Miracle-dz/UniFAR-FTUs huggingface dataset:
Phosphorus recovery from digested sludge centrate using seawater-driven 1 forward osmosis 2 Revised Manuscript Submitted to 3 Separation Purification Technology 4

In this study, we demonstrate a novel seawater-driven forward osmosis (FO) process to recover calcium phosphate precipitates from digested sludge centrate without any chemical addition and draw solute regeneration. The FO process effectively pre-concentrated phosphate and calcium in the digested sludge centrate. Spontaneous precipitation of calcium phosphate minerals in the digested sludge centrate was achieved by the sustained concentrative action of the FO process and the gradual pH increase due to the diffusion of protons to the draw solution. Pre-concentrating digested sludge centrate by three-fold resulted in a 92% recovery of phosphate via precipitation. The phosphate precipitate only constituted 3% of the total inorganic solids recovered, therefore subsequent treatment steps would be required to recover phosphorus in a useable form. A water flux decline of 30% from the initial value was observed as the digested sludge was concentrated by three-fold. This observed water flux decline was mostly attributed to the decrease in the effective osmotic driving force due to the increasingly concentrated feed solution and diluted draw solution. It is also noteworthy that membrane fouling was readily reversible. By flushing the membrane with deionised water and subjecting the membrane to feed and draw solutions with the same osmotic pressure as the initial conditions, complete water flux recovery could be achieved. Disciplines Engineering | Science and Technology Studies Publication Details Ansari, A. J., Hai, F. I. , Price, W. E. & Nghiem, L. D. (2016). Phosphorus recovery from digested sludge centrate using seawater-driven forward osmosis. Separation and Purification Technology, 163 1-7. This journal article is available at Research Online: http://ro.uow. edu.au/eispapers/5179 Phosphorus recovery from digested sludge centrate using seawater-driven 1 forward osmosis 2 Revised Manuscript Submitted to 3 Separation Purification Technology 4 Ashley J. Ansari, Faisal I. Hai, William E. Price, and Long D. Nghiem* 5 1 Strategic Water Infrastructure Laboratory, School of Civil, Mining and Environmental 6 Engineering, University of Wollongong, Wollongong, NSW 2522, Australia 7 2 Strategic Water Infrastructure Laboratory, School of Chemistry, University of Wollongong, 8 Wollongong NSW 2522, Australia 9 10
Incorporating Locality of Images to Generate Targeted Transferable Adversarial Examples

Despite that leveraging the transferability of adversarial examples can attain a fairly high attack success rate for non-targeted attacks, it does not work well in targeted attacks since the gradient directions from a source image to a targeted class are usually different in different DNNs. To increase the transferability of target attacks, recent studies make efforts in aligning the feature of the generated adversarial example with the feature distributions of the targeted class learned from an auxiliary network or a generative adversarial network. However, these works assume that the training dataset is available and require a lot of time to train networks, which makes it hard to apply to real-world scenarios. In this paper, we re-visit adversarial examples with targeted transferability from the perspective of universality and ﬁnd that highly universal adversarial perturbations tend to be more transferable. Based on this observation, we propose the Locality of Images (LI) attack to improve targeted transferability. Speciﬁcally, instead of using the classiﬁcation loss only, LI introduces a feature similarity loss between intermediate features from adversarial perturbed original images and randomly cropped images, which makes the features from adversarial perturbations to be more dominant than that of benign images, hence improving targeted transferability. Through incorporating locality of images into optimizing perturbations, the LI attack emphasizes that targeted perturbations should be universal to diverse input patterns, even local image patches. Extensive experiments demonstrate that LI can achieve high success rates for transfer-based targeted attacks. On attacking the ImageNet-compatible dataset, LI yields an improvement of 12% compared with existing state-of-the-art methods.
Optimization-based framework for low-voltage grid reinforcement assessment under various levels of flexibility and coordination

The rapid electrification of residential heating and mobility sectors is expected to drive the existing distribution grid assets beyond their planned operating conditions. This change will also reveal new potentials through sector coupling, flexibilities, and the local exchange of decentralized generation. This paper thus presents an optimization framework for multi-modal energy systems at the low voltage (LV) distribution grid level. In this, we focus on the reinforcement requirements of the grid and the techno-economic assessment of flexibility components and coordination between agents. By employing a multi-level approach, computational complexity is reduced, and various levels of coordination and flexibilities are implemented. We conclude the work with a case study for a representative rural grid in Germany, in which we observe high economic potential in the flexible operation of buildings, majorly thanks to better integration of photovoltaics. Across all paradigms barring a best-case benchmark, grid reinforcement based on a mix of passive and active measures was necessary. A synergy effect is observed between flexibilities and coordination, as their combination reduces the peaks to the extent of completely avoiding grid reinforcement. The presented framework can be applied with a wide range of grid and component types to outline the broad landscape of future LV distribution grids.
Galactic Cosmic Rays and Solar Energetic Particles in Cis-Lunar Space

The particle and radiation environment in cis-lunar space is becoming increasingly important as more hardware and human assets occupy various orbits around the Earth and space exploration efforts turn to the Moon and beyond. Since 2020, the total number of satellites in orbit has approximately doubled, highlighting the growing dependence on space-based resources. Through NASA's upcoming Artemis missions, humans will spend more time in cis-lunar space than ever before supported by the expansive infrastructure required for extended missions to the Moon, including a surface habitat, a communications network, and the Lunar Gateway. This paper focuses on galactic cosmic rays (GCRs) and solar energetic particles (SEPs) that create a dynamic and varying radiation environment within these regions. GCRs are particles of hundreds of MeV/nucleon (MeV/n) and above generated in highly energetic astrophysical environments in the Milky Way Galaxy, such as supernovae and pulsars, and beyond. These particles impinge isotropically on the heliosphere and are filtered down to 1 AU, experiencing modulation in energy and intensity on multiple timescales, from hours to decades, due to the solar magnetic cycle and other transient phenomena. SEPs are particles with energies up to thousands of MeV/n that are accelerated in eruptive events on the Sun and flood the inner heliosphere causing sudden and drastic increases in the particle environment on timescales of minutes to days. This paper highlights a current and prospective future gap in energetic particle measurements in the hundreds of MeV/n. We recommend key observations near Earth to act as a baseline as well as distributed measurements in the heliosphere, magnetosphere, and lunar surface to improve the scientific understanding of these particle populations and sources.
HIFI++: A Unified Framework for Bandwidth Extension and Speech Enhancement

Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models. In this paper, we show that this success can be extended to other tasks of conditional audio generation. In particular, building upon HiFi vocoders, we propose a novel HiFi++ general frame-work for bandwidth extension and speech enhancement. We show that with the improved generator architecture, HiFi++ performs better or comparably with the state-of-the-art in these tasks while spending significantly less computational resources. The effectiveness of our approach is validated through a series of extensive experiments.
Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction

This study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved solution accuracy in satisfying the interim point constraints placed on the performance output variables. The proposed approach is to linearize the dynamics around the baseline values of its arguments and then to solve for the corrective input required to transfer the perturbed trajectory to precisely known or desired values at specific time points, in other words, the interim points. Depending on the type of decision variables to adjust, parameter correction and control function correction methods are developed. These incremental correction methods can be used as a means to compensate for the prediction errors of pretrained neural networks in real-time applications where high accuracy of the prediction of dynamical systems at prescribed time points is imperative. In this regard, the online update approach can be useful for enhancing overall targeting accuracy of finite-horizon control subject to point constraints using a neural policy. A numerical example demonstrates the effectiveness of the proposed approach in an application to a powered descent problem on Mars.
Optimal personalized therapies in colon-cancer induced immune response using a Fokker-Planck framework

In this paper, a new stochastic framework to determine optimal combination therapies in colon cancer-induced immune response is presented. The dynamics of colon cancer is described through an It\"o stochastic process, whose probability density function evolution is governed by the Fokker-Planck equation. An open-loop control optimization problem is proposed to determine the optimal combination therapies. Numerical results with combination therapies comprising of the chemotherapy drug \ind{Doxorubicin} and immunotherapy drug IL-2 validate the proposed framework.
A stabilised Benders decomposition with adaptive oracles applied to investment planning of multi-region power systems with short-term and long-term uncertainty

Benders decomposition with adaptive oracles was proposed to solve large-scale optimisation problems with a column bounded block-diagonal structure, where subproblems differ on the right-hand side and cost coefficients. Adaptive Benders reduces computational effort significantly by iteratively building inexact cutting planes and valid upper and lower bounds. However, Adaptive Benders and standard Benders may suffer severe oscillation when solving a multi-region investment planning problem. Therefore, we propose stabilising Adaptive Benders with the level set method and adaptively selecting the subproblems to solve per iteration for more accurate information. Furthermore, we propose a dynamic level set method to improve the robustness of stabilised Adaptive Benders by adjusting the level set per iteration. We compare stabilised Adaptive Benders with the unstabilised versions of Adaptive Benders with one subproblem solved per iteration and standard Benders on a multi-region long-term power system investment planning problem with short-term and long-term uncertainty. The problem is formulated as multi-horizon stochastic programming. Four algorithms were implemented to solve linear programming with up to 1 billion variables and 4.5 billion constraints. The computational results show that: a) for a 1.00% convergence tolerance, the proposed stabilised method is up to 113.7 times faster than standard Benders and 2.14 times faster than unstabilised Adaptive Benders; b) for a 0.10% convergence tolerance, the proposed stabilised method is up to 45.5 times faster than standard Benders and unstabilised Adaptive Benders cannot solve the largest instance to convergence tolerance due to severe oscillation and c) dynamic level set method makes stabilisation more robust.
Nonlinear Dynamics in Complexity Quantification

Chaotic systems which are due to nonlinearity have attracted a great concern in the current world and chaotic models. Systems for a wide range of operation conditions have their application in almost all branches of engineering and science. In the history of chaotic studies and nonlinearity, many different but co-existent phases can be distinguished [1]. In the initial phase, chaos was considered as a deterministic regime which, most probably, was responsible for the variations that was regarded as noise and thus was being modeled as a stochastic process. In the second phase, it was of great significance to establish criteria for detecting chaotic dynamics and thus establishing dynamical invariants which were necessary in quantifying chaos. The third step which was to develop machine learning models which could learn the dynamics and chaos from the data of the strange attractor [2]. With respect to this aspect, various model structures were developed and investigated on their ability to detect chaos from a given set of data [3]. These model structures included radial basis functions, and local linear mapping among others. The third phase is currently being investigated together with other issues surrounding nonlinear dynamics and chaos, for instance in noise reduction and control, among other issues.
Extracting ultralight boson properties from boson clouds around postmerger remnants

Ultralight bosons are a class of hypothetical particles that could potentially solve critical problems in fields ranging from cosmology to astrophysics and fundamental physics. If ultralight bosons exist, they form clouds around spinning black holes with sizes comparable to their particle Compton wavelength through superradiance, a well-understood classical wave amplification process that has been studied for decades. After these clouds form, they dissipate and emit continuous gravitational waves through the annihilation of ultralight bosons into gravitons. These gravitons could be detected with ground-based gravitational-wave detectors using continuous-wave searches. However, it is conceivable for other continuous-wave sources to mimic the emission from the clouds, which could lead to false detections. Here we investigate how one can use continuous waves from clouds formed around known merger remnants to alleviate this problem. In particular, we simulate a catalogue of merger remnants that form clouds around them and demonstrate with select"golden"merger remnants how one can perform a Bayesian cross-verification of the ultralight boson hypothesis that has the potential to rule out alternative explanations. Our proof-of-concept study suggest that, in the future, there is a possibility that a merger remnant exists close enough for us to perform the analysis and test the boson hypothesis if the bosons exist in the relevant mass range. Future research will focus on building more sophisticated continuous-wave tools to perform this analysis in practice.
Trees maximizing the number of almost-perfect matchings

We characterize the extremal trees that maximize the number of almost-perfect matchings, which are matchings covering all but one or two vertices, and those that maximize the number of strong almost-perfect matchings, which are matchings missing only one or two leaves. We also determine the trees that minimize the number of maximal matchings. We apply these results to extremal problems on the weighted Hosoya index for several choices of vertex-degree-based weight function.
Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction

In recent years, graph neural networks (GNNs) combined with variants of recurrent neural networks (RNNs) have reached state-of-the-art performance in spatiotemporal forecasting tasks. This is particularly the case for traffic forecasting, where GNN models use the graph structure of road networks to account for spatial correlation between links and nodes. Recent solutions are either based on complex graph operations or avoiding predefined graphs. This paper proposes a new sequence-to-sequence architecture to extract the spatiotemporal correlation at multiple levels of abstraction using GNN-RNN cells with sparse architecture to decrease training time compared to more complex designs. Encoding the same input sequence through multiple encoders, with an incremental increase in encoder layers, enables the network to learn general and detailed information through multilevel abstraction. We further present a new benchmark dataset of street-level segment traffic data from Montreal, Canada. Unlike highways, urban road segments are cyclic and characterized by complicated spatial dependencies. Experimental results on the METR-LA benchmark highway and our MSLTD street-level segment datasets demonstrate that our model improves performance by more than 7% for one-hour prediction compared to the baseline methods while reducing computing resource requirements by more than half compared to other competing methods.
The solar magnetic field

The magnetic field of the Sun is the underlying cause of the many diverse phenomena combined under the heading of solar activity. Here we describe the magnetic field as it threads its way from the bottom of the convection zone, where it is built up by the solar dynamo, to the solar surface, where it manifests itself in the form of sunspots and faculae, and beyond into the outer solar atmosphere and, finally, into the heliosphere. On the way it transports energy from the surface and the subsurface layers into the solar corona, where it heats the gas and accelerates the solar wind.
Fermat's Last Theorem over some small real quadratic fields

Using modularity, level lowering, and explicit computations with Hilbert modular forms, Galois representations and ray class groups, we show that for $3 \le d \le 23$ squarefree, $d \ne 5$, $17$, the Fermat equation $x^n+y^n=z^n$ has no non-trivial solutions over the quadratic field $\mathbb{Q}(\sqrt{d})$ for $n \ge 4$. Furthermore, we show for $d=17$ that the same holds for prime exponents $n \equiv 3$, $5 \pmod{8}$.
Reinforcing effect of a thin basalt fiber-reinforced polymer plywood coating

The strengthening effect of basalt fiber-reinforced epoxy coatings was investigated with regard to their areal weight and position on the compression or tension side of plywood. Beach plywood was coated on one side with a basalt fiber-reinforced epoxy matrix. Two biaxial and one twilled fabric with areal weights of 170 g/m2, 210 g/m2, and 340 g/m2 respectively were used. The thickness of the plywood was 21 mm. The results showed the best reinforcing effect was obtained with the highest weight when mounted on the tension side of the parallel specimens. The bending strength of these specimens was improved by 15.7%. The perpendicular specimens were positively reinforced by the fiber-reinforced polymers on both the compression and tension sides. The tension reinforcement provided a higher deflection, which was further analyzed using digital image correlation. The evaluated data indicated significant displacement of the neutral axis. The impact strength of the parallel specimens was not improved by the reinforcement, but all of the reinforced perpendicular specimens were significantly strengthened.
A Generalized Convective Inhibition Energy

A substantially water-free creme having from about 20% to about 50% of a rapidly melting confectionery fat and 50% to about 80% of a carbohydrate is disclosed. This creme has a disordering temperature of between 80 DEG F. and less than 102 DEG F. and a melt rate of 15 seconds to 75 seconds at 92 DEG F. It has a back extrusion force of from about 0.3 to about 6 lbs at 70 DEG F. Disordering temperature relates to the temperature at which the creme and saliva mixture forms a water-continuous phase. The back extrusion force is a measure of the apparent viscosity of the creme. The creme's viscosity remains stable over a broad temperature range.
Stars Form By Gravitational Collapse, Not Competitive Accretion

There are now two dominant models of how stars form: gravitational collapse theory holds that star-forming molecular clumps, typically hundreds to thousands of solar masses in mass, fragment into gaseous cores that subsequently collapse to make individual stars or small multiple systems. In contrast, competitive accretion theory suggests that at birth all stars are much smaller than the typical stellar mass (~0.5 solar masses), and that final stellar masses are determined by the subsequent accretion of unbound gas from the clump. Competitive accretion models explain brown dwarfs and free-floating planets as protostars ejected from star-forming clumps before accreting much mass, predicting that they should lack disks, have high velocity dispersions, and form more frequently in denser clumps. They also predict that mean stellar mass should vary within the Galaxy. Here we derive a simple estimate for the rate of competitive accretion as a function of the star-forming environment, based partly on simulations, and determine in what types of environments competitive accretion can occur. We show that no observed star-forming region produces significant competitive accretion, and that simulations that show competitive accretion do so because their properties differ from those determined by observation. Our result shows that stars form by gravitational collapse, and explains why observations have failed to confirm predictions of the competitive accretion scenario.
Study of EAS hadronic component with hadron energy > 50 GeV

An experiment has been performed using Baksan "Carpet-2" Air Shower array. We present here preliminary experimental data for hadrons with energy more than 50 GeV in EAS with Ne ≥ 10 5 at core distances of (45-55) m, obtained by a new method. The experimental data are compared with results of Monte Carlo simulations using CORSIKA code (HDPM model).
Stellar yields as a function of initial metallicity and mass limit for black hole formation

Grids of evolutionary star models from 1 to 120 M ⊙ with metallicities Z = 0.001 and 0.020 are used to derive chemical yields for studies on the chemical evolution of galaxies. The stellar models include the new opacities by Rogers & Iglesias (1992) and take into account, in addition to the usual metal dependent opacity and nuclear effects, the changes of the mass loss rates with metallicity predicted by wind models and also the dependence of the mass loss rates of WR stars on their actual masses. Moderate core overshooting is also accounted for
Gravitational Cheshire eﬀect: Nonminimally coupled scalar ﬁelds may not curve spacetime

: It is shown that ﬂat spacetime can be dressed with a real scalar ﬁeld that satisﬁes the nonlinear Klein-Gordon equation without curving spacetime. Surprisingly, this possibility arises from the nonminimal coupling of the scalar ﬁeld with the curvature, since a footprint of the coupling remains in the energy-momentum tensor even when gravity is switched oﬀ. Requiring the existence of solutions with vanishing energy-momentum tensor ﬁxes the self-interaction potential as a local function of the scalar ﬁeld depending on two coupling constants. The solutions describe shock waves and, in the Euclidean continuation, instanton conﬁgurations in any dimension. As a consequence of this eﬀect, the tachyonic solutions of the free massive Klein-Gordon equation become part of the vacuum.