A neural network algorithm for servicing jobs with sequential and parallel machines

We consider the FJ‖C _max problem of optimal servicing with respect to performance for a given set of jobs by sequential and parallel machines. The problem FJ‖C _max is a generalization of the classical J‖C _max problem for the case when the servicing system has not only sequential but also parallel (identical) machines. We propose a two-stage algorithm for a heuristic solution of problem FJ‖C _max On the first stage, we solve the problem J‖C _max, i.e., we assume that the servicing system does not have parallel machines. On the second stage, operations are distributed over parallel machines. On both stages of the algorithm, we use neural network decision making models. The efficiency of a neural network algorithm for the problem J‖C _max and problem FJ‖C _max was evaluated on 20 test examples obtained from 20 known J‖C _max problems by including into the servicing system a random number of copies of sequential machines.     

Clinical Decision Support and Closed‐Loop Control for Intensive Care Unit Sedation

Critical care patients undergoing surgery require drug administration to regulate physiological variables such as blood pressure, cardiac output, heart rate, and degree of consciousness. The rate of infusion of each administered drug is critical, requiring constant monitoring and frequent adjustments. Patients in the intensive care unit who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. Open‐loop control (manual control) by clinical personnel can be tedious, imprecise, time‐consuming, and sometimes of poor quality, depending on the skills and judgment of the clinician. Dynamical system pharmacokinetic and pharmacodynamic modeling and closed‐loop control system design methodologies can significantly advance our understanding of the wide effects of pharmacological agents and anesthetics, as well as advance the state‐of‐the‐art in active control of drug delivery systems for clinical pharmacology. In this paper, we discuss the challenges and opportunities of clinical decision support and closed‐loop control for intensive care unit sedation.     

Stochastic controller as an active queue management based on B-spline kernel observer via particle swarm optimization

Given the fact that the current Internet is getting more difficult in handling the traffic congestion control, the proposed method is compatible with the stochastic nature of network dynamics. Most conventional active queue management is based on the first stochastic moment. In stochastic theory, the first moment is not efficient for non-Gaussian systems that are the same as the network queue size. We propose a new stochastic active queue management technique, based on stochastic control and B-spline window observer, called intelligent probability density function AQM (IPDF-AQM). The IPDF-AQM is based on a PDF control and particle swarm optimization, which not only considers the average queue length at the current time slot, but also takes into consideration the PDF of queue lengths within a round-trip time. We provide a guideline for the selection of the probability of dropping as control input for TCP/AQM system to make the PDF of queue length converge at a certain PDF target based on B-spline approximation and improve the network performance. Simulation results show that the proposed stochastic AQM scheme does improve the end-to-end performance.     

An immune algorithm for scheduling a hybrid flow shop with sequence-dependent setup times and machines with random breakdowns

Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems that have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real-life industries a machine can be unavailable for many reasons, such as unanticipated breakdowns (stochastic unavailability), or due to scheduled preventive maintenance where the periods of unavailability are known in advance (deterministic unavailability). This paper deals with hybrid flow shop scheduling problems in which there are sequence-dependent setup times (SDSTs), and machines suffer stochastic breakdowns, to optimise objectives based on the expected makespan. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. An immune algorithm (IA) can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. In this research, a computational method based on a clonal selection principle and an affinity maturation mechanism of the immune response is used. This paper describes how we can incorporate simulation into an immune algorithm for the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdowns. The results obtained are analysed using a Taguchi experimental design.     

Green information systems use in social enterprise: the case of a community-led eco-localization website in the West Midlands region of the UK

This study examines the intention of environmentally-oriented social enterprises to continue using Green IS (an eco-localization website here) in Herefordshire located in the West Midlands region of the United Kingdom. The Herefordshire Greenlinks (HGL) Association is a local business community that operates primarily through the HGL Website in order to help local social enterprises find and trade local eco-friendly goods and services. It is an example of a “market-based” approach to solving social and environmental issues that is inspired by a bottom-up view of community development and eco-localization. Based on the theory of uses and gratification of media use and the new environmental paradigm, we propose a research model that outlines the various factors contributing to intention to continue using an eco-localization website. The research model and its associated hypotheses were empirically tested using a dataset generated from a survey of 83 small social enterprises. The results demonstrate the importance of three different types of gratification – content, social and performance gratification as well as users’ environmental concerns in determining the intention to continue using an eco-localization website. This study contributes to the Green IS literature by extending research to the social sector environment, and it also provides practical insight for designers of such community-led eco-localization websites.     

PDE-constrained optimization in medical image analysis

PDE-constrained optimization problems find many applications in medical image analysis, for example, neuroimaging, cardiovascular imaging, and oncologic imaging. We review the related literature and give examples of the formulation, discretization, and numerical solution of PDE-constrained optimization problems for medical imaging. We discuss three examples. The first is image registration, the second is data assimilation for brain tumor patients, and the third is data assimilation in cardiovascular imaging. The image registration problem is a classical task in medical image analysis and seeks to find pointwise correspondences between two or more images. Data assimilation problems use a PDE-constrained formulation to link a biophysical model to patient-specific data obtained from medical images. The associated optimality systems turn out to be sets of nonlinear, multicomponent PDEs that are challenging to solve in an efficient way. The ultimate goal of our work is the design of inversion methods that integrate complementary data, and rigorously follow mathematical and physical principles, in an attempt to support clinical decision making. This requires reliable, high-fidelity algorithms with a short time-to-solution. This task is complicated by model and data uncertainties, and by the fact that PDE-constrained optimization problems are ill-posed in nature, and in general yield high-dimensional, severely ill-conditioned systems after discretization. These features make regularization, effective preconditioners, and iterative solvers that, in many cases, have to be implemented on distributed-memory architectures to be practical, a prerequisite. We showcase state-of-the-art techniques in scientific computing to tackle these challenges.     

Shrinkage behaviour of superplasticised RCCP and its relationship with internal temperature

Shrinkage properties of roller-compacted concrete pavement (RCCP) and its relationship with internal temperature variations in superplasticised RCCP were studied. The control and superplasticised RCCPs containing different amounts of sodium naphthalene sulphonate superplasticiser between 1 and 4% by weight of cement were compacted at their optimum water content. The short- and long-term shrinkage and internal temperature variations were monitored in isolated and non-isolated conditions. Micro-structural analysis was implemented using the electron scanning microscope and X-ray diffraction methods. The initial internal temperature of superplasticised RCCP during the first hours of setting was higher than the control mix. A dormant period of low internal temperature was found for superplasticised mixes which started after about 7–13 and 6–10 h in non-isolated and isolated conditions, respectively. For non-isolated condition, the dormant period lasted about 3 days. Under isolated condition, similar trend was found after 4–8 days. During the first day of curing, the total and drying shrinkages of the control RCCP were considerably higher than the superplasticised mixes. On the contrary, the long-term monitoring indicated higher total and drying shrinkages in superplasticised mixes. Based on statistical analysis, the relationship between the autogenous shrinkage and area under the internal temperature diagram was significant.     

Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns

Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems which have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real life industries, a machine can be unavailable for many reasons, such as unanticipated breakdowns, i.e., stochastic unavailability, or due to a scheduled preventive maintenance where the periods of unavailability are known in advance, i.e., deterministic unavailability. This paper deals with the hybrid flow shop scheduling problems in which there are sequence-dependent setup times, commonly known as the SDST, and machines which suffer stochastic breakdown to optimize objectives based on expected makespan. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. The genetic algorithm can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes how we can incorporate simulation into genetic algorithm approach to the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdown. An overview of the hybrid flow shops and scheduling under stochastic unavailability of machines are presented. Subsequently, the details of incorporated simulation into genetic algorithm approach are described and implemented. Consequently, the results obtained are analyzed with Taguchi experimental design.     

Investigation on hydrophilicity of micro-arc oxidized TiO2 nano/micro-porous layers

Titania porous layers with a rough surface were synthesized via micro-arc oxidation (MAO) and the effect of the applied voltage and electrolyte concentration on surface structure, and chemical composition of the layers was studied. Morphological and topographical investigations, performed by SEM and AFM, revealed that pore size and surface roughness of the layers increased with the applied voltage and the electrolyte concentration. Based on the XRD and XPS results, the grown layers consisted of anatase and rutile phases with varying fractions depending on growth conditions. It was found that anatase/rutile relative content reached its maximum value at medium applied voltages or electrolyte concentrations. Finally, hydrophilicity of the grown layers was determined using a water contact angle apparatus, and a correlation between measured contact angles and MAO-parameters was suggested. It was observed that the layers synthesized under the applied voltage of 400 V in the electrolytes with a concentration of 10 g l⁻¹ exhibited the highest hydrophilicity.