Cross-borehole delineation of a conductive ore deposit in a resistive host-experimental design

Journal ArticleThe finite-difference time-domain method is used for high-resolution full-wave analysis of cross-borehole electromagnetic surveys of buried nickel sulfide deposits. The method is validated against analytical methods for simple cases, but is shown to be a valuable tool for analysis of complicated geological structures such as faulted or layered regions. The magnetic fields generated by a wire loop in a borehole near a nickel sulfide deposit are presented for several cases. The full-wave solution is obtained up to 200 MHz, where quasi-static methods would have failed. The dielectric response is included in the solution, and the diffractive nature of the field is observed. The sensitivity of each receiver in a vertical line in the cross borehole is presented and analyzed to provide an optimal weighting for receivers that can be applied to an experimental study

First report on the occurrence of Rickettsia slovaca and Rickettsia raoultii in Dermacentor silvarum in China

10.1186/1756-3305-5-19Parasites and Vectors511

Adaptive distributed differential evolution

Due to the increasing complexity of optimization problems, distributed differential evolution (DDE) has become a promising approach for global optimization. However, similar to the centralized algorithms, DDE also faces the difficulty of strategies' selection and parameters' setting. To deal with such problems effectively, this article proposes an adaptive DDE (ADDE) to relieve the sensitivity of strategies and parameters. In ADDE, three populations called exploration population, exploitation population, and balance population are co-evolved concurrently by using the master-slave multipopulation distributed framework. Different populations will adaptively choose their suitable mutation strategies based on the evolutionary state estimation to make full use of the feedback information from both individuals and the whole corresponding population. Besides, the historical successful experience and best solution improvement are collected and used to adaptively update the individual parameters (amplification factor F and crossover rate CR) and population parameter (population size N), respectively. The performance of ADDE is evaluated on all 30 widely used benchmark functions from the CEC 2014 test suite and all 22 widely used real-world application problems from the CEC 2011 test suite. The experimental results show that ADDE has great superiority compared with the other state-of-the-art DDE and adaptive differential evolution variants

Stent Selection for Endoscopic Ultrasound-Guided Drainage of Pancreatic Fluid Collections: A Multicenter Study in China

Aims. We attempted to establish some guidelines for the selection of transmural stents during endoscopic drainage of PFCs by retrospective review of the clinical data obtained from three tertiary hospitals. Patients and Methods. Clinical data of 93 patients with attempted endoscopic drainage of symptomatic PFCs were obtained through chart review and prospective follow-up. Results. Treatment success for acute pseudocyst (n=67), chronic pseudocyst (n=9), and WOPN (n=17) was 95.3%, 100%, and 88.2%, respectively (P=0.309). Clinical success for single-stent drainage was 93.9% (46/49) versus 97.4% (37/38) for multiple-stent drainage (P=0.799). Secondary infection for single-stent drainage was 18.4% (9/49) versus 5.3% (2/38) for multiple-stent drainage (P=0.134). Secondary infection for stent diameter less than or equal to 8.5 F was 3.4% (1/29) versus 17.2% (10/58) for stent diameter larger than or equal to 10 F (P=0.138). Conclusion. EUS-guided transmural drainage is an effective therapy for PFCs. Single-stent transmural drainage of PFCs is enough and does not seem to influence clinical success. The number or diameter of stents does not seem to be associated with secondary infection

Adaptive granularity learning distributed particle swarm optimization for large-scale optimization

Large-scale optimization has become a significant and challenging research topic in the evolutionary computation (EC) community. Although many improved EC algorithms have been proposed for large-scale optimization, the slow convergence in the huge search space and the trap into local optima among massive suboptima are still the challenges. Targeted to these two issues, this article proposes an adaptive granularity learning distributed particle swarm optimization (AGLDPSO) with the help of machine-learning techniques, including clustering analysis based on locality-sensitive hashing (LSH) and adaptive granularity control based on logistic regression (LR). In AGLDPSO, a master-slave multisubpopulation distributed model is adopted, where the entire population is divided into multiple subpopulations, and these subpopulations are co-evolved. Compared with other large-scale optimization algorithms with single population evolution or centralized mechanism, the multisubpopulation distributed co-evolution mechanism will fully exchange the evolutionary information among different subpopulations to further enhance the population diversity. Furthermore, we propose an adaptive granularity learning strategy (AGLS) based on LSH and LR. The AGLS is helpful to determine an appropriate subpopulation size to control the learning granularity of the distributed subpopulations in different evolutionary states to balance the exploration ability for escaping from massive suboptima and the exploitation ability for converging in the huge search space. The experimental results show that AGLDPSO performs better than or at least comparable with some other state-of-the-art large-scale optimization algorithms, even the winner of the competition on large-scale optimization, on all the 35 benchmark functions from both IEEE Congress on Evolutionary Computation (IEEE CEC2010) and IEEE CEC2013 large-scale optimization test suites

Boosting data-driven evolutionary algorithm with localized data generation

By efficiently building and exploiting surrogates, data-driven evolutionary algorithms (DDEAs) can be very helpful in solving expensive and computationally intensive problems. However, they still often suffer from two difficulties. First, many existing methods for building a single ad hoc surrogate are suitable for some special problems but may not work well on some other problems. Second, the optimization accuracy of DDEAs deteriorates if available data are not enough for building accurate surrogates, which is common in expensive optimization problems. To this end, this article proposes a novel DDEA with two efficient components. First, a boosting strategy (BS) is proposed for self-aware model managements, which can iteratively build and combine surrogates to obtain suitable surrogate models for different problems. Second, a localized data generation (LDG) method is proposed to generate synthetic data to alleviate data shortage and increase data quantity, which is achieved by approximating fitness through data positions. By integrating the BS and the LDG, the BDDEA-LDG algorithm is able to improve model accuracy and data quantity at the same time automatically according to the problems at hand. Besides, a tradeoff is empirically considered to strike a better balance between the effectiveness of surrogates and the time cost for building them. The experimental results show that the proposed BDDEA-LDG algorithm can generally outperform both traditional methods without surrogates and other state-of-the-art DDEA son widely used benchmarks and an arterial traffic signal timing real-world optimization problem. Furthermore, the proposed BDDEA-LDG algorithm can use only about 2% computational budgets of traditional methods for producing competitive results

Ulinastatin attenuates oxidation, inflammation and neural apoptosis in the cerebral cortex of adult rats with ventricular fibrillation after cardiopulmonary resuscitation

OBJECTIVE: The role of Ulinastatin in neuronal injury after cardiopulmonary resuscitation has not been elucidated. We aim to evaluate the effects of Ulinastatin on inflammation, oxidation, and neuronal injury in the cerebral cortex after cardiopulmonary resuscitation. METHODS: Ventricular fibrillation was induced in 76 adult male Wistar rats for 6 min, after which cardiopulmonary resuscitation was initiated. After spontaneous circulation returned, the rats were split into two groups: the Ulinastatin 100,000 unit/kg group or the PBS-treated control group. Blood and cerebral cortex samples were obtained and compared at 2, 4, and 8 h after return of spontaneous circulation. The protein levels of tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6) were assayed using an enzyme-linked immunosorbent assay, and mRNA levels were quantified via real-time polymerase chain reaction. Myeloperoxidase and Malondialdehyde were measured by spectrophotometry. The translocation of nuclear factor-κB p65 was assayed by Western blot. The viable and apoptotic neurons were detected by Nissl and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL). RESULTS: Ulinastatin treatment decreased plasma levels of TNF-α and IL-6, expression of mRNA, and Myeloperoxidase and Malondialdehyde in the cerebral cortex. In addition, Ulinastatin attenuated the translocation of nuclear factor-κB p65 at 2, 4, and 8 hours after the return of spontaneous circulation. Ulinastatin increased the number of living neurons and decreased TUNEL-positive neuron numbers in the cortex at 72 h after the return of spontaneous circulation. CONCLUSIONS: Ulinastatin preserved neuronal survival and inhibited neuron apoptosis after the return of spontaneous circulation in Wistar rats via attenuation of the oxidative stress response and translocation of nuclear factor-κB p65 in the cortex. In addition, Ulinastatin decreased the production of TNF-α, IL-6, Myeloperoxidase, and Malondialdehyde