A knowledge construction methodology to automate case-based learning using clinical documents

The case-based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer-based domain knowledge to medical students for solving real-world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine-readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM-CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM-CD methodology, an interactive case-based learning system(iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.     

Electrical and magnetic properties of lithium borate glass containing iron

Journal of Materials Science Letters -     

Effect of the addition of Fe2O3 and heat treatment duration on the magnetic susceptibility of V2O5-P2O5-B2O3 glass system

The effect of the addition of Fe₂O₃ and heat treatment duration on the magnetic susceptibility of vanadium borophosphate glass were studied. The magnetic susceptibility of glass samples was found to increase with increasing Fe₂O₃ content, which may be explained by the formation of the FeO₆ group and the change of Fe²⁺ to Fe³⁺ which has higher paramagnetic properties. No detectable changes in the magnetic susceptibility with heat treatment for the samples containing 0.0, 0.5 and 1.0 mol% Fe₂O₃ was observed. The magnetic susceptibility for the heat treated samples containing 2.5, 5.0 and 7.5 mol% Fe₂O₃ decreases sharply with increasing duration of heat treatment up to 6 h and then remains almost constant. The sharp decrease in magnetic susceptibility of 2.5 mol% Fe₂O₃ is attributed to the increase in the number of ferrous ions. The sharp decrease for samples containing 5.0 and 7.5 mol% Fe₂O₃ is attributed to the increase in the number of Fe³⁺ in tetrahedral co-ordination. The rate of crystallization owing to the heat treatment was calculated and was found to increase with increasing iron oxide content. The geometry of crystallization was found to be in three-, two-and one-dimension(s) for samples containing 2.5, 5.0 and 7.5 mol% Fe₂O₃, respectively.     

An efficient numerical algorithm for the study of time fractional Tricomi and Keldysh type equations

Engineering with Computers - This work addresses a hybrid scheme for the numerical solutions of time fractional Tricomi and Keldysh type equations. In proposed methodology, Haar wavelets are used...     

Brain tumor classification from multi-modality MRI using wavelets and machine learning

In this paper, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance imaging scans. The data from multi-modal brain tumor segmentation challenge (MICCAI BraTS 2013) are utilized which are co-registered and skull-stripped, and the histogram matching is performed with a reference volume of high contrast. From the preprocessed images, the following features are then extracted: intensity, intensity differences, local neighborhood and wavelet texture. The integrated features are subsequently provided to the random forest classifier to predict five classes: background, necrosis, edema, enhancing tumor and non-enhancing tumor, and then these class labels are used to hierarchically compute three different regions (complete tumor, active tumor and enhancing tumor). We performed a leave-one-out cross-validation and achieved 88% Dice overlap for the complete tumor region, 75% for the core tumor region and 95% for enhancing tumor region, which is higher than the Dice overlap reported from MICCAI BraTS challenge.     

Integration of millimeter-wave and optical link for duplex transmission of hierarchically modulated signal over a single carrier and fiber for future 5G communication systems

Telecommunication Systems - Integration of optical and millimeter-wave systems provide a promising solution for future giga-bits per second wireless communication systems. We have proposed and...     

Potential Chemical Markers for the Identification of Irradiated Sausages

Abstract: Hydrocarbons, gas compounds, and off‐odor volatiles were determined for irradiated (0 or 5 kGy) commercial sausages with different fat contents (16% and 29%) during a 60‐d storage period at 4 °C. Total of 4 hydrocarbons (C14:1, C15:0, C16:2, and C17:1) were detected only in irradiated sausages: the amount of C16:2 was the highest, followed by C17:1, C14:1, and C15:0. The concentrations of hydrocarbons decreased significantly (P < 0.05) with storage, but were still detectable at the end of 60‐d storage. Irradiated sausages produced significantly higher amounts of CO than the nonirradiated ones. CH₄ was detected only in irradiated sausages. Dimethyl disulfide was detected only in irradiated sausages and its concentration decreased significantly (P < 0.05) with storage. Fat content of sausages showed a significant effect on the production and retention of hydrocarbons, gas compounds, and sulfur volatiles in irradiated sausages during storage. Some hydrocarbons (C16:2, C17:1, C14:1, and C15:0), CH₄, and dimethyl disulfide were only found in irradiated sausages indicating that these compounds can be used as potential markers for irradiated sausages.