Normalised fuzzy index for research ranking

ABSTRACT

There are great interests of designing research metrics and indices to measure the research impacts in research institutes. Unfortunately, most of those indices ignore critical design issues, e.g. the disparity between domains, the impact of journals or conferences in which papers are published, normalising the range of the index values to certain intervals, and the scalability of using the index to rank different research entities. In this paper, a new normalised fuzzy index, (NF_index), is proposed as a fuzzy-based research impact metric. The proposed index is a scalable index whose values are normalised to the percentage levels. NF_index achieves both inter-discipline normalisation and intra-discipline consistency. The capability of NF_index to achieve the inter-discipline normalisation enables fair comparison between different research domains regardless their nature in terms of influence and contribution to other research areas, e.g. natural science. Therefore, NF_index gives a universal normalised single-number metric that can be used by research institutes to solve the problem of inter-discipline scholar ranking. Moreover, it can help universal ranking of universities and research institutes according to their research capabilities and impacts. The obtained results, on diverse research areas, prove the potential of NF_index in terms of both intra-discipline consistency and inter-discipline normalisation.     

A Genetic Algorithm-Based,Dynamic Clustering Method Towards Improved WSN Longevity

The dynamic nature of wireless sensor networks (WSNs) and numerous possible cluster configurations make searching for an optimal network structure on-the-fly an open challenge. To address this problem, we propose a genetic algorithm-based, self-organizing network clustering (GASONeC) method that provides a framework to dynamically optimize wireless sensor node clusters. In GASONeC, the residual energy, the expected energy expenditure, the distance to the base station, and the number of nodes in the vicinity are employed in search for an optimal, dynamic network structure. Balancing these factors is the key of organizing nodes into appropriate clusters and designating a surrogate node as cluster head. Compared to the state-of-the-art methods, GASONeC greatly extends the network life and the improvement up to 43.44 %. The node density greatly affects the network longevity. Due to the increased distance between nodes, the network life is usually shortened. In addition, when the base station is placed far from the sensor field, it is preferred that more clusters are formed to conserve energy. The overall average time of GASONeC is 0.58 s with a standard deviation of 0.05.     

Facial expression recognition based on image pyramid and single-branch decision tree

In this paper, a new approach has been proposed for improved facial expression recognition. The new approach is inspired by the compressive sensing theory and multiresolution approach to facial expression problems. Initially, each image sample is decomposed into desired pyramid levels at different sizes and resolutions. Pyramid features at all levels are concatenated to form a pyramid feature vector. The vectors are further reinforced and reduced in dimension using a measurement matrix based on compressive sensing theory. For classification, a multilevel classification approach based on single-branch decision tree has been proposed. The proposed multilevel classification approach trains a number of binary support vector machines equal to the number of classes in the datasets. Class of test data is evaluated through the nodes of the tree from the root to its apex. The results obtained from the approach are impressive and outperform most of its counterparts in the literature under the same databases and settings.     

How artificial intelligence and machine learning can help healthcare systems respond to COVID-19

Machine Learning - The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The...     

Influence of inherent particle characteristics on hopper flow rate

The inherent factors influencing the shear strength of particulate materials are also believed to influence the flow rate through a hopper. These inherent particle characteristics include particle size, particle-size distribution, particle shape, angularity, hardness, and surface roughness. To determine the effect of inherent particle characteristics on flow rate and pluviated void ratio, a wide range of materials were tested. These materials ranged from manufactured sands and glass beads to natural sands. Determination of particle size, particle-size distribution, and hardness was achieved by conducting conventional tests such as sieve analyses and specific gravity tests. Quantification of the shape and angularity parameter is more difficult due to the nonavailability of any standard techniques. To achieve the objective of quantifying shape and angularity, a new technique was developed utilizing the image analyzer. Two shape parameters, namely, Shape Factor and Angularity Factor, were determined for various materials. Shape and Angularity Factors were correlated with flow rate as well as the pluviated void ratio. Overall, the results indicate that as shape and angularity of particles increase, the flow rate decreases, and pluviated void ratio increase. A good correlation between drained shear strength properties and the flow rates measured in the cone was also found to exist. Therefore, index tests such as flow rate through a flow cone (hopper) can be used to estimate the drained monotonic strength of particulate materials.     

Uscharin,the most potent molluscicidal compound tested against land snails

Calotropis procera, is a shrub with broad ovate fleshy leaves that grows wild in the Egyptian deserts. The plant was discovered to be highly toxic to the land snailsThepa pisana. The active ingredient responsible for the molluscicidal activity was isolated from its latex by solvent extraction and partitioning and was finally purified by fractional crystallization from 95% aqueous ethanol. The purity of the isolated material was monitored by TLC. Chemical identification was carried out using mass, infrared, and proton magnetic resonance spectroscopic methods. The active compound was found to be uscharin, and its identity was confirmed by comparing its spectroscopic data with the literature values. The isolated compound was 128 times more toxic than methomyl to the snails tested.     

Generalized fractional magneto-thermo-viscoelasticity

The new model of the equations of the linear theory of magneto-thermo-viscoelasticity with two relaxation times and fractional heat transfer involving fractional relaxation operator is given. The resulting formulation is applied to thermal shock problems for a perfect electrically conducting half-space in the presence of a transverse magnetic field. Laplace transform techniques are used. Some essential theorems on the linear coupled and generalized theories of thermo-viscoelasticity with two relaxation times are established. According to the numerical results and its graphs, conclusion about the new theory has been constructed. The effects of the fractional relaxation operator on viscoelastic material like poly (methyl methacrylate) (Perspex) are discussed.

     

A Low Voltage and Low Power Current-Mode Analog Computational Circuit

A new current-mode analog computational circuit is presented. The circuit can be digitally controlled to produce multiplying, squaring and inverse functions. The design is based on using MOSFETs operating in sub-threshold region to achieve ultra low power dissipation. The circuit is operated from a ±0.75 V DC supply. The proposed circuit has been simulated using Tanner in 0.35-μm TSMC CMOS process. Simulation results confirm the functionality of the circuit. The total power consumption is 2.3 μW, total harmonic distortion is 1.1 %, maximum linearity error is 0.3 % and the bandwidth is 2.3 MHz.