Electrospun PCL and PLA hybrid nanofibrous scaffolds containing Nigella sativa herbal extract for effective wound healing

Black seed (Nigella sativa [NS]) is used in traditional medicine as an antibacterial agent. In this study, novel hybrid scaffolds were manufactured from poly(ɛ-caprolactone[PCL])/ Poly(lactic acid [PLA]) with NS extract by double-nozzle electrospinning for wound healing application. Optimal conditions were found using response surface methodology including feed rate 0.8 ml/hr, voltage 20 kV and PLA 8% /PCL 10% concentrations. The effect of NS extract on the properties of the scaffold was evaluated by scanning electron microscopy, contact angle, and mechanical test. The PLA-PCL/NS 20% beadles, and smooth nanofibrous web was obtained as the optimum scaffold with an average diameter of 638 ± 69 nm and the contact angle of 44°. In addition, the biological properties of the scaffolds such as antibacterial against Staphylococcus aureus (gram-positive) and Escherichia coli (gram-negative) bacteria, MTT assay, extract release, and cell growth (human mesenchymal stem cells) were examined. Incorporation of NS extract into the nanofibers caused to enhance the biological properties, cell viability and cell proliferation without toxicity.     

Application of poly (amide-<Emphasis Type="Italic">b</Emphasis>-ethylene oxide)/zeolitic imidazolate framework nanocomposite membrane in gas separation

Mixed matrix membranes (MMMs) are gaining increasing interest in academic and industrial research due to their combined, desirable properties of both polymers and organic/inorganic filler as important materials. In this work, synthesized zeolitic imidazolate framework (ZIF-8) suspension (10–50 wt%) was directly incorporated into a [poly (amide-b-ethylene oxide) Pebax^® 1657] matrix in order to improve the gas separation performance of the membrane. Dynamic light scattering (DLS) analysis showed an average diameter of 77.4 nm for the prepared nanoparticles. The transparent membranes were characterized by scanning electron microscopy (SEM), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), Fourier transform infrared (FTIR) spectroscopy, and X-ray diffractometry (XRD). These indicated excellent dispersion of nanoparticles, which was achieved by ultrasonication before casting the solution. Incorporation of ZIF-8 as filler in the polymer matrix led to improved thermal and mechanical stability of the membranes. This was confirmed by TGA and tensile analyses, indicating good contacts provided at the polymer/filler interfaces. The effect of ZIF-8 loading (up to 50 wt%) on membrane performance was investigated and it showed an optimum loading of 30 %. Single gas (CO₂, N₂ and CH₄) permeation tests revealed rapid, enhanced permeability of the nanocomposite membranes without significant changes in selectivity (compared to those of the pristine polymeric membrane). The permeability increases for CO₂, CH₄ and N₂ in the optimum Pebax^® 1657/ZIF-8 (30 wt%) membrane were found in the stated order as 111, 88 and 99 %. The study revealed that Pebax^® 1657/ZIF-8 membranes displayed better gas permeation properties compared to those of Pebax^® 1657.     

Atomic Sn, Sb, and Ge photodissociation lasers

Stimulated emission has been observed on the 380.1 nm line of atomic tin, the 326.7 nm line of atomic antimony, and the 326.9 nm line of atomic germanium following photodissociation of SnI2, SbI3, and GeI4molecular vapors. A 193 nm ArF laser was used as the excitation source. Bond energies for the dissociation of monoiodides were estimated. Output energies of 200, 80, and 75 μJ were obtained for 380.1, 326.7, and 326.9 nm transitions, respectively.     

Modeling robustness behavior using aspect-oriented modeling to support robustness testing of industrial systems

Model-based robustness testing requires precise and complete behavioral, robustness modeling. For example, state machines can be used to model software behavior when hardware (e.g., sensors) breaks down and be fed to a tool to automate test case generation. But robustness behavior is a crosscutting behavior and, if modeled directly, often results in large, complex state machines. These in practice tend to be error prone and difficult to read and understand. As a result, modeling robustness behavior in this way is not scalable for complex industrial systems. To overcome these problems, aspect-oriented modeling (AOM) can be employed to model robustness behavior as aspects in the form of state machines specifically designed to model robustness behavior. In this paper, we present a RobUstness Modeling Methodology (RUMM) that allows modeling robustness behavior as aspects. Our goal is to have a complete and practical methodology that covers all features of state machines and aspect concepts necessary for model-based robustness testing. At the core of RUMM is a UML profile (AspectSM) that allows modeling UML state machine aspects as UML state machines (aspect state machines). Such an approach, relying on a standard and using the target notation as the basis to model the aspects themselves, is expected to make the practical adoption of aspect modeling easier in industrial contexts. We have used AspectSM to model the crosscutting robustness behavior of a videoconferencing system and discuss the benefits of doing so in terms of reduced modeling effort and improved readability.     

Transient chaotic neural network-based disjoint multipath routing for mobile ad-hoc networks

Due to mobility of wireless hosts, routing in mobile ad-hoc networks (MANETs) is a challenging task. Multipath routing is employed to provide reliable communication, load balancing, and improving quality of service of MANETs. Multiple paths are selected to be node-disjoint or link-disjoint to improve transmission reliability. However, selecting an optimal disjoint multipath set is an NP-complete problem. Neural networks are powerful tools for a wide variety of combinatorial optimization problems. In this study, a transient chaotic neural network (TCNN) is presented as multipath routing algorithm in MANETs. Each node in the network can be equipped with a neural network, and all the network nodes can be trained and used to obtain optimal or sub-optimal high reliable disjoint paths. This algorithm can find both node-disjoint and link-disjoint paths with no extra overhead. The simulation results show that the proposed method can find the high reliable disjoint path set in MANETs. In this paper, the performance of the proposed algorithm is compared to the shortest path algorithm, disjoint path set selection protocol algorithm, and Hopfield neural network (HNN)-based model. Experimental results show that the disjoint path set reliability of the proposed algorithm is up to 4.5 times more than the shortest path reliability. Also, the proposed algorithm has better performance in both reliability and the number of paths and shows up to 56% improvement in path set reliability and up to 20% improvement in the number of paths in the path set. The proposed TCNN-based algorithm also selects more reliable paths as compared to HNN-based algorithm in less number of iterations.     

Application of Fuzzy C-means algorithm as a novel approach to predict solubility of hydrocarbons in carbon dioxide

In the recent years, declination of oil reservoir causes the importance of researches on enhancement of oil recovery processes become more important. One of wide applicable approaches in enhancement of oil recovery is carbon dioxide injection which becomes interested because of relative low cost, good displacement and environmentally aspects. The injection of carbon dioxide to oil reservoir causes the lighter hydrocarbons of crude oil are extracted by CO₂. This phenomena can be affected by various factors such the solubility of hydrocarbons in carbon dioxide so in the present investigation Fuzzy c-means (FCM) as a novel approach for estimation of solubility of alkanes in carbon dioxide in terms of temperature, pressure and carbon number of alkane were utilized. The predicting algorithm FCM has reliable ability to estimate solubility based on graphical and statistical results. The coefficient of determination (R²) for training and testing data are calculated as 0.9856 and 0.9529 respectively.     

Atomic lead photodissociation laser

Stimulated emission has been observed on the 405.8, 368.3, and 364.0 nm lines of neutral atomic lead following photodissociation of lead dibromide and lead diiodide molecules with the 193 nm output of an ArF laser. Two-photon dissociation processes are involved in the excitation mechanism.