X-ray source design optimization using differential evolution algorithms-A case study.

Traditional x-ray sources used today for multiple applications, such as medical imaging (computed tomography, radiography, mammography, and interventional radiology) or industrial inspection, are vacuum based electron beam devices that include several key components, such as electron emitters, electron guns/cathodes, and anodes/targets. The associated electronics for electron beam generation, focusing and control, and beam acceleration are located outside the vacuum chamber. The general topology of these tubes has been directionally unchanged for more than 100 years; however, tube design remains a long, inefficient, tedious, and complex process; blind design of experiments do not necessarily make the process more efficient. As a case study, in this paper, we introduce the differential evolution (DE), an artificial intelligence-based optimization algorithm, for the design optimization of x-ray source beam optics. Using a small-scale design problem, we demonstrate that DE can be an effective optimization method for x-ray source beam optics design.

Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques.

Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT's turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts' interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology's effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction.

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines.

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures.

Toward automatic time-series forecasting using neural networks.

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

The 559-to-600 nm shift observed in red fluorescent protein eqFP611 is attributed to cis-trans isomerization of the chromophore in an anionic protein pocket.

Fluorescent proteins are commonly used as molecular labels, noninvasive markers of gene expression, and reporters of environmental conditions in live cells. We investigate the structural and spectroscopic properties of the chromophore of a far-red fluorescent protein eqFP611. Both the cis and trans isomers of the chromophore are examined within the protein for which both anionic and neutral states of protonation are considered. Spectroscopic properties are examined using time-dependent density functional theory (TDDFT), employing the B3LYP, PBE and B3PW91 density functionals. Intermolecular and long-range contributions to the structure and spectroscopy were treated using the own n-layered integrated molecular orbital and molecular mechanics (ONIOM) approach. The results indicated that the chromophore before excitation is in an anionic, protonated state, with the long-range contributions inducing a blue shift in the absorption and fluorescence maxima of the chromophore. Moreover, the calculated changes of the lowest pi-pi* excitation energy upon isomerization match the observed shift from 559 to 600 nm in the absorption maximum of the system following prolonged irradiation. Furthermore, decomposition analysis of the electrostatic contributions from individual residues indicated that the interactions from four residues Arg92, Lys67, Glu145, and His197 to the chromophore play a key role in the absorption and fluorescence spectra of eqFP611, suggesting that mutations at these sites should provide very useful mechanistic information.

Electronic excitations of green fluorescent proteins: modeling solvatochromatic shifts of red fluorescent protein chromophore model compound in aqueous solutions.

While green fluorescent proteins (GFPs) have been widely used as tools in biochemistry, cell biology, and molecular genetics, novel red fluorescent proteins (RFPs) with red fluorescence emission have also been identified, as complements to the existing GFP technology. The unusual spectrophotometric and fluorescence properties of GFPs and RFPs are controlled by the protonation states and possibly cis/trans isomerization within their chromophores. In this work, we have investigated the electronic structures, liquid structures, and solvent shifts of the possible neutral and anionic protonated states and the cis/trans isomerization of a RFP chromophore model compound HBMPI in aqueous solutions. The calculations reproduced the experimental absorption solvatochromatic shifts of dilute HBMPI in water under neutral and anionic conditions. Unlike the GFP chromophore, the RFP chromophore model compound HBMPI in basic solution can only adopt a conformation where the C=C bond between the bridge group and the imidazolinone ring and the C-C bond between the imidazolinone and ethylene groups exist in cis and trans conformations, respectively. Moreover, the solvent-solute hydrogen-bonding interactions are found to contribute significantly to the total solvent shifts of pi-pi* excitations of aqueous HBMPI solutions, signifying the importance of protein environment in the determination of the conformation of the chromophores in red fluorescent proteins.

A theoretical study of solvent effects on tautomerism and electronic absorption spectra of 3-hydroxy-2-mercaptopyridine and 2,3-dihydroxypyridine.

The tautomerization equilibria of 3-hydroxy-2-mercaptopyridine (HMP) and 2,3-dihydroxypyridine (DHP) in vacuo and in ethanol solution have been studied using the density functional theory (DFT) at B3LYP/6-31Gd level. The results indicate that the thione form of HMP and the keto form of DHP are the most stable tautomers in the equilibrium, and the energy barrier for the thiol-thione and enol-keto proton transfer decreases significantly when the tautomerism is mediated by a specific ethanol molecule in solution. The time-dependent density functional theory--polarizable continuum model (TDDFT-PCM) calculations on all tautomers of HMP and DHP in vacuo and in ethanol have assigned the lowest pi --> pi* excitations of thione and keto tautomers to the observed absorption bands of HMP and DHP in solutions. The solvation is predicted to have relatively small effect on these pi --> pi* excitations in ethanol.

[310 cases of thymectomy for myasthenia gravis].

OBJECTIVE: To evaluate the clinical characteristics and long-term outcome of 310 cases of thymectomy for myasthenia Gravis. METHODS: The data of 310 patients with thymectomy were analyzed retrospectively to study the patient selection, operative techniques, perioperative management and results for myasthenia Gravis. Absolute and relative scores for clinical evaluation were used as the criteria to determine the therapeutic effects of thymectomy. RESULTS: There were no operative death and postoperative complication rates were 8.7% (27/310). The extra anatomic thymic tissue was found in up to 38.7% (120/310) patients and thymus hyperplasia occurred in 92.9% (288/310) cases. 92.6% (287/310) postoperative patients were followed up for 3 or more months; the percentage of patients being remitted, essentially remitted, significantly effective, effective and non-effective were 7.1% (22/310), 11.3% (35/310) 40.0% (124/310), 27.1% (84/310), 7.1% (22/310) respectively. The total long-term effective rate was 85.5% (265/310). The effective rate for type I, IIa, IIb, III, IV was 90.9% (20/22), 97.6% (40/41), 95.3% (162/170), 80.6% (29/36), 77.8% (14/18) respectively. CONCLUSIONS: Generalized typed and properly selected recurrent ocular-typed patients with Myasthenia Gravis undergoing extensive thymectomy would have good long-term outcomes.