A Multi-Objective Optimization for Supply Chain Network Using the Bees Algorithm

A supply chain is a complex network which involves the products, services and information flows between suppliers and customers. A typical supply chain is composed of different levels, hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between the multi-objectives such as cost minimization and lead-time minimization. There are several multi-objective optimization methods which have been applied to find the optimum solutions set based on the Pareto front line. In this study, a swarm-based optimization method, namely, the bees algorithm is proposed in dealing with the multi-objective supply chain model to find the optimum configuration of a given supply chain problem which minimizes the total cost and the total lead-time. The supply chain problem utilized in this study is taken from literature and several experiments have been conducted in order to show the performance of the proposed model; in addition, the results have been compared to those achieved by the ant colony optimization method. The results show that the proposed bees algorithm is able to achieve better Pareto solutions for the supply chain problem

Subject-Independent ERP-Based Brain-Computer Interfaces

© 2001-2011 IEEE. Brain-computer interfaces (BCIs) are desirable for people to express their thoughts, especially those with profound disabilities in communication. The classification of brain patterns for each different subject requires an extensively time-consuming learning stage specific to that person, in order to reach satisfactory accuracy performance. The training session could also be infeasible for disabled patients as they may not fully understand the training instructions. In this paper, we propose a unified classification scheme based on ensemble classifier, dynamic stopping, and adaptive learning. We apply this scheme on the P300-based BCI, with the subject-independent manner, where no learning session is required for new experimental users. According to our theoretical analysis and empirical results, the harmonized integration of these three methods can significantly boost up the average accuracy from 75.00% to 91.26%, while at the same time reduce the average spelling time from 12.62 to 6.78 iterations, approximately to two-fold faster. The experiments were conducted on a large public dataset which had been used in other related studies. Direct comparisons between our work with the others' are also reported in details

Diffusivity and configurational entropy maxima in short range attractive colloids

We study tagged particle diffusion at large packing fractions, for a model of particles interacting with a generalized Lennard-Jones 2n-n potential, with large n. The resulting short-range potential mimics interactions in colloidal systems. In agreement with previous calculations for short-range potential, we observe a diffusivity maximum as a function of temperature. By studying the temperature dependence of the configurational entropy -- which we evaluate with two different methods -- we show that a configurational entropy maximum is observed at a temperature close to that of the diffusivity maximum. Our findings suggest a relationbetween dynamics and number of distinct states for short-range potentials.Comment: 4 pages, 3 figures, submited to Physical Review Lette

Neural Modeling and Control of Diesel Engine with Pollution Constraints

The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identification and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are flexible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The presented work extends optimal neuro-control to the multivariable case and shows the flexibility of neural optimisers. Considering the preliminary results, it appears that neural networks can be used as embedded models for engine control, to satisfy the more and more restricting pollutant emission legislation. Particularly, they are able to model nonlinear dynamics and outperform during transients the control schemes based on static mappings.Comment: 15 page

New insights in post-traumatic headache with cluster headache phenotype: a cohort study

OBJECTIVES: To define the characteristics of post-traumatic headache with cluster headache phenotype (PTH-CH) and to compare these characteristics with primary CH. METHODS: A retrospective study was conducted of patients seen between 2007 and 2017 in a headache centre and diagnosed with PTH-CH that developed within 7 days of head trauma. A control cohort included 553 patients with primary CH without any history of trauma who attended the headache clinic during the same period. Data including demographics, attack characteristics and response to treatments were recorded. RESULTS: Twenty-six patients with PTH-CH were identified. Multivariate analysis revealed significant associations between PTH-CH and family history of CH (OR 3.32, 95% CI 1.31 to 8.63), chronic form (OR 3.29, 95% CI 1.70 to 6.49), parietal (OR 14.82, 95% CI 6.32 to 37.39) or temporal (OR 2.04, 95% CI 1.10 to 3.84) location of pain, and presence of prominent cranial autonomic features during attacks (miosis OR 11.24, 95% CI 3.21 to 41.34; eyelid oedema OR 5.79, 95% CI 2.57 to 13.82; rhinorrhoea OR 2.65, 95% CI 1.26 to 5.86; facial sweating OR 2.53, 95% CI 1.33 to 4.93). Patients with PTH-CH were at a higher risk of being intractable to acute (OR 12.34, 95% CI 2.51 to 64.73) and preventive (OR 16.98, 95% CI 6.88 to 45.52) treatments and of suffering from associated chronic migraine (OR 10.35, 95% CI 3.96 to 28.82). CONCLUSION: This largest series of PTH-CH defines it as a unique entity with specific evolutive profile. Patients with PTH-CH are more likely to suffer from the chronic variant, have marked autonomic features, be intractable to treatment and have associated chronic migraine compared with primary CH

Charge carrier localization induced by excess Fe in the Fe1+y(Te,Se) superconductor system

We have investigated the effect of Fe nonstoichiometry on properties of the Fe1+y(Te, Se) superconductor system by means of resistivity, Hall coefficient, magnetic susceptibility, and specific heat measurements. We find that the excess Fe at interstitial sites of the (Te, Se) layers not only suppresses superconductivity, but also results in a weakly localized electronic state. We argue that these effects originate from the magnetic coupling between the excess Fe and the adjacent Fe square planar sheets, which favors a short-range magnetic order.Comment: 15 pages, 6 figures accepted for publication in PR

Transition between nuclear and quark-gluon descriptions of hadrons and light nuclei

We provide a perspective on studies aimed at observing the transition between hadronic and quark-gluonic descriptions of reactions involving light nuclei. We begin by summarizing the results for relatively simple reactions such as the pion form factor and the neutral pion transition form factor as well as that for the nucleon and end with exclusive photoreactions in our simplest nuclei. A particular focus will be on reactions involving the deuteron. It is noted that a firm understanding of these issues is essential for unraveling important structure information from processes such as deeply virtual Compton scattering as well as deeply virtual meson production. The connection to exotic phenomena such as color transparency will be discussed. A number of outstanding challenges will require new experiments at modern facilities on the horizon as well as further theoretical developments.Comment: 37 pages, 17 figures, submitted to Reports on Progress in Physic

Morphology and Magnetic Properties of Sulfonated Poly[styrene-(ethylene/butylene)-styrene]/Iron Oxide Composites

α-Fe2O3 structures were initiated in the sulfonated polystyrene block domains of poly[styrene–(ethylene/butylene)–styrene] (SEBS) block copolymers via a domain-targeted in-situ chemical precipitation method. The crystal structure of these particles was determined using wide-angle X-ray diffraction and selected area electron diffraction using a transmission electron microscope (TEM). TEM revealed that for less sulfonated SEBS (10 mole%), nanoparticles were aggregated with aggregate size range of 100–150 nm whereas for high sulfonation (16 and 20 mole% sSEBS) there were needle-like structures with length and width of 200–250 nm and 50 nm, respectively. Dynamic mechanical analyses suggest that initial iron oxide nanoparticle growth takes place in the sulfonated polystyrene block domains. The magnetic properties of these nanocomposites were probed with a superconducting quantum interference device magnetometer at 5 and 150 K as well as with an alternating gradient magnetometer at 300 K. The materials exhibited superparamagnetism at 150 K and 300 K and ferrimagnetism at 5 K