Numerical investigation on 2D viscoelastic fluid due to exponentially stretching surface with magnetic effects: an application of non-Fourier flux theory

Two-dimensional flow of Casson fluid toward an exponentially stretched surface in view of Cattaneo–Christove flux theory is discoursed in current communication. Flow pattern within boundary layer under the effectiveness of magnetic field is also contemplated in the communication. Non-dimensionalized governing expressions are attained through transformation procedure. To anticipate the fascinating features of present work, solution of resulted nonlinear differential system is computed with the collaborated help of shooting scheme and Runge–Kutta method. The influence of involved variables on velocity and temperature fields is scrutinized. Contribution of thermal relaxation is explicitly pointed out. Evaluation of convective heat transfer and friction factor in the fluid flow is visualized through graphs and tables. Additionally, the assurance of present work is affirmed by developing comparison with previous findings in the literature which sets a trade mark for the implementation of numerical approach. It is inferred from the thorough examination of the analysis that present formulation reduces to classical Fourier’s problem by considering \(\varLambda = 0\). Furthermore, decreasing pattern in temperature distribution is depicted in the presence of Cattaneo–Christove flux law as compared to heat transfer due to the Fourier’s law.

     

Toward analyzing impact of disjoint axioms for merging heterogeneous ontologies

In recent years, the question on Automatic Ontology Merging (AOM) become challenging to address for the researchers. Our research and development for the Disjoint Knowledge Perservation based Automatic Ontology Merging (DKP-AOM) is a milestone in the same direction. This paper provides a more specific discussion about disjoint knowledge axioms in DKP-AOM and makes an assessment of our merge algorithm that looks-up within disjoint partitions of concept hierarchies of ontologies. The significant use of disjoint knowledge is corroborated by testing conference and vertebrate ontologies. The results reveal that disjoint knowledge axioms help identifying initial inaccurate mappings and remove ambiguity when the concept with same symbolic identifier has a different meaning in different local ontologies in the process of ontology merging. Disjoint axioms separate the knowledge in distinct chunks and enable concept matching within the boundaries of sub-hierarchies of the entire ontology concept hierarchy. While finding matches between concepts of ontologies, disjoint partitions with the disjoint knowledge about concepts in source ontologies minimize the search space and reduce the runtime complexity of ontology merging. We also discuss encouraging results obtained by our DKP-AOM system within the OAEI 2015 campaign.     

Scale coding bag of deep features for human attribute and action recognition

Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.     

Fault detection and identification—A filter investigation

This paper develops a new active fault‐tolerant control system based on the concept of analytical redundancy. The novel design presented here consists of an observation filter–based fault detection and identification system integrated with a nonlinear model predictive controller. A number of observation filters are designed, integrated with the nonlinear controller, and tested before reaching the final design, which comprises an unscented Kalman filter for fault detection and identification together with a nonlinear model predictive controller to form an active fault‐tolerant control system.     

A framework for harmonizing internet of things (IoT) in cloud: analyses and implementation

Internet of Things (IoT) refers to uniquely identifiable entities. Its vision is the world of connected objects. Due to its connected nature the data produced by IoT is being used for different purposes. Since IoT generates huge amount of data, we need some scalable storage to store and compute the data sensed from the sensors. To overcome this issue, we need the integration of cloud and IoT, so that the data might be stored and computed in a scalable environment. Harmonization of IoT in Cloud might be a novel solution in this regard. IoT devices will interact with each other using Constrained Application Protocol (CoAP). In this paper, we have implemented harmonizing IoT in Cloud. We have used CoAP to get things connected to each other through the Internet. For the implementation we have used two sensors, fire detector and the sensor attached with the door which is responsible for opening it. Thus our implementation will be storing and retrieving the sensed data from the cloud. We have also compared our implementation with different parameters. The comparison shows that our implementation significantly improves the performance compared to the existing system.

     

Multi-Fidelity High-Throughput Optimization of Electrical Conductivity in P3HT-CNT Composites

Combining high-throughput experiments with machine learning accelerates materials and process optimization toward user-specified target properties. In this study, a rapid machine learning-driven automated flow mixing setup with a high-throughput drop-casting system is introduced for thin film preparation, followed by fast characterization of proxy optical and target electrical properties that completes one cycle of learning with 160 unique samples in a single day, a > 10 ×  improvement relative to quantified, manual-controlled baseline. Regio-regular poly-3-hexylthiophene is combined with various types of carbon nanotubes, to identify the optimum composition and synthesis conditions to realize electrical conductivities as high as state-of-the-art 1000 S cm⁻¹. The results are subsequently verified and explained using offline high-fidelity experiments. Graph-based model selection strategies with classical regression that optimize among multi-fidelity noisy input-output measurements are introduced. These strategies present a robust machine-learning driven high-throughput experimental scheme that can be effectively applied to understand, optimize, and design new materials and composites.     

A Robust Wearable Point-of-Care CNT-Based Strain Sensor for Wirelessly Monitoring Throat-Related Illnesses

Point-of-care testing (POC) has the ability to detect chronic and infectious diseases early or at the time of occurrence and provide a state-of-the-art personalized healthcare system. Recently, wearable and flexible sensors have been employed to analyze sweat, glucose, blood, and human skin conditions. However, a flexible sensing system that allows for the real-time monitoring of throat-related illnesses, such as salivary parotid gland swelling caused by flu and mumps, is necessary. Here, for the first time, a wearable, highly flexible, and stretchable piezoresistive sensing patch based on carbon nanotubes (CNTs) is reported, which can record muscle expansion or relaxation in real-time, and thus act as a next-generation POC sensor. The patch offers an excellent gauge factor for in-plane stretching and spatial expansion with low hysteresis. The actual extent of muscle expansion is calculated and the gauge factor for applications entailing volumetric deformations is redefined. Additionally, a bluetooth-low-energy system that tracks muscle activity in real-time and transmits the output signals wirelessly to a smartphone app is utilized. Numerical calculations verify that the low stress and strain lead to excellent mechanical reliability and repeatability. Finally, a dummy muscle is inflated using a pneumatic-based actuator to demonstrate the application of the affixed wearable next-generation POC sensor.     

A closed-loop healthcare processing approach based on deep reinforcement learning

Multimedia Tools and Applications - In healthcare, the human body is a controlled input-output system, which generates different observations with the variations of external interventions. The...     

Speech emotion recognition using optimized genetic algorithm-extreme learning machine

Multimedia Tools and Applications - Automatic Emotion Speech Recognition (ESR) is considered as an active research field in the Human-Computer Interface (HCI). Typically, the ESR system is...