Improved particle swarm optimization based on particles' explorative capability enhancement

Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.

Determination of blast-induced ground vibration equations for rocks using mechanical and geological properties

In the recent decades, effects of blast loads on natural and man-made structures have gained considerable attention due to increase in threat from various man-made activities. Site-specific empirical relationships for calculation of blast-induced vibration parameters like peak particle velocity （PPV） and peak particle displacement （PPD） are commonly used for estimation of blast loads in design. However, these relation- ships are not able to consider the variation in rock parameters and uncertainty of in situ conditions. In this paper, a total of 1089 published blast data of various researchers in different rock sites have been collected and used to propose generalized empirical model for PPV by considering the effects of rock parameters like unit weight, rock quality designation （ROD）, geological strength index （GSI）, and uniaxial compressive strength （UCS）. The proposed PPV model has a good correlation coefficient and hence it can be directly used in prediction of blast-induced vibrations in rocks. Standard errors and coefficient of correlations of the predicted blast-induced vibration parameters are obtained with respect to the observed field data. The proposed empirical model for PPV has also been compared with the empirical models available for blast vibrations predictions given by other researchers and found to be in good agreement with specific cases.

Effect of traffic restriction on atmospheric particle concentrations and their size distributions in urban Lanzhou, Northwestern China

During the 2012 Lanzhou International Marathon, the local government made a significant effort to improve traffic conditions and air quality by implementing traffic restriction measures. To evaluate the direct effect of these measures on urban air quality, especially particle concentrations and their size distributions, atmospheric particle size distributions （0.5-20μm） obtained using an aerodynamic particle sizer （model 3321, TSI, USA） in June 2012 were analyzed. It was found that the particle number, surface area and volume concentrations for size range 0.5-10 μm were （15.0±2.1） cm-3, （11.8±2.6） μm2/cm3 and （1.9±0.6） μm2/cm3, respectively, on the traffic-restricted day （Sunday）, which is 63.2%, 53.0% and 47.2% lower than those on a normal Sunday. For number and surface area concentrations, the most affected size range was 0.5-0.7 and 0.5-0.8 μm, respectively, while for volume concentration, the most affected size ranges were 0.5-0.8, 1.7-2.0 and 5.0-5.4 μm. Number and volume concentrations of particles in size range 0.5-1.0μm correlated well with the number of non-CNG （Compressed Natural Gas） powered vehicles, while their correlation with the number of CNG-powered vehicles was very low, suggesting that reasonable urban traffic controls along with vehicle technology improvements could play an important role in improving urban air quality.

Influence of staff number and internal constellation on surgical site infection in an operating room

Prediction of bacteria-carrying particle （BCP） dispersion and particle distribution released from staffmem- bers in an operating room （OR） is very important for creating and sustaining a safe indoor environment. Postoperative wound infections cause significant morbidity and mortality, and contribute to increased hospitalization time. Increasing the number of personnel within the OR disrupts the ventilation airflow pattern and causes enhanced contamination risk in the area of an open wound. Whether the amount of staffwithin the OR influences the BCP distribution in the surgical zone has rarely been investigated. This study was conducted to explore the influence of the number of personnel in the OR on the airflow field and the BCP distribution. This was performed by applying a numerical calculation to map the airflow field and Lagrangian particle tracking （LPT） for the BCP phase. The results are reported both for active sampling and passive monitoring approaches. Not surprisingly, a growing trend in the BCP concentration （cfu/ms） was observed as the amount of staff in the OR increased. Passive sampling shows unpredictable results due to the sedimentation rate, especially for small particles （5-10 i^m）. Risk factors for surgical site infections （SSls） must be well understood to develop more effective prevention programs.

Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM(1,1) model

An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guantlng reservoir inlet and outlet （China）. The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What＇s more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting.