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.     

Cyclic preference scheduling of nurses using a Lagrangian-based heuristic

This paper addresses the problem of developing cyclic schedules for nurses while taking into account the quality of individual rosters. In this context, quality is gauged by the absence of certain undesirable shift patterns. The problem is formulated as an integer program (IP) and then decomposed using Lagrangian relaxation. Two approaches were explored, the first based on the relaxation of the preference constraints and the second based on the relaxation of the demand constraints. A theoretical examination of the first approach indicated that it was not likely to yield good bounds. The second approach showed more promise and was subsequently used to develop a solution methodology that combined subgradient optimization, the bundle method, heuristics, and variable fixing. After the Lagrangian dual problem was solved, though, there was no obvious way to perform branch and bound when a duality gap existed between the lower bound and the best objective function value provided by an IP-based feasibility heuristic. This led to the introduction of a variable fixing scheme to speed convergence. The full algorithm was tested on data provided by a medium-size U.S. hospital. Computational results showed that in most cases, problem instances with up to 100 nurses and 20 rotational profiles could be solved to near-optimality in less than 20 min.     

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.     

Numerical analysis of plastic deformation of a circular sheet metal subjected to transverse impact loading

Based on the concept of Tensor Code a numerical method is presented for analysis of the plastic deformation process of circular sheet metals subjected to transverse impact loading. In the solution process, the equation of motion is solved explicitly with finite difference method in a series of small time steps over a Lagrangian mesh of zones. Using this method, a code has been developed and utilized for investigation of the deformation behavior of an explosively loaded circular sheet metal under various conditions. Deformation characteristics of a sheet under rectangular and triangular pressure distributions are discussed. It is shown that when these simple distributions are combined with each other, their individual effects on the deformation behavior are also combined in the deformation process. Effects of shape and duration of pressure pulse as well as boundary conditions have been explored. Moreover, results of the numerical simulation have been compared with those of theoretical solution and experiments reported by other researchers. Good agreements between them show the validity of the developed code.     

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.     

Experimental Test of a Two-Stage Kalman Filter for Actuator Fault Detection and Diagnosis of an Unmanned Quadrotor Helicopter

This paper addresses the problem of Faut Detection and Diagnosis (FDD) of a quadrotor helicopter system in the presence of actuator faults. To this end a Two-Stage Kalman Filter (TSKF) is used to simultaneously estimate and isolate possible faults in each actuator. The faults are modelled as losses in control effectiveness of rotors. Three fault scenarios are investigated: loss of control effectiveness in one single actuator, simultaneous loss of control effectiveness in all motors, and loss of control effectiveness in three motors with different magnitudes. The developed FDD algorithm is evaluated through experimental application to an unmanned quadrotor helicopter testbed available at the Department of Mechanical and Industrial Engineering of Concordia University, called Qball-X4. The obtained results show the effectiveness of the proposed FDD method.     

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.     

VANET QoS-OLSR: QoS-based clustering protocol for Vehicular Ad hoc Networks

In this paper, we address the problem of clustering in Vehicular Ad hoc Networks (VANETs) using Quality of Service Optimized Link State Routing (QoS-OLSR) protocol. Several clustering algorithms have been proposed for VANET and MANET. However, the mobility-based algorithms ignore the Quality of Service requirements that are important for VANET safety, emergency, and multimedia services while the QoS-based algorithms ignore the high speed mobility constraints since they are dedicated for Mobile Ad hoc Networks (MANETs). Our solution is a new QoS-based clustering algorithm that considers a tradeoff between QoS requirements and high speed mobility constraints. The goal is to form stable clusters and maintain the stability during communications and link failures while satisfying the Quality of Service requirements. This is achieved by: (1) considering the high mobility metrics while computing the QoS, (2) using Ant Colony Optimization for MPRs selection, and (3) using MPR recovery algorithm able to select alternatives and keep the network connected in case of link failures. Performance analysis and simulation results show that the proposed model can maintain the network stability, reduce the end-to-end delay, increase the packet delivery ratio, and reduce the communications overhead.