Changes during manufacture and ripening of cheddar cheese prepared with fungal rennet substitute of Rhizopus oligosporus

Studies were made on the manufacture and ripening of cheddar cheese prepared with a milk-clotting enzyme of R. oligosporus and compared with rennet cheese. The curd working properties, yields and quality of green cheese from R. oligosporus enzyme were fairly comparable with calf-rennet. During the ripening period the changes in moisture, acidity, pH and fat content of both the cheese were similar. The soluble nitrogen was comparatively low while maturity index was higher during ripening in R. oligosporus cheese than calf-rennet cheese. Organoleptically, R. oligosporus cheese was rated lower than calf-rennet cheese, developing transient bitterness during ripening like other fungal rennet substitutes.     

Comparing the performance of neural networks developed by using Levenberg-Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process

Monitoring and control of multiple process quality characteristics (responses) in grinding plays a critical role in precision parts manufacturing industries. Precise and accurate mathematical modelling of multiple response process behaviour holds the key for a better quality product with minimum variability in the process. Artificial neural network (ANN)-based nonlinear grinding process model using backpropagation weight adjustment algorithm (BPNN) is used extensively by researchers and practitioners. However, suitability and systematic approach to implement Levenberg-Marquardt (L-M) and Boyden, Fletcher, Goldfarb and Shanno (BFGS) update Quasi-Newton (Q-N) algorithm for modelling and control of grinding process is seldom explored. This paper provides L-M and BFGS algorithm-based BPNN models for grinding process, and verified their effectiveness by using a real life industrial situation. Based on the real life data, the performance of L-M and BFGS update Q-N are compared with an adaptive learning (A-L) and gradient descent algorithm-based BPNN model. The results clearly indicate that L-M and BFGS-based networks converge faster and can predict the nonlinear behaviour of multiple response grinding process with same level of accuracy as A-L based network.     

Identifying correlated heavy-hitters in a two-dimensional data stream

We consider online mining of correlated heavy-hitters (CHH) from a data stream. Given a stream of two-dimensional data, a correlated aggregate query first extracts a substream by applying a predicate along a primary dimension, and then computes an aggregate along a secondary dimension. Prior work on identifying heavy-hitters in streams has almost exclusively focused on identifying heavy-hitters on a single dimensional stream, and these yield little insight into the properties of heavy-hitters along other dimensions. In typical applications however, an analyst is interested not only in identifying heavy-hitters, but also in understanding further properties such as: what other items appear frequently along with a heavy-hitter, or what is the frequency distribution of items that appear along with the heavy-hitters. We consider queries of the following form: “In a stream S of (x, y) tuples, on the substream H of all x values that are heavy-hitters, maintain those y values that occur frequently with the x values in H”. We call this problem as CHH. We formulate an approximate formulation of CHH identification, and present an algorithm for tracking CHHs on a data stream. The algorithm is easy to implement and uses workspace much smaller than the stream itself. We present provable guarantees on the maximum error, as well as detailed experimental results that demonstrate the space-accuracy trade-off.     

Enhanced dielectric and electrical properties of three-component PMMA–NaNbO3–starch percolative composite films

Journal of Materials Science: Materials in Electronics - Three-component PMMA–NaNbO3–starch percolative composites with starch and sodium niobate (NaNbO3; NN) particles embedded into...     

Kiln-fired clay bricks synergizing nickel–chromium plating sludge and fly ash: mechanical characteristics and cradle-to-gate life cycle assessment

Clean Technologies and Environmental Policy - Life cycle assessment (LCA) of novel fired clay bricks with synergistic co-valorization of nickel-chrome plating sludge (NCPS) and fly ash (FA) is...     

Nonlinear Dynamic System Identification of ARX Model for Speech Signal Identification

System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems. As most practical systems don’t have prior information about the system behaviour thus, mathematical modelling is required. The authors have proposed a stacked Bidirectional Long-Short Term Memory (Bi-LSTM) model to handle the problem of nonlinear dynamic system identification in this paper. The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions. The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways: one from the past to the future, and the other from the future to the past. In this proposed model a backward-running Long-Short Term Memory (LSTM) can store information from the future along with application of two hidden states together allows for storing information from the past and future at any moment in time. The proposed model is tested with a recorded speech signal to prove its superiority with the performance being evaluated through Mean Square Error (MSE) and Root Means Square Error (RMSE). The RMSE and MSE performances obtained by the proposed model are found to be 0.0218 and 0.0162 respectively for 500 Epochs. The comparison of results and further analysis illustrates that the proposed model achieves better performance over other models and can obtain higher prediction accuracy along with faster convergence speed.