High Correlation Values Research Articles

Excellent agreement of Norwegian trauma registry data compared to corresponding data in electronic patient records

BackgroundThe Norwegian Trauma Registry (NTR) is designed to monitor and improve the quality and outcome of trauma care delivered by Norwegian trauma hospitals. Patient care is evaluated through specific quality indicators, which are constructed of variables reported to the registry by certified registrars. Having high-quality data recorded in the registry is essential for the validity, trust and use of data. This study aims to perform a data quality check of a subset of core data elements in the registry by assessing agreement between data in the NTR and corresponding data in electronic patient records (EPRs).MethodsWe validated 49 of the 118 variables registered in the NTR by comparing those with the corresponding ones in electronic patient records for 180 patients with a trauma diagnosis admitted in 2019 at eight public hospitals. Agreement was quantified by calculating observed agreement, Cohen’s Kappa and Gwet’s first agreement coefficient (AC1) with 95% confidence intervals (CIs) for 27 nominal variables, quadratic weighted Cohen’s Kappa and Gwet’s second agreement coefficient (AC2) for five ordinal variables. For nine continuous, one date and seven time variables, we calculated intraclass correlation coefficient (ICC).ResultsAlmost perfect agreement (AC1 /AC2/ ICC > 0.80) was observed for all examined variables. Nominal and ordinal variables showed Gwet’s agreement coefficients ranging from 0.85 (95% CI: 0.79–0.91) to 1.00 (95% CI: 1.00–1.00). For continuous and time variables there were detected high values of intraclass correlation coefficients (ICC) between 0.88 (95% CI: 0.83–0.91) and 1.00 (CI 95%: 1.00–1.00). While missing values in both the NTR and EPRs were in general negligeable, we found a substantial amount of missing registrations for a continuous “Base excess” in the NTR. For some of the time variables missing values both in the NTR and EPRs were high.ConclusionAll tested variables in the Norwegian Trauma Registry displayed excellent agreement with the corresponding variables in electronic patient records. Variables in the registry that showed missing data need further examination.

Performance of the new MC-80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600.

Mindray MC-80 is an automated system for digital imaging of white blood cells (WBCs) and their pre-classification. The objective of this work is to analyse its performance comparing it with the CellaVision® DM9600. A total of 445 samples were used, 194 normal and 251 abnormal: acute leukaemia (100), myelodysplastic syndromes/myeloproliferative neoplasms (33), lymphoid neoplasms (50), plasma cell neoplasms (14), infections (49) and thrombocytopenia (5). WBC pre-classification values with the MC-80 and DM9600 were compared with (1) the microscope, (2) Mindray BC-6800Plus differentials in only normal samples, and (3) confirmed or reclassified images (post-classification). Pearson's correlation, Lin's concordance, Passing-Bablok regression, and Bland-Altman plots were used. Sensitivity, specificity, positive (PPV) and negative (NPV) predictive values for abnormal cells using the MC-80 were calculated. The PPV and NPV were above 98% and 99%, for normal samples. For immature granulocytes (IG), NPV and PPV were 100% and 74.2%. When comparing the WBC differentials using the MC-80, the microscope and the BC-6800Plus, no differences were found except for basophils and IG. Our results showed good agreement between the pre- and post-classification of normal WBC, including IG, quantified by high correlation and concordance values (0.91-1). Sensitivity and specificity for blasts were 0.984 and 0.640. The MC-80 detected abnormal lymphocytes in 30% of the smears from patients with lymphoid neoplasm. Plasma cell identification was better using the DM9600. The sensitivity and specificity for erythroblast detection were 1 and 0.890. We found that the MC-80 shows high performance for WBC differentials for both normal samples and patients with haematological diseases.

КАРДИОВАСКУЛЯРНАЯ ПАТОЛОГИЯ И ПРОГНОЗИРОВАНИЕ ПЕРЕЛОМОВ У МУЖЧИН СТАРШЕ 45 ЛЕТ

Introduction. The combination of cardiovascular pathology with osteoporosis and fractures in men, especially at a younger age, requires the study of the causes and risk factors for the strategy of early diagnosis, treatment and prevention. Target. To study the clinical and pathogenetic relationships of cardiovascular pathology with the risk of fractures in men over 45 years of age. Materials and methods: 201 men over the age of 45 were examined, mean age 51.1 [46.1; 56.3]. The accuracy and statistical significance of the distribution of groups was assessed using discriminant analysis. High values of canonical correlation (Canonic.R=0.941) and chi-square test (χ²=630.4) with zero probability of not rejecting the null hypothesis (p=0.000) and coincidence of the results of discrimination and cluster analysis in 95% of cases made it possible to assume the reliability of the model , built on the basis of the proposed variables. Results. The frequency of risk factors in the examined patients was: smoking - 107 people. (53.2%), abdominal obesity 74 people. (36.8%); arterial hypertension - 75 people. (37.3%), hypercholesterolemia (total cholesterol> 5.0 mmol / l) - 39 people. (19.4%), increased low-density lipoprotein cholesterol (LDL cholesterol)> 3.0 mmol / l - 32 people. (15.9%), hypertriglyceridemia (TG> 1.7 mmol / l) - 27 people. (13.4%); fasting glycemia or diabetes mellitus - 28 people. (13.9%). Among patients with fractures, men with an increased body mass index (26.5±3.3 kg/m² and 24.1±4.5 kg/m² p=0.008) and abdominal obesity (104.1±7.0 and 100.1±6.2 cm, p=0.004), vitamin D deficiency or insufficiency (37 (18.4%) versus 4 (1.9%) p=0.000) and androgen deficiency (15 (7 .4%) and 7 (3.4%) p=0.048). Fractures were significantly more often associated with diabetes mellitus (19 (9.4%) and 4 (1.9%) p=0.029), respectively. Based on the identified factors, the discriminant analysis made it possible to build a mathematical model for predicting fractures in men over 45 years of age. The canonical correlation coefficient R was 0.841. Wilkson's lambda statistics values of 0.292 indicate good discrimination (with χ² - 223.7 p<0.01). The established most significant signs were assigned gradations (X1 - 6) and numerical values, where: X1 - history of fractures: no - 0, yes - 1; Х2 – age of the first fracture older than 50 years: no – 0, yes – 1; smoking: no - 0, yes - 1; diabetes mellitus: no - 0, yes - 1; walking less than 1 hour a day: no - 0, yes - 1; X6 - osteoporosis or osteopenia: no - 0, yes - 1. X1-X6 values are standardized initial data calculated by the formula: Z = (X I - X) / s, where Z - initial distribution values, X I - results dimensions of the distribution with the original dimension. The values of the constants F1 and F2 are determined: F1 = -5.99; F2 = -4.54. Having established the gradations and numerical values of the risk factors, the predictive coefficients F1 and F2 are determined according to the following formulas: F1= -5.99–0.854×X1–0.192×X2+0.855×X3–0.179×X4+8.755×X5+3.904×X6. F2= –4.54+1.24×X1+ 2.94×X2+2.165×X3+1.60×X4–0.77×X5+5.75×X6. With an absolute value of F2 greater than the absolute value of F1, a high risk is predicted, and with an F2 value less than F1, a low risk of fractures in comorbid men is predicted. Conclusions. Among men in a random sample over 45 years of age, a high incidence of cardiovascular risk factors was established. A third of the examined men had bone fractures of various localization. The mathematical model makes it possible to predict the development of fractures in men over 45 years of age, the risk of which increases in the presence of the following risk factors: a history of fractures before the age of 50, physical inactivity, smoking, the presence of diabetes mellitus and osteoporosis according to x-ray osteodensitometry.

Study of Regularized Generalized Structured Component Analysis to Overcome Multicollinearity in Component-Based SEM

Multicollinearity is one of the issues that may arise in the analysis of Structural Equation Modeling (SEM). An indication of multicollinearity is the high correlation between latent variables and the correlation between indicators forming the latent construct. Multicollinearity causes the interpretation of SEM analysis to be inappropriate. In this study, Regularized generalized structured component analysis (RGSCA) is used as a solution to overcome multicollinearity in component-based SEM. The research aims to apply RGSCA to East Java poverty data, which contains multicollinearity. The first step is analyze data using GSCA, however the weights of and indicators are not significant, and the three estimated path coefficients are also not significant at the 95% confidence interval. The high correlation value between the indicators further indicates the presence of multicollinearity. Futhermore, the data are analyzed using RGSCA with ridge parameters namely which provides minimum prediction error (CV). The results of the analysis reveal that all estimation of loading factors, weights and path coefficients are significant at 95% confidence intervals. The interpretation of the path coefficient results suggests that education, health, and economy significantly influence poverty, while health and economy also have a significant effect on education, and health additionally exhibits a significant effect on economy. The overall model evaluation results obtained a FIT value of 0.662, indicating that the model can explain about 66.2% of the data variation.

Manufacturing and Machining Performance Analysis of Al/SiC5 MMC using Soft Computing Techniques

Now a day’s, metal matrix composites (MMCs) are taking a superior position among the available material. The presented work aims to investigate the ease of machining for Aluminium based MMC (AlSiC5- with 5% Silicate). These MMCs are extensively used in space, defence, automobile, etc. To analyze the process, an effective two techniques ie. Response surface method (RSM) & artificial neural networks (ANN) have been selected to analyze the process. Taguchi’s L27 plan was used for data collection. The parameters like current (AMP) and pulse on time (TON) are the basically dominant parameters, followed by voltage (VOLT) and pulse off time (TOFF). The ease of machining performance measured in terms of surface roughness (Ra). From obtained result, it is seen that the ANN & RSM presents an exemplary commitment. An adequate contract was observed with the high correlation coefficient value (R2 = 0 9863) in RSM and (R2 = 0 9764).

Radiological examination of the skull of Siirt-coloured mohair goat, Romanov and Hamdani sheep.

The skull is a vitally important complex structure formed by the combination of many bones. Craniometric studies are performed with various imaging methods to determine morphometric characteristics in animals. One of these methods is radiography. In our study, skulls of 14 Hamdani sheep, 20 Siirt-coloured mohair goats and 20 Romanov sheep breeds were used. Skull sections were taken with a computed tomography device and saved in DICOM format. Nine separate measurements and six index calculations were made from the images. In the study, firstly, the gender difference was ignored and the similarities and differences between the species were recorded. In addition, gender differences were determined statistically, without making any distinction between species. Correlation analysis was performed to compare measurement parameters between animal species. As a result of the study, the highest skull length (SL) and cranial length (CL) values were determined in the Siirt-coloured mohair goat. It was determined that the difference between Romanov sheep and Siirt-coloured mohair goat in CL parameter was not statistically significant (p > 0.05). In addition, a statistically significant difference was observed between the Romanov sheep breed and the Hamdani sheep breed (p < 0.01). The external length of the cranium (ELC) measurement parameter was found to have a statistically significant difference in three animals (p < 0.01). According to the results of the correlation analysis, the highest correlation value was determined between cranial width (CW) and CL parameters (r = 0.920). When the species difference was ignored, and the statistical difference of the internal height of the cranium (IHC) parameter was found to be significant between female and male animals (p < 0.05). In addition, it was determined that SL, CW and internal cranium index (ICI) parameters had a significant difference depending on sex (p < 0.01). It is foreseen that the obtained data can be used in sciences such as zoo archaeology, anatomy, surgery and forensic medicine.

High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold Model

Ratoon rice, an effective rice cultivation system, allows paddy rice to be harvested twice from the same stubble, playing an important role in ensuring food security and adapting to climate change with its unique growth characteristics. However, there is an absence of research related to remote-sensing monitoring of ratoon rice, and the presence of other rice cropping systems (e.g., double-season rice) with similar characteristics poses a hindrance to the accurate identification of ratoon rice. Furthermore, cloudy and rainy regions have limited available remote-sensing images, meaning that remote-sensing monitoring is limited. To address this issue, taking Yongchuan District, a typical cloud-prone region in Chongqing, China, as an example, this study proposed the construction of a time-series optical dataset using the Modified Neighborhood Similar Pixel Interpolator (MNSPI) method for cloud-removal interpolation and the Flexible Spatiotemporal DAta Fusion (FSDAF) model for fusing multi-source optical remote-sensing data, in combination with vegetation index features and phenological information to build a threshold model to map ratoon rice at high-resolution (10 m). The mapping performance of ratoon rice was evaluated using independent field samples to obtain the overall accuracy and kappa coefficient. The findings indicate that the combination of the MNSPI method and FSDAF model had a stable and effective performance, characterized by high correlation coefficient (r) values and low root mean square error (RMSE) values between the restored/predicted images and the true images. Notably, it was possible to effectively capture the distinct characteristics of ratoon rice in cloudy and rainy regions using the proposed threshold model. Specifically, the identified area of ratoon rice in the study region was 194.17 km2, which was close to the official data (158–180 km2), and the overall accuracy and kappa coefficient of ratoon rice identification result were 90.73% and 0.81, respectively. These results demonstrate that our proposed threshold model can effectively distinguish ratoon rice during vital phenological stages from other crop types, enrich the technical system of rice remote-sensing monitoring, and provide a reference for agricultural remote-sensing applications in cloudy and rainy regions.

Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach

Ceramic nanoparticles have become increasingly popular owing to their wide range of engineering applications in the industry. Silica is one of the most promising nanomaterials for heat transfer applications due to its favorable thermophysical characteristics and dispersion stability. This work investigates the thermophysical properties of polydisperse SiO2 nanoparticles in a glycerol and water mixture. Aqueous glycerol nanofluid comprised 30% glycerol (30 GW) by weight, and SiO2 with mean particle sizes of 15, 50, and 100 nm were prepared for 0.5 and 1.0% volume concentrations using a two-step method. Measurements of properties are made in the temperature range of 30–100oC. The nanofluids behaved as a single-phase liquid remaining stable for four weeks. The viscosity and density of the base liquid and nanofluid decreased while the thermal conductivity increased with temperature. The base liquid-specific heat increased slightly while that of nanofluid decreased significantly with temperature. For 0.5% SiO2 concentration, the thermal conductivity and viscosity varied by 11.1% and 32%, respectively at 60oC compared to the base liquid. Correlations are developed for the estimation of nanofluid properties. An Explainable Artificial Intelligence (XAI) technique called Bayesian approach optimized Gaussian Process Regression was employed to develop a prognostic model for the thermophysical properties of test nanofluids. In addition, the model's predictability and explainability are both enhanced by the use of kernel functions, which makes it possible to include historical data in the representation of a phenomenon. The test XAI approaches were shown as robust one because of the high correlation values, which ranged from 99.68% to 99.99%, along with minimal modeling errors.

Effectiveness of modified nitrogen fertilizer on soil chemical properties and rice plant growth in the textile industrial area

The imbalance of nutrients and heavy metal contamination is a challenge in increasing plant growth surround the textile industry area. This study aimed to determine the effectiveness of the dosage of Biologically Agent N Organo Mineral Fertilizer (Biomix-N) as a fertilizer and an ameliorant in paddy soil contaminated with textile liquid waste. This study used a randomized block design of eight treatments with four replications. Parameters observed were soil chemical properties after application of Biomix-N (Na-ex, total N, EC, Cd, and pH), rice plant growth (plant height and tiller number), Cd concentration in the plant, and relative agronomic effectiveness (RAE). Data analysis used analysis of variance, Duncan's test at a 5% level, regression, and correlation analysis. The results showed that Biomix-N 500 kg ha-1 affected the value of EC and total N, while it did not affect Na-ex, Cd, pH and Cd uptake by the paddy plant. Biomix-N fertilization affects the height and number of tillers of rice plants at the age of 70 DAP. Biomix-N fertilization 500 kg ha-1 gave an RAE value of 126-176% compared to the control treatment. The total N content of the soil has a high value of coefficient determinant and correlation (R2= 0.76; R = 0.9) on the growth of rice plants. Applying Biomix-N 500 kg ha-1 equal to 300 kg ha-1 of urea was an effective dose for paddy soil surround the textile industrial area.Keywords: Cadmium, nutrient balance, soil amendment

Single-trial ERP Quantification Using Neural Networks.

Traditional approaches to quantify components in event-related potentials (ERPs) are based on averaging EEG responses. However, this method ignores the trial-to-trial variability in the component's latency, resulting in a smeared version of the component and underestimates of its amplitude. Different techniques to quantify ERP components in single trials have therefore been described in literature. In this study, two approaches based on neural networks are proposed and their performance was compared with other techniques using simulated data and two experimental datasets. On the simulated dataset, the neural networks outperformed other techniques for most signal-to-noise ratios and resulted in better estimates of the topography and shape of the ERP component. In the first experimental dataset, the highest correlation values between the estimated latencies of the P300 component and the reaction times were obtained using the neural networks. In the second dataset, the single-trial latency estimation techniques showed an amplitude reduction of the N400 effect with age and ascertained this effect could not be attributed to differences in latency variability. These results illustrate the applicability and the added value of neural networks for the quantification of ERP components in individual trials. A limitation, however, is that simulated data is needed to train the neural networks, which can be difficult when the ERP components to be found are not known a priori. Nevertheless, the neural networks-based approaches offer more information on the variability of the timing of the component and result in better estimates of the shape and topography of ERP components.

Application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachate

Sustainable municipal solid waste leachate (MSWL) management requires a paradigm shift from removing contaminants to effectively recovering resources and decreasing contaminants simultaneously. In this study, two types of humic substances, fulvic acid (FA) and humic acid (HA) were extracted from MSWL. HA was extracted using HCl and NaOH solution, followed by FA using a column bed under diversified operations such as flow rate, input concentration, and bed height. Also, this work aims to evaluate efficiency of Artificial Neural Network (ANN) and Dynamic adsorption models in predicting FA. With the flow rate of 0.3 mL/min, bed height of 15.5 cm, and input concentration of 4.27 g/mL, the maximum capacity of FA was obtained at 23.03 mg/g. FTIR analysis in HA and FA revealed several oxygen-containing functional groups including carboxylic, phenolic, aliphatic, and ketone. The high correlation coefficient value (R2) and a lower mean squared error value (MSE) were obtained using the ANN, indicating the superior ability of ANN to predict adsorption capacity compared to traditional modeling.

Novel Impact Model for Mobile Learning Adoption in Higher Education

In successful mobile learning (ML) integration, factors associated with live-ware, software, and infrastructure are important. The main objective of this study is to investigate and model the influencing factors for learners and teachers at once to adopt ML in higher education. The proposed model consists of five impact factors: teacher, learner, mobile devices, ML tools, ML contents, communication technologies, and higher education institutes. Then the proposed model was implemented using a modified Moodle mobile application and evaluated using 60 teachers and 60 learners attached to the University of Kelaniya, Sri Lanka in 2021. According to the experimental research design approach, the proposed impact model was assessed as pre-test and post-test surveys using seven questionnaires. According to the Pearson correlation coefficient test, the most significant factor for learners and teachers to adopt ML is the mobile device. Learning content and communication technology were elected as the second most significant adoption factors for teachers and learners consecutively. However, higher correlation values were obtained for all factors denoting that they are greatly influenced the participant to adopt ML. The significant influencing factor of each impact factor was also investigated. In conclusion, it was recommended that featured smart devices, quality learning content, user-satisfied communication technology, academic enriched ML tools and higher education institutes with sound educational facilities are crucial for the university community to adopt ML in higher education. These findings help design academic community acceptable ML environments for higher education context.

Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor.

Research advancement has spurred the usage of electroencephalography (EEG)-based neural oscillatory rhythms as a biomarker to complement clinical rehabilitation strategies for the recovery of motor functions in stroke survivors. However, the inevitable contamination of EEG signals with artifacts from various sources limits its utilization and effectiveness. Thus, the integration of Independent Component Analysis (ICA) and Independent Component Label (ICLabel) has been widely employed to separate neural activity from artifacts. A crucial step in the ICLabel preprocessing pipeline is the artifactual ICs rejection threshold (TH) parameter, which determines the overall signal's quality. For instance, selecting a high TH will cause many ICs to be rejected, thereby leading to signal over-cleaning, and choosing a low TH may result in under-cleaning of the signal. Toward determining the optimal TH parameter, this study investigates the effect of six different TH groups (NO-TH and TH1-TH6) on EEG signals recorded from post-stroke patients who performed four distinct motor imagery (MI) tasks including wrist and grasping movements. Utilizing the EEG-beta band signal at the brain's sensorimotor cortex, the performance of the TH groups was evaluated using three notable EEG quantifiers. Overall, the obtained result shows that the considered THs will significantly alter neural oscillatory patterns. Comparing the performance of the TH-groups, TH-3 with a confidence level of 60% showed consistently stronger signal desynchronization and lateralization. The correlation result shows that most of the electrode pairs with high correlation values are replicable across all the MI tasks. It also revealed that brain activity correlates linearly with distance, and a strong correlation between electrode pairs is independent of the different brain cortices. The study outcome may facilitate adequate therapeutic intervention for stroke rehab.Clinical Relevance: This study indicated that optimal selection of the ICLabel artifactual rejection threshold is essential for EEG enhancement for adequate signal characterization. Thus, a TH-values with a confidence level between 50% - 70% would be suggested for artifactual ICs rejection in MI-EEG.

The Best Scenario for Geostatistical Modeling of Porosity in the Sarvak Reservoir in an Iranian Oil Field, Using Electrofacies, Seismic Facies, and Seismic Attributes

Summary The lateral and vertical variations in porosity significantly impact the reservoir quality and the volumetric calculations in heterogeneous reservoirs. With a case study from Iran’s Zagros Basin Sarvak reservoir in the Dezful Embayment, this paper aims to demonstrate an efficient methodology for distributing porosity. Four facies models (based on electrofacies analysis data and seismic facies) with different geostatistical algorithms were used to examine the effect of different facies types on porosity propagation. Both deterministic and stochastic methods are adopted to check the impact of geostatistical algorithms on porosity modeling in the static model. A total of 40 scenarios were run and validated for porosity distribution through a blind test procedure to check the reliability of the models. The study’s findings revealed high correlation values in the blind test data for all porosity realizations linked to seismic facies, ranging from 0.778 to 0.876. In addition, co-kriging to acoustic impedance (AI), as a secondary variable, increases the correlation coefficient in all related cases. Unlike deterministic algorithms, using stochastic methods reduces the uncertainty and causes the porosity model to have an identical histogram compared with the original data. This study introduced a comprehensive workflow for porosity distribution in the studied carbonate Sarvak reservoir, considering the electrofacies, and seismic facies, and applying different geostatistical algorithms. As a result, based on this workflow, simultaneously linking the porosity distribution to seismic facies, co-kriging to AI, and applying the sequential Gaussian simulation (SGS) algorithm result in the best spatial modeling of porosity.

Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study

The impact of the suspended sediment load (SSL) on environmental health, agricultural operations, and water resources planning, is significant. The deposit of SSL restricts the streamflow region, affecting aquatic life migration and finally causing a river course shift. As a result, data on suspended sediments and their fluctuations are essential for a number of authorities especially for water resources decision makers. SSL prediction is often difficult due to a number of issues such as site-specific data, site-specific models, lack of several substantial components to use in prediction, and complexity its pattern. In the past two decades, many machine learning algorithms have shown huge potential for SSL river prediction. However, these models did not provide very reliable results, which led to the conclusion that the accuracy of SSL prediction should be improved. As a result, in order to solve past concerns, this research proposes a Long Short-Term Memory (LSTM) model for SSL prediction. The proposed model was applied for SSL prediction in Johor River located in Malaysia. The study allocated data for suspended sediment load and river flow for period 2010 to 2020. In the current research, four alternative models—Multi-Layer Perceptron (MLP) neural network, Support Vector Regression (SVR), Random Forest (RF), and Long Short-term Memory (LSTM) were investigated to predict the suspended sediment load. The proposed model attained a high correlation value between predicted and actual SSL (0.97), with a minimum RMSE (148.4 ton/day and a minimum MAE (33.43 ton/day).and can thus be generalized for application in similar rivers around the world.

Water distribution system calibration using the Finite Element Method coupled to a Genetic Algorithm

Water distribution systems (WDS) are a fundamental part of the infrastructure in our cities, for this reason increasingly capable numerical models have been developed in recent years to allow hydraulic systems be analyzed in great detail. Calibration process let get accurate and reliable information for those involved in design, operation, construction and maintenance of public WDS. In this study a calibration process is proposed using a Genetic Algorithms (GA) methodology with real number coding coupled to the Finite Element Method as hydraulic simulator GA-FEMH. The method proposed was applied to two calibration cases; one reported in literature and a real network.The results show that the proposed calibration process is reliable, and the WDS model can represent real conditions of the network according to the regulations that international organizations such as AWWA and WAA recommend. Moreover high correlation values were obtained after the calibration process, with values of R2 higher than 95 % for pressure and flow. Likewise, the FEM can be an alternative to use it as a hydraulic simulator due to its rapid convergence and its ease of integrating hydraulic accessories.

A Quantification of Heat Storage Change-Based Evaporation Behavior in Middle–Large-Sized Lakes in the Inland of the Tibetan Plateau and Their Temporal and Spatial Variations

A large number of different-sized lakes exist in the inland area of the Tibetan Plateau (TP), which are examples of the important connection between the atmosphere and hydrosphere through the analysis of lake surface convergence and evaporation processes. The evaporation level changes that occur in middle–large-sized lakes (surface area &gt; 50 km2) in the area directly influence the regional mass and energy balance values, atmospheric boundary layer heat and humidity structures, and weather processes occurring in the lower-reach areas. The studies conducted in the literature at present, concerning lake evaporation processes, generally overlook the differences in lake heat storage behavior due to the reduced amount of data in the literature concerning lake bathymetry. According to the in situ bathymetric data obtained for 68 middle–large-sized lakes in the inner basin of the TP, in this study, we calculated their heat storage (G) change values by using the different vertical-depth water-temperature-change integral method, and we established a regression equation for the heat storage and lake surface net radiation values for 68 lakes. The evaporation rates of 134 middle–large-sized lakes larger than 50 km2 in the inland are of the TP were calculated by obtaining the G regression result and adopting it into the Penman model, as well as estimating the evaporation losses of theses 134 lakes from 2002 to 2018. The result shows that the annual average evaporation rate for these lakes is 927.39 mm/year, with an insignificant upward trend (0.10 mm/year). This method achieved good accuracy compared with the Bowen ratio method, which estimates the evaporation rate during the ice-free season, with a high correlation coefficient (R) value of 0.95 and least root mean square error (RMSE) value of 61 mm. The annual mean evaporation rate can be divided into the southern and northern lake groups along a 34°N line with a difference of 314.41 mm/year. The annual average evaporation volume of these lakes was 25.02 km3 and showed an upward trend of 0.35 km3/year. Among them, the annual average evaporation volume contribution ratio of level-1 lakes (50 km2 ≤ lake’s area &lt; 100 km2, 61 lakes) was 14.04%, showing an upward trend, and the contribution of level-3 lakes (lake’s area ≥ 500 km2, 10 lakes) was 41.50%, showing a downward trend. There were no obvious changes in the level-2 lakes (100 km2 ≤ lake’s area &lt; 500 km2, 63 lakes), which maintained at the same level in approximately 44.46%. Air temperature is the most important factor affecting the evaporation rate of lakes, while the lake surface area is the main factor affecting lake evaporation volume. Our study, considering the actual lake heat storage value, provides a useful reference for further improving lake water budget balance values and watershed hydrologic features in the inland closed lakes located in the TP.

Delayed response of low latitudes TEC during thirty-six geomagnetic storms from 2014 to 2017

Ionospheric response to the onset of geomagnetic storms is an important aspect for developing models towards better understanding and prediction of ionospheric parameters, particularly over the equatorial and low latitude sectors that are associated with several complexities. Our paper discusses the time response of the ionosphere (Δtiono), where Δtiono is the time elapsed from the onset of sudden storm commencement (SSC) of a magnetic storm to the absolute maximum value of DVTEC (TEC: total electron content). Over the period 2014 to 2017, thirty-six storms are reviewed, and their Δtiono are analyzed along with the magnetic and solar parameters. We defined a threshold value of TEC to be 8 TECU. Three storms are studied in detail as a reference for the entire range of storms (March 2015, June 2015, and September 2015). The stations used are Kourou (KOUR; 5.25°N/52.80°W) in the American longitude sector, Addis Ababa (AAE; 9.03N°/38.76°E) in the African longitude, Port Blair (PBRI; 11.63°N/92.71°E) and Patumwan (CUSV; 13.73°N/100.53°E) in the Asian longitude sector. In the Asian and American sectors, for all storms combined, there is no significant correlation (0.44 for the Asian sector and 0.22 for the American sector) between the delay (SSC time/time of maximum DVTEC) and the minimum of the Dst as found by previous studies. Instead, we considered geomagnetic storms satisfying the criteria: a) SSC occurs on the day side, and b) the origin of the magnetic storm on the solar disk must be far from the limb or does not belong to the far side, to obtain a much better correlation. The highest correlation value is observed at Thailand (0.84), followed by India (0.79) and South America (0.759), and the minimum value at Africa (0.641). The average response time in our study was found to be about 27.2 h. We observed positive and negative ionospheric storms, five negative storms in South America followed by two in Asia and a single negative storm in Africa from which it is realized that the most negative storms are in the time range of 9–15 LT. Nevertheless, there is hardly any relationship between the strength of DVTEC amplitude and the intensity of magnetic storms irrespective of longitude sectors.

Evaluation of Near-Infrared Chemical Imaging (NIR-CI) for the Authentication of Antibiotics

Counterfeit medicines represent a public health threat that results in treatment failure and may even have lethal effects in the worst-case scenario. Near-infrared Chemical Imaging (NIR-CI) offers an informative and in-depth tool for several applications in the pharmaceutical industry, particularly for medicine authentication. The current study aimed to authenticate antibiotic tablets using NIR-CI. These tablets were measured non-destructively using a near-infrared microscope within their blister packaging, without their blisters, sectioned and crushed. The results showed that there was no marked difference in measuring the tablets within or without their blister packaging. The mean spectra of tablets showed high correlation coefficient values against the active pharmaceutical ingredient, in case of authentic tablets. On the other hand, counterfeit tablets showed key differences from their authentic alternatives with low correlation coefficient values. More specifically, counterfeit tablets showed poor distribution of the active pharmaceutical ingredient and excipients. It has been proved from the results that NIR-CI process is an authentic process for the evaluation of counterfeit tablets, non-destructively.

Circulating endothelial cells as a marker of endothelial dysfunction in patients with psoriatic arthritis

Abstract. Psoriatic arthritis (PsA) is a chronic progressive inflammatory disease that involves damage of the skin and joints and leads to the development of cardiopulmonary diseases. Mortality among this cohort is 1.28 times higher than the population level. The presence of a prolonged systemic inflammatory process is a significant predictor of the development of cardiovascular (CV) diseases due to the progression of endothelial dysfunction (ED). Objective. To evaluate the role of circulating endothelial cell (CEC) levels as a marker of endothelial dysfunction in patients with psoriatic arthritis. Materials and Methods. We examined 97 patients diagnosed with PsA and 25 clinically healthy patients who formed the control group. All patients underwent diagnostic methods characterizing the course of the disease, assessment of endothelium-dependent vasodilation (EDVD) in response to reactive hyperaemia and CEC levels. Results. The results of the study of endothelial function in patients with PsA demonstrated that reduced EDVD (less than 10 %) was more common among patients with PsA compared to the control group (75.3 % vs. 11.8 %, respectively). The analysis of the CEC content in peripheral blood showed a significant difference (7.15 ± 0.19 and 4.05 ± 0.11, respectively, p &lt; 0.001) between patients with PsA and the control group. In addition, were established the relationships between endothelial dysfunction and the state of endothelial vasodilatory function (rs = -0.91, p &lt; 0.05), disease duration (rs = 0.69, p &lt; 0.05), disease activity (DAPSA (rs = 0.65, p &lt; 0.05), inflammatory process itself (ESR (rs = 0.39, p &lt; 0.05), CRP (rs = 0.52, p &lt; 0.05) and skin lesion prevalence (PASI (rs = - 0.42, p &lt; 0.05)). Conclusions. Our studies suggest a significant prevalence of ED in patients with PsA. The high values of negative correlation between EDVD and CEC are quite expected and comparable to modern studies and allow us to consider desquamated endothelial cells as an independent marker for assessing the state of the endothelium in patients with PsA and are consistent with the literature.