8,421 research outputs found
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
Anti-JNK2 peptide–siRNA nanostructures improve plaque endothelium and reduce thrombotic risk in atherosclerotic mice
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
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