An artificial neural network based heuristic for flow shop scheduling problems

The objective of this paper is to find a sequence of jobs in the flow shop to minimize makespan. A feed forward back propagation neural network is used to solve the problem. The network is trained with the optimal sequences of completely enumerated five, six and seven jobs, ten machine problem and this trained network is then used to solve the problem with greater number of jobs. The sequence obtained using artificial neural network (ANN) is given as the initial sequence to a heuristic proposed by Suliman and also to genetic algorithm (GA) as one of the sequences of the population for further improvement. The approaches are referred as ANN-Suliman heuristic and ANN-GA heuristic respectively. Makespan of the sequences obtained by these heuristics are compared with the makespan of the sequences obtained using the heuristic proposed by Nawaz, Enscore and Ham (NEH) and Suliman Heuristic initialized with Campbell Dudek and Smith (CDS) heuristic called as CDS-Suliman approach. It is found that the ANN-GA and ANN-Suliman heuristic approaches perform better than NEH and CDS-Suliman heuristics for the problems considered.     

The continuous non-linear approximation of procedurally defined curves using integral B-splines

This paper outlines an algorithm for the continuous non-linear approximation of procedurally defined curves. Unlike conventional approximation methods using the discrete L_2 form metric with sampling points, this algorithm uses the continuous L_2 form metric based on minimizing the integral of the least square error metric between the original and approximate curves. Expressions for the optimality criteria are derived based on exact B-spline integration. Although numerical integration may be necessary for some complicated curves, the use of numerical integration is minimized by a priori explicit evaluations. Plane or space curves with high curvatures and/or discontinuities can also be handled by means of an adaptive knot placement strategy. It has been found that the proposed scheme is more efficient and accurate compared to currently existing interpolation and approximation methods.     

Molecular Chaperones and Thyroid Cancer

Thyroid cancers are the most common of the endocrine system malignancies and progress must be made in the areas of differential diagnosis and treatment to improve patient management. Advances in the understanding of carcinogenic mechanisms have occurred in various fronts, including studies of the chaperone system (CS). Components of the CS are found to be quantitatively increased or decreased, and some correlations have been established between the quantitative changes and tumor type, prognosis, and response to treatment. These correlations provide the basis for identifying distinctive patterns useful in differential diagnosis and for planning experiments aiming at elucidating the role of the CS in tumorigenesis. Here, we discuss studies of the CS components in various thyroid cancers (TC). The chaperones belonging to the families of the small heat-shock proteins Hsp70 and Hsp90 and the chaperonin of Group I, Hsp60, have been quantified mostly by immunohistochemistry and Western blot in tumor and normal control tissues and in extracellular vesicles. Distinctive differences were revealed between the various thyroid tumor types. The most frequent finding was an increase in the chaperones, which can be attributed to the augmented need for chaperones the tumor cells have because of their accelerated metabolism, growth, and division rate. Thus, chaperones help the tumor cell rather than protect the patient, exemplifying chaperonopathies by mistake or collaborationism. This highlights the need for research on chaperonotherapy, namely the development of means to eliminate/inhibit pathogenic chaperones.     

Memory-based approaches for eliminating premature convergence in particle swarm optimization

Applied Intelligence - Particle Swarm Optimization (PSO) is a computational method in which a group of particles moves in search space in search of an optimal solution. During this movement, each...     

Feature based fall detection system for elders using compressed sensing in WVSN

Wireless Networks - In general there is a steep increase in the number of cases related to elderly people falling down and getting hospitalized since they are living alone. This increases the need...     

Tuning the Transconductance of Organic Electrochemical Transistors

Organic electrochemical transistors (OECTs) operate at very low voltages, transduce ions into electronic signals, and reach extremely large transconductance values, making them ideally suited for bio-sensing applications. However, despite their promising performance, the dependence of their maximum transconductance on device geometry and applied voltages are not correctly captured by current capacitive device models. Here, current scaling laws are revised in the light of a recently developed 2D device model that adequately accounts for drift and diffusion of ions inside the polymer channel. It is shown that the maximum transconductance of the devices is found at the transition between the depletion and accumulation region of the transistors, which as well provides an explanation for the observed shift of the transconductance peak with geometric dimensions and the drain potential. Overall, the results provide a better understanding of the working mechanisms of OECTs, and facilitate design rules to optimize OECT performance further.