Assessment of the methods for determining net radiation at different time-scales of meteorological variables

When modeling the soil/atmosphere interaction, it is of paramount importance to determine the net radiation flux. There are two common calculation methods for this purpose. Method 1 relies on use of air temperature, while Method 2 relies on use of both air and soil temperatures. Nowadays, there has been no consensus on the application of these two methods. In this study, the half-hourly data of solar radiation recorded at an experimental embankment are used to calculate the net radiation and long-wave radiation at different time-scales (half-hourly, hourly, and daily) using the two methods. The results show that, compared with Method 2 which has been widely adopted in agronomical, geotechnical and geo-environmental applications, Method 1 is more feasible for its simplicity and accuracy at shorter time-scale. Moreover, in case of longer time-scale, daily for instance, less variations of net radiation and long-wave radiation are obtained, suggesting that no detailed soil temperature variations can be obtained. In other words, shorter time-scales are preferred in determining net radiation flux.     

Analysis of the effect of traffic exposure on the recovery properties of cut pile carpet using analytical and viscoelastic modeling

Viscoelastic models composing of different combination of spring and dashpot are usually used to explain the mechanical behavior of textile materials. In this work, a viscoelastic model was presented to analyze the effect of traffic exposure on the compression and recovery performance of the pile carpet. Wear test, performed by a Hexapod tumbling machine, was conducted to simulate the traffic exposure. Using a tensile tester, adjusted in compression mode, one cycle of compression–decompression was applied to the samples. The standard nonlinear model was presented to fit the experimental data. Best curve fitting based on the least square method was then used to fit the model to the experimental curve. Different attributions of compression were then analyzed and discussed. The results showed that the standard nonlinear model was fitted to the experimental curves with an acceptable coefficient of regression (R²). The district model parameters, i.e. the spring and dashpot constants, were both decreased as the wear cycles increased. At the higher level of wear cycles, the model parameters showed some increment. The initial compression modulus showed the same trend. This may be explained by the more compactness of the carpet at higher wear cycles. The decompression modulus, compression and the decompression work also decreased with the increase of wear cycles. However, no significant increase of the formers was observed at the higher wear cycles.     

Fabrication and characterization of electrospun Plantago major seed mucilage/PVA nanofibers

Electrospinning is a well-known technique for producing nanofibers using synthetic and natural polymers like mucilage. In this study, Plantago major Mucilage (PMM) was blended with polyvinyl alcohol (PVA) as a nontoxic adding agent, in order to produce electrospun nanofiber. Electrospinning parameters (voltage, tip-to-collector distance, feed rate, and PMM/PVA ratio) were optimized and solution properties were analyzed. The morphology of nanofibers was investigated using scanning electron microscopy (SEM), Fourier transform infrared (FTIR), X-ray diffraction (XRD), and Brunauer–Emmett–Teller (BET). Mechanical strength of nanofibers was determined, and cell viability on nanofibers was discussed by MTT assay. The results of SEM indicated that the PMM/PVA (50/50) nanofibers obtained with average diameter of 250 nm. Viscosity, electrical conductivity, and surface tension of PMM/PVA solution were 550 Cp, 575 μS/cm, and 47.044 mN/m, respectively. FTIR and XRD results verified the exiting PMM in produced nanofibers and no chemical reaction between PMM and PVA. Improvement in mechanical strength and cell viability of nanofibers by adding PMM to PVA nanofibers indicated the potential application of PMM-based nanofibers for medical and food industries. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47852.     

Thermosensitive folic acid-targeted poly (ethylene-<Emphasis Type="Italic">co</Emphasis>-vinyl alcohol) hemisuccinate polymeric nanoparticles for delivery of epirubicin to breast cancer cells

A novel thermosensitive folic acid (FA)-targeted succinylated poly (ethylene-co-vinyl alcohol) (EVOH) (EVOHS-FA) nanocarrier was synthesized for the specific delivery of epirubicin (EPI) to MCF-7 breast cancer cell line. Three different ratios of synthesized EVOH-Suc were reacted with FA. The structure of the desired products (EVOHS₄₀-FA, EVOHS₆₀-FA and EVOHS₈₀-FA) was confirmed by ¹H NMR and FTIR techniques. Nanoparticles were obtained by nano-precipitation procedure using DMSO/H₂O as solvent/anti-solvent. The particle size, zeta potential, entrapment efficacy and in vitro release profile of the final formulations in different temperatures were measured. The optimized nanoparticles had the particle size of 214 ± 8.5 nm, zeta potential of ?29.6 mV, PDI of 0.198 ± 0.04, and a high encapsulation efficiency that released the drug efficiently within 450 h at the temperature of 40 °C compared to 37 °C. The morphology of nanoparticles was studied by scanning electron microscopy. The in vitro cytotoxicity was evaluated using the MTT assay on MCF-7 cell lines in response to temperatures of 37 and 40 °C. The MTT assay indicated that the targeted nanoparticles carrying EPI were significantly more cytotoxic than the non-targeted nanoparticles and the free drug at 40 °C.     

Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application’s owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application’s workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG’s specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.     

Dynamic modeling and CPG-based trajectory generation for a masticatory rehab robot

Human mastication is a complex and rhythmic biomechanical process which is regulated by a brain stem central pattern generator (CPG). Masticatory patterns, frequency and amplitude of mastication are different from person to person and significantly depend on food properties. The central nervous system controls the activity of muscles to produce smooth transitions between different movements. Therefore, to rehab human mandibular system, there is a real need to use the concept of CPG for development of a new methodology in jaw exercises and to help jaw movements recovery. This paper proposes a novel method for real-time trajectory generation of a mastication rehab robot. The proposed method combines several methods and concepts including kinematics, dynamics, trajectory generation and CPG. The purpose of this article is to provide a methodology to enable physiotherapists to perform the human jaw rehabilitation. In this paper, the robotic setup includes two Gough–Stewart platforms. The first platform is used as the rehab robot, while the second one is used to model the human jaw system. Once the modeling is completed, the second robot will be replaced by an actual patient for the selected physiotherapy. Gibbs–Appell’s formulation is used to obtain the dynamics equations of the rehab robot. Then, a method based on the Fourier series is employed to tune parameters of the CPG. It is shown that changes in leg lengths, due to the online changes of the mastication parameters, occur in a smooth and continuous manner. The key feature of the proposed method, when applied to human mastication, is its ability to adapt to the environment and change the chewing pattern in real-time parameters, such as amplitudes as well as jaw movements velocity during mastication.     

An analytical hierarchy process-based approach to solve the multi-objective multiple traveling salesman problem

We consider the problem of assigning a team of autonomous robots to target locations in the context of a disaster management scenario while optimizing several objectives. This problem can be cast as a multiple traveling salesman problem, where several robots must visit designated locations. This paper provides an analytical hierarchy process (AHP)-based approach to this problem, while minimizing three objectives: the total traveled distance, the maximum tour, and the deviation rate. The AHP-based approach involves three phases. In the first phase, we use the AHP process to define a specific weight for each objective. The second phase consists in allocating the available targets, wherein we define and use three approaches: market-based, robot and task mean allocation-based, and balanced-based. Finally, the third phase involves the improvement in the solutions generated in the second phase. To validate the efficiency of the AHP-based approach, we used MATLAB to conduct an extensive comparative simulation study with other algorithms reported in the literature. The performance comparison of the three approaches shows a gap between the market-based approach and the other two approaches of up to 30%. Further, the results show that the AHP-based approach provides a better balance between the objectives, as compared to other state-of-the-art approaches. In particular, we observed an improvement in the total traveled distance when using the AHP-based approach in comparison with the distance traveled when using a clustering-based approach.