Route Mean Square Research Articles

Evaluation of the operational performance of Karbala waste water treatment plant under variable flow using GPS-X model

Abstract As Karbala City is a religious tourism destination, millions of tourists visit the city annually, so there is a high fluctuation of flow in the wastewater between the plants during the year. The aim of this study is to evaluate the performance of the wastewater treatment plant (WWTP) in Karbala, Iraq, in removing pollutants for different flow rates in five scenarios using the GPS-X model. The most important phase in modeling, which greatly impacts simulation accuracy, is characterizing the influent composition to meet the mass balance. As a result, the influent wastewater was initially described and thoroughly examined. The model has been calibrated, followed by the collected data’s validation. The sensitivity of different stoichiometric and kinetic factors in the GPS-X was examined and screened to calibrate the model. To demonstrate the consistency between the simulated and measured data, the route mean square error was used in this instance. The result showed that the Karbala WWTP complies with Iraqi environmental regulations for water discharged to surface water or water for other uses and has an appropriate efficiency of wastewater treatment even if the flow entering the plant reaches the peak flow rate of 180,000/day. An improvement in orthophosphate removal efficiency was observed as the flow rate increased because of the contact time in the anaerobic basins, which gradually decreased as the flow increased until it reached the appropriate time for PO 4 3 − {\text{PO}}_{4}^{3-} removal. The outcomes of the present study provide an impression to the operators of the treatment plant of the impact of fluctuating flow on the treatment plant. The developed model can also be used for future studies.

A comparative study of ensemble machine learning algorithms for brucellosis disease prediction

Brucellosis, caused by Brucella spp., is a global public health concern, particularly in underdeveloped regions. Cattle, predominantly infected with B. abortus, encounter reproductive challenges, reduced productivity, and fertility issues. Effective control measures, including serological tests like iELISA (indirect Enzyme-linked Immunosorbent Assay) are vital. This research harnesses machine learning techniques, encompassing AdaBoostM1, Vote, Bagging, and LogitBoost, to forecast Brucella infection in cattle, utilizing comprehensive data sourced from Qazvin, Iran. Detailed model descriptions are provided, highlighting AdaBoostM1 as the optimal choice, boasting a robust 75% correlation, low RMSE (Route Mean Square Error), MAE (Mean Absolute Error), and a commendable Kappa Statistics score of 0.4965. Ensemble machine learning demonstrates significant potential in Brucellosis prediction, adept at handling intricate datasets, and enhancing predictive accuracy. AdaBoostM1 stands out as the preferred model, offering valuable insights for Brucellosis prediction and contributing to the enhancement of disease control strategies.

LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

The Brain-Computer Interface (BCI) is a scientific field aimed at helping people with neuromotor disabilities. Among the current drawbacks of BCI research is the need for a cost-effective software instrument for simple integration with portable EEG headsets, the lack of a comparative assessment approach of various techniques underlying recognizing the most precise BCI control signal –voluntary eye-blinking, and the need for EEG datasets allowing the classification of multiple voluntary eye-blinks. The proposed BCI research-related virtual instrument accomplishes the data acquisition, processing, features extraction, and the ANN-based classification of the EEG signal detected by the NeuroSky embedded biosensor. The developed software application automatically generated fifty mixtures between selected EEG rhythms and statistical features. The EEG rhythms are related to the time and frequency domains of the raw, delta, theta, alpha, beta, and gamma. The extracted statistical features contain the mean, median, standard deviation, route mean square, Kurtosis coefficient, mode, sum, skewness, maximum, and range = maximum-minimum. The results include 100 EEG datasets to classify multiple voluntary eye-blinks: 50 datasets with 4000 recordings and 50 with 800 recordings. The LabVIEW application determined the optimal ANN models for classifying the EEG temporal sequences corresponding to detecting zero, one, two, or three voluntary eye-blinks.

Towards Ge-based electronic devices: Increased longevity of alkanethiol-passivated Ge(100) in low humidity environments

Germanium is a critically important material for future complementary metal-oxide-semiconductor devices, however, to maximise its potential it is necessary to develop a robust passivation process that prevents Ge re-oxidation for a queue time of 24 h. Self-assembled monolayers (SAMs) of alkanethiols on Ge have previously been shown to inhibit oxidation; however, re-oxidation eventually occurs when exposed to ambient conditions. Herein, it is shown that humidity plays a key role in the degradation of the SAM, ultimately resulting in re-oxidation. To demonstrate this, thiol-passivated Ge(100) surfaces are exposed to controlled humidity environments with different levels of relative humidity (RH). The rate of re-oxidation of the Ge surfaces are tracked using X-ray photoelectron spectroscopy and water contact angle analysis to discern what role RH plays in the re-oxidation of the Ge and the degradation of the SAM passivation. Atomic force microscopy data is presented to show that humidity-mediated re-oxidation of the Ge has little or no impact on the route mean square roughness of those surfaces. Finally, atomistic modelling of thiol-SAM passivated Ge in the presence of water molecules has been studied using first principles density functional theory in order to simulate experimental conditions and to understand the atomic level processes that determine stability in hydrophilic and hydrophobic configurations.

Disease monitoring using lung function trajectory in lymphangioleiomyomatosis: assessment in two national cohorts

Study questionIn lymphangioleiomyomatosis, airflow obstruction and impairment of gas transfer progress at variable rates and serial lung function is recommended for disease monitoring. As these measurements are variable, recognising subjects...

Study of zinc salt concentration effect on the growth of zno nanostructured oxides obtained by direct electrodeposition

"Effect of zinc salt concentration on the growth of electrodeposited zinc oxide phases was investigated. ZnO thin films were elaborated via chronoamperometry on fluorine doped tin oxide (FTO) substrates from aqueous mixed bath of zinc nitrate and chlorate at 70°C. X ray diffraction patterns showed nanocrystalline films of hexagonal wurtzite phase with (101) preferential orientation for ZnO as grown in low concentration of Zn2+ salt. High concentration of Zn2+ favor the formation of Zn5(OH)8Cl2H2O simonkolleite phase. After heat treatment at 400 °C for 1h this later decomposes to ZnO oxide which presents no preferential orientation. The route mean square RMS roughness of surfaces estimated from AFM technique shows that ZnO generated from heat treated Zn5(OH)8Cl2H2O present more increasing roughness than ZnO deposits obtained at lower salt concentration. The photoelectrochemical answer indicated important generated photocurrents for both films which present n-type electrical conductivity"

Antiviral drug discovery by targeting the SARS-CoV-2 polyprotein processing by inhibition of the main protease.

The spread of SARS-CoV-2, the causative agent for COVID-19, has led to a global and deadly pandemic. To date, few drugs have been approved for treating SARS-CoV-2 infections. In this study, a structure-based approach was adopted using the SARS-CoV-2 main protease (Mpro) and a carefully selected dataset of 37,060 compounds comprising Mpro and antiviral protein-specific libraries. The compounds passed two-step docking filtration, starting with standard precision (SP) followed by extra precision (XP) runs. Fourteen compounds with the highest XP docking scores were examined by 20 ns molecular dynamics simulations (MDs). Based on backbone route mean square deviations (RMSD) and molecular mechanics/generalized Born surface area (MM/GBSA) binding energy, four drugs were selected for comprehensive MDs analysis at 100 ns. Results indicated that birinapant, atazanavir, and ritonavir potently bound and stabilized SARS-CoV-2 Mpro structure. Binding energies higher than −102 kcal/mol, RMSD values <0.22 nm, formation of several hydrogen bonds with Mpro, favourable electrostatic contributions, and low radii of gyration were among the estimated factors contributing to the strength of the binding of these three compounds with Mpro. The top two compounds, atazanavir and birinapant, were tested for their ability to prevent SARS-CoV-2 plaque formation. At 10 µM of birinapant concentration, antiviral tests against SARS-CoV-2 demonstrated a 37% reduction of virus multiplication. Antiviral assays demonstrated that birinapant has high anti-SARS-CoV-2 activity in the low micromolar range, with an IC50 value of 18 ± 3.6 µM. Therefore, birinapant is a candidate for further investigation to determine whether it is a feasible therapy option.

Marine predators algorithm for parameters estimation of photovoltaic modules considering various weather conditions

The accurate models of photovoltaic systems are the core of solar energy studies that are describing the system performance and behavior under different operating conditions. The improving of photovoltaic models based on optimizing method is recently the main simulation tool to construct I-V and P–V characteristic curves with the aid of system parameters. These parameters are extracted by using powerful optimal techniques that are building-up from datasheet of manufacturers or experimental data. This paper presents a novel proposed photovoltaic model based on Marine Predators Algorithm to estimate the optimal model parameters of solar cells or modules. Also, it can extract parameters of single diode, double diode and three diode models. Moreover, the Route Mean Square Error value between each model computed parameters and measured results of photovoltaic components are considered as the objective function. R.T.C. France Solar Cell, Photowatt-PWP201 and thin-film photovoltaic modules have been implemented to extract parameters for all three previously mentioned models at different irradiance intensities or temperature degrees. The proposed algorithm results of such cells and modules for each diode model are compared with other research works and manufacturer's results. Moreover, the evaluation of proposed algorithm has been presented considering the complexity analysis and statistical tests. The compared results show that, the accuracy of algorithm results is the best and their I-V and P–V characteristic curves are highly coinciding with manufacturer's curves. Therefore, the results of the proposed algorithm are satisfied with high superiority and better reliability to optimize parameters under different operating conditions.

Surface Modification of Sputtered Carbon Supercapacitor Electrode by Hydrogen Annealing

In the applications of renewable energy; use of energy in the electric vehicles and many other electronic devices such as mobile devices and computers; electrical energy storage is essential. Batteries are used to store electrical energy but have low power density and lower cycle life. Using extremely porous electrode materials for supercapacitors, based on quick ion transport, are specialized to provide high power density, long stability and effective energy storage. Using graphene-based electrode is the best way to boost the energy density of supercapacitor. Graphene synthesized by chemical exfoliation, ultrasonic exfoliation and solution based chemical reduction suffers agglomerations that tends to restack the graphene sheets. In the present work, we studied the option of hydrogen gas annealing to obtain graphene from amorphous carbon film, coated on Cu substrate using sputtering. For electrochemical assessment, in situ developed film was compared with graphene applied from other methods of graphene synthesis. Atomic force microscopy (AFM) results revealed that annealed carbon sputtered electrode has high route mean square (RMS) roughness i.e 181.5 nm, most probably because of graphene formation. Cyclic voltammogram (CV) results show less area curve for annealed electrode which depicts high active area for charge storage and enhanced conductivity due to deposited graphene layer.

Artificial intelligence forecasting models of uniaxial compressive strength

The uniaxial compressive strength (UCS) is a vital rock geomechanical parameter widely used in rock engineering projects such as tunnels, dams, and rock slope stability. Since the acquisition of high-quality core samples is not always possible, researchers often indirectly estimate these parameters. The main objective of the present study is to evaluate the performance of the long short term memory (LSTM), deep neural networks (DNN), K-nearest neighbor (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision tree (DT) to predict the UCS of different rock types of Claystone, Granite, Schist and Sandstone, Travertine, Limestone, Slate, Dolomite and Marl acquired from almost all quarry locations of Iran. 170 data sets, including porosity (n), Schmidt hammer (SH), P-wave velocity (Vp), and point load index (Is(50)) were applied in the methods. Finally, a comparison was made between the results made by the prediction methods. To assess the performance ability of the applied methods, the 5-fold cross-validation (CV) was considered. The results proved that computational intelligence approaches are capable of predicting UCS. On the whole, the GPR with a correlation coefficient (R2) of 0.9955 and a route mean square error (RMSE) of 0.52169, performs best. Lastly, the UCS prediction intelligence methods were ordered as GPR, DT, SVR, LSTM, DNN and KNN, respectively.

Comparison of direct measurements of sea ice thickness and snow height, CryoSat-2 observations and PIOMAS numerical estimates

The paper presents the results of comparison of contact measurements of ice thicknesses and snow heights performed at the points of the hydrological stations of the “Transarktika-2019” expedition in April 2019 north of Franz-Josef Land archipelago, with altimetry observations of the Cryosat-2 satellite and numerical estimates of the PIOMAS (Pan-Arctic numerical Ice and Ocean Modeling system and data Assimilation). A significantly better correspondence is predictably shown between the variability of the ice thickness directly measured and observed using the CryoSat-2 satellite than that for the numerical PIOMAS system estimates. A trial correction of the algorithm for calculating the ice thickness by replacing the climatic values of the ice density, snow density and height with data from direct measurements also predictably improves the quality of calculating the ice thickness from satellite observations. The mean / route mean square differences obtained for ice thicknesses (+44/+96 cm for uncorrected and +30/+95 cm for corrected CryoSat-2 satellite, –14/+81 cm for PIOMAS system) and snow height (–4/+12 cm for CryoSat-2 satellite, –15/+12 cm for PIOMAS system) show the scale of uncertainty in estimating sea ice thickness and snow height for areas dominated by medium and thick first-year ice. An anomaly of the ice thicknesses observed during the expedition is given in comparison with the background characteristics based on historical ice charting data for 1970s — 1990s, earlier High-Arctic aircraft “Sever” expeditions during 1950s — 1970s and the stated remote observations and numerical estimates for 2000s — 2019. Comparison shows that the AARI expedition was actually carried out in one of the most favorable years for ice research in the last decade for this region — the average ice thickness in April 2019 was 15 — 28 cm higher than that for the interval 2011 — 2019 with a slightly lower (1 — 2 cm) height of the snow cover. In the earlier period of the 1970 — 1990s this area was characterized by significantly thicker old ice with characteristic thicknesses ~ 60 cm more than in April 2019.

Studies of sintering temperature on the microstructure, magnetic and dielectric behavior of CaCu3Ti3.5Mn0.5O12 ceramic synthesized by semi-wet route

The CCTMO (CaCu3Ti3.5Mn0.5O12) ceramic has been prepared by using the semi-wet method and sintered at 1223 K, 1323 K, and 1373 K, respectively for 8 h. The XRD, as well as TEM, have been to confirm the phase formation of CCTMO ceramics. The crystalline size of CCTMO was found in the range of 38–70 nm at different sintering temperatures. The route-mean square, as well as average roughness, was occurred by AFM technique 7.55 nm and 9.23 nm, respectively, sintered at 1373 K. The P-E hysteresis loop shows the ferromagnetic behavior of CCTMO at room temperature for all samples. The zero-field cooled (ZFC), field cooled (FC) and Magnetic hysteresis (M-H) curve shows temperature-dependent ferromagnetic behavior of CCTMO ceramic. The dielectric constant (ɛr) at 10 kHz decreases with increasing sintering temperature due to semiconducting grains and insulating boundaries supported by internal barrier layer capacitance modal (IBLC) in Mn-doped CCTO ceramic.

Studies on dielectric and magnetic properties of CaCu3Ti3MnO12 ceramic synthesized via semi-wet route

CaCu3Ti3MnO12 (CCTMO) ceramic has been successfully synthesized by the semi-wet route and sintered at 1223 K for 8 h, which is confirmed by XRD analysis to ensure CaCu3Ti3MnO12 (CCTMO) phase formation. The microstructure, phase-structure, and thermal behavior were examined by scanning electron microscopy (SEM), X-ray diffraction (XRD), and thermogravimetric analysis (TGA), respectively. After Mn-doping, the dielectric constant decreases from 104 to 102. The particle size as well as grain size measured by TEM and SEM techniques which were found to be 43.76 ± 10 nm and 1.46 μm, respectively. The route mean square and average roughness observed by atomic force microscope (AFM) analysis were 0.141 μm and 0.109 μm, respectively. The temperature-dependent ferromagnetic nature of CCTMO ceramic was confirmed by zero field cooled (ZFC), field cooled (FC), and M-H hysteresis curves. The investigated magnetic property of CCTMO confirmed paramagnetic behavior at 300 K and ferromagnetic behavior at 5 K. The dielectric constant (ɛr) increases when temperature increases, although dielectric constant and dielectric loss were observed 100 and 0.1, respectively.

Biocomputational Analysis and In Silico Characterization of an Angiogenic Protein (RNase5) in Zebrafish (Danio rerio)

Several types of RNase protein has been identified and characterized from different group of organism using advanced biocomputational techniques. Some of these RNase have crucial role for understanding and discovering the practical mechanism of cellular angiogenesis. To characterize the zebrafish (Danio rerio) RNase5 for its angiogenic properties multiple modern bioinformatics tools and server applied in present research. For accurate structural profiling of RNase5 protein through Ramachandran plot of PROCHECK and ProSA-web servers were applied. Subsequently, the CABS-flex server has been introduced to compute the Route Mean Square Fluctuation of all atoms for dynamic system simulation with a minimal residue fluctuation. Prediction of RNase5 protein’s sub-cellular localization, the TMHMM server also applied and confirmed its extracellular or secretory nature. Moreover, the sequence alignment with human angiogenin protein authenticates higher level of sequence similarity and reveals the conserved regions within protein of interest. Additionally, molecular docking with very low ACE value − 131.67 between the VEGF-binding domain of FLT-1 protein and zebrafish RNase5 able to generate the effective confirmation of cellular angiogenic activity. The molecular dynamic simulation by Normal Mode Analysis (NMA) also performed for functional mobility and structural stability of docking complex. Therefore, such In silico efforts definitely support the proper understanding of RNase5 enzyme mediated angiogenic activity. This effort certainly provides like a potential aid towards the future study of angiogenesis mechanism, as an important phenomenon of cancer metastasis in vertebrate model.

Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification

Simulation is of primal importance in the prediction of the produced power and automatic fault detection in PV grid-connected systems (PVGCS). The accuracy of simulation results depends on the models used for main components of the PV system, especially for the PV module. The present paper compares two PV array models, the five-parameter model (5PM) and the Sandia Array Performance Model (SAPM). Five different algorithms are used for estimating the unknown parameters of both PV models in order to see how they affect the accuracy of simulations in reproducing the outdoor behavior of three PVGCS. The arrays of the PVGCS are of three different PV module technologies: Crystalline silicon (c-Si), amorphous silicon (a-Si:H) and micromorph silicon (a-Si:H/μc-Si:H).The accuracy of PV module models based on the five algorithms is evaluated by means of the Route Mean Square Error (RMSE) and the Normalized Mean Absolute Error (NMAE), calculated for different weather conditions (clear sky, semi-cloudy and cloudy days). For both models considered in this study, the best accuracy is obtained from simulations using the estimated values of unknown parameters delivered by the ABC algorithm. Where, the maximum error values of RMSE and NMAE stay below 6.61% and 2.66% respectively.

Accurate THD Formulas for Multilevel Inverters with MILP Optimization

This study aims to derive simple formulas for calculating the accurate Total Harmonic Distortion (THD) of Multilevel Inverter (MLIs), that considers all low and high order harmonics, for single phase and three phase MLIs., since the THD is a main criterion when judging the performance of a MLI. In addition, a formula for the percentage route mean square value of high order harmonics %V HOrms is derived. These formulas could be applied for all types of MLIs that use different switching techniques for harmonics elimination or reduction, so long as the switching angles of the phase voltage are known and it is specially suitable to be applied with the Mixed Integer Linear Programming (MILP) optimization model, which is constructed for determining the switching angles of the MLI that minimize the absolute values of any undesired harmonics. This study aims also to show that this MILP model can produce solutions that have low values of the accurate THD, by giving the detailed solutions of two cases taken from the references.

Lattice summations for spread out particles: Applications to neutral and charged systems

This work is concerned with the lattice energy of periodic assemblies of mass and charge distributions of the form, exp (-αp(2)), where α is an adjustable positive variable and p(̱) is the vector from the lattice site or average position. The energy of interaction between two distributions is the density-weighted integral of the interactions between the volume elements of each distribution. Reciprocal space lattice summation formulas derived for particles represented by gaussian smeared-out density distributions are applied to the gaussian potential and a bounded version of the soft-sphere potential for a range of exponents. Two types of spatial broadening are considered, continuous or physical broadening (PB) and broadening resulting from the time average of point particle positions, so-called "time" broadening (TB). For neutral mass distributions a reciprocal space lattice summation formula is derived which is applied to the bounded soft-sphere potential. For the charged systems, the methodology described in Heyes [J. Chem. Phys. 74, 1924 (1981)] is used, which for the PB case gives the Ewald-like formulas derived by Gingrich and Wilson [Chem. Phys. Lett. 500, 178 (2010)] using a different method. Another expression for the lattice energy of the spread out charge distributions is derived which is cast entirely in terms of a summation over the reciprocal lattice vectors, without the arbitrary charge spreading function used in the Ewald method. The effects of charge spreading on a generalized definition of the Madelung constant (M) for a selection of crystal lattices are shown to be insignificant for route mean square displacements up to values typical of melting of an ionic crystal. When the length scale of the charge distribution becomes comparable to or greater than the mean inter particle spacing, however, the effects of charge broadening on the lattice energy are shown to be significant. In the PB case, M → 0 for the uniform charge density or α → 0 limit, and M ultimately becomes negative in the TB case for a large enough root mean square displacement (or small enough α).

Mapping of noise pollution by different interpolation methods in recovery section of Ghandi telecommunication Cables Company

Received: September 2013, Accepted: January 2013 Background: Noise pollution and workers' noise exposure are common in industrial factories in Iran. In order to reduce this noise pollution, evaluation and investigation of noise emission are both necessary. In this study, different noise mapping methodsare used for determining the distribution of noise. Materials and Methods: In the present study, for preparing a noise map in a hall of an industrial factory, sampling was carried out in 6×6 m grid. After data normalization the variogram was developed. For interpolation of mentioned parameter, kriging and Inverse Distance Weighting methods were used. The best model for interpolation was selected by cross validation and error evaluation methods, such as Route Mean Square Error(RMSE). Results: The results showed that kriging method is better than other methods for prediction of noise property. The noise map was prepared, using the best interpolation method in Geographical Information System environment. Conclusion: Workers in this industrial hall were exposed to noise which is mainly induced by noisy machines. Noise maps which were produced in this study showed the distribution of noise and, also revealed that workers suffer from serious noise pollution.

Development of Soft Sensors for Crude Distillation Unit Control

AbstractSoft sensors for distillation end point (D95) on-line estimation in crude distillation unit (CDU) are developed. Experimental data are acquired from the refinery distributed control system (DCS) and include on-line available continuously measured variables and laboratory assays. Soft sensors are developed using different linear and nonlinear identification methods. Additional laboratory data for model identification are generated by Multivariate Adaptive Regression Splines (MARSplines). The models are evaluated based on Route Mean Square (RMS), Absolute Error (AE), FIT and Final Prediction Error (FPE) criteria. The best results are achieved with Box Jenkins (BJ), Output Error (OE) and Hammerstein–Wiener (HW) model. Based on developed soft sensors it is possible to estimate fuel properties in continuous manner and apply inferential control. By real plant application of developed soft sensors considerable savings could be expected, as well as compliance with strict law regulations for product quality specifications.

Quantum chemical calculations to reveal the relationship between the chemical structure and the fluorescence characteristics of phenylquinolinylethynes and phenylisoquinolinylethynes derivatives, and to predict their relative fluorescence intensity

In this paper the relationship between the chemical structure and fluorescence characteristics of 30 phenylquinolinylethyne (PhQE), and phenylisoquinolinylethyne (PhIE) derivatives compounds employing ab initio calculations have been elucidated. Quantum chemical calculations (6-31G) were carried out to obtain: the optimized geometry, energy levels, charges and dipole moments of these compounds, in the singlet (steady and excited states) and triplet states. The relationship between quantum chemical descriptors, and wavelength of maximum excitation and emission indicated that these two parameters have the most correlation with quantum chemical hardness ( η). Also, stokes shift has the most correlation with the square of difference between the maximum of positive charges in the singlet steady and singlet excited states. The quantitative structure–property relationship (QSPR) of PhQE and PhIE was studied for relative fluorescence intensity (RFI). The genetic algorithm (GA) was applied to select the variables that resulted in the best-fit models. After the variable selection, multiple linear regression (MLR) and support vector machine (SVM) were both utilized to construct linear and non-linear QSPR models, respectively. The SVM model demonstrated a better performance than that of the MLR model. The route mean square error (RMSE) in the training and the test sets for the SVM model was 0.195 and 0.324, and the correlation coefficients were 0.965 and 0.960, respectively, thus revealing the reliability of this model. The resulting data indicated that SVM could be used as a powerful modeling tool for QSPR studies. According to the best of our knowledge, this is the first research on QSPR studies to predict RFI for a series of PhQE and PhIE derivative compounds using SVM.