.1%. In conclusion, corn DDGS could be included in dual-purpose breeding hen’s diet up to 20% without adverse effects on the sustainability of the quality of eggs and semen, fertility and hatchability, and blood biochemistry. Furthermore, enzyme cocktail at 500 gm/ton feed and yeast at 1 kg/ton feed supplementations significantly and similarly improved eggshell thickness by 1.44-1.72%, and yeast increased sperm concentration and total sperm output per ejaculate. Key words: Distillers dried grains with soluble; Breeding hens; Multi-enzymes; Yeast; Fertility; Hatchability; Semen quality.

Real-time gypsum quality estimation in an industrial calciner: A neural network-based approach

Total bound moisture (TBM) is a typical quality indicator of industrial-grade gypsum. This gypsum is comprised of three distinct phases, namely anhydrite, dihydrate, and hemihydrate, of which only the latter is of much industrial use. TBM analysis is a lengthy laboratory procedure, and an artificial neural network (ANN) TBM inference measurement is proposed as a fast and online alternative. An ANN inference model for gypsum TBM based on plant data was developed. The inputs to the network were primarily focused on the plant's calciner, and different network topologies, data divisions, and transfer functions were investigated. Furthermore, the applicability of the TBM value as a quality indicator was investigated based on a gypsum phase analysis. A strong correlation between TBM and the gypsum hemihydrate and anhydrite content was found, validating the plant target TBM of 5.8% as a quality indicator. A network topology consisting of one hidden layer with logarithmic-sigmoid (logsig) and pure linear (purelin) transfer functions showed the best performance (R &gt; 90%).

Evaluation of Multiple Satellite, Reanalysis, and Merged Precipitation Products for Hydrological Modeling in the Data-Scarce Tributaries of the Pearl River Basin, China

Satellite and reanalysis precipitation estimates of high quality are widely used for hydrological modeling, especially in ungauged or data-scarce regions. To improve flood simulations by merging different precipitation inputs or directly merging streamflow outputs, this study comprehensively evaluates the accuracy and hydrological utility of nine corrected and uncorrected precipitation products (TMPA-3B42V7, TMPA-3B42RT, IMERG-cal, IMERG-uncal, ERA5, ERA-Interim, GSMaP, GSMaP-RNL, and PERSIANN-CCS) from 2006 to 2018 on a daily timescale using the Coupled Routing and Excess Storage (CREST) hydrological model in two flood-prone tributaries, the Beijiang and Dongjiang Rivers, of the Pearl River Basin, China. The results indicate that (1) all the corrected precipitation products had better performance (higher CC, CSI, KGE’, and NSCE values) than the uncorrected ones, particularly in the Beijiang River, which has a larger drainage area; (2) after re-calibration under Scenario II, the two daily merged precipitation products (NSCE values: 0.73–0.87 and 0.69–0.82 over the Beijiang and Dongjiang Rivers, respectively) outperformed their original members for hydrological modeling in terms of BIAS and RMSE values; (3) in Scenario III, four evaluation metrics illustrated that merging multi-source streamflow simulations achieved better performance in streamflow simulation than merging multi-source precipitation products; and (4) under increasing flood levels, almost all the performances of streamflow simulations were reduced, and the two merging schemes had a similar performance. These findings will provide valuable information for improving flood simulations and will also be useful for further hydrometeorological applications of remote sensing data.