Application of Parameter-Less Rao Algorithm in Optimization of Water Distribution Networks Through Pressure-Driven Analysis

Water Resources Management - Water distribution networks (WDNs) connect consumers to the source of water. The primary goal of optimizing WDNs is to minimize the network costs as WDNs entail high...     

Application of quantum dot gate nonvolatile memory (QDNVM) in image segmentation

This paper presents the application of quantum dot gate nonvolatile memory (QDNVM) in image processing application. The charge accumulation in the gate region varies the threshold voltage of QDNVM, which can be used as a reference voltage source in a comparator circuit. A simplified comparator circuit can be implemented using the QDNVM. In this work, the use of QDNVM-based comparators in image processing specially image segmentation is demonstrated, which can be efficient in future image processing application.     

Application of nitrogen-enriched nanobiopolymer for the effective removal of Cr(VI) from tannery effluent

Nitrogen-enriched nanobiopolymer has been fabricated using (2,3-epoxypropyl) trimethylammonium chloride (EPTMAC) and explored for the removal of Cr(VI) from tannery effluent. The removal efficiency of nanobiopolymer was found to be 23.99 mg g^?1 (95.94%) under optimized conditions. The sorption data agrees well with the Langmuir and pseudo-second-order model. Equilibrium parameter (R_L) and sorption energy show the favorability and physical binding of Cr(VI) on the nanobiopolymer’s backbone. The values of ?G° (?7.84 kJ mol^?1), ?S° (65.97 J mol^?1K^?1) and ?H° (11.82 kJ mol^?1) reflect the feasible nature of the sorption process. Reusability study was also conducted to state the performance of the nanobiopolymer.     

Prediction modeling of Coriolis type mass flow: Sensor using neural network

The neural network (NN) technique has been utilized for prediction of performance of omegatube type Coriolis mass flow sensor. The results show that a well trained and well tested NN model has the capability to predict the performance of mass flow sensor for varying design parameters depending on the availability of the data and can be used as an alternative to the physical models in the sense that the results can be produced in a fast and cost effective way. The values of correlation coefficient (R) for the training, testing and whole datasets indicates that the NN results are in good agreement with the experimental results.     

Handwritten signature verification using shallow convolutional neural network

Multimedia Tools and Applications - Handwritten signatures are an undeniable and unique way to prove the identity of persons. Owing to the simplicity and uniqueness, it finds an essential place in...     

Predicting Spam Messages Using Back Propagation Neural Network

Wireless Personal Communications - With the increase in popularity of smartphones, text-based communication has also gained popularity. Availability of messaging services at low cost has resulted...     

Plastic collapse moment of 90° long radius elbows with internal circumferential surface crack at intrados under in-plane bending

Piping elbows under in-plane bending moment are vulnerable to cracking. The crack initiates at the surface and eventually reaches through the thickness and may lead to failure. The structural integrity assessment requires knowledge of the limit load. Limit load solutions for elbows with through-wall crack configurations are available in the open literature. But solutions for surface crack are not available. This paper presents a closed form expression for the plastic collapse moment (PCM) of 90°, long radius elbows with circumferential surface cracks at the intrados, under in-plane bending moment. The expression is derived, based on the results of non-linear (geometric and material) FE analyses covering a wide range of geometries and crack sizes. These plastic collapse moments evaluated herein will help in structural integrity assessment.