Implications to stormwater management as a result of lot scale rainwater tank systems: a case study in Western Sydney, Australia

Rainwater tanks are increasingly adopted in Australia to reduce potable water demand and are perceived to reduce the volume of stormwater discharge from developments. This paper investigates the water balance of rainwater tanks, in particular the possible impacts these tanks could have in controlling the stormwater discharge volume. The study collected water quantity data from two sites in the Hawkesbury City Council area, New South Wales, Australia and utilised the collected data in a simple water balance model to assess the effectiveness of rainwater tanks in reducing the stormwater discharge volume. The results indicate that a significant reduction in discharge volume from a lot scale development can be achieved if the rainwater tank is connected to multiple end-uses, but is minimal when using irrigation alone. In addition, the commonly used volumetric runoff coefficient of 0.9 was found to over-estimate the runoff from the roof areas and to thereby under-estimate the available volume within the rainwater tanks for retention or detention. Also, sole reliance on the water in the rainwater tanks can make the users aware of their water use pattern and water availability, resulting in significant reductions in water use as the supply dwindles, through self-imposed water restrictions.     

Adaptive sliding mode approach for learning in a feedforward neural network

An adaptive learning algorithm is proposed for a feedforward neural network. The design principle is based on the sliding mode concept. Unlike the existing algorithms, the adaptive learning algorithm developed does not require a prioriknowledge of upper bounds of bounded signals. The convergence of the algorithm is established and conditions given. Simulations are presented to show the effectiveness of the algorithm.     

An expert system for process planning

Fully integrated manufacturing systems, the ultimate goal of today's production engineering research, cannot be realized without automated process planning systems. A knowledge-based expert system incorporating heuristic algorithms as well as analytical and empirical models and which can thus provide a partial replacement for human expertise is the only key to automated process planning.This paper presents an attempt to develop an expert system for automatic process planning using artificial intelligence. In this system, a structural database was incorporated with a knowledgebase for the execution of rules. The programs developed are user-friendly and highly interactive. They allow dialog between the system and the user and require only that the operator answer questions in a familiar workshop language.     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.     

Light Spectral Composition Modifies Polyamine Metabolism in Young Wheat Plants

Although light-emitting diode (LED) technology has extended the research on targeted photomorphogenic, physiological, and biochemical responses in plants, there is not enough direct information about how light affects polyamine metabolism. In this study, the effect of three spectral compositions (referred to by their most typical characteristic: blue, red, and the combination of blue and red [pink] lights) on polyamine metabolism was compared to those obtained under white light conditions at the same light intensity. Although light quality induced pronounced differences in plant morphology, pigment contents, and the expression of polyamine metabolism-related genes, endogenous polyamine levels did not differ substantially. When exogenous polyamines were applied, their roborative effect were detected under all light conditions, but these beneficial changes were correlated with an increase in polyamine content and polyamine metabolism-related gene expression only under blue light. The effect of the polyamines on leaf gene expression under red light was the opposite, with a decreasing tendency. Results suggest that light quality may optimize plant growth through the adjustment of polyamine metabolism at the gene expression level. Polyamine treatments induced different strategies in fine-tuning of polyamine metabolism, which were induced for optimal plant growth and development under different spectral compositions.     

Advent of 2D Rhenium Disulfide (ReS2): Fundamentals to Applications

Rhenium disulfide (ReS₂) is a two‐dimensional (2D) group VII transition metal dichalcogenide (TMD). It is attributed with structural and vibrational anisotropy, layer‐independent electrical and optical properties, and metal‐free magnetism properties. These properties are unusual compared with more widely used group VI‐TMDs, e.g., MoS₂, MoSe₂, WS₂ and WSe₂. Consequently, it has attracted significant interest in recent years and is now being used for a variety of applications including solid state electronics, catalysis, and, energy harvesting and energy storage. It is anticipated that ReS₂ has the potential to be equally used in parallel with isotropic TMDs from group VI for all known applications and beyond. Therefore, a review on ReS₂ is very timely. In this first review on ReS₂, we critically analyze the available synthesis procedures and their pros/cons, atomic structure and lattice symmetry, crystal structure, and growth mechanisms with an insight into the orientation and architecture of domain and grain boundaries, decoupling of structural and vibrational properties, anisotropic electrical, optical, and magnetic properties impacted by crystal imperfections, doping and adatoms adsorptions, and contemporary applications in different areas.     

DVCC-Based Non-linear Feedback Neural Circuit for?Solving System of Linear Equations

A neural circuit to solve a system of simultaneous linear equations is presented. The circuit employs non-linear feedback to achieve a transcendental energy function that ensures fast convergence to the exact solution while enjoying reduction in hardware complexity over existing schemes. A new building block for analog signal processing, the digitally controlled differential voltage current conveyor (DC-DVCC) is introduced and is utilized for the non-linear synaptic interconnections between neurons. The proof of the energy function has been given and it is shown that the gradient network converges exactly to the solution of the system of equations. PSPICE simulation results are presented for linear systems of equations of various sizes and are found to be in close agreement with the algebraic solution. The use of CMOS DC-DVCCs and operational amplifiers facilitates monolithic integration.     

Stochastic multiscale fracture analysis of three-dimensional functionally graded composites

A new moment-modified polynomial dimensional decomposition (PDD) method is presented for stochastic multiscale fracture analysis of three-dimensional, particle-matrix, functionally graded materials (FGMs) subject to arbitrary boundary conditions. The method involves Fourier-polynomial expansions of component functions by orthonormal polynomial bases, an additive control variate in conjunction with Monte Carlo simulation for calculating the expansion coefficients, and a moment-modified random output to account for the effects of particle locations and geometry. A numerical verification conducted on a two-dimensional FGM reveals that the new method, notably the univariate PDD method, produces the same crude Monte Carlo results with a five-fold reduction in the computational effort. The numerical results from a three-dimensional, edge-cracked, FGM specimen under a mixed-mode deformation demonstrate that the statistical moments or probability distributions of crack-driving forces and the conditional probability of fracture initiation can be efficiently generated by the univariate PDD method. There exist significant variations in the probabilistic characteristics of the stress-intensity factors and fracture-initiation probability along the crack front. Furthermore, the results are insensitive to the subdomain size from concurrent multiscale analysis, which, if selected judiciously, leads to computationally efficient estimates of the probabilistic solutions.     

Effect of phosphoric acid on the performance of low antimony grid of Pb‐acid cell under constant current charging and discharging

Effect of phosphoric acid on the performance of Pb‐1.7%Sb grid of lead‐acid cell is studied in 5 M H₂SO₄ by cyclic galvanostatic polarization and impedance spectroscopy. An increase in capacitance to a maximum is recorded during the initial stages of the electro‐reduction of PbO₂ into Pb(II) compounds and attributed to concurrent compositional and dimensional changes. These changes include removal of O₂ bubbles, insertion of large amounts of H₂SO₄ and H₂O. Efficiency of PbO₂ formation decreases, while its rate of self‐discharge increases with increasing the charging current and in the presence of H₃PO₄. The charge capacity increases with increasing the discharging current due to the decrease in the self‐discharge. The charge capacity is lower in the presence of H₃PO₄. On increasing the cycle number, the corrodibility of the grid increases, because more layers of the surface Pb are involved in the self‐discharge. H₃PO₄ significantly retards the effect of cycle number.