Handling boundary constraints for particle swarm optimization in high-dimensional search space

Despite the fact that the popular particle swarm optimizer (PSO) is currently being extensively applied to many real-world problems that often have high-dimensional and complex fitness landscapes, the effects of boundary constraints on PSO have not attracted adequate attention in the literature. However, in accordance with the theoretical analysis in [11], our numerical experiments show that particles tend to fly outside of the boundary in the first few iterations at a very high probability in high-dimensional search spaces. Consequently, the method used to handle boundary violations is critical to the performance of PSO. In this study, we reveal that the widely used random and absorbing bound-handling schemes may paralyze PSO for high-dimensional and complex problems. We also explore in detail the distinct mechanisms responsible for the failures of these two bound-handling schemes. Finally, we suggest that using high-dimensional and complex benchmark functions, such as the composition functions in [19], is a prerequisite to identifying the potential problems in applying PSO to many real-world applications because certain properties of standard benchmark functions make problems inexplicit.     

Evidence for organosulfates in secondary organic aerosol

Recent work has shown that particle-phase reactions contribute to the formation of secondary organic aerosol (SOA), with enhancements of SOA yields in the presence of acidic seed aerosol. In this study, the chemical composition of SOA from the photooxidations of alpha-pinene and isoprene, in the presence or absence of sulfate seed aerosol, is investigated through a series of controlled chamber experiments in two separate laboratories. By using electrospray ionization-mass spectrometry, sulfate esters in SOA produced in laboratory photooxidation experiments are identified for the first time. Sulfate esters are found to account for a larger fraction of the SOA mass when the acidity of seed aerosol is increased, a result consistent with aerosol acidity increasing SOA formation. Many of the isoprene and alpha-pinene sulfate esters identified in these chamber experiments are also found in ambient aerosol collected at several locations in the southeastern U.S. It is likely that this pathway is important for other biogenic terpenes, and may be important in the formation of humic-like substances (HULIS) in ambient aerosol.     

An integrated design methodology based on the use of group AHP-DEA approach for measuring lean tools efficiency with undesirable output

The selection of lean tools is one of the crucial factors for decision makers and practitioners in a competitive environment. A few efforts have been made based on problem selection. Conversely, numerical studies have been done on analytical hierarchy process (AHP)–data envelopment analysis (DEA) as well as DEA-undesirable variables separately. Thus, there is a shortage of lean practitioners as well as the methods involved. The present research aims at integrating AHP and DEA with desirable and undesirable factors to evaluate the lean tools and techniques and to rank the aspect of efficacy. We suggest a logical procedure to measure the efficacy of lean tools on leanness and to prioritize them as decision makers. In this extensive research, we apply the integrated multicriteria decision-making approach, including the hybrid groups AHP and DEA models with desirable and undesirable variables, to assess the relative efficiency of lean manufacturing tools and techniques. Case studies are used to demonstrate the lean implementation in companies while being validated by a panel of experts. The integration of these approaches has created synergy and shown to be even more powerful. Thus, the proposed integrated AHP-DEA model can evaluate and rank different alternatives while considering desirable and undesirable variables in the production processes.     

Satellite‐based precipitation estimation using watershed segmentation and growing hierarchical self‐organizing map

This paper outlines the development of a multi‐satellite precipitation estimation methodology that draws on techniques from machine learning and morphology to produce high‐resolution, short‐duration rainfall estimates in an automated fashion. First, cloud systems are identified from geostationary infrared imagery using morphology based watershed segmentation algorithm. Second, a novel pattern recognition technique, growing hierarchical self‐organizing map (GHSOM), is used to classify clouds into a number of clusters with hierarchical architecture. Finally, each cloud cluster is associated with co‐registered passive microwave rainfall observations through a cumulative histogram matching approach. The network was initially trained using remotely sensed geostationary infrared satellite imagery and hourly ground‐radar data in lieu of a dense constellation of polar‐orbiting spacecraft such as the proposed global precipitation measurement (GPM) mission. Ground‐radar and gauge rainfall measurements were used to evaluate this technique for both warm (June 2004) and cold seasons (December 2004–February 2005) at various temporal (daily and monthly) and spatial (0.04° and 0.25°) scales. Significant improvements of estimation accuracy are found classifying the clouds into hierarchical sub‐layers rather than a single layer. Furthermore, 2‐year (2003–2004) satellite rainfall estimates generated by the current algorithm were compared with gauge‐corrected Stage IV radar rainfall at various time scales over continental United States. This study demonstrates the usefulness of the watershed segmentation and the GHSOM in satellite‐based rainfall estimations.     

Hygroscopic and Chemical Properties of Aerosols Collected near a Copper Smelter: Implications for Public and Environmental Health

Particulate matter emissions near active copper smelters and mine tailings in the southwestern United States pose a potential threat to nearby environments owing to toxic species that can be inhaled and deposited in various regions of the body depending on the composition and size of the particles, which are linked by particle hygroscopic properties. This study reports the first simultaneous measurements of size-resolved chemical and hygroscopic properties of particles next to an active copper smelter and mine tailings by the towns of Hayden and Winkelman in southern Arizona. Size-resolved particulate matter samples were examined with inductively coupled plasma mass spectrometry, ion chromatography, and a humidified tandem differential mobility analyzer. Aerosol particles collected at the measurement site are enriched in metals and metalloids (e.g., arsenic, lead, and cadmium) and water-uptake measurements of aqueous extracts of collected samples indicate that the particle diameter range of particles most enriched with these species (0.18-0.55 μm) overlaps with the most hygroscopic mode at a relative humidity of 90% (0.10-0.32 μm). These measurements have implications for public health, microphysical effects of aerosols, and regional impacts owing to the transport and deposition of contaminated aerosol particles.     

The challenge of predicting flash floods from thunderstorm rainfall

A major characteristic of the hydrometeorology of semi-arid regions is the occurrence of intense thunderstorms that develop very rapidly and cause severe flooding. In summer, monsoon air mass is often of subtropical origin and is characterized by convective instability. The existing observational network has major deficiencies for those regions in providing information that is important to run-off generation. Further, because of the complex interactions between the land surface and the atmosphere, mesoscale atmospheric models are currently able to reproduce only general features of the initiation and development of convective systems. In our research, several interrelated components including the use of satellite data to monitor precipitation, data assimilation of a mesoscale regional atmospheric model, modification of the land component of the mesoscale model to better represent the semi-arid region surface processes that control run-off generation, and the use of ensemble forecasting techniques to improve forecasts of precipitation and run-off potential are investigated. This presentation discusses our ongoing research in this area; preliminary results including an investigation related to the unprecedented flash floods that occurred across the Las Vegas valley (Nevada, USA) in July of 1999 are discussed.     

Air pollution prediction by using an artificial neural network model

Clean Technologies and Environmental Policy - Air pollutants impact public health, socioeconomics, politics, agriculture, and the environment. The objective of this study was to evaluate the...     

Rapid,Size-Resolved Aerosol Hygroscopic Growth Measurements: Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP)

We report on a new instrument developed to perform rapid, size-resolved aerosol hygroscopicity measurements. The differential aerosol sizing and hygroscopicity spectrometer probe (DASH-SP) employs differential mobility analysis in-concert with multiple humidification and optical sizing steps to determine dry optical size and hygroscopic growth factors for size-selected aerosols simultaneously at three elevated relative humidities. The DASH-SP has been designed especially for aircraft-based measurements, with time resolution as short as a few seconds. The minimum particle diameter detected with 50% efficiency in the optical particle counters (OPCs) is 135 ± 8 nm, while the maximum detectable particle diameter is in excess of 1 μm. An iterative data processing algorithm quantifies growth factors and “effective” refractive indices for humidified particles using an empirically derived three-dimensional surface (OPC pulse height–refractive index–particle size), based on a calculated value of the “effective” dry particle refractive index. Excellent agreement is obtained between DASH-SP laboratory data and thermodynamic model predictions for growth factor dependence on relative humidity for various inorganic salts. Growth factor data are also presented for several organic acids. Oxalic, malonic, glutaric, and glyoxylic acids grow gradually with increasing relative humidity up to 94%, while succinic and adipic acids show no growth. Airborne measurements of hygroscopic growth factors of ship exhaust aerosol during the 2007 Marine Stratus/Stratocumulus Experiment (MASE II) field campaign off the central coast of California are presented as the first report of the aircraft integration of the DASH-SP.     

A new evolutionary search strategy for global optimization of high-dimensional problems

Global optimization of high-dimensional problems in practical applications remains a major challenge to the research community of evolutionary computation. The weakness of randomization-based evolutionary algorithms in searching high-dimensional spaces is demonstrated in this paper. A new strategy, SP-UCI is developed to treat complexity caused by high dimensionalities. This strategy features a slope-based searching kernel and a scheme of maintaining the particle population’s capability of searching over the full search space. Examinations of this strategy on a suite of sophisticated composition benchmark functions demonstrate that SP-UCI surpasses two popular algorithms, particle swarm optimizer (PSO) and differential evolution (DE), on high-dimensional problems. Experimental results also corroborate the argument that, in high-dimensional optimization, only problems with well-formative fitness landscapes are solvable, and slope-based schemes are preferable to randomization-based ones.