Soft computing applications in dynamic model identification of polymer extrusion process

This paper proposes the application of soft computing to deal with the constraints in conventional modelling techniques of the dynamic extrusion process. The proposed technique increases the efficiency in utilising the available information during the model identification. The resultant model can be classified as a ‘grey-box model’ or has been termed as a ‘semi-physical model’ in the context. The extrusion process contains a number of parameters that are sensitive to the operating environment. Fuzzy ruled-based system is introduced into the analytical model of the extrusion by means of sub-models to approximate those operational-sensitive parameters. In drawing the optimal structure for the sub-models, a hybrid algorithm of genetic algorithm with fuzzy system (GA-Fuzzy) has been implemented. The sub-models obtained show advantages such as linguistic interpretability, simpler rule-base and less membership functions. The developed model is adaptive with its learning ability through the steepest decent error back-propagation algorithm. This ability might help to minimise the deviation of the model prediction when the operational-sensitive parameters adapt to the changing operating environment in the real situation. The model is first evaluated through simulations on the consistency of model prediction to the theoretical analysis. Then, the effectiveness of adaptive sub-models in approximating the operational-sensitive parameters during the operation is further investigated

Cognitive Rehabilitation for Executive Dysfunction in Parkinson's Disease: Application and Current Directions

Cognitive dysfunction in Parkinson's disease contributes to disability, caregiver strain, and diminished quality of life. Cognitive rehabilitation, a behavioral approach to improve cognitive skills, has potential as a treatment option to improve and maintain cognitive skills and increase quality of life for those with Parkinson's disease-related cognitive dysfunction. Four cognitive rehabilitation programs in individuals with PD are identified from the literature. Characteristics of the programs and outcomes are reviewed and critiqued. Current studies on cognitive rehabilitation in PD demonstrate feasibility and acceptability of a cognitive rehabilitation program for patients with PD, but are limited by their small sample size and data regarding generalization of effects over the long term. Because PD involves progressive heterogeneous physical, neurological, and affective difficulties, future cognitive rehabilitation programs should aim for flexibility and individualization, according to each patient's strengths and deficits

Feasibility of Using Cranial Electrotherapy Stimulation for Pain in Persons with Parkinson's Disease

Objectives. To assess the feasibility of treating musculoskeletal pain in the lower back and/or lower extremities in persons with Parkinson's disease (PD) with cranial electrotherapy stimulation (CES). Design. Randomized, controlled, double-blind trial. Setting. Veterans Affairs Medical Center, Community. Participants. Nineteen persons with PD and pain in the lower back and/or lower extremities. Thirteen provided daily pain rating data. Intervention. Of the thirteen participants who provided daily pain data, 6 were randomly provided with active CES devices and 7 with sham devices to use at home 40 minutes per day for six weeks. They recorded their pain ratings on a 0-to-10 scale immediately before and after each session. Main Outcome Measure. Average daily change in pain intensity. Results. Persons receiving active CES had, on average, a 1.14-point decrease in pain compared with a 0.23-point decrease for those receiving sham CES (Wilcoxon Z = −2.20, P = .028). Conclusion. Use of CES at home by persons with PD is feasible and may be somewhat helpful in decreasing pain. A larger study is needed to determine the characteristics of persons who may experience meaningful pain reduction with CES. Guidelines for future studies are provided

Applications of exact solution for strongly interacting one dimensional bose-fermi mixture: low-temperature correlation functions, density profiles and collective modes

We consider one dimensional interacting bose-fermi mixture with equal masses of bosons and fermions, and with equal and repulsive interactions between bose-fermi and bose-bose particles. Such a system can be realized in current experiments with ultracold bose-fermi mixtures.We apply the Bethe-ansatz technique to find the exact ground state energy at zero temperature for any value of interaction strength and density ratio between bosons and fermions. We use it to prove the absence of the demixing, contrary to prediction of a mean field approximation. Combining exact solution with local density approximation (LDA) in a harmonic trap, we calculate the density profiles and frequencies of collective modes in various limits. In the strongly interacting regime, we predict the appearance of low-lying collective oscillations which correspond to the counterflow of the two species. In the strongly interacting regime we use exact wavefunction to calculate the single particle correlation functions for bosons and fermions at low temperatures under periodic boundary conditions. We derive an analytical formula, which allows to calculate correlation functions at all distances numerically for a polynomial time in the system size. We investigate numerically two strong singularities of the momentum distribution for fermions at $k_f$ and $k_f+2k_b.$ We show, that in strongly interacting regime correlation functions change dramatically as temperature changes from 0 to a small temperature $\sim E_f/\gamma.$ A strong change of the momentum distribution in a small range of temperatures can be used to perform a thermometry at very small temperatures.Comment: v2 More extensive discussion of collective modes, newfigures adde

Acute Stroke, Hematocrit, and Blood Pressure.

A population-based study of the relation between hematocrit and stroke subtype was carried out among 2,077 individuals using the Lehigh Valley Stroke Register. This register identifies all stroke patients admitted to the 8 acute care hospitals serving the Lehigh Valley area of eastern Pennsylvania-western New Jersey. The mean hematocrit was higher in patients with lacunes than with thrombotic or embolic strokes (p = 0.02). However, when blood pressure was also considered the increase in hematocrit in patients with lacunar stroke was significant only when systolic hypertension (greater than or equal to 150 mm Hg) was also present (p = 0.029); no significant difference in hematocrit was found between stroke subtypes in normotensive individuals. Therefore, we cannot exclude the possibility that hypertension interacts with hematocrit in accounting for the observed association with lacunar infarcts. There was no trend for increased in-hospital mortality for stroke patients in either the low (less than or equal to 30, 30-36%) or high (greater than or equal to 47%) hematocrit groups

Coronavirus-positive Nasopharyngeal Aspirate as Predictor for Severe Acute Respiratory Syndrome Mortality

Severe acute respiratory syndrome (SARS) has caused a major epidemic worldwide. A novel coronavirus is deemed to be the causative agent. Early diagnosis can be made with reverse transcriptase-polymerase chain reaction (RT-PCR) of nasopharyngeal aspirate samples. We compared symptoms of 156 SARS-positive and 62 SARS-negative patients in Hong Kong; SARS was confirmed by RT-PCR. The RT-PCR–positive patients had significantly more shortness of breath, a lower lymphocyte count, and a lower lactate dehydrogenase level; they were also more likely to have bilateral and multifocal chest radiograph involvement, to be admitted to intensive care, to need mechanical ventilation, and to have higher mortality rates. By multivariate analysis, positive RT-PCR on nasopharyngeal aspirate samples was an independent predictor of death within 30 days

Combination of G72 Genetic Variation and G72 Protein Level to Detect Schizophrenia: Machine Learning Approaches

The D-amino acid oxidase activator (DAOA, also known as G72) gene is a strong schizophrenia susceptibility gene. Higher G72 protein levels have been implicated in patients with schizophrenia. The current study aimed to differentiate patients with schizophrenia from healthy individuals using G72 single nucleotide polymorphisms (SNPs) and G72 protein levels by leveraging computational artificial intelligence and machine learning tools. A total of 149 subjects with 89 patients with schizophrenia and 60 healthy controls were recruited. Two G72 genotypes (including rs1421292 and rs2391191) and G72 protein levels were measured with the peripheral blood. We utilized three machine learning algorithms (including logistic regression, naive Bayes, and C4.5 decision tree) to build the optimal predictive model for distinguishing schizophrenia patients from healthy controls. The naive Bayes model using two factors, including G72 rs1421292 and G72 protein, appeared to be the best model for disease susceptibility (sensitivity = 0.7969, specificity = 0.9372, area under the receiver operating characteristic curve (AUC) = 0.9356). However, a model integrating G72 rs1421292 only slightly increased the discriminative power than a model with G72 protein alone (sensitivity = 0.7941, specificity = 0.9503, AUC = 0.9324). Among the three models with G72 protein alone, the naive Bayes with G72 protein alone had the best specificity (0.9503), while logistic regression with G72 protein alone was the most sensitive (0.8765). The findings remained similar after adjusting for age and gender. This study suggests that G72 protein alone, without incorporating the two G72 SNPs, may have been suitable enough to identify schizophrenia patients. We also recommend applying both naive Bayes and logistic regression models for the best specificity and sensitivity, respectively. Larger-scale studies are warranted to confirm the findings