Propagation of the Multi-Scalar Aggregative Standardized Precipitation Temperature Index and its Application

Water Resources Management - Nowadays, drought monitoring with various probabilistic indices has become common. However, the interpretation and applicability issues of multi-scalar drought indices...     

Fuzzy harmony search based optimal control strategy for wireless cyber physical system with industry 4.0

Journal of Intelligent Manufacturing - Recently, Industry 4.0 facilitates implementing several modular smart factories particularly the Cyber-Physical System. Due to enhanced growth in the...     

Cognitive frequency diverse array radar with symmetric non-uniform frequency offset

Frequency diverse array (FDA) radar with uniform inter-element frequency offset generates a beam pattern with maxima at multiple range and angle values. Multiple maxima property allows interferers located at any of the maxima to affect the target-returns. As a result the signal to interference noise ratio (SINR) and probability of detection decreases. In this paper, we propose a cognitive uniformly-spaced FDA with non-uniform but symmetric frequency offsets to achieve a single maximum beam pattern at the target position. Moreover, these non-uniform frequency offsets are calculated using well known mu-law formulae. The design sharpens or broadens the transmitted beam pattern based on the receiver feedback to achieve a better detection probability and an improved SINR as compared to the previous designs. The performance is also analyzed by considering the Cramer-Rao lower bound (CRLB) on target angle and range estimation.     

Evidence-based understanding of introductions of research articles

Scientometrics - Achieving publications in high-impact journals is a major cornerstone for academic careers in the US and elsewhere in the world. However, apart from novel insights and relevant...     

Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations

Recommendation services become an essential and hot research topic for researchers nowadays. Social data such as Reviews play an important role in the recommendation of the products. Improvement was achieved by deep learning approaches for capturing user and product information from a short text. However, such previously used approaches do not fairly and efficiently incorporate users’ preferences and product characteristics. The proposed novel Hybrid Deep Collaborative Filtering (HDCF) model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations. To overcome the cold start problem, the new overall rating is generated by aggregating the Deep Multivariate Rating DMR (Votes, Likes, Stars, and Sentiment scores of reviews) from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision, either product is truly popular or not. The proposed novel HDCF model consists of four major modules such as User Product Attention, Deep Collaborative Filtering, Neural Sentiment Classifier, and Deep Multivariate Rating (UPA-DCF + NSC + DMR) to solve the addressed problems. Experimental results demonstrate that our novel model is outperforming state-of-the-art IMDb, Yelp2013, and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy, confidence, and trust of recommendation services.     

Multiscale Modeling of Flood-Induced Piping in River Levees

A three-dimensional transient fully coupled fluid-particle model is utilized to simulate flood-induced piping under river levees and taking into account the effects of soil-fluid-structure interactions. The porous soil medium is modeled as a mixture of two phases, namely the fluid phase (water) and the particulate solid phase. The fluid is idealized as a continuum by using an averaged form of Navier–Stokes equations that accounts for the presence of the solid particles. These particles are modeled at a microscale using the discrete element method. The interphase momentum transfer is modeled using an established relationship that accounts for the dynamic change in porosity and possible occurrence of nonlinear losses. The hydraulic structure (levee) is modeled as an impervious rigid block and its motion is described by a combination of external and internal forces from the surrounding fluid and solid particles. A computational simulation is conducted to investigate the response of a granular deposit when subjected to a rapidly increasing head difference. The simulation provided information at the microscale level for the solid phase as well as at the macroscopic level for the pore-water flow. The settlement and failure mechanism of the structure were captured as the hydraulic head difference gradually increased and the solid phase underwent subsequent deformations. The results suggest that failure of such structures may occur suddenly and at hydraulic gradients well below the critical gradient. The proposed computational framework for analyzing river and flood-protection levees would provide a new dimension to the design of such vital geotechnical systems. The technique can be effectively used to investigate failure mechanisms under complex loading and flow conditions.     

Efficient Algorithms for Mining and Incremental Update of Maximal Frequent Sequences

We study two problems: (1) mining frequent sequences from a transactional database, and (2) incremental update of frequent sequences when the underlying database changes over time. We review existing sequence mining algorithms including GSP, PrefixSpan, SPADE, and ISM. We point out the large memory requirement of Pref ixSpan, SPADE, and ISM, and evaluate the performance of GSP. We discuss the high I/O cost of GSP, particularly when the database contains long frequent sequences. To reduce the I/O requirement, we propose an algorithm MFS, which could be considered as a generalization of GSP. The general strategy of MFS is to first find an approximate solution to the set of frequent sequences and then perform successive refinement until the exact set of frequent sequences is obtained. We show that this successive refinement approach results in a significant improvement in I/O cost. We discuss how MFS can be applied to the incremental update problem. In particular, the result of a previous mining exercise can be used (by MFS) as a good initial approximate solution for the mining of an updated database. This results in an I/O efficient algorithm. To improve processing efficiency, we devise pruning techniques that, when coupled with GSP or MFS, result in algorithms that are both CPU and I/O efficient.This research is supported by Hong Kong Research Grants Council grant HKU 7040/02E.     

Lysis of oocytes by bovine sperm and seminal plasma

This study examined the lytic activity of bovine seminal plasma on zona-free oocytes commonly used to assess bovine capacitation. Exposure of hamster oocytes (intact or zona-free) to undiluted seminal plasma resulted in lysis within 5 min. With intact oocytes, 1:5 to 1:20 seminal plasma resulted in swelling of the oocytes. With zona-free oocytes, seminal plasma (1:0 to 1:1000) resulted in lysis within 1 min to 3 h depending on dilution. Heating seminal plasma to inactivate complement did not reduce lytic activity, but boiling destroyed it. Lytic activity was present in seminal plasma from vasectomized bulls and in seminal vesicle fluid. After elution of seminal plasma through Sephadex G-200, lytic activity was only associated with the fraction containing proteins of 200,000 to 45,000 dal. Lytic activity remained with washed capacitated bull sperm only when 10(7) sperm/ml or more were coincubated with zona-free oocytes at 37 degrees C for 3 h. In conclusion, both bovine seminal plasma and capacitated sperm were lytic in the zona-free hamster oocyte assay. The results may explain why so few sperm are normally found at the site of fertilization.     

Learning to Recognize Volcanoes on Venus

Dramatic improvements in sensor and image acquisition technology have created a demand for automated tools that can aid in the analysis of large image databases. We describe the development of JARtool, a trainable software system that learns to recognize volcanoes in a large data set of Venusian imagery. A machine learning approach is used because it is much easier for geologists to identify examples of volcanoes in the imagery than it is to specify domain knowledge as a set of pixel-level constraints. This approach can also provide portability to other domains without the need for explicit reprogramming; the user simply supplies the system with a new set of training examples. We show how the development of such a system requires a completely different set of skills than are required for applying machine learning to toy world domains. This paper discusses important aspects of the application process not commonly encountered in the toy world, including obtaining labeled training data, the difficulties of working with pixel data, and the automatic extraction of higher-level features.     

Vector Based Genetic Algorithm to optimize predictive analysis in network security

A new Intrusion Detection System (IDS) for network security is proposed making use of a Vector-Based Genetic Algorithm (VBGA) inspired by evolutionary approaches. The novelty in the algorithm is to represent chromosomes as vectors and training data as matrices. This approach allows multiple pathways to calculate fitness function out of which one particular methodology is used and tested. The proposed method uses the overlap of the matrices with vector chromosomes for model building. The fitness of the chromosomes is calculated from the comparison of true and false positives in test data. The algorithm is flexible to train the chromosomes for one particular attack type or to detect the maximum number of attacks. The VBGA has been tested on two datasets (KDD Cup-99 and CTU-13). The proposed algorithm gives high detection rate and low false positives as compared to traditional Genetic Algorithm. A detailed comparative analysis is given of proposed VBGA with the traditional string-based genetic algorithm on the basis of accuracy and false positive rates. The results show that vector based genetic algorithm provides a significant improvement in detection rates keeping false positives at minimum.