Mathematical model for predicting solidification and cooling of steel inside mould and in air

A two-dimensional mathematical model has been developed to describe the solidification and cooling of steel inside the mould after teeming and in the air after stripping. Partial differential equations describing the processes have been discretized using control volume approach. The discretization equations obtained are of Tri-diagonal matrix form, which have been solved using well known Tri-diagonal matrix algorithm (TDMA) and Alternate direction implicit (ADI) solver. The model has been validated by measuring surface temperatures of mould and ingot using Infrared thermo-vision scanner. This is then used to compute charging temperature and solidification status of ingot as function of track time and type of ingot

On the in vitro fatigue behavior of human dentin with implications for life prediction

Although human dentin is known to be susceptible to failure under repetitive cyclic fatigue loading, there are few reports in the literature that reliably quantify this phenomenon

Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye

We study involuntary micro-movements of the eye for biometric identification. While prior studies extract lower-frequency macro-movements from the output of video-based eye-tracking systems and engineer explicit features of these macro-movements, we develop a deep convolutional architecture that processes the raw eye-tracking signal. Compared to prior work, the network attains a lower error rate by one order of magnitude and is faster by two orders of magnitude: it identifies users accurately within seconds

A multi-biometric iris recognition system based on a deep learning approach

YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person

Alcohol Consumption Among Older Adults in Primary Care

BACKGROUND: Alcohol misuse is a growing public health concern for older adults, particularly among primary care patients. OBJECTIVES: To determine alcohol consumption patterns and the characteristics associated with at-risk drinking in a large sample of elderly primary care patients. DESIGN: Cross-sectional analysis of multisite screening data from 6 VA Medical Centers, 2 hospital-based health care networks, and 3 Community Health Centers. PARTICIPANTS: Patients, 43,606, aged 65 to 103 years, with scheduled primary care appointments were approached for screening; 27,714 (63.6%) consented to be screened. The final sample of persons with completed screens comprised 24,863 patients. MEASUREMENTS: Quantity and frequency of alcohol use, demographics, social support measures, and measures of depression/anxiety. RESULTS: Of the 24,863 older adults screened, 70.0% reported no consumption of alcohol in the past year, 21.5% were moderate drinkers (1–7 drinks/week), 4.1% were at-risk drinkers (8–14 drinks/week), and 4.5% were heavy (>14 drinks/week) or binge drinkers. Heavy drinking showed significant positive association with depressive/anxiety symptoms [Odds ratio (OR) (95% CI): 1.79 (1.30, 2.45)] and less social support [OR (95% CI): 2.01 (1.14, 2.56)]. Heavy drinking combined with binging was similarly positively associated with depressive/anxiety symptoms [OR (95%): 1.70 (1.33, 2.17)] and perceived poor health [OR (95% CI): 1.27 (1.03, 1.57)], while at-risk drinking was not associated with any of these variables. CONCLUSIONS: The majority of participants were nondrinkers; among alcohol users, at-risk drinkers did not differ significantly from moderate drinkers in their characteristics or for the 3 health parameters evaluated. In contrast, heavy drinking was associated with depression and anxiety and less social support, and heavy drinking combined with binge drinking was associated with depressive/anxiety symptoms and perceived poor health

Structural hierarchies define toughness and defect-tolerance despite simple and mechanically inferior brittle building blocks

Mineralized biological materials such as bone, sea sponges or diatoms provide load-bearing and armor functions and universally feature structural hierarchies from nano to macro. Here we report a systematic investigation of the effect of hierarchical structures on toughness and defect-tolerance based on a single and mechanically inferior brittle base material, silica, using a bottom-up approach rooted in atomistic modeling. Our analysis reveals drastic changes in the material crack-propagation resistance (R-curve) solely due to the introduction of hierarchical structures that also result in a vastly increased toughness and defect-tolerance, enabling stable crack propagation over an extensive range of crack sizes. Over a range of up to four hierarchy levels, we find an exponential increase in the defect-tolerance approaching hundred micrometers without introducing additional mechanisms or materials. This presents a significant departure from the defect-tolerance of the base material, silica, which is brittle and highly sensitive even to extremely small nanometer-scale defects