Alternative continuous- and discrete-time neural networks for image restoration.

This paper presents alternative continuous- and discrete-time neural networks for image restoration in real time by introducing new vectors and transforming its optimization conditions into a system of double projection equations. The proposed neural networks are shown to be stable in the sense of Lyapunov and convergent for any starting point. Compared with the existing neural networks for image restoration, the proposed models have the least neurons, a one-layer structure and the faster convergence, and is suitable to parallel implementation. The validity and transient behaviour of the proposed neural network is demonstrated by numerical examples.

Odor impact assessment of trace sulfur compounds from working faces of landfills in Beijing, China.

Odor pollution from landfills is causing a growing number of public complaints and concerns. Compared with hydrogen sulfide (H2S) and ammonia (NH3), odor impacts of trace sulfur compounds (TSCs) are arousing concerns due to their low odor threshold values (OTVs). Working face on landfill sites has been claimed as major source of odor impacts. This study estimated the odor impacts of fugitive TSCs from the working face of a large typical municipal solid waste (MSW) landfill in Beijing, China. A modified wind tunnel system was introduced to estimate emission rates of TSCs, which is a basic requirement for odor impact assessment. The odor activity value (OAV) method was introduced for odor evaluation. Fieldwork in the selected landfill was conducted from 2014 to 2015. Methyl mercaptan (CH3SH), dimethyl sulfide, dimethyl disulfide (DMDS), and carbon disulfide (CS2) were the TSCs studied in this work. The spatial concentration distributions of the TSCs were calculated on the basis of the Gaussian dispersion model in a "normal case" scenario and a "worst case" scenario. DMDS showed the highest emission rate (7.18 µg m-2 s-1), and CH3SH was the dominant odorous compound with an average emission rate of 4.58 µg m-2 s-1. The dispersion modeling indicated that the odor impact distances of the TSCs in the studied landfill for the normal case and worst case scenarios were 495 ±â€¯96 m and 9230 m at the downwind regions, respectively. Results of this study can benefit the formulation of strategies for odor control and abatement in landfill sites.

A full-scale study on thermal degradation of polychlorinated dibenzo- p-dioxins and dibenzofurans in municipal solid waste incinerator fly ash and its secondary air pollution control in China.

Degradation of polychlorinated dibenzo- p-dioxins and dibenzofurans in municipal solid waste incinerator fly ash is beneficial to its risk control. Fly ash was treated in a full-scale thermal degradation system (capacity 1 t d-1) to remove polychlorinated dibenzo- p-dioxins and dibenzofurans. Apart from the confirmation of the polychlorinated dibenzo- p-dioxin and dibenzofuran decomposition efficiency, we focused on two major issues that are the major obstacles for commercialising this decomposition technology in China, desorption and regeneration of dioxins and control of secondary air pollution. The toxic equivalent quantity values of polychlorinated dibenzo- p-dioxins and dibenzofurans decreased to <6 ng kg-1 and the detoxification rate was ⩾97% after treatment for 1 h at 400 °C under oxygen-deficient conditions. About 8.49% of the polychlorinated dibenzo- p-dioxins and dibenzofurans in toxic equivalent quantity (TEQ) of the original fly ash were desorbed or regenerated. The extreme high polychlorinated dibenzo- p-dioxin and dibenzofuran levels and dibenzo- p-dioxin and dibenzofuran congener profiles in the dust of the flue gas showed that desorption was the main reason, rather than de novo synthesis of polychlorinated dibenzo- p-dioxins and dibenzofurans in the exhaust pipe. Degradation furnace flue gas was introduced to the municipal solid waste incinerator economiser, and then co-processed in the air pollution control system. The degradation furnace released relatively large amounts of cadmium, lead and polychlorinated dibenzo- p-dioxins and dibenzofurans compared with the municipal solid waste incinerator, but the amounts emitted to the atmosphere did not exceed the Chinese national emission limits. Thermal degradation can therefore be used as a polychlorinated dibenzo- p-dioxin and dibenzofuran abatement method for municipal solid waste incinerator source in China.

Novel Continuous-and Discrete-Time Neural Networks for Solving Quadratic Minimax Problems With Linear Equality Constraints.

This article presents two novel continuous-and discrete-time neural networks (NNs) for solving quadratic minimax problems with linear equality constraints. These two NNs are established based on the conditions of the saddle point of the underlying function. For the two NNs, a proper Lyapunov function is constructed so that they are stable in the sense of Lyapunov, and will converge to some saddle point(s) for any starting point under some mild conditions. Compared with the existing NNs for solving quadratic minimax problems, the proposed NNs require weaker stability conditions. The validity and transient behavior of the proposed models are illustrated by some simulation results.

Exponential State Estimation for Stochastically Disturbed Discrete-Time Memristive Neural Networks: Multiobjective Approach.

The state estimation of the discrete-time memristive model is studied in this article. By applying the stochastic analysis technique, sufficient formulas are established to ensure the exponentially mean-square stability of the error model. Moreover, the derived control gain matrix can be calculated via the linear matrix inequality (LMI). It should be mentioned that, by extending the derived conclusion to a multiobjective optimization problem, the maximum bound of the active function and the minimum bound of the disturbance attenuation are derived. The corresponding simulation figures are provided in the end.

Improved impact assessment of odorous compounds from landfills using Monte Carlo simulation.

Landfills are city infrastructures used for the treatment of municipal solid waste (MSW) in China. However, due to technical failure and/or management problem most of them are facing serious secondary pollution such as groundwater contamination and odor nuisance. The latter is the main reason causing a growing number of public complaints. Atmospheric dispersion models are routinely adopted for odor impact assessment, but these models provide deterministic predictions only. To determine the potential odorant paths and treat the uncertainty of odor pollution, Monte Carlo simulation coupled with an odor dispersion model was proposed and named Monte Carlo-dispersion simulation method (MCDSM). By introducing a series of random values of error components in the dispersion model, MCDSM can produce probabilistic odor impact results. Values of these variances were randomly selected according to their probability density functions (PDFs) due to the imprecise knowledge of the meteorological and emission conditions. After running the odor dispersion model for numerous times, the randomization produces a set of possible results that closely resembles the expected behavior of the odorants. This study applied MCDSM to estimate the odor impact of methyl mercaptan (CH3SH) on an MSW landfill in Beijing, China. The PDF of the CH3SH emission rate was derived from the field data. The uncertainty of odor impact was analyzed statistically, and the results were summarized using the probability of odor exceedance (POE). A POE map of CH3SH was plotted for a particular interest, in which the north downwind direction was the most polluted area. MCDSM provides a scientific approach for the assessment of odor pollution from individual odorant, which can benefit the formulation of standard for odor impact assessment in landfill sites.

Low-temperature dechlorination of hexachlorobenzene on solid supports and the pathway hypothesis.

The dechlorination of HCB was carried out under low-temperature and oxygen deficient conditions on different solid supports such as SiO(2), CaO, CaSiO(3), cement and treated fly ash (tFA). All the tested supports except SiO(2) showed a HCB dechlorination potential. The dechlorination efficiencies (D(1)) of HCB by CaO, CaSiO(3), tFA and cement reached 64.62%, 76.15%, 79.97% and 32.21% at 350 degrees C for 4h, respectively. It was thought electrons in the vacancies originated from the unsaturated metallic ions and O(2-) on the crystal surfaces made the D(1) different between SiO(2), CaO and CaSiO(3). Comparing the D(1) by tFA and cement, the high dechlorination potential of tFA was due to the more free electrons from the crystal defects and the transition metals, and the more active points for the gas-solid phase reaction, which both had positive effects on dechlorination reaction. The effect of Cu addition (0.2-5.0%) on HCB dechlorination might result from the Ullmann coupling which was not notable in enhancing the dechlorination reaction. From the study, we can draw the conclusion that the dechlorination potential mainly depends on the support characteristic rather than the transition metal content. Based on this study and previous references, the dechlorination/polymerization induced by the electron transfer mode was thought to be the dominant pathway while the hydrogen transfer mode was minor. The electron was originated from the crystal defects or induced by transition metals, and the dissociation of a chloride ion happened forming a radical, and then the polymerization of radicals led to the formation of high-molecular-weight compounds which seemed to cause the material imbalance.

A Novel Neural Network for Generally Constrained Variational Inequalities.

This paper presents a novel neural network for solving generally constrained variational inequality problems by constructing a system of double projection equations. By defining proper convex energy functions, the proposed neural network is proved to be stable in the sense of Lyapunov and converges to an exact solution of the original problem for any starting point under the weaker cocoercivity condition or the monotonicity condition of the gradient mapping on the linear equation set. Furthermore, two sufficient conditions are provided to ensure the stability of the proposed neural network for a special case. The proposed model overcomes some shortcomings of existing continuous-time neural networks for constrained variational inequality, and its stability only requires some monotonicity conditions of the underlying mapping and the concavity of nonlinear inequality constraints on the equation set. The validity and transient behavior of the proposed neural network are demonstrated by some simulation results.

Assessment of the temporal and spatial distribution of atmospheric PCNs and their air-soil exchange using passive air samplers in Shanghai, East China.

A total of 47 passive air samples and 25 soil samples were collected to study the temporal trend, distribution, and air-soil exchange of polychlorinated naphthalenes (PCNs) in Shanghai, China. Atmospheric PCNs ranged from 3.44 to 44.1 pg/m3 (average of 21.9 pg/m3) in summer and 13.6 to 153 pg/m3 (average of 40.0 pg/m3) in winter. In the soil samples, PCN concentrations were 54.7-1382 pg/g dry weight (average of 319 pg/g). Tri-CNs and tetra-CNs were two dominant homolog groups in air samples, while di-CNs were also found at comparable proportions to tri-CNs and tetra-CNs in soil samples. Most air and soil samples from the industrial and urban areas showed higher PCN concentrations than those from suburban areas. However, some soil samples in urban centers presented higher PCN concentrations than industrial areas. Analysis of PCN sources indicated that both industrial thermal process and historical usage of commercial PCN mixtures contributed to the PCN burden in most areas. The fugacity fraction results indicated a strong tendency of volatilization for lighter PCNs (tri- to hexa-CNs) in both seasons, and air-soil deposition for octa-CNs. Moreover, air-soil exchange fluxes indicate that soil was an important source of atmospheric PCNs in some areas. The results of this study provide information for use in the evaluation of the potential impact and human health risk of PCNs around the study areas.

A study on the chemical and mineralogical characterization of MSWI fly ash using a sequential extraction procedure.

The presence of heavy metals in municipal solid waste incineration (MSWI) fly ash is of environmental concern due to their leaching potential in landfill environments. Sequential chemical extraction was performed on fly ash samples from a large-scale municipal solid waste incineration plant in East China. The transformation of the mineralogical species of fly ash during the sequential extraction was studied using X-ray fluorescence (XRF) and X-ray powder diffraction (XRD). The leaching behavior of heavy metals such as zinc, lead, cadmium and copper in MSWI fly ash was considered to have a dependency relationship with the components of calcium, such as aphthitalite, calcite, anhydrite and calcium aluminate or calcium aluminosilicate.

Thermal dechlorination of heavily PCB-contaminated soils from a sealed site of PCB-containing electrical equipment.

A large amount of soils are contaminated by leakage of polychlorinated biphenyls (PCBs) from sealed-up PCB-containing electrical equipment in China. Thermal dechlorination of soils contaminated with PCBs at a level of 108 mg g(-1) and PCB77 (3,3',4,4'-tetrachlorobiphenyl) as a model isomer in conjunction with calcium oxide was investigated in this study. The PCB dechlorination rate improved with increased temperature and time. The highest dechlorination rate was 85.3 %, and temperature was the main influencing factor. Pentachlorobiphenyl and tetrachlorobiphenyl in soils decreased or disappeared in response to treatment at 350 and 400 °C for 4 h, while monochlorinated biphenyl and biphenyl were detected after the reaction, indicating the presence of a dechlorination/hydrogenation pathway. Discrepancy in chlorine balance was observed after low-temperature thermal dechlorination. The species of dechlorination products were identified as amorphous carbon containing a crystalline graphite plane structure and a carbonyl group-containing polymerized product, demonstrating the existence of a dechlorination/polymerization pathway. The yield of amorphous carbon and high-molecular-weight intermediates increased with heating time. The results showed that the discrepancy in chlorine balance was because of the generation of polymerized products and undetected intermediates.

Solidification and immobilization of MSWI fly ash through aluminate geopolymerization: Based on partial charge model analysis.

This study presents an integrated synopsis of the solidification and immobilization mechanisms of fly ash-based geopolymers. A rational analysis of the ion reactions involved in geopolymerization was conducted using the partial charge model (PCM). The following conclusions were obtained: (1) heavy metal cations cannot be immobilized as counter cations through exchange with Na+; (2) isomorphous substitution of heavy metals in the geopolymer can be expected from the condensation reaction between the hydrolyzed heavy metal species and aluminosilicate; (3) the hydrolyzed species condensation could result in solidification and immobilization and be promoted by aluminates; and (4) a geopolymer with the highest immobilization and solidification efficiency can be obtained at an intermediate pH value. The partial charges on the framework of Si, Al, and O in the primary building blocks of aluminosilicate and heavy metal-doped aluminosilicate were confirmed through XPS and 29Si NMR spectroscopy analyses. The effects of activator dosage and types on fly ash-based geopolymers were also investigated, and the results verify the PCM analysis. A geopolymer with the highest strength was produced at an intermediate alkaline dosage. Silicate or aluminate introduced into the activator improved the strength and immobilization efficiency, and aluminate exhibited better performance. Heavy metals bound to the exchangeable or acid-soluble fraction were transformed into aluminosilicate species during geopolymerization.

Seasonal and spatial distributions of atmospheric polychlorinated naphthalenes in Shanghai, China.

Air samples were collected in Shanghai during summer and winter 2013, and the gas and particulate concentrations of polychlorinated naphthalenes (PCNs) were measured. All 75 congeners were quantified and the corresponding toxic equivalents (TEQs) were calculated. PCN concentrations were higher in summer than winter, at 8.22-102 pg/m(3) (average of 61.3 pg/m(3)) in summer and 16.5-61.1 pg/m(3) (average of 37.7 pg/m(3)) in winter. Their seasonal TEQ values were in contrast, at 1.35-7.31 fg/m(3) (average of 3.84 fg/m(3)) in summer and 4.08-23.3 fg/m(3) (average of 8.80 fg/m(3)) in winter, because of the seasonal change in congener profiles. Tri-CNs were the predominant homologs in both the summer and winter samples. However, the major congeners in summer were PCNs containing less chlorine, but these decreased over winter. Air mass back trajectories suggested that wind direction over various sites was similar in the summer and winter seasons, yet there were clear seasonal variations in atmospheric PCN concentrations. Ratios of several characteristic congeners were calculated and the results indicated that the ratios varied only to a limited extent with PCN emissions profile from industrial thermal sources, but varied strongly with profiles of technical PCN and PCN contaminants in polychlorinated biphenyl mixtures. The results of principal component analysis suggest that local industrial thermal emissions (thermal processes containing waste incineration and secondary metal smelting processes) still play a considerable role in influencing the atmospheric PCNs in Shanghai.

Seasonal variations in concentrations, distributions, and air-soil exchange fluxes of dioxin-like polychlorinated biphenyls in Shanghai, China.

Dioxin-like polychlorinated biphenyl (dl-PCB) concentrations in ambient air and soil in Shanghai, China, were measured to allow seasonal and spatial differences in the dl-PCB concentrations, profiles, distributions, fugacity fractions, and air-soil fluxes to be determined. The toxic equivalent (TEQ) DL-PCB concentrations in the air were higher in summer (mean 9.46 fg m(-3), range 1.32-26.3 fg m(-3)) than in winter (mean 4.57 fg m(-3), range 1.55-10.9 fg m(-3)). The DL-PCB concentrations in air were different in different areas, and the concentrations decreased in the order industrial areas > commercial and residential areas > suburban areas > rural area. The mean DL-PCB concentration in soil was 0.25 pg TEQ g(-1) dry weight (dw) and the range was 0.05-0.90 pg TEQ g(-1) dw. The highest DL-PCB concentration in soil was found in a sample from a commercial/residential area. The DL-PCB fluxes were negative (-216 pg m(-2) h(-1) in summer and -41.1 pg m(-2) h(-1) in winter), and the fugacity fractions were below 0.5, indicating that dl-PCBs in Shanghai are deposited from the air to the soil in all seasons. The net fluxes were higher in summer than in winter, and the deposition fluxes were higher in industrial areas than in other areas in both summer and winter.

A novel neural network for variational inequalities with linear and nonlinear constraints.

Variational inequality is a uniform approach for many important optimization and equilibrium problems. Based on the sufficient and necessary conditions of the solution, this paper presents a novel neural network model for solving variational inequalities with linear and nonlinear constraints. Three sufficient conditions are provided to ensure that the proposed network with an asymmetric mapping is stable in the sense of Lyapunov and converges to an exact solution of the original problem. Meanwhile, the proposed network with a gradient mapping is also proved to be stable in the sense of Lyapunov and to have a finite-time convergence under some mild condition by using a new energy function. Compared with the existing neural networks, the new model can be applied to solve some nonmonotone problems, has no adjustable parameter, and has lower complexity. Thus, the structure of the proposed network is very simple. Since the proposed network can be used to solve a broad class of optimization problems, it has great application potential. The validity and transient behavior of the proposed neural network are demonstrated by several numerical examples.

Solidification/stabilization and leaching behavior of PbCl2 in fly-ash hydrated silicate matrix and fly-ash geopolymer matrix.

Fly ash (FA) for reuse as a construction material is activated using two methods, to produce hydrated silicate and geopolymer gels. We investigated the solidification/stabilization and leaching behavior of PbCl2 in a geopolymer matrix (GM) and hydrated silicate matrix (HSM), based on FA as the source material, to evaluate the environmental and health risks. The GM and HSM synthetic conditions were 60 °C, 20 % relative humidity (RH), and 12 wt% (6 mol/L) NaOH, and 20 ± 2 °C, ≥ 90 % RH, and 30 wt.%, respectively, based on their compressive strength performances. X-ray diffraction (XRD) showed that Pb participated in hydration and geopolymerization, and was incorporated in the structural components of the hydrated silicate and geopolymer. In leaching experiments, the solidification/stabilization effects of Pb and Cl in the HSM and GM improved with increasing curing time. After long-term curing (28 days), the immobility of Pb in the GM was better than that in the HSM. Sodalite improved the Cl-stabilizing ability of the GM compared with that of the HSM. In static monolithic leaching experiments, HSM and GM had the same Pb-leaching behaviors. Based on the changes in the location of the neutral sphere layer with decreasing acid-neutralizing capacity, Pb release was divided into alkaline-release, stagnation, and acid-release stages. The neutral sphere layer contained the highest Pb concentration during permeation toward the block center from the block edge. This behavior regulation could also apply to other amphoteric metals immobilized by GMs and HSMs.

Environmental risks of HBCDD from construction and demolition waste: a contemporary and future issue.

Hexabromocyclododecane (HBCDD), as one of the most widely used brominated flame retardants (BFRs), is of great concern globally because of its persistence in the environment and negative impacts on humans and animals. HBCDD has been mainly used in flame-retarded expanded (EPS) and extruded (XPS) polystyrene foams for insulation in the construction industry. Most of these products will become a part of the construction and demolition (C&D) waste at the end of their life cycle (30-50 years) which is typically disposed of into landfills or incineration. However, the recycling of this material takes quite a low share compared with landfill and incineration. Consequently, high environmental risks will exist in these disposal approaches due to the HBCDD in C&D waste. Currently, XPS or EPS products containing HBCDD in the construction industry have not reached the end of their life cycle in most countries. Relatively little attention has been paid to this emergency issue by either the government or public. Furthermore, C&D waste is most likely disposed of by direct dumping, simple stacking, or open burning in developing countries. Therefore, this paper highlights the global environmental risks of HBCDD from C&D waste. Areas of research for key problems of HBCDD contained in C&D waste are suggested to help control and finally eliminate the impact.

Neural networks for nonlinear and mixed complementarity problems and their applications.

This paper presents two feedback neural networks for solving a nonlinear and mixed complementarity problem. The first feedback neural network is designed to solve the strictly monotone problem. This one has no parameter and possesses a very simple structure for implementation in hardware. Based on a new idea, the second feedback neural network for solving the monotone problem is constructed by using the first one as a subnetwork. This feedback neural network has the least number of state variables. The stability of a solution of the problem is proved. When the problem is strictly monotone, the unique solution is uniformly and asymptotically stable in the large. When the problem has many solutions, it is guaranteed that, for any initial point, the trajectory of the network does converge to an exact solution of the problem. Feasibility and efficiency of the proposed neural networks are supported by simulation experiments. Moreover, the feedback neural network can also be applied to solve general nonlinear convex programming and nonlinear monotone variational inequalities problems with convex constraints.

Exponential stability of globally projected dynamic systems.

In this paper, we further analyze and prove the stability and convergence of the dynamic system proposed by Friesz et al.(1994), whose equilibria solve the associated variational inequality problems. Two sufficient conditions are provided to ensure the asymptotic stability of this system with a monotone and asymmetric mapping by means of an energy function. Meanwhile this system with a monotone and gradient mapping is also proved to be asymptotically stable using another energy function. Furthermore, the exponential stability of this system is also shown under strongly monotone condition. Some obtained results improve the existing ones and the given conditions can be easily checked in practice. Since this dynamic system has wide applications, the obtained results are significant in both theory and applications.

A novel neural network for nonlinear convex programming.

In this paper, we present a neural network for solving the nonlinear convex programming problem in real time by means of the projection method. The main idea is to convert the convex programming problem into a variational inequality problem. Then a dynamical system and a convex energy function are constructed for resulting variational inequality problem. It is shown that the proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. Compared with the existing neural networks for solving the nonlinear convex programming problem, the proposed neural network has no Lipschitz condition, no adjustable parameter, and its structure is simple. The validity and transient behavior of the proposed neural network are demonstrated by some simulation results.