Spider specie identification and verification based on pattern recognition of it cobweb

Biodiversity conservation is a global priority where the study of every type of living form is a fundamental task. Inside the huge number of the planet species, spiders play an important role in almost every habitat. This paper presents a comprehensive study on the reliability of the most used features extractors to face the problem of spider specie recognition by using their cobwebs, both in identification and verification modes. We have applied a preprocessing to the cobwebs images in order to obtain only the valid information and compute the optimal size to reach the highest performance. We have used the principal component analysis (PCA), independent component analysis (ICA), Discrete Cosine Transform (DCT), Wavelet Transform (DWT) and discriminative common vectors as features extractors, and proposed the fusion of several of them to improve the system’s performance. Finally, we have used the Least Square Vector Support Machine with radial basis function as a classifier. We have implemented K-Fold and Hold-Out cross-validation techniques in order to obtain reliable results. PCA provided the best performance, reaching a 99.65% ± 0.21 of success rate in identification mode and 99.98% ± 0.04 of the area under de Reveicer Operating Characteristic (ROC) curve in verification mode. The best combination of features extractors was PCA, DCT, DWT and ICA, which achieved a 99.96% ± 0.16 of success rate in identification mode and perfect verification.     

Modified Köhler illumination for LED-based projection display

Common projection optics use Köhler illumination to achieve a required lighting. These systems always prevent the realization of a compact optical configuration along with a high lumen output. Based on conventional Köhler illumination, a modified Köhler illumination system for LED-based projection display is presented in this paper, which can significantly reduce the system volume while allowing for adequate and homogeneous illumination. Equipped with the proposed system, a pocket-sized CF-LCoS projector with a physical dimension of 27.4 mm × 19.4 mm × 9.6 mm is designed, simulated and analyzed. Compared to conventional approaches, this design could offer an average 43% volume reduction with acceptable tolerance. To the best of our knowledge, the screen uniformity of 90.2% and the light efficiency of 56.5% are competitive as compared with those of the currently commercialized pocket-sized CF-LCoS projectors.     

Effect of temperature on humido-sensitive conducting polymer actuators

Temperature dependence of water vapor sorption and electro-active polymer actuating behavior of free-standing films made of poly(3,4-ethylenedioxythiophene) doped with poly(4-styrenesulfonate) (PEDOT/PSS) was investigated by means of sorption isotherm and electromechanical analyses. The non-porous PEDOT/PSS film, having a specific surface area of 0.13 m² g^?1, sorbed water vapor of 1080 cm³(STP) g^?1, corresponding to 87 wt%, at relative water vapor pressure of 0.95. A temperature rise from 25 °C to 40 °C lowered sorption degree, indicative of an exothermic process, where isosteric heat of sorption decreased with increasing water vapor sorption and the value reached 43.9 kJ mol^?1, being consistent with the heat of water condensation (44 kJ mol^?1). Upon application of 10 V, the film underwent contraction of 2.46% at 5 °C caused by desorption of water vapor due to Joule heating, which slightly decreased to 2.10% at 45 °C. The speed of contraction was one order of magnitude faster than that of expansion and less dependent on the temperature since water vapor sorbed in the film were forced to desorb by Joule heating. In contrast, the higher the temperature the faster the film expansion because diffusion coefficient increased as the temperature became higher.     

Inter-relationship between preparation methods,nickel loading,characteristics and performance in the reforming of crude ethanol over Ni/Al2O3 catalysts: A neural network approach

Artificial neural network (ANN) approach was used to design an optimum Ni/Al₂O₃ catalyst for the production of hydrogen by the catalytic reforming of crude ethanol based on determining the inter-relationships between catalyst-preparation methods, nickel loading, catalyst characteristics and catalyst performance. ANN could predict hydrogen production performance of various Ni/Al₂O₃ catalysts of various elemental compositions and methods of preparation in the production of hydrogen by the catalytic reforming of crude ethanol in terms of crude-ethanol conversion, hydrogen selectivity and hydrogen yield. Specifically on catalyst design, ANN was used to determine the optimum catalyst conditions for obtaining maximum hydrogen production performance of a Ni/Al₂O₃ catalyst for the production of hydrogen by the catalytic reforming of crude ethanol. The optimal hydrogen yield was 4.4 mol %, and the associated crude-ethanol conversion and H₂ selectivity for the optimal hydrogen yield were 79.6 and 91.4 mol%, respectively. The optimal catalyst was the one prepared by the coprecipitation method with the optimal nickel loading of 12.4 wt% and an optimal aluminum composition of 42.5 wt%.     

Measurement of phase equilibria in Ti-Co-Pt ternary system

Phase equilibria in the Ti-Co-Pt ternary system were measured through diffusion triple and alloy sampling. Based on the results from Electron Probe Microanalysis (EPMA) and X-ray diffraction (XRD) techniques, the isothermal sections of the Ti-Co-Pt system were constructed, which consist of 16 and 13 three-phased regions at 973 and 1173 K, respectively. A new ternary phase τ was detected, which contains 23.6–29.9 at% Pt at 973 K and 27.4–40.1 at% Pt at 1173 K. Furthermore, an invariant reaction between 973 and 1173 K was deduced, i.e. τ + Ti₄Pt₃ ↔ Ti₃Pt + TiPt. By the way, the solubilities of Pt mainly substituting for Co in TiCo and TiCo₃ respectively increase from 22.4 at% and 26.1 at% at 973 K to 23.8 at% and 33.1 at% at 1173 K.     

A micro shear stress sensor based on laterally aligned carbon nanotubes

This paper reports the development of a micro thermal shear stress sensor that utilizes multiwalled carbon nanotubes as the sensing element. The sensor was fabricated by laterally aligning randomly distributed nanotubes into a 360 μm long and 90 μm wide conductive trace between two triangular shaped micro electrodes through the use of a high frequency AC electric field. During operation, the aligned nanotubes are electrically heated to an elevated temperature and surface shear stress is measured indirectly by the amount of convective heat transfer from the heated nanotubes to the surrounding fluid flow.The nanotube alignment process was primarily controlled by three different phenomena: dielectrophoresis, joule heating, and Brownian motion. Numerical simulations, together with experimental verifications, indicated that a successful alignment could only be realized if: (1) the dielectrophoretic force was positive, (2) the electro-thermal force was also positive, and (3) the dielectrophoretic force was high enough to overcome Brownian motion. The aligned nanotube trace has a room-temperature resistance of 580 Ω, which corresponds to a conductivity of 2.7 × 10⁴ S/m. The absolute temperature coefficient of resistivity ranges from 0.01 to 0.04% °C⁻¹. This is about one order of magnitude smaller than the highly doped polysilicon sensing material used in the MEMS micro shear stress sensor. The shear stress sensitivity of the nanotube trace operated at a 3% overheat ratio is found to follow the theoretical sensor power ∝ (shear stress)^1/3 relationship, provided the shear stress level is higher than 0.34 mPa. This result confirms the feasibility of using aligned multi-walled carbon nanotubes as a thermal shear stress sensing material.     

Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans

PurposeTo compare the diagnostic performances of artificial neural networks (ANNs) and multivariable logistic regression (LR) analyses for differentiating between malignant and benign lung nodules on computed tomography (CT) scans.MethodsThis study evaluated 135 malignant nodules and 65 benign nodules. For each nodule, morphologic features (size, margins, contour, internal characteristics) on CT images and the patient’s age, sex and history of bloody sputum were recorded. Based on 200 bootstrap samples generated from the initial dataset, 200 pairs of ANN and LR models were built and tested. The area under the receiver operating characteristic (ROC) curve, Hosmer–Lemeshow statistic and overall accuracy rate were used for the performance comparison.ResultsANNs had a higher discriminative performance than LR models (area under the ROC curve: 0.955 ± 0.015 (mean ± standard error) and 0.929 ± 0.017, respectively, p < 0.05). The overall accuracy rate for ANNs (90.0 ± 2.0%) was greater than that for LR models (86.9 ± 1.6%, p < 0.05). The Hosmer–Lemeshow statistic for the ANNs was 8.76 ± 6.59 vs. 6.62 ± 4.03 (p > 0.05) for the LR models.ConclusionsWhen used to differentiate between malignant and benign lung nodules on CT scans based on both objective and subjective features, ANNs outperformed LR models in both discrimination and clinical usefulness, but did not outperform for the calibration.     

On the mechanism of substrate/non-substrate recognition by P-glycoprotein

P-Glycoprotein (P-gp, multi-drug resistance protein, MDR1) plays a gatekeeper role, interfering delivery of multiple pharmaceuticals to the target tissues and cells. We performed Molecular Dynamics (MD) simulations to generate fifty side-chain variants for P-gp (PDB ID: 4Q9H-L) followed by docking of 31 drugs (0.6 ≤ ER ≤ 22.7) to the whole surface except the ATPase domains and the extracellular part. A selection of the most negative energy complex for each ligand followed. All compounds docked to the two areas – the main binding cavity at the top of P-gp (12.5% of compounds with ER < 1; 44.4% of 1 ≤ ER ≤ 2; and 100% of ER > 2), and the binding sites in the middle of P-gp (87.5% of ER < 1; 55.6% of 1 ≤ ER ≤ 2; and 0% of ER > 2). Our results show that anti-substrates (ER < 1), intermediate compounds (1 ≤ ER ≤ 2) and strong substrates (ER > 2) might behave differently in relation to the P-gp. According to our calculations, the anti-substrates almost do not bind the main binding cavity (MBC) of P-gp and rather approach the other binding sites on the protein; the substrates preferably bind the MBC; the intermediate compounds with 1 ≤ ER ≤ 2 bind both MBC and other binding sites almost equally. The modelling results are in line with the known hypothesis that binding the MBC is prerequisite for the pumping the compound off the P-gp.     

Improvement in smoothness of anisotropically etched silicon surfaces: Effects of surfactant and TMAH concentrations

We investigated the anisotropic etching properties of single-crystal silicon using tetramethyl-ammonium-hydroxide (TMAH) water solutions containing poly-oxethylene-alkyl-phenyl-ether (NC-200) as a surfactant. When the surfactant was added at 0.1% of the total volume of the etchant, the etched surface morphologies drastically changed, along with the anisotropy of the etching rate. We found that by using the surfactant at the low TMAH concentration region, a smooth mirror-like surface can be etched in both (1 0 0) and (1 1 0) orientations simultaneously. Although the addition of the surfactant reduces the etching rate, we show how this procedure can be used to improve the roughness of an etched surface without significantly increasing the overall processing time.     

Consensus scoring model for the molecular docking study of mTOR kinase inhibitor

The discovery of mammalian target of rapamycin (mTOR) kinase inhibitors has always been a research hotspot of antitumor drugs. Consensus scoring used in the docking study of mTOR kinase inhibitors usually improves hit rate of virtual screening. Herein, we attempt to build a series of consensus scoring models based on a set of the common scoring functions. In this paper, twenty-five kinds of mTOR inhibitors (16 clinical candidate compounds and 9 promising preclinical compounds) are carefully collected, and selected for the molecular docking study used by the Glide docking programs within the standard precise (SP) mode. The predicted poses of these ligands are saved, and revaluated by twenty-six available scoring functions, respectively. Subsequently, consensus scoring models are trained based on the obtained rescoring results by the partial least squares (PLS) method, and validated by Leave-one-out (LOO) method. In addition, three kinds of ligand efficiency indices (BEI, SEI, and LLE) instead of pIC₅₀ as the activity could greatly improve the statistical quality of build models. Two best calculated models 10 and 22 using the same BEI indice have following statistical parameters, respectively: for model 10, training set R² = 0.767, Q² = 0.647, RMSE = 0.024, and for test set R² = 0.932, RMSE = 0.026; for model 22, raining set R² = 0.790, Q² = 0.627, RMSE = 0.023, and for test set R² = 0.955, RMSE = 0.020. These two consensus scoring model would be used for the docking virtual screening of novel mTOR inhibitors.     

Thermodynamics of Fe–S at ultra-high pressure

Earth's core is believed to consist of a solid inner core and an outer liquid core. Since the inner core is mostly solid iron, most geophysical work has focused on melting of pure iron at core conditions. The inner core density is well matched with seismological data if some S is added to iron. The available phase equilibrium experimental data in the binary Fe–S system to pressures as high as ~200 GPa is used to create a thermodynamic database extending to core pressures that can be used to calculate the inner core density if S were the only other constituent. Such a calculation gives the maximum temperature of the solid inner core as 4428 (±500) K (363.85 GPa, density=13.09 g/cm³) with a sulfur content of ~15 wt%. To be consistent with the seismically determined density, the outer liquid core requires mixing of yet another light element or elements; both oxygen and carbon are suitable.     

Preparation of TiO2 coated silicate micro-spheres for enhancing the light diffusion property of polycarbonate composites

The TiO₂ coated silicate micro-spheres (SMS) core–shell particles (SMS@TiO₂) were synthesized using the sol–gel reaction followed by calcination. The SMS@TiO₂ particles were used to enhance the light diffusion property of polycarbonate (PC) composites. Our experimental analysis shows that the TiO₂ was coated on the SMS particles and the optimum parameters of the reaction were 4:1 of the Si/Ti molar ratio and 500 °C of the calcination temperature. The UV–Vis spectra indicate that SMS@TiO₂ can absorb or hinder the UV light, which may prolong the service life of PC light diffusion materials. Compared to that of PC composites physically mixed with SMS + TiO₂, the haze of the PC/SMS@TiO₂ composites was little affected, while the transmittance was obviously enhanced, which can be increased from 55.5% for PC/TiO₂/SMS to 70.3% for PC/SMS@TiO₂ with only 0.6 wt% filler loading.     

Fuzzy logic and statistical-based modelling of the Marshall Stability of asphalt concrete under varying temperatures and exposure times

In this study, the Marshall Stability (MS) of asphalt concrete under varying temperature and exposure times was modelled by using fuzzy logic and statistical method. This is an experimental study conducted using statistics and fuzzy logic methods. In order to investigate the Marshall Stability of asphalt concrete based on exposure time and environment temperature, exposure times of 1.5, 3, 4.5 and 6 h and temperatures of 30, 40 and 50 °C were selected. The MS of the asphalt concrete at 17 °C (in laboratory environment temperature) was used as reference. The results showed that the MS of the asphalt core samples decreased 40.16% at 30 °C after 1.5 h and 62.39% after 6 h. At 40 °C the decrease was 74.31% after 1.5 h, and 78.10% after 6 h. At 50 °C the stability of the asphalt decreased 83.22% after 1.5 h, 88.66% after 6 h. The relationships between experimental results, fuzzy logic model and statistical results exhibited good correlation. The correlation coefficient was R = 0.99 for fuzzy logic model and R² = 0.9 for statistical method. Based on the results of the study, it could be said that both the fuzzy logic method and statistical methods could be used for modelling of the stability of asphalt concrete under varying temperature and exposure time.     

Ultra high performance planar InGaAs PIN photodiodes for high speed optical fiber communication

This paper reports a front-illuminated planar InGaAs PIN photodiode with very low dark current, very low capacitance and very high responsivity on S-doped InP substrate. The presented device which has a thick absorption layer of 2.92 μm and a photosensitive area 73 μm in diameter exhibited the high performance of a very low capacitance of 0.47 pF, a very low dark current of 0.041 nA, a very high responsivity of 0.99 A/W (79% quantum efficiency) at λ = 1.55 μm, the 3 dB bandwidths of 6.89 GHz (−5 V), 7.48 GHz (−12 V) for bare chips and 4.48 GHz (−5 V), 5.02 GHz (−12 V) for the devices packaged in TO can, respectively. Furthermore, the developed PIN photodiodes possess high breakdown voltage of less than −25 V.     

Experimental blood glucose interval identification of patients with type 1 diabetes

Many problems are confronted when characterizing a type 1 diabetic patient such as model mismatches, noisy inputs, measurement errors and huge variability in the glucose profiles. In this work we introduce a new identification method based on interval analysis where variability and model imprecisions are represented by an interval model as parametric uncertainty.The minimization of a composite cost index comprising: (1) the glucose envelope width predicted by the interval model, and (2) a Hausdorff-distance-based prediction error with respect to the envelope, is proposed. The method is evaluated with clinical data consisting in insulin and blood glucose reference measurements from 12 patients for four different lunchtime postprandial periods each.Following a “leave-one-day-out” cross-validation study, model prediction capabilities for validation days were encouraging (medians of: relative error = 5.45%, samples predicted = 57%, prediction width = 79.1 mg/dL). The consideration of the days with maximum patient variability represented as identification days, resulted in improved prediction capabilities for the identified model (medians of: relative error = 0.03%, samples predicted = 96.8%, prediction width = 101.3 mg/dL). Feasibility of interval models identification in the context of type 1 diabetes was demonstrated.     

An architecture of a high-speed digital hologram generator based on FPGA

In this paper, a hardware architecture to generate a computer-generated hologram (CGH) in a real-time is proposed and implemented in FPGAs. The algorithm that generates digital hologram is reinterpreted and rearranged for higher operation speed. In order to optimize the hardware architecture and performance, the precision is analyzed using fixed-point simulation. The bit-width inside the hardware is obtained by numerical and visual precision analysis. The structure of the basic calculational unit (CGH Cell), an arrangement of these cells (CGH Kernel) to calculate a row of a hologram, and a processor (CGH Processor) with the kernels to perform the modified CGH algorithm are proposed.The proposed processor was implemented with Xilinx XC2VP70 FPGAs. A 1408 × 1050 sized hologram for a 3D object consisting of 10,000 light sources can be generated in 0.0093 [s] at the operating frequency of 285 MHz. Our architecture showed 37.32% and 87.32% higher speed than the best previous work when 1408 cells and 5632 cells are used, respectively.     

Exploring depression and problematic internet use among college females: A multisite study

ObjectiveThe purpose of this study was to assess associations between depression and problematic internet use (PIU) among female college students, and determine whether Internet use time moderates this relationship.MethodThis cross-sectional survey included 265 female college students from four U.S. universities. Students completed the Patient Health Questionnaire-9 (PHQ-9), the Problematic and Risky Internet Use Screening Scale (PRIUSS) and self-reported daily Internet use. Analyses included multivariate analysis of variance and Poisson regression.ResultsParticipants reported mean age of 20.2 years (SD = 1.7) and were 84.9% Caucasian. The mean PHQ-9 score was 5.4 (SD = 4.6); the mean PRIUSS score was 16.4 (SD = 11.1). Participants’ risk for PIU increased by 27% with each additional 30 min spent online using a computer (RR = 1.27, 95% CI: 1.14–1.42, p < .0001). Risk for PIU was significantly increased among those who met criteria for severe depression (RR = 8.16 95% CI: 4.27–15.6, p < .0001). The PHQ-9 items describing trouble concentrating, psychomotor dysregulation and suicidal ideation were most strongly associated with PIU risk.ConclusionsThe positive relationship between depression and PIU among female college students supports screening for both conditions, particularly among students reporting particular depression symptoms.