Annual Rainfall Forecasting Using Hybrid Artificial Intelligence Model: Integration of Multilayer Perceptron with Whale Optimization Algorithm

Water Resources Management - Rainfall, as one of the key components of hydrological cycle, plays an undeniable role for accurate modelling of other hydrological components. Therefore, a precise...     

Interference mitigation and avoidance in uplink OFDMA with collaborative distributed intracell scheduling

A distributed collaborative uplink scheduling model in OFDMA systems is extended to a multicell scenario. On the intercell level, the scenarios with and without base station cooperation are investigated. In the cooperative case, base stations collaborate by performing pricing-based power control based on exchanging interference information. In the absence of intercell collaboration, probabilistic transmission is applied in each cell for interference avoidance. Simulations show that the proposed intercell interference mitigation and avoidance schemes lead to enhanced results.     

Preparation,characterization and dissolution behavior of artemisinin microparticles

In the present study, a modified 4-fluid nozzle spray drier was used to prepare microparticles of a poorly water soluble drug, artemisinin with the aim of improving its solubility. We also investigated the effect of process variables on the physical properties and dissolution rate of spray dried artemisinin. A full factorial experimentally designed study was performed to investigate the following spray drying variables: inlet temperature and feed concentration. The artemisinin powder and spray dried artemisinin microparticles were characterized by scanning electron microscopy (SEM), differential scanning calorimetric (DSC), X-ray diffraction (XRD) and dissolution. SEM study suggested that the inlet temperature and feed concentration impacted on the particle size of the spray dried particles. The crystallinity of spray dried particles was slightly decreased with increasing inlet temperature and concentration. The dissolution of spray dried particles was markedly improved as compared to commercial artemisinin. A dissolution surface-response model was used to elucidate the significant and direct relationships between drug feed concentration and inlet temperature on one hand and dissolution on the other hand. The best dissolution was found to be 117.00 ± 5.15 μg/mL at the drug feed concentration of 10 g/L and inlet temperature of 140 °C.     

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.     

Non catheter‐related bacteremia caused by Pseudomonas oryzihabitans in a patient undergoing hemodialysis

Pseudomonas oryzihabitans (P. orizyhabitans) has already been reported both as a human and a zoonotic pathogen. A few cases of P. orizyhabitans bacteremia have been reported among patients who underwent peritoneal dialysis. P. orizyhabitans bacteremia has never been reported among patients on hemodialysis. We report the first case of P. orizyhabitans bacteremia in a chronic hemodialysis patient; this patient did not have a central venous catheter angioaccess as a potential portal of entry.     

Copper complexes of some tetra- and pentapeptides containing polyfunctional amino acids

Copper complexes of some tetra- and pentapeptides [(I)-(VI)] containing lysine and ornithine are spectrophotometrically examined. It is shown that all tetra- and pentapeptides form red or reddish violet copper complexes with λ_max 515–540 nm, and copper-peptide ratio 1:1. The results achieved confirmed that the N⁶-amino group of the lysine residues or the N⁵-amino group of the ornithine residues do not participate in complex formation of tetra- and pentapeptides even when lysine or ornithine form the N-terminal amino acid in the peptide.     

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.     

Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation

Soft computing models are known as an efficient tool for modelling temporal and spatial variation of surface water quality variables and particularly in rivers. These model’s performance relies on how effective their simulation processes are accomplished. Fuzzy logic approach is one of the authoritative intelligent model in solving complex problems that deal with uncertainty and vagueness data. River water quality nature is involved with high stochasticity and redundancy due to the its correlation with several hydrological and environmental aspects. Yet, the fuzzy logic theory can give robust solution for modelling river water quality problem. In addition, this approach likewise can be coordinated with an expert system framework for giving reliable and trustful information for decision makers in enhancing river system sustainability and factual strategies. In this research, different hybrid intelligence models based on adaptive neuro-fuzzy inference system (ANFIS) integrated with fuzzy c-means data clustering (FCM), grid partition (GP) and subtractive clustering (SC) models are used in modelling river water quality index (WQI). Monthly measurement records belong to Selangor River located in Malaysia were selected to build the predictive models. The modelling process was included several water quality terms counting physical, chemical and biological variables whereas WQI was the target variable. At the first stage of the research, statistical analysis for each water quality parameter was analyzed toward the WQI. Whereas in the second stage, the predictive models were established. The finding of the current research provides an authorized soft computing model to determine WQI that can be used instead of the conventional procedure that consumes time, cost, efforts and sometimes computation errors.     

Effects of the slip boundary condition on dynamics and pull-in instability of carbon nanotubes conveying fluid

This paper addresses the effects of the slip boundary condition on dynamics and pull-in instability of carbon nanotubes (CNTs) containing internal fluid flow. Both the clamped–clamped and the cantilever boundary conditions are considered. The structure of CNTs is modelled using the size-dependent strain gradient theory (SGT) of continuum mechanics. It is shown that the Knudsen number (Kn) has a significant effect on the static and dynamic CNT response due to pull-in voltage loading and the existence of the instability region.