Effect of cysteine on the electrochemical behavior of Cu10Ni alloy in sulfide polluted environments: Experimental and theoretical aspects

Electrochemical techniques were used to investigate the effect of cysteine (cys) on the dissolution behavior of Cu10Ni alloy in sulfide-polluted salt water through the analysis of the alloy electrochemical behavior including Tafel polarization and electrochemical frequency modulation (EFM) along with scanning electron microscopy (SEM) and energy dispersive X-ray diffraction (EDX). The results showed that cys acts as a mixed type inhibitor. The dissolution rate decreases with increasing cys concentration and increases with increasing the temperature. An adherent layer of inhibitor molecules on the alloy surface is proposed to account for their inhibitive action. The adsorption of cys on the active cathodic sites was found to follow the Langmuir adsorption isotherm from which the thermodynamic parameters of adsorption and corrosion processes were determined and discussed. A synergistic effect was observed between cys and potassium iodide (KI). EFM data are in good agreement with SEM and EDX results.     

Multivariate determinants of the need for postoperative ventilation in myasthenia gravis

PURPOSE: Following transsternal thymectomy, up to 50% of patients may require postoperative ventilation. The aim of this study was to identify the variables most useful in predicting the myasthenic patient who needs postoperative mechanical ventilation. METHODS: We applied multivariate discriminant analysis to preoperative physical, historical, laboratory and intraoperative data of 51 myasthenic patients who underwent transcervical-transsternal thymectomy to select those variables most useful in predicting the postoperative need for mechanical ventilation. The receiver operating characteristic (ROC) curve was also used to describe the discrimination abilities and to explore the trade-offs between sensitivity and specificity of the model. RESULTS: Discriminant analysis identified seven risk factors that correlated with the need for postoperative ventilation: FVC, FEF25-75%, MEF50% and their percentages of the predicted values, as well as, sex. The model correctly predicted the actual ventilatory outcome in 88.2% of patients. The area under the ROC curve verified that our model correctly predicted the actual ventilatory outcome with a probability of 88.2%. CONCLUSIONS: This model can be used for predicting the need for postoperative mechanical ventilation in myasthenia gravis patients.     

A novel optimized neutrosophic <Emphasis Type="Italic">k</Emphasis>-means using genetic algorithm for skin lesion detection in dermoscopy images

This paper implemented a new skin lesion detection method based on the genetic algorithm (GA) for optimizing the neutrosophic set (NS) operation to reduce the indeterminacy on the dermoscopy images. Then, k-means clustering is applied to segment the skin lesion regions. Therefore, the proposed method is called optimized neutrosophic k-means (ONKM). On the training images set, an initial value of \(\alpha \) in the \(\alpha \)-mean operation of the NS is used with the GA to determine the optimized \(\alpha \) value. The Jaccard index is used as the fitness function during the optimization process. The GA found the optimal \(\alpha \) in the \(\alpha \)-mean operation as \(\alpha _{\mathrm{optimal}} =0.0014\) in the NS, which achieved the best performance using five fold cross-validation. Afterward, the dermoscopy images are transformed into the neutrosophic domain via three memberships, namely true, indeterminate, and false, using \(\alpha _{\mathrm{optimal}}\). The proposed ONKM method is carried out to segment the dermoscopy images. Different random subsets of 50 images from the ISIC 2016 challenge dataset are used from the training dataset during the fivefold cross-validation to train the proposed system and determine \(\alpha _{\mathrm{optimal}}\). Several evaluation metrics, namely the Dice coefficient, specificity, sensitivity, and accuracy, are measured for performance evaluation of the test images using the proposed ONKM method with \(\alpha _{\mathrm{optimal}} =0.0014\) compared to the k-means, and the \(\gamma \)–k-means methods. The results depicted the dominance of the ONKM method with \(99.29\pm 1.61\%\) average accuracy compared with k-means and \(\gamma \)–k-means methods.     

A simulation analysis of the impact of FAHP–MAUT triage algorithm on the Emergency Department performance measures

Emergency Department (ED) crowding is a major problem in the U.S. like in many other countries world-wide. This problem is adversely affecting the safety of the patients who rely on receiving a timely treatment in EDs. As a part of solving this problem, a triage process is utilized. Triage is a pre-hospital process by which patients are sorted according to the severity of their illnesses or injuries. Improvements to this process would affect the patient flow positively, and in turn would enhance patient satisfaction and quality of care. In a previous study, we developed a triage algorithm that uses Fuzzy Analytic Hierarchy Process (FAHP) and Multi-Attribute Utility Theory (MAUT) to rank the patients according to their characteristics: chief complaint, age, gender, pain level, and vital signs. The main purpose of this study is to compare two triage systems using Discrete Event Simulation (DES); one system uses the typical Emergency Severity Index (ESI), and the other uses the FAHP and MAUT algorithm. Overall, there was no strong statistical evidence that either system would do better than the other for all the performance measures when the average is taken across all ESI levels. On the other hand, the collected simulated data by each ESI level showed that the FAHP–MAUT algorithm tends to balance the time-to-bed (TTB) and length of stay (LOS) for ESI levels 2–5. In terms of the percentage of tardy patients, FAHP–MAUT system significantly outperforms the ESI system for ESI levels 4 and 5; 34% vs. 61% and 25% vs. 70%, respectively. Both systems were performing about equally for ESI level 1 and level 3 patients; 25% vs. 26% and 64% vs. 67%, respectively. While ESI system slightly outperforms FAHP–MAUT system for ESI level 2 patients, 56% vs. 66%. Based on these results, we recommend using FAHP–MAUT not only because it performs better in terms of minimizing the number of patients with longer than the allotted upper limits of wait times, but also it reduces potential bias and errors in decision making in clinical settings; and thus, it can be used as the basis of an expert system to advise triage nurses.     

Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in X-ray image datasets

Multimedia Tools and Applications - The imbalance and multi-dimension are two common problems in the medical image datasets, which affect the performances of the image processing procedures. The...     

Influence of pressurized carbon dioxide on ketoprofen-incorporated hot-melt extruded low molecular weight hydroxypropylcellulose

Objectives: The aim of the current research project was to investigate the effect of pressurized carbon dioxide (P-CO₂) on the physico-mechanical properties of ketoprofen (KTP)-incorporated hydroxypropylcellulose (HPC) (Klucel? ELF, EF, and LF) produced using hot-melt extrusion (HME) techniques and to assess the plasticization effect of P-CO₂ on the various polymers tested.

Methods: The physico-mechanical properties of extrudates with and without injection of P-CO₂ were examined and compared with extrudates with the addition of 5% liquid plasticizer of propylene glycol (PG). The extrudates were milled and compressed into tablets. Tablet characteristics of the extrudates with and without injection of P-CO₂ were evaluated.

Results and conclusion: P-CO₂ acted as a plasticizer for tested polymers, which allowed for the reduction in extrusion processing temperature. The microscopic morphology of the extrudates was changed to a foam-like structure due to the expansion of the CO₂ at the extrusion die. The foamy extrudates demonstrated enhanced KTP release compared with the extrudates processed without P-CO₂ due to the increase of porosity and surface area of those extrudates. Furthermore, the hardness of the tablets prepared by foamy extrudates was increased and the percent friability was decreased. Thus, the good binding properties and compressibility of the extrudates were positively influenced by utilizing P-CO₂ processing.     

Effect of sulfide ions on the stress corrosion behaviour of Al-brass and Cu10Ni alloys in salt water

The stress corrosion cracking (SCC) behavior of Al-brass and Cu10Ni alloys was investigated in 3.5% NaCl solution in absence and in presence of different concentrations of Na₂S under open-circuit potentials using the constant slow strain rate technique. The results indicated that the Cu10Ni alloy is more susceptible to stress corrosion cracking than as-received Al-brass at strain rate of 3.5 × 10^–6 s^–1 in 3.5% NaCl in presence of high concentration of sulfide ions (1000 ppm). The sulfide ions (up to 500 ppm) has no effect on the stress corrosion cracking of the annealed Al-brass in 3.5% NaCl at two strain rates of 7.4 × 10^–6 and 3.5 × 10^–6 s^–1. The results support film rupture for Al-brass and sulfide stress corrosion cracking assisted with pitting corrosion for Cu10Ni at slip steps as the operating mechanisms.     

Model comparison in Emergency Severity Index level prediction

Emergency Department (ED) triage is a process of determining illness severity and accordingly assigning patient priority. The Emergency Severity Index (ESI) is a 5-level acuity categorization system that aides in triage. This paper compared the capabilities of predicting ESI level using ordinal logistic regression (OLR), artificial neural networks (NNs), and naïve Bayesian networks (NBNs). Data were obtained from Susquehanna Williamsport Hospital for 947 patients over a one month period in 2008. It contained the assigned ESI level, chief complaint, systolic blood pressure, pulse, respiration rate, temperature, oxygen saturation level (SaO₂), age, gender, and pain level. An OLR model was fit using a subset of these covariates. NBNs and NNs were modeled to relax the inherent assumptions of linearity and covariate independence in logistic regression. These three techniques were compared using incremental training dataset sizes between 50% and 100% of given data. All models were >60% accurate using the entire dataset for training. It was found that NBNs and NNs were robust to data size changes and all models had evaluation speeds of less than 0.5 s. At this time the use of NBNs is recommended considering speed, accuracy, data utilization, model flexibility, and interpretability of the model.     

Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem

Neural Computing and Applications - Robotics are extensively utilized in modern industry to replace human labor and achieve high automation and flexibility. In order to produce large-size products,...