Non-linear diffusion of image noise with minimal iterativity

Non-linear diffusion (ND) is an iterative difference equation used in several image processing applications such as denoising, segmentation, or compression. The number of iterations required to achieve optimal processing can be very high, making ND not suitable for real-time requirements. In this paper, we study how to reduce complexity of ND so as to achieve minimal number of iterations for real-time image denoising. To do this, we first study the relations between parameters of the iterative equation: the number of iterations, the time step, and the edge strength. We then proceed by estimating the minimally required number of iterations to achieve effective denoising. Then, we relate the edge strength to the number of iterations, to noise, and to the image structure. The resulted minimal iterativity ND is very fast, while still achieves similar or better noise reduction compared to related ND work. This paper also shows how the proposed spatial filter is suitable for structure-sensitive object segmentation and temporal noise reduction.     

Modeling moisture sorption isotherms in roasted green wheat using least square regression and neural-fuzzy techniques

Roasted green wheat at moisture content from 0.052 to 0.25 (decimal d.b.) and temperatures from 25 to 43 °C was used to model moisture sorption isotherms using conventional non-linear least square regression (NLR) and neural-fuzzy (NF) techniques. The results showed that neural-fuzzy techniques provided a better fit than conventional least square regression with: R² = 0.99 and 0.97, RMSE = 0.01 and 0.0038, E% = 1.01 and 5.9 and SSE = 0.0008 and 0.009, for NF and NLR techniques, respectively. Differential enthalpy decreased from 477.9 kJ/kg at 0.052 (decimal d.b. mc) to 44.7 kJ/kg at 0.25 (decimal d.b. mc) and entropy decreased from 1.16 kJ/kg K at 0.052 (decimal d.b. mc) to 0.014 kJ/kg K at 0.25 (decimal d.b. mc). A linear plot between enthalpy and entropy showed that compensation exists. The isokinetic temperature T_β was 376.13 K which was larger than the harmonic mean temperature T_hm = 307.31 K, showing that the water sorption was entropy-driven. The free energy change ΔG was positive (+38.42 kJ/kg) indicating a non-spontaneous water sorption process.     

Banana peel: an effective biosorbent for aflatoxins

This work reports the application of banana peel as a novel bioadsorbent for in vitro removal of five mycotoxins (aflatoxins (AFB1, AFB2, AFG1, AFG2) and ochratoxin A). The effect of operational parameters including initial pH, adsorbent dose, contact time and temperature were studied in batch adsorption experiments. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and point of zero charge (pH_pzc) analysis were used to characterise the adsorbent material. Aflatoxins’ adsorption equilibrium was achieved in 15 min, with highest adsorption at alkaline pH (6–8), while ochratoxin has not shown any significant adsorption due to surface charge repulsion. The experimental equilibrium data were tested by Langmuir, Freundlich and Hill isotherms. The Langmuir isotherm was found to be the best fitted model for aflatoxins, and the maximum monolayer coverage (Q₀) was determined to be 8.4, 9.5, 0.4 and 1.1 ng mg^?1 for AFB1, AFB2, AFG1 and AFG2 respectively. Thermodynamic parameters including changes in free energy (ΔG), enthalpy (ΔH) and entropy (ΔS) were determined for the four aflatoxins. Free energy change and enthalpy change demonstrated that the adsorption process was exothermic and spontaneous. Adsorption and desorption study at different pH further demonstrated that the sorption of toxins was strong enough to sustain pH changes that would be experienced in the gastrointestinal tract. This study suggests that biosorption of aflatoxins by dried banana peel may be an effective low-cost decontamination method for incorporation in animal feed diets.     

A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers

This paper presents a robust, efficient and parameter-setting-free evolutionary approach for the optimal design of compact heat exchangers. A learning automata based particle swarm optimization (LAPSO) is developed for optimization task. Seven design parameters, including discreet and continuous ones, are considered as optimization variables. To make the constraint handling straightforward, a self-adaptive penalty function method is employed. The efficiency and the accuracy of the proposed method are demonstrated through two illustrative examples that include three objectives, namely minimum total annual cost, minimum weight and minimum number of entropy generation units. Numerical results indicate that the presented approach generates the optimum configuration with higher accuracy and a higher success rate when compared with genetic algorithms (GAs) and particle swarm optimization (PSO).     

Synthesis and Characterization of Poly[bis(ethyl salicylate)phosphazenes] and Poly[bis(ethyl salicylate diethylamino)phosphazenes] and Their Hydrolytic Degradation

The polydichlorophosphazenes were synthesized from hexachlorocyclotriphosphazenes by ring opening polymerization in the presence of AlCl₃ as a catalyst. Poly[bis(ethyl salicylate)phosphazenes] (PESP) and poly[bis(ethyl salicylate diethylamino)phosphazenes] (PESDEAP) were synthesized via macromolecular substitution reactions using ethyl salicylate and (or) diethylamine as side groups. The synthesis results were proved by nuclear magnetic resonance (¹H NMR, and ³¹P NMR) and gel permeation chromatography. In addition, the hydrolytic degradation of PESP and PESDEAP was investigated at constant temperature in neutral medium.     

A reaction mechanism for gasoline surrogate fuels for large polycyclic aromatic hydrocarbons

This work aims to develop a reaction mechanism for gasoline surrogate fuels (n-heptane, iso-octane and toluene) with an emphasis on the formation of large polycyclic aromatic hydrocarbons (PAHs). Starting from an existing base mechanism for gasoline surrogate fuels with the largest chemical species being pyrene (C₁₆H₁₀), this new mechanism is generated by adding PAH sub-mechanisms to account for the formation and growth of PAHs up to coronene (C₂₄H₁₂). The density functional theory (DFT) and the transition state theory (TST) have been adopted to evaluate the rate constants for several PAH reactions. The mechanism is validated in the premixed laminar flames of n-heptane, iso-octane, benzene and ethylene. The characteristics of PAH formation in the counterflow diffusion flames of iso-octane/toluene and n-heptane/toluene mixtures have also been tested for both the soot formation and soot formation/oxidation flame conditions. The predictions of the concentrations of large PAHs in the premixed flames having available experimental data are significantly improved with the new mechanism as compared to the base mechanism. The major pathways for the formation of large PAHs are identified. The test of the counterflow diffusion flames successfully predicts the PAH behavior exhibiting a synergistic effect observed experimentally for the mixture fuels, irrespective of the type of flame (soot formation flame or soot formation/oxidation flame). The reactions that lead to this synergistic effect in PAH formation are identified through the rate-of-production analysis.     

Experimental investigation of a cook-off temperature in a hot barrel

The experimental investigations of the effect of contact time/temperature on initiating the cook-off using 7.62 mm calibre cartridge cases （CC） were conducted previously. These cartridges were filled with commercial off-the-shelf （COTS） double based （DB） propellant （Bulls Eye） and were loaded in a hot chamber. The thermal explosion temperature is of great significance to both weapon designers and safety inspectors as it provides the operational limit and safe operating temperature. For CC under test, it was found that the cook-off temperatures of this propellant were encountered with the heat transfer profile of the simulated gun barrel between 151.4 ℃ and 153.4 ℃, with a reaction occurring in less than 300 s after the round was chambered. Usefully, each experiment was found to be consistent and repeatable.     

Heavy Metals In Water,Suspended Particles,Sediments And Aquatic Plants Of Habbaniya Lake,Iraq

Seasonal samples were taken from four selected stations on the Habbaniya lake, middle of Iraq during 1997, to study six heavy metals (Cu, Cd, Pb, Ni, Mn, and Zn), in water suspended particles, sediments and aquatic plants. Five plant species were collected, represented the most dominant in the lake, namely Myriophyllum verticillatum, Potamogeton crispus, P. pectinatus, Ceratophyllum demersum and Vallisnaria spiralis . Zn had the highest concentration among the studied metals in filtered water and suspended particles with an average of 4.08 w g/l and 26088 w g/g, respectively. Whereas, Mn had the highest in the sediments and all studied aquatic plants, which ranged between 213.3- 2027 w g/g and 100-1725 w g/g, respectively, Meanwhile, Cd had the lowest concentration in all studied samples.     

Aluminum extraction from aluminum industrial wastes

Aluminum dross tailings, an industrial waste from the Egyptian Aluminum Company (Egyptalum), was used to produce two types of alums: aluminum sulfate alum (Al₂(SO₄)₃·12H₂O) and ammonium aluminum alum {(NH₄)₂SO₄AL₂ (SO₄)₃·24H₂O}. This was carried out in two processes. The first involves leaching the impurities using diluted H₂SO₄ with different solid/liquid ratios at different temperatures to dissolve the impurities present in the starting material in the form of aluminum sulfates. The second process is the extraction of aluminum (as aluminum sulfate) from the purified aluminum dross tailings thus produced. This was carried out in an autoclave. The effects of temperature, time of reaction, and acid concentration on pressure leaching and extraction processes were studied in order to specify the optimum conditions to be applied in the bench scale production as well as the kinetics of leaching process.     

Development of planning level transportation safety tools using Geographically Weighted Poisson Regression

A common technique used for the calibration of collision prediction models is the Generalized Linear Modeling (GLM) procedure with the assumption of Negative Binomial or Poisson error distribution. In this technique, fixed coefficients that represent the average relationship between the dependent variable and each explanatory variable are estimated. However, the stationary relationship assumed may hide some important spatial factors of the number of collisions at a particular traffic analysis zone. Consequently, the accuracy of such models for explaining the relationship between the dependent variable and the explanatory variables may be suspected since collision frequency is likely influenced by many spatially defined factors such as land use, demographic characteristics, and traffic volume patterns. The primary objective of this study is to investigate the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors, using the Geographically Weighted Poisson Regression modeling technique. The secondary objective is to build on knowledge comparing the accuracy of Geographically Weighted Poisson Regression models to that of Generalized Linear Models. The results show that the Geographically Weighted Poisson Regression models are useful for capturing spatially dependent relationships and generally perform better than the conventional Generalized Linear Models.