The influence of baking fuel on residues of polycyclic aromatic hydrocarbons and heavy metals in bread

The influence of fuel type used to bake bread on the spectrum and concentrations of some polycyclic aromatic hydrocarbons and heavy metals in baked bread was assessed. Bread samples were collected from different bakeries operated by either electricity, solar, mazot or solid waste and their residue content of PAHs and heavy metals was assessed. The total concentration of PAHs detected in mazot, solar, solid waste and electricity operated bakeries had an average of 320.6, 158.4, 317.3 and 25.5 μg kg⁻¹, respectively. Samples collected from mazot, solar and solid waste operated bakeries have had a wide spectrum of PAHs, in comparison to that detected in bread samples collected from electricity operated bakeries. Lead had the highest concentrations in the four group of bread samples, followed by nickel, while the concentrations of zinc and cadmium were the least. The concentration of lead detected in bread samples produced from mazot, solar, solid waste and electricity fueled bakeries were 1375.5, 1114, 1234, and 257.3 μg kg⁻¹, respectively. Estimated daily intake of PAHs based on bread consumption were 48.2, 28.5, 80.1, and 4.8 μg per person per day for bread produced in bakeries using mazot, solar, solid waste and electricity, respectively. Meanwhile, the estimated daily intake of benzo (a) pyrene were 3.69, 2.65, 8.1, and 0.81 μg per person per day for bread sample baked with mazot, solar, solid waste and electricity, respectively. The daily intake of lead, based on bread consumption was 291, 200.5, 222, and 46.31 μg per person per day for bread sample baked with mazot, solar, solid waste and electricity, respectively. The present work has indicated the comparatively high level of daily intake of benzo (a) pyrene and lead in comparison to levels reported from many other countries and those recommended by international regulatory bodies. It is probable that residues detected in bread samples are partially cereal-borne but there is strong evidence that the process of baking and the gases emitted are responsible for most of the contamination load.     

Nonlinearity and frequency-path modelling of three-longitudinal-mode nanometric displacement measurement system

The effects of periodic nonlinearity, intermodulation distortion, phase detection uncertainty and electrical cross-talk in a three-longitudinal-mode laser heterodyne Doppler interferometer (TLMI) displacement measurement system are presented. A frequency-path model consisting of three frequency components and two paths is also designed. As a result, there will be six frequency-path elements and 21 distinct interference terms. The periodic nonlinearity in the TLMI is mathematically modelled and simulated. Simulation results are compared with the typical two-mode heterodyne interferometer to confirm the designed model performance. The authors demonstrate that the nonlinearity of TLMI can be greatly reduced using the same compensation method as used in a two-frequency interferometer proposed by Hou et al.     

A Finite Volumes-Based 3-D Low Dispersion FDTD Algorithm

An algorithm extension to three dimensions is developed and presented for the highly phase-coherent modified second-order in time, fourth-order in space (or M24) finite-difference time-domain (FDTD) algorithm. A finite-volumes approach in conjunction with Yee's standard FDTD lattice is used for algorithm development. The corresponding dispersion relation is also developed, analyzed and compared to both the standard second-order and fourth-order FDTD algorithms as well as to two closely related high-order phase-coherent algorithms. Wideband algorithm attributes are also presented as well as sets of ready to use optimized algorithm coefficients.     

Investigating the effect of nanoparticles on the rutting behaviour of hot-mix asphalt

Rutting is considered as one of the major damages in asphalt mixtures. In this study, different types of nanoparticles such as TiO₂, Al₂O₃, Fe₂O₃ and ZnO in different percentages were added to the base asphalt binder in order to decrease the rutting potential of hot-mix asphalt (HMA). In the first step, asphalt binder tests for characteristics such as penetration grade, ductility, softening point and viscosity were performed on the asphalt binder modified by the nanoparticles. Then, after preparing HMA samples, the static creep test was done at two stress levels at a specific temperature. Results of this study showed that using the nanoparticles improved the behavioural properties of the asphalt binder and decreased rutting in asphalt mix samples. Furthermore, scanning electron microscope images taken from the asphalt binder samples modified by the nanoparticles demonstrated that these nanoparticles were properly distributed in the asphalt binder space and had a positive effect on the rutting performance of the asphalt mixes.     

Scheduling with an outsourcing option on both manufacturer and subcontractors

This paper addresses a single-stage scheduling problem with outsourcing allowed where each job can be either scheduled for in-house production or outsourced to one of the outside subcontractors available. The manufacturer has an unrelated parallel machine system, and each subcontractor has its own single machine. Subcontractors are capable to process all the jobs. Unlike most of past research, our study considers the joint scheduling of both in-house and outsourced jobs simultaneously. The objective is to minimize sum of the total weighted completion time and total outsourcing cost. An integer programming formulation is presented and then improved through an optimality property on job orders. A heuristic algorithm is also introduced to decompose the problem into smaller and easier subproblems and solve them to optimality.     

A powerful hybrid clustering method based on modified stem cells and Fuzzy C-means algorithms

One of the simple techniques for Data Clustering is based on Fuzzy C-means (FCM) clustering which describes the belongingness of each data to a cluster by a fuzzy membership function instead of a crisp value. However, the results of fuzzy clustering depend highly on the initial state selection and there is also a high risk for getting the best results when the datasets are large. In this paper, we present a hybrid algorithm based on FCM and modified stem cells algorithms, we called it SC-FCM algorithm, for optimum clustering of a dataset into K clusters. The experimental results obtained by using the new algorithm on different well-known datasets compared with those obtained by K-means algorithm, FCM, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) Algorithm demonstrate the better performance of the new algorithm.     

Crop yield prediction from remotely sensed vegetation indices and primary productivity in arid and semi-arid lands

Global demands for biomass and arable lands are expected to double in the next 35 years. Scarcity of water resources in arid and semi-arid areas poses a serious threat to their agricultural productivity and hence their food security. In this study, we examine whether crop yields can be predicted from remotely sensed vegetation indices and remotely sensed estimates of primary productivity. Spatial relationships between remotely sensed enhanced vegetation index (EVI), net photosynthesis (P_Net), and gross and net primary production (GPP and NPP, respectively) in irrigated semi-arid and arid agro-ecosystems since the beginning of the century are analysed. The conflict-affected country of Syria is selected as the case study. Relationships between EVI and crop yield are investigated in an effort to enhance food production estimates in affected areas outside governmental jurisdictions. Estimates of NPP derived from reported irrigated agriculture crop data in a semi-arid and an arid zone are compared to remotely sensed NPP in a geospatial environment. Results show that winter crop yields are correlated with spring GPP in semi-arid zones of the study area (R² = 0.85). Summer crop yield can be predicted from either cumulative summer EVI (R² = 0.77) or P_Net in most zones. Where fully irrigated fields are surrounded by hyper-arid landscape, summer P_Net was negative in all instances and EVI was inversely correlated with yield. NPP from crops was much higher (290 gC m^?2 year^?1) in those regions than MOD17 NPP (70 gC m^–2), where 1.0 g of carbon is equivalent to 2.2 g of oven-dry organic matter (= 45% carbon by weight). The gap was less in semi-arid zones (2–39% difference). Overall crop-derived NPP for the period 2000–2013 was 322 versus 300 gC m^–2 for that remotely sensed within the cropped zones of the political units. The results of this study are crucial to derive accurate estimates of irrigated agriculture productivity and to study the effect of the latter on net ecosystem carbon storage.     

PASTA: a power-aware solution to scheduling of precedence-constrained tasks on heterogeneous computing resources

Power efficiency is one of the main challenges in large-scale distributed systems such as datacenters, Grids, and Clouds. One can study the scheduling of applications in such large-scale distributed systems by representing applications as a set of precedence-constrained tasks and modeling them by a Directed Acyclic Graph. In this paper we address the problem of scheduling a set of tasks with precedence constraints on a heterogeneous set of Computing Resources (CRs) with the dual objective of minimizing the overall makespan and reducing the aggregate power consumption of CRs. Most of the related works in this area use Dynamic Voltage and Frequency Scaling (DVFS) approach to achieve these objectives. However, DVFS requires special hardware support that may not be available on all processors in large-scale distributed systems. In contrast, we propose a novel two-phase solution called PASTA that does not require any special hardware support. In its first phase, it uses a novel algorithm to select a subset of available CRs for running an application that can balance between lower overall power consumption of CRs and shorter makespan of application task schedules. In its second phase, it uses a low-complexity power-aware algorithm that creates a schedule for running application tasks on the selected CRs. We show that the overall time complexity of PASTA is $O(p.v^{2})$ where $p$ is the number of CRs and $v$ is the number of tasks. By using simulative experiments on real-world task graphs, we show that the makespan of schedules produced by PASTA are approximately 20 % longer than the ones produced by the well-known HEFT algorithm. However, the schedules produced by PASTA consume nearly 60 % less energy than those produced by HEFT. Empirical experiments on a physical test-bed confirm the power efficiency of PASTA in comparison with HEFT too.