Hypergraph Models and Algorithms for Data-Pattern-Based Clustering

In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depends on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two-phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework.     

Power Flattening in ARIES-RS Fusion Breeder Reactor Using Mixed Fuels

This study presents the possibility of the power flattening in the ARIES-RS breeder reactor using mixed (Th,U)C or (Th,U)N fuels. Two different types of mixing, namely, homogeneous mixing (HM) and linear mixing (LM) were used to investigate the uniformity of fission power distribution through the fuel zone. In HM, fraction of uranium content were kept constant in all rows of the fuel zone whereas, in LM the fraction of the uranium were linearly increased from the first to last fuel row in the fuel zone. Neutron transport calculations were performed with the aid of the SCALE4.3 system by solving the Boltzmann transport equation with the XSDRNPM code in 238 neutron groups and a S₈–P₃ approximation. Flat fission power distribution was maintained successfully for the blanket using linearly mixed fuels. However, the fission density profile was not uniform in the blanket with homogeneously mixed fuels. It decreased exponentially form the 1st to 10th fuel row.     

Recent developments in catalyst synthesis using DBD plasma for reforming applications

The catalyst has a significant role in gas processing applications such as reforming technologies for H₂ and syngas production. The stable catalyst is requisite for any industrial catalysis application to make it commercially viable. Several methods are employed to synthesize the catalysts. However, there is still a challenge to achieve a controlled morphology and pure catalyst which majorly influences the catalytic activity in reforming applications. The conventional methods are expansive, and the removal of the impurities are major challenges. Nevertheless, it is not straightforward to achieve the desired structure and stability. Therefore, significant interest has been developed on the advanced techniques to take control of the physicochemical properties of the catalyst through non-thermal plasma (NTP) techniques. In this review, the systematic evolution of the catalyst synthesis using NTP technique is elucidated. The emerging DBD plasma to synthesized and effective surface treatment is reviewed. DBD plasma synthesized catalyst performance in reforming application for H₂ and syngas production is summarised. Furthermore, the status of DBD plasma for catalyst synthesis and proposed future avenues to design environmentally suitable and cost-effective synthesis techniques are discussed.     

Evaluation of damage to light structures erected on a fill material rich in expansive soil

The paper reports a study of the cause of defects in light structures and the toppling of a wall constructed on a fill material rich in Ankara clay. Laboratory tests were carried out on vertical and horizontal samples from boreholes and a trial pit was excavated near the damaged structures. The results showed that in the vicinity of the toppled wall, swelling pressures in the horizontal direction were greater than those measured in the vertical direction. The swelling properties of the fill material were higher than those of original Ankara clay as determined previously by other investigators, suggesting that breakdown of the cementing bonds and a change in the fabric are the main factors affecting the swelling pressure of disturbed and compacted expansive soils. The calculations to predict uplift showed a good agreement with the observations in the damaged structures. It is concluded that swelling was the main cause of the damage to the light structures at the study site and resulted from the highly expansive nature of the fill material, poor drainage, the semi-arid climate, poor construction methods and ineffective precautions. Some recommendations for minimizing the effects of swelling at the study site are briefly outlined.     

Hierarchical behavior categorization using correlation based adaptive resonance theory

This paper introduces a new model for robot behavior categorization. Correlation based adaptive resonance theory (CobART) networks are integrated hierarchically in order to develop an adequate categorization, and to elicit various behaviors performed by the robot. The proposed model is developed by adding a second layer CobART network which receives first layer CobART network categories as an input, and back-propagates the matching information to the first layer networks. The first layer CobART networks categorize self-behavior data of a robot or an object in the environment while the second layer CobART network categorizes the robot's behavior with respect to its effect on the object. Experiments show that the proposed model generates reasonable categorization of behaviors being tested. Moreover, it can learn different forms of the behaviors, and it can detect the relations between them. In essence, the model has an expandable architecture and it contains reusable parts. The first layer CobART networks can be integrated with other CobART networks for another categorization task. Hence, the model presents a way to reveal all behaviors performed by the robot at the same time.     

Combined effect of microwave and ultrasonication treatments on the quality and stability of sugarcane juice during cold storage

The main objective of this study was to investigate the combined effect of microwave (MW) treatment (2450 MHz for 120 s), ultrasonication (US) (24 KHz, 20 °C for 20 min) and combined treatment (MW-US) on the quality and stability of sugarcane juice (SCJ) during 21 days of storage at 4 °C. The effect of the treatments and storage time on physicochemical, bioactive compounds (total phenolic, flavonoids and ascorbic acid content) and microbial analysis of SCJ. No significant (P < 0.05) changes were observed in °Brix, while there was an increase in pH and a decrease in titratable acidity in all treatments. Compared to US and MW, MW-US treatments was more effective in preserving colour attributes, total phenolic and flavonoids contents, ascorbic acid and antioxidant capacities of the SCJ during storage. The results regarding the microbial count indicate that more microbial safety and longer shelf life was achieved by MW-US. MW-US treatment is an effective technology for improving the safety and shelf life of SCJ by minimising quality changes, retaining bioactive compounds and reducing microbial growth during storage.     

Fraudulent Firm Classification: A Case Study of an External Audit

This paper is a case study of visiting an external audit company to explore the usefulness of machine learning algorithms for improving the quality of an audit work. Annual data of 777 firms from 14 different sectors are collected. The Particle Swarm Optimization (PSO) algorithm is used as a feature selection method. Ten different state-of-the-art classification models are compared in terms of their accuracy, error rate, sensitivity, specificity, F measures, Mathew’s Correlation Coefficient (MCC), Type-I error, Type-II error, and Area Under the Curve (AUC) using Multi-Criteria Decision-Making methods like Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results of Bayes Net and J48 demonstrate an accuracy of 93% for suspicious firm classification. With the appearance of tremendous growth of financial fraud cases, machine learning will play a big part in improving the quality of an audit field work in the future.     

Cobalt loaded organic acid modified kaolin clay for the enhanced catalytic activity of hydrogen release via hydrolysis of sodium borohydride

Inorganic acids such as hydrochloric acid (HCl), nitric acid (HNO₃) and sulphuric acid (H₂SO₄) are generally used in the acid modification of clays. Here, CoB catalyst was synthesized on the acetic acid-activated kaolin support material (CH₃COOH -kaolin- CoB) with an alternative approach. This prepared catalyst, firstly, was used to catalyze the hydrolysis of NaBH₄ (NaBH₄-HR). The structure of the raw kaolin, kaolin-CH₃COOH, and CH₃COOH-kaolin-CoB samples were characterized by X-ray diffraction spectroscopy (XRD), Fourier transforms infrared spectroscopy (FTIR), scanning electron microscope (SEM), and nitrogen adsorption. At the same time, this catalyst performance was examined by Co loading, NaBH₄ concentration, NaOH concentration, temperature and reusability parameters. The end times of this hydrolysis reaction using raw kaolin-CoB and CH₃COOH-kaolin-CoB were found to be approximately 140 and 245 min, respectively. The maximum hydrogen generation rates (HGRs) obtained at temperatures 30 °C and 50 °C were 1533 and 3400 mL/min/g_catalyst, respectively. At the same time, the activation energy was found to be 49.41 kJ/mol.