Prediction of thermal and fluid flow characteristics in helically coiled tubes using ANFIS and GA based correlations

This study introduces the ability of Adaptive Neuro-Fuzzy Inference System (ANFIS) and genetic algorithm (GA) based correlations for estimating the hydrodynamics and heat transfer characteristics in coiled tubes. The experimental data related to the heat transfer and pressure drop in helically coiled tubes with deferent geometrical parameters (coil diameter and pitch) were used. In the experiments, hot water was passed in the coiled tubes, which were placed in a cold bath. Two ANFIS models were developed for predicting the Nusselt number (Nu) and friction factor (f) in the coiled tubes and the geometric parameters were employed as input data. Moreover, empirical correlations for estimating the Nu and f were developed by a phenomenological argument in the form of classical power–law correlations and their constants were found using the GA technique. The mean relative errors (MRE) of the developed ANFIS models for estimation of Nu and f are 6.24% and 3.54%, respectively. On the other hand, for empirical correlations, a MRE of 8.06% was found for prediction Nu while MRE of 5.03% was obtained for f. The results show that the ANFIS models can predict Nu and f with the higher accuracy than the developed correlations.     

Oxadiazoline and Ketoimine Palladium(II) Complexes as Highly Efficient Catalysts for Suzuki–Miyaura Cross‐Coupling Reactions in Supercritical Carbon Dioxide

The [2+3] cycloaddition of nitriles (RCN) with 2,2‐dimethyl‐3,4‐dihydro‐2H‐pyrrole 1‐oxide, in the presence of palladium dichloride (PdCl₂) gives the corresponding 2,3‐dihydro[1.2.4]oxadiazole (Δ⁴‐1,2,4‐oxadiazoline) palladium(II) complexes 1 – 4 in good yields. However, the Pd(II)‐assisted reaction of pentafluorobenzonitrile with the same pyrroline N‐oxide gives a mixture of oxadiazoline 5 , ketoimine 6 and pyrrolylbenzamide‐ketoimine 7 Pd(II) complexes, which affords upon heating in refluxing acetone the unprecedented fused tricyclic ketoimine complex 8 as the exclusive product. Under heating, compounds 5 and 7 transform to 6 , the latter undergoing intramolecular cyclization by nucleophilic attack of the amino moiety to the ortho carbon of the pentafluorophenyl ring leading ultimately to 8 . The compounds were characterized by IR, ¹H and ¹³C NMR, ESI⁺‐MS, elemental analyses and, in the cases of 3 , 6 , 7 and 8 , also by X‐ray diffraction analyses. The catalytic properties of the Pd complexes were evaluated in Suzuki–Miyaura cross‐coupling reactions, using supercritical carbon dioxide (scCO₂) as a green solvent. Cross‐couplings of aryl halides with phenylboronic acid give the desired biaryl products in quantitative yields, in a short reaction time, for substrate‐to‐catalyst molar ratios as high as 4.0⋅10⁴.     

Tension behavior of unidirectional glass/epoxy composites under different strain rates

By considering wide applications of composite materials, having a proper knowledge of them under dynamic loading is necessary. In order to study the effects of strain rates on the behavior of the materials, special testing machines are needed. Most of the research in this field is focused on applying real loading and gripping boundary conditions on the testing specimens. In this study, behavior of unidirectional glass fiber reinforced polymeric composites under uni-axial loading is determined at quasi-static and intermediate strain rates of 0.001–100 s⁻¹. The tests were performed using a servo-hydraulic testing apparatus equipped with a strain rate increase mechanism. For performing the tests, a jig and a fixture are designed and manufactured. The performance of the test jig was evaluated and found to be adequate for testing of composites. Dynamic tests results are compared with the results of static tensile tests carried out on specimens with identical geometry. Experimental results show a significant increase of the tensile strength by increasing the strain rate. The tensile modulus and strain to failure are also observed to increase slightly by increasing the strain rate.     

Estimation of animal and olive solid wastes in Jordan and their potential as a supplementary energy source: An overview

Biomass is a potential source of energy that can reduce our dependency on oil as the main source of energy. In addition to municipal solid waste, animal and olive wastes are the main sources of organic waste in Jordan. In 2005, there were more than 2.4 million heads of sheep, about 72 thousand cows, and 40 million hens being raised in farms distributed in all governorates of Jordan. These animals produce 5.3 million tons (as exerted) of solid waste per year. If these quantities can be effectively collected they may constitute a valuable source of energy. This paper is aiming to estimate the amounts of animal and solid wastes generated in Jordan and their energy potential.The total amount of BOD from animal waste is estimated at 200,000 tons per year. Significant quantities of organic waste can also be collected from olive mills distributed in the country. This waste known locally as “Jift” is currently being collected and used for heating during the winter. The amount of olive waste produced in 2005 was about 27,000 tons. The potential for energy recovery from these wastes was investigated. Assuming an overall waste collection efficiency of 70%, the total heating value of these wastes was found to be 6600 million MJ. This quantity is equivalent to 157 thousand tons of oil equivalent (toe). This quantity represents 84% of Jordan's local crude oil and natural gas production. However, it only represents 2% of the total primary energy consumption of 7187 thousand toe. In addition, the scattering of farms and olive mills in the country will make the collection of their waste costly. Therefore, any potential project for energy recovery from animal and olive wastes in a centralized plant may have low economic merit; however, its environmental benefits are tangible. Decentralized collection and processing of these wastes may be a better option.     

Numerical study of temperature distribution in an inverse moving boundary problem using a meshless method

In this paper, we consider the inverse one-phase one-dimensional Stefan problem to study the thermal processes with phase change in a moving boundary problem and calculate the temperature distribution in the given domain, as well as approximate the temperature and the heat flux on a boundary of the region. For this problem, the location of the moving boundary and temperature distribution on this curve are available as the extra specifications. First, we use the Landau’s transformation to get a rectangular domain and then apply the Crank–Nicolson finite-difference scheme to discretize the time dimension and reduce the problem to a linear system of differential equations. Next, we employ the radial basis function collocation technique to approximate the spatial unknown function and its derivatives at each time level. Finally, the linear systems of algebraic equations constructed in this way are solved using the LU factorization method. To show the numerical convergence and stability of the proposed method, we solve two benchmark examples when the boundary data are exact or contaminated with additive noises.

     

An Ensemble Approach to Identify Firearm Listing on Tor Hidden-Services

The ubiquitous nature of the internet has made it easier for criminals to carry out illegal activities online. The sale of illegal firearms and weaponry on dark web cryptomarkets is one such example of it. To aid the law enforcement agencies in curbing the illicit trade of firearms on cryptomarkets, this paper has proposed an automated technique employing ensemble machine learning models to detect the firearms listings on cryptomarkets. In this work, we have used part-of-speech (PoS) tagged features in conjunction with n-gram models to construct the feature set for the ensemble model. We studied the effectiveness of the proposed features in the performance of the classification model and the relative change in the dimensionality of the feature set. The experiments and evaluations are performed on the data belonging to the three popular cryptomarkets on the Tor dark web from a publicly available dataset. The prediction of the classification model can be utilized to identify the key vendors in the ecosystem of the illegal trade of firearms. This information can then be used by law enforcement agencies to bust firearm trafficking on the dark web.     

A systematic literature review on hardware implementation of artificial intelligence algorithms

Artificial intelligence (AI) and machine learning (ML) tools play a significant role in the recent evolution of smart systems. AI solutions are pushing towards a significant shift in many fields such as healthcare, autonomous airplanes and vehicles, security, marketing customer profiling and other diverse areas. One of the main challenges hindering the AI potential is the demand for high-performance computation resources. Recently, hardware accelerators are developed in order to provide the needed computational power for the AI and ML tools. In the literature, hardware accelerators are built using FPGAs, GPUs and ASICs to accelerate computationally intensive tasks. These accelerators provide high-performance hardware while preserving the required accuracy. In this work, we present a systematic literature review that focuses on exploring the available hardware accelerators for the AI and ML tools. More than 169 different research papers published between the years 2009 and 2019 are studied and analysed.

     

Data fusion for skin detection

Two methods of data fusion to improve the performance of skin detection were tested. The first method fuses two chrominance components from the same color space, while the second method fuses the outputs of two skin detection methods each based on a different color space. The color spaces used are the normalized red, green, blue (RGB) color space, referred to here as pixel intensity normalization, and a new method of obtaining the R, G, and B components of the normalized RGB color space called maximum intensity normalization. The multilayer perceptron (MLP) neural network and histogram thresholding were used for skin detection. It was found that fusion of two chrominance components gives a lower skin detection error than a single chrominance component regardless of the database or the color space for both skin detection methods. In addition, the fusion of the outputs of two skin detection methods further reduces the skin detection error. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Ranking Fuzzy Cognitive Map based scenarios using ELECTRE III: Applied on housing market

Policy makers often deal with a wide range of alternative probable future states for the entity they work for – a country, for example. To strive for the most desirable state, the policy makers must evaluate and rank all probable future scenarios. To this end, ‘scenario methods’ gained recent popularity are increasingly being employed. Nevertheless, currently, decisions made based on insight gained from scenarios are not made in an integrated systematic process. Current variants of the method help study the role of any research concept individually; thus, they do not provide a complete picture of the research situation. A more suitable variant of the method for today's world should provide a holistic view of the research situation by modelling possible links among research concepts. This paper, introduces a stepwise methodology that can guide the building, developing, and ranking of possible future scenarios, having factored in the possible causal interrelations among research concepts. The method is enriched with a combination of Fuzzy Cognitive Maps, a widely used soft computing method, and ELECTRE III, a popular method of Multi Attribute Decision Making. This paper also presents the results of the application of the proposed methodology for Iran's housing market, highlighting the advantages of the proposed methodology.     

Verifying concurrent probabilistic systems using probabilistic-epistemic logic specifications

In this paper, we address the problem of verifying probabilistic and epistemic properties in concurrent probabilistic systems expressed in PCTLK. PCTLK is an extension of the Probabilistic Computation Tree Logic (PCTL) augmented with Knowledge (K). In fact, PCTLK enjoys two epistemic modalities K_i for knowledge and \(Pr_{\triangledown b}K_{i}\) for probabilistic knowledge. The approach presented for verifying PCTLK specifications in such concurrent systems is based on a transformation technique. More precisely, we convert PCTLK model checking into the problem of model checking Probabilistic Branching Time Logic (PBTL), which enjoys path quantifiers in the range of adversaries. We then prove that model checking a formula of PCTLK in concurrent probabilistic programs is PSPACE-complete. Furthermore, we represent models associated with PCTLK logic symbolically with Multi-Terminal Binary Decision Diagrams (MTBDDs), which are supported by the probabilistic model checker PRISM. Finally, an application, namely the NetBill online shopping payment protocol, and an example about synchronization illustrated through the dining philosophers problem are implemented with the MTBDD engine of this model checker to verify probabilistic epistemic properties and evaluate the practical complexity of this verification.