Patient mood flux on‐ and off‐hemodialysis

Mood in hemodialysis patients is most often evaluated off‐dialysis, possibly underestimating mood during dialysis. We compared mood in patients on‐ and off‐dialysis using the Positive and Negative Affect Schedule for 6 consecutive days. Initially, scores are normal, but subsequently positive affect falls below, and negative affect increases above, off‐dialysis values, suggesting increasing depression and anxiety, particularly in women. Quality‐of‐life questions confirm the effects of the dialysis session on mood. Prevalence and severity of depression evaluated off‐dialysis, or once only, may be underestimated, especially in women, because hemodialysis patients undergo mood swings centering on the dialysis session. Focusing insight on the dialysis session could improve coping among patients and caretakers.     

Rapid response concentration-controlled desorption of activated carbon to dampen concentration fluctuations

Fluctuations in concentration of organic vapors in gas streams that are treated by devices such as biofilters or oxidizers make it challenging to remove the vapors from the gas streams in an efficient and economic manner. Combining adsorption with concentration-controlled desorption provides an active buffer between the source of vapors and the control device for better control of concentration and flow rate of the gas stream that is treated by the secondary control device, hence further enhancing the performance or reducing the size of the devices. Activated carbon fiber cloth is used with microwave swing adsorption to remove methyl ethyl ketone (MEK) from air streams and then provide a readily controllable feed stream of that vapor in air at a specified concentration and gas flow rate with steady-state tracking desorption. MEK was captured with >99.8% efficiency during the adsorption cycle. The MEK concentration during the regeneration cycle was readily controlled at concentration set-points between 170 and 5000 ppmv, within relative standard deviations of 1.8 and 4.9%, respectively, and at 20% of the gas flow rate that was treated during the adsorption cycle. Such capability of the system allows the secondary control device to be optimized for select constant concentrations and low gas flow rates that is not possible without such pretreatment.     

A segmented conical electric lens for optimization of the beam spot of the low-energy muon facility at PSI: a Geant4 simulation analysis

The low-energy muon facility at PSI provides nearly fully polarized positive muons with tunable energies in the keV range to carry out muon spin rotation (LE-μSR)experiments with nanometer depth resolution on thin films,heterostructures,and near-surface regions.The low-energy muon beam is focused and transported to the sample by electrostatic lenses.In order to achieve a minimum beam spot size at the sample position and to enable the steering of the beam in the horizontal and vertical direction,a special electrostatic device has been implemented close to the sample position.It consists of a cylinder at ground potential followed by four conically shaped electrodes,which can be operated at different electric potential.In LEμSR experiments,an electric field at the sample along the beam direction can be applied to accelerate/decelerate muons to different energies (0.5-30 keV).Additionally,a horizontal or vertical magnetic field can be superimposed for transverse or longitudinal field μSR experiments.The focusing properties of the conical lens in the presence of these additional electric and magnetic fields have been investigated and optimized by Geant4 simulations.Some experimental tests were also performed and show that the simulation well describes the experimental setup.     

The behaviour of some derivatives of 1,2,4-triazine toward organomagnesium halides

6-Phenyl-3,5-dioxo-2,3,4,5-tetrahydro-1,2,4-triazine 1 , 3-oxo-5,6-diphenyl-2,3-dihydro-1,2,4-triazine 3 , 3,5,6-triphenyl-1,2,4-triazine 4 , and 3-chloro-5,6-diphenyl-1,2,4-triazine 8 , react with phenylmagnesium bromide. The reactions involved are represented by scheme A. Compound 3 reacts with o- and/or p-methoxyphenylmagnesium bromide to give compounds believed to have structures like 10 and 11 respectively. Compound 1 reacts with methylmagnesium bromide and with benzylmagnesium chloride to give products believed to have structures like 12 a , b .     

Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme learning machines, including the standard, online sequential, and kernel extreme learning machines, in addition to the artificial neural network, classification and regression tree model, and statistical multiple linear regression model. The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models. The input variability was assessed based on two scenarios prior to the application of the predictive models. For the first assessment, the machine learning models were developed using all the available cement and concrete mixture input variables; the second assessment was conducted based on the gamma test approach, which is a sensitivity analysis technique. Subsequently, the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches. The adopted methodology attained optimistic and reliable modeling results. The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.     

Cottonseed colour fixed pigments. Part II. Role of hexane isomers on oil quality

Colour fixed pigments can be detected in refined and bleached oils rather than in crude oils, since in the latter their absorption is masked up by the gossypol pigments. Isohexane is the most desirable hexane isomer. Though it produces darker crude oils, yet refinability and bleachability increases as the percentage of isohexane in normal hexane increases. It has also the advantage of extracting oils with lowest refining loss and with the highest gossypol content. Recovery of gossypol, which is alkali refinable, with the oil results in a superior quality of meal. Benzene, on the other hand, is the least desirable constituent in commercial hexane. Its presence with n-hexane results in darker coloured crude, refined and bleached oil colours, indicating the selectivity of benzene to colour fixed pigments. Highest refining loss were found in oils extracted with benzene or benzene-hexane solvent mixtures. Cyclohexane presence with n-hexane (35% to 60%) and methylcyclopentane (6.0% to 12.0%) do not increase the degree of extraction of colour fixed pigments, however increase the refining loss of the extracted oils.     

Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows

An investigation is presented in this paper to study the performance of Artificial Intelligence running Multiple Models (AIMM) using time series of river flows. This is a modelling strategy, which is formed by first running two Artificial Intelligence (AI) models: Support Vector Machine (SVM) and its hybrid with the Fire-Fly Algorithm (FFA) and they both form supervised learning at Level 1. The outputs of Level 1 models serve as inputs to another AI Model at Level 2. The AIMM strategy at Level 2 is run by Artificial Neural Network (MM-ANN) and this is compared with the Simple Averaging (MM-SA) of both inputs. The study of the performances of these models (SVM, SVM-FFA, MM-SA and MM-ANN) in the paper shows that the ability of SVM-FFA in matching observed values is significantly better than that of SVM and that of MM-ANN is considerably better than each SVM and/or SVM-FFA but the performances are deteriorated by using the MM-SA strategy. The results also show that the residuals of MM-ANN are less noisy than those shown by the models at  Level 1 and those at Level 2 do not display any trend.     

Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

In this study, a new hybrid model integrated adaptive neuro fuzzy inference system with Firefly Optimization algorithm (ANFIS-FFA), is proposed for forecasting monthly rainfall with one-month lead time. The proposed ANFIS-FFA model is compared with standard ANFIS model, achieved using predictor-predictand data from the Pahang river catchment located in the Malaysian Peninsular. To develop the predictive models, a total of fifteen years of data were selected, split into nine years for training and six years for testing the accuracy of the proposed ANFIS-FFA model. To attain optimal models, several input combinations of antecedents’ rainfall data were used as predictor variables with sixteen different model combination considered for rainfall prediction. The performances of ANFIS-FFA models were evaluated using five statistical indices: the coefficient of determination (R ² ), Nash-Sutcliffe efficiency (NSE), Willmott’s Index (WI), root mean square error (RMSE) and mean absolute error (MAE). The results attained show that, the ANFIS-FFA model performed better than the standard ANFIS model, with high values of R ² , NSE and WI and low values of RMSE and MAE. In test phase, the monthly rainfall predictions using ANFIS-FFA yielded R ² , NSE and WI of about 0.999, 0.998 and 0.999, respectively, while the RMSE and MAE values were found to be about 0.272 mm and 0.133 mm, respectively. It was also evident that the performances of the ANFIS-FFA and ANFIS models were very much governed by the input data size where the ANFIS-FFA model resulted in an increase in the value of R ² , NSE and WI from 0.463, 0.207 and 0.548, using only one antecedent month of data as an input (t-1), to almost 0.999, 0.998 and 0.999, respectively, using five antecedent months of predictor data (t-1, t-2, t-3, t-6, t-12, t-24). We ascertain that the ANFIS-FFA is a prudent modelling approach that could be adopted for the simulation of monthly rainfall in the present study region.     

A facile synthesis of 3,5-dimethyl-4-hydroxybenzaldehyde via copper-mediated selective oxidation of 2,4,6-trimethylphenol

2,4,6-Trimethylphenol was selectively oxidized to 3,5-dimethyl-4-hydroxybenzaldehyde in very good yields using catalytic or equivalent amounts of CuCl₂ in the presence of K₂CO₃ and H₂O₂ in i-PrOH at 65 °C. The effect of the molar ratios of CuCl₂, K₂CO₃ and H₂O₂ on the yields and product distribution was examined. The oxidation reaction was found to proceed smoothly without the use of additives or ligands which were reported to be necessary.     

Effect of γ-Radiation on the Electronic Spectra of Poly(Methyl Methacrylate) Doped with Some Divalent Nd10 Cation Dithizones

The electronic absorption spectra of dithizone (H₂DZ) and its metal complexes of Hg. Cd, and Zn doped in poly(methyl methacrylate), PMMA has been investigated before and after γ-irradiation. It is found that the complex formation resulted in significant changes in both the position and the intensity of the electronic bands of H₂DZ. γ-Radiation causes remarkable decrease in the intensity of the most absorption bands of the metal complexes. The rate of decrease depends on the nature of the metal ions.