Optimization the initial weights of artificial neural networks via genetic algorithm applied to hip bone fracture prediction

This paper aims to find the optimal set of initial weights to enhance the accuracy of artificial neural networks (ANNs) by using genetic algorithms (GA). The sample in this study included 228 patients with first low-trauma hip fracture and 215 patients without hip fracture, both of them were interviewed with 78 questions. We used logistic regression to select 5 important factors (i.e., bone mineral density, experience of fracture, average hand grip strength, intake of coffee, and peak expiratory flow rate) for building artificial neural networks to predict the probabilities of hip fractures. Three-layer (one hidden layer) ANNs models with back-propagation training algorithms were adopted. The purpose in this paper is to find the optimal initial weights of neural networks via genetic algorithm to improve the predictability. Area under the ROC curve (AUC) was used to assess the performance of neural networks. The study results showed the genetic algorithm obtained an AUC of 0.858±0.00493 on modeling data and 0.802 ± 0.03318 on testing data. They were slightly better than the results of our previous study (0.868±0.00387 and 0.796±0.02559, resp.). Thus, the preliminary study for only using simple GA has been proved to be effective for improving the accuracy of artificial neural networks.This research was supported by the National Science Council (NSC) of Taiwan (Grant no. NSC98-2915-I-155-005), the Department of Education grant of Excellent Teaching Program of Yuan Ze University (Grant no. 217517) and the Center for Dynamical Biomarkers and Translational Medicine supported by National Science Council (Grant no. NSC 100- 2911-I-008-001)

Automatic age estimation system for face images

Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications. © 2012 Lin et al

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.This research is supported by the Center forDynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it is supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302)

Citrulline protects mice from experimental cerebral malaria by ameliorating hypoargininemia, urea cycle changes and vascular leak

© 2019 Gramaglia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Clinical and model studies indicate that low nitric oxide (NO) bioavailability due in part to profound hypoargininemia contributes to cerebral malaria (CM) pathogenesis. Protection against CM pathogenesis may be achieved by altering the diet before infection with Plasmodium falcIParum infection (nutraceutical) or by administering adjunctive therapy that decreases CM mortality (adjunctive therapy). This hypothesis was tested by administering citrulline or arginine in experimental CM (eCM). We report that citrulline injected as prophylaxis immediately post infection (PI) protected virtually all mice by ameliorating (i) hypoargininemia, (ii) urea cycle impairment, and (iii) disruption of blood brain barrier. Citrulline prophylaxis inhibited plasma arginase activity. Parasitemia was similar in citrulline- And vehicle control-groups, indicating that protection from pathogenesis was not due to decreased parasitemia. Both citrulline and arginine administered from day 1 PI in the drinking water significantly protected mice from eCM. These observations collectively indicate that increasing dietary citrulline or arginine decreases eCM mortality. Citrulline injected IP on day 4 PI with quinine-injected IP on day 6 PI partially protected mice from eCM; citrulline plus scavenging of superoxide with pegylated superoxide dismutase and pegylated catalase protected all recIPients from eCM. These findings indicate that ameliorating hypoargininemia with citrulline plus superoxide scavenging decreases eCM mortality

A single sub-km Kuiper Belt object from a stellar Occultation in archival data

The Kuiper belt is a remnant of the primordial Solar System. Measurements of its size distribution constrain its accretion and collisional history, and the importance of material strength of Kuiper belt objects (KBOs). Small, sub-km sized, KBOs elude direct detection, but the signature of their occultations of background stars should be detectable. Observations at both optical and X-ray wavelengths claim to have detected such occultations, but their implied KBO abundances are inconsistent with each other and far exceed theoretical expectations. Here, we report an analysis of archival data that reveals an occultation by a body with a 500 m radius at a distance of 45 AU. The probability of this event to occur due to random statistical fluctuations within our data set is about 2%. Our survey yields a surface density of KBOs with radii larger than 250 m of 2.1^{+4.8}_{-1.7} x 10^7 deg^{-2}, ruling out inferred surface densities from previous claimed detections by more than 5 sigma. The fact that we detected only one event, firmly shows a deficit of sub-km sized KBOs compared to a population extrapolated from objects with r>50 km. This implies that sub-km sized KBOs are undergoing collisional erosion, just like debris disks observed around other stars.Comment: To appear in Nature on December 17, 2009. Under press embargo until 1800 hours London time on 16 December. 19 pages; 7 figure

Spin-Nematic Squeezed Vacuum in a Quantum Gas

Using squeezed states it is possible to surpass the standard quantum limit of measurement uncertainty by reducing the measurement uncertainty of one property at the expense of another complementary property. Squeezed states were first demonstrated in optical fields and later with ensembles of pseudo spin-1/2 atoms using non-linear atom-light interactions. Recently, collisional interactions in ultracold atomic gases have been used to generate a large degree of quadrature spin squeezing in two-component Bose condensates. For pseudo spin-1/2 systems, the complementary properties are the different components of the total spin vector , which fully characterize the state on an SU(2) Bloch sphere. Here, we measure squeezing in a spin-1 Bose condensate, an SU(3) system, which requires measurement of the rank-2 nematic or quadrupole tensor as well to fully characterize the state. Following a quench through a nematic to ferromagnetic quantum phase transition, squeezing is observed in the variance of the quadratures up to -8.3(-0.7 +0.6) dB (-10.3(-0.9 +0.7) dB corrected for detection noise) below the standard quantum limit. This spin-nematic squeezing is observed for negligible occupation of the squeezed modes and is analogous to optical two-mode vacuum squeezing. This work has potential applications to continuous variable quantum information and quantum-enhanced magnetometry

Gout penyakit lama dihidapi manusia

This paper proposes a differential evolution with local information for TSK-type neuro-fuzzy system optimization. The differential evolution with local information consider neighborhood between each individual to keep the diversity of population. An adaptive parameter tuning based on 1/5th rule is used to trade off between local search and global search. For structure learning algorithm, the on-line clustering algorithm is used for rule generation. The structure learning algorithm generates a new rule which compares the firing strength. Initially, there is no rule in neuro-fuzzy system model. The rules are automatically generated by fuzzy measure. For parameter learning, the parameters are optimized by differential evolution algorithm. Finally, the proposed neuro-fuzzy system with novel differential evolution model is applied in chaotic sequence prediction problem. Results of this paper demonstrate the effectiveness of the proposed model. © 2011 IEEE

Electromagnetically Induced Transparency and Slow Light with Optomechanics

Controlling the interaction between localized optical and mechanical excitations has recently become possible following advances in micro- and nano-fabrication techniques. To date, most experimental studies of optomechanics have focused on measurement and control of the mechanical subsystem through its interaction with optics, and have led to the experimental demonstration of dynamical back-action cooling and optical rigidity of the mechanical system. Conversely, the optical response of these systems is also modified in the presence of mechanical interactions, leading to strong nonlinear effects such as Electromagnetically Induced Transparency (EIT) and parametric normal-mode splitting. In atomic systems, seminal experiments and proposals to slow and stop the propagation of light, and their applicability to modern optical networks, and future quantum networks, have thrust EIT to the forefront of experimental study during the last two decades. In a similar fashion, here we use the optomechanical nonlinearity to control the velocity of light via engineered photon-phonon interactions. Our results demonstrate EIT and tunable optical delays in a nanoscale optomechanical crystal device, fabricated by simply etching holes into a thin film of silicon (Si). At low temperature (8.7 K), we show an optically-tunable delay of 50 ns with near-unity optical transparency, and superluminal light with a 1.4 microseconds signal advance. These results, while indicating significant progress towards an integrated quantum optomechanical memory, are also relevant to classical signal processing applications. Measurements at room temperature and in the analogous regime of Electromagnetically Induced Absorption (EIA) show the utility of these chip-scale optomechanical systems for optical buffering, amplification, and filtering of microwave-over-optical signals.Comment: 15 pages, 9 figure