RQL: a recursive query language

Different classes of recursive queries in the relational databases are identified. It is shown that existing proposals to extend the relational query languages are either not powerful enough to express queries in many of these classes or use nonfirst normal form constructs. RQL, a recursive database query language that can be used to express recursive queries on all the classes identified, is presented. RQL is based on the relational algebra. In addition to functions that correspond to the standard and extended relational algebra operators, RQL supports functions required to express general recursive queries. The elements of RQL and the ways in which they are used to formulate complicated, but useful, recursive queries are described. The effects of the extensions embodied in RQL on the termination of recursive query evaluation are discussed     

Differential scanning calorimetry of aqueous polymer solutions and gels

The melting phenomena of aqueous polymer solutions and gels have been investigated by differential scanning calorimetry (DSC). The polymers used were synthetic polyacrylamide and poly(vinyl alcohol) samples as well as guar and xanthan gums. By using an empirical relation, the energy measured from the area under the melting peak yielded heats of mixing and sorption, when fitted by an association factor computed from the data. This factor (independent of the concentration) is a measure of the water fraction associated with the polymer and has a definite and characteristic value for a given polymer in water. When a crosslinking agent (potassium pyroantimoniate or chromic nitrate) was added to the water–polymer system, the association factor varied with the polymer concentration; the macromolecular chains thus become less accessible to penetrating water. If a branched gel was obtained owing to the formation of chemical crosslinks, a hump appeared on the melting peak.     

KINETICS AND ABSORPTION RATE OF CO2 INTO PARTIALLY CARBONATED AMMONIA SOLUTIONS

In this research, kinetics and absorption rate of CO₂ were studied using partially carbonated ammonia solutions. A correlation was proposed to calculate CO₂ absorption rate based on two dimensionless parameters: conversion film parameter and carbonation ratio. Absorption rate experiments have been performed employing a laboratory absorption packed column. More than 60 items of experimental data were used for obtaining the correlation parameters. In the experiments, total ammonia concentration range was 30 to 750 (mol · m^?3), carbonation ratio range was 0.22 to 0.785, and CO₂ partial pressure in the gas mixture was 10, 14, or 18 (kPa). A comparison of the predictions indicated that the proposed correlation was more accurate than other correlations reported in the literature.     

A family of memristor‐based reactance‐less oscillators

In this paper, we present for the first time a family of memristor‐based reactance‐less oscillators (MRLOs). The proposed oscillators require no reactive components, that is, inductors or capacitors, rather, the ‘resistance storage’ property of memristor is exploited to generate the oscillation. Different types of MRLO family are presented, and for each type, closed form expressions are derived for the oscillation condition, oscillation frequency, and range of oscillation. Derived equations are further verified using transient circuit simulations. A comparison between different MRLO types is also discussed. In addition, detailed fabrication steps of a memristor device and experimental results for the first MRLO physical realization are presented. Copyright © 2013 John Wiley & Sons, Ltd.     

Possibilities of urban flood reduction through distributed-scale rainwater harvesting

Urban flooding in Chittagong City usually occurs during the monsoon season and a rainwater harvesting(RWH) system can be used as a remedial measure. This study examines the feasibility of rain barrel RWH system at a distributed scale within an urbanized area located in the northwestern part of Chittagong City that experiences flash flooding on a regular basis. For flood modeling, the storm water management model(SWMM) was employed with rain barrel low-impact development(LID) as a flood reduction measure. The Hydrologic Engineering Center's River Analysis System(HEC-RAS) inundation model was coupled with SWMM to observe the detailed and spatial extent of flood reduction.Compared to SWMM simulated floods, the simulated inundation depth using remote sensing data and the HEC-RAS showed a reasonable match,i.e., the correlation coefficients were found to be 0.70 and 0.98, respectively. Finally, using LID, i.e., RWH, a reduction of 28.66% could be achieved for reducing flood extent. Moreover, the study showed that 10%e60% imperviousness of the subcatchment area can yield a monthly RWH potential of 0.04 e0.45 m~3 from a square meter of rooftop area. The model can be used for necessary decision making for flood reduction and to establish a distributed RWH system in the study area.     

CO2 capture by benzene-based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology

In this research, porous benzene-based hypercrosslinked polymeric adsorbents with different morphological properties were synthesized through Friedel–Crafts alkylation reaction. The resulting samples were applied for CO₂ capture at different operational conditions. Two modelling approaches, including artificial neural network (radial basis function [RBF] and multi layer perceptron [MLP]) and response surface methodology (RSM), were employed to investigate the effect of independent parameters on adsorption capacity. A semi-empirical quadratic model for adsorption capacity was presented based on RSM-central composite design technique. Additionally, the optimal structure of RBF was determined with 200 neurons, and the optimal structure of MLP was determined with three hidden layers and 10, 8, and 7 neurons. The modelling results demonstrate the better prediction of MLP and RBF approaches than the RSM method with correlation coefficient values of 0.999, 0.989, and 0.931, respectively. Finally, process optimization was carried out using RSM optimization module and the optimized values of synthesis time, crosslinker ratio (formaldehyde dimethyl acetal [FDA]/benzene), adsorption time, pressure, and temperature were obtained at 10.11 h, 1, 220 s, 9 bar, and 55°C, respectively. The optimum value of CO₂ uptake capacity was obtained around 167 (mg/g).     

Cu Current Collector with Binder-Free Lithiophilic Nanowire Coating for High Energy Density Lithium Metal Batteries

Despite significant efforts to fabricate high energy density (ED) lithium (Li) metal anodes, problems such as dendrite formation and the need for excess Li (leading to low N/P ratios) have hampered Li metal battery (LMB) development. Here, the use of germanium (Ge) nanowires (NWs) directly grown on copper (Cu) substrates (Cu-Ge) to induce lithiophilicity and subsequently guide Li ions for uniform Li metal deposition/stripping during electrochemical cycling is reported. The NW morphology along with the formation of the Li₁₅Ge₄ phase promotes uniform Li-ion flux and fast charge kinetic, resulting in the Cu-Ge substrate demonstrating low nucleation overpotentials of 10 mV (four times lower than planar Cu) and high Columbic efficiency (CE) efficiency during Li plating/stripping. Within a full-cell configuration, the Cu-Ge@Li – NMC cell delivered a 63.6% weight reduction at the anode level compared to a standard graphite-based anode, with impressive capacity retention and average CE of over 86.5% and 99.2% respectively. The Cu-Ge anodes are also paired with high specific capacity sulfur (S) cathodes, further demonstrating the benefits of developing surface-modified lithiophilic Cu current collectors, which can easily be integrated at the industrial scale.     

Machine learning modelling and evaluation of jet fires from natural gas processing,storage, and transport

Jet fires and their repercussions play a significant role in catastrophic incidents that typically have a cascading impact in process industries. Several hydrocarbon experiments from 19 papers were incorporated into the current endeavour to develop simulations of jet flames using machine learning (ML) models. Dimensionless characteristics have been used as output and input variables, including mass flow rates, fuel density, jet flame length, and heat release fluxes. When training three layers of the multi-layer feedforward neural network (MLFFNN) method, a Bayesian regularization backpropagation approach was adopted and evaluated with the radial based functions (RBF) algorithm. Through an optimization procedure, the first and second hidden layers of the MLFFNN have been optimized to include 10 and five neurons, respectively. The RBF algorithm with 40 neurons in a single layer has been computed using the same method. The best mean square error (MSE) validation results for RBF and MLFFNN were 0.006 and 0.0002, respectively, for 40 and 100 epochs. The MLFFNN and RBF models' respective regression statistical analysis outputs were 0.9949 and 0.9645. The ML method has been identified as a potentially useful technique for precisely predicting the geometrical and radiative characteristics of jet flames.