Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation on internal structural properties of asphalt mixtures subjected to loading using image analysis

ABSTRACT Asphalt concrete is a heterogeneous material consisting of asphalt binder, aggregates, and air voids; spatial distributions and interactions of these components affect the performance of the material. In past decades, increasing interest has been shown in the use of image analysis techniques for characterising the internal structure and performance of asphalt mixtures; however, the influence of load applications is still lacking. In this study, a 2-D imaging analysis was used to evaluate the changes in asphalt concrete microstructure due to loading of laboratory-produced specimens and field-core specimens. Uniaxial compression tests were conducted to apply different loading levels to the laboratory-produced specimens, while field-core specimens were subjected to different levels of accumulated traffic load. Results indicate that mixture microstructural properties, including number of aggregate contacts, total contact length, and internal structure index (ISI), significantly decreased with increased strain levels resulting from increased loading levels, whereas average contact orientation and vector magnitude did not change significantly with deformation. Relationships between ISI and loading levels were plotted and their slopes were determined for all laboratory-produced asphalt mixes. Results suggest that under a given load application, the type of asphalt binder is a critical factor influencing the rutting performance of asphalt mixture.
Development and external validation of an acute kidney injury risk score for use in the general population

Abstract Background Improving recognition of patients at increased risk of acute kidney injury (AKI) in the community may facilitate earlier detection and implementation of proactive prevention measures that mitigate the impact of AKI. The aim of this study was to develop and externally validate a practical risk score to predict the risk of AKI in either hospital or community settings using routinely collected data. Methods Routinely collected linked datasets from Tayside, Scotland, were used to develop the risk score and datasets from Kent in the UK and Alberta in Canada were used to externally validate it. AKI was defined using the Kidney Disease: Improving Global Outcomes serum creatinine–based criteria. Multivariable logistic regression analysis was performed with occurrence of AKI within 1 year as the dependent variable. Model performance was determined by assessing discrimination (C-statistic) and calibration. Results The risk score was developed in 273 450 patients from the Tayside region of Scotland and externally validated into two populations: 218 091 individuals from Kent, UK and 1 173 607 individuals from Alberta, Canada. Four variables were independent predictors for AKI by logistic regression: older age, lower baseline estimated glomerular filtration rate, diabetes and heart failure. A risk score including these four variables had good predictive performance, with a C-statistic of 0.80 [95% confidence interval (CI) 0.80–0.81] in the development cohort and 0.71 (95% CI 0.70–0.72) in the Kent, UK external validation cohort and 0.76 (95% CI 0.75–0.76) in the Canadian validation cohort. Conclusion We have devised and externally validated a simple risk score from routinely collected data that can aid both primary and secondary care physicians in identifying patients at high risk of AKI.
Revisiting the Middle Eocene Climatic Optimum “Carbon Cycle Conundrum” With New Estimates of Atmospheric pCO2 From Boron Isotopes

The Middle Eocene Climatic Optimum (MECO) was a gradual warming event and carbon cycle perturbation that occurred between 40.5 and 40.1 Ma. A number of characteristics, including greater‐than‐expected deep‐sea carbonate dissolution, a lack of globally coherent negative δ13C excursion in marine carbonates, a duration longer than the characteristic timescale of carbon cycle recovery, and the absence of a clear trigger mechanism, challenge our current understanding of the Earth system and its regulatory feedbacks. This makes the MECO one of the most enigmatic events in the Cenozoic, dubbed a middle Eocene “carbon cycle conundrum. ” Here we use boron isotopes in planktic foraminifera to better constrain pCO2 changes over the event. Over the MECO itself, we find that pCO2 rose by only 0.55–0.75 doublings, thus requiring a much more modest carbon injection than previously indicated by the alkenone δ13C‐pCO2 proxy. In addition, this rise in pCO2 was focused around the peak of the 400 kyr warming trend. Before this, considerable global carbonate δ18O change was asynchronous with any coherent ocean pH (and hence pCO2) excursion. This finding suggests that middle Eocene climate (and perhaps a nascent cryosphere) was highly sensitive to small changes in radiative forcing.
Sparse Bayesian learning approach for propagation distance recognition and damage localization in plate-like structures using guided waves

In ultrasonic guided wave–based damage detection, the propagation distance recognition of wave packets is an essential step. However, it is difficult to perform direct distance extraction from guided wave signals since the multimode, mode conversion, and dispersion effects typically lead to wave packet overlapping and distortion. In addition, the identified damage location may be incorrect due to inevitable uncertainties in the procedure of propagation distance recognition and damage localization. Motivated by these difficulties, a novel two-stage approach for propagation distance recognition and damage localization is proposed based on sparse Bayesian learning framework. In the first stage, prior knowledge of a small number of wave packets contained in a signal is exploited to sparsely represent the guided wave signal and then the corresponding propagation distance and amplitude information of each wave packet can be obtained. In the next stage, only a small number of damages occurring in a structure are exploited and a vector consisting of the propagation distances extracted from the previous stage is used to match the atoms in a pre-defined over-complete distance dictionary matrix, to achieve our goal of localizing structural damage. Both procedures of the two stages are realized by the sparse Bayesian learning algorithm, which obtains the most probable value and the corresponding uncertainty. A sampling strategy is presented to transfer the uncertainty of the propagation distance recognition to the subsequent damage localization. Finally, the effectiveness of the proposed method is validated using numerical simulation and experimental investigation on aluminum plates. The proposed method is only valid for single damage localization in the present form, but it has the potential to be extended for multiple damage localization.
Innovations in creative education for tertiary sector in Australia: present and future challenges

Abstract Recent significant changes in technology such as Artificial Intelligence (AI) and big data analysis have a wide impact in many areas of human societies, not least in education. Advances in efficiency of new technologies, with impacts in renewable energy and transport affected by environmental concerns and climate change mitigation, demand a reassessment and adjustment of the industrial structure of the economy. Technological advances enhancing creativity and analytical skills mean people must learn and adapt to a fast-changing economic environment. This paper present some key changes in Australian educational areas in recent years that are important and relevant to the subject of education innovation and philosophy to achieve outcomes in training and sustainable economic development, as anticipated by the community and government. Education courses and curricula in many countries have been transformed by technological advance. It is the swift incorporation of new technologies that best prepares students for the dynamics of economic and social change. Even though technological responsiveness is not a new phenomenon, it is established that the teaching methods found to be most successful include complementary and supplemental courses producing multi-skilled and creative graduates. These courses are delivered with significant emphasis upon self-development skills and are less focused upon traditional content. A move toward horizontal focus and a student-centric balance is the current trend, displacing vertical over-specialisation of learning. Yet, the central role of educator is seen as still appropriate and necessary where the aim is to equip graduates with creative and adaptive skills.
Evolution of Guangzhou Biomedical Industry Innovation Network Structure and Its Proximity Mechanism

The topological structure and multidimensional proximity evolution mechanism of Guangzhou biomedical industry innovation network are studied by using cooperative patents and cooperative papers from 1998 to 2018 with Ucinet and PLS-SEM. Research shows: ① The evolution of the Guangzhou Biomedical Industry Innovation Network can be divided into three stages, which are the primary budding stage of 1998–2005, the steady development stage of 2006–2012, and the rapid development stage of 2013–2018; ② the number of subjects in the innovation network is increasing, and the types of subjects are constantly increasing. The innovation network has gone from small to large, from closed to open, and from simple to complex. Patents and papers cooperation networks show different characteristics at various stages; ③ the relationship between geographical proximity, cognitive proximity, organizational proximity, and innovation network differs in stages. The interaction of proximity varies with the content of innovation cooperation. Based on this result, we have put forward policy recommendations for different innovative content.
A sol–gel synthesis to prepare size and shape-controlled mesoporous nanostructures of binary (II–VI) metal oxides

A base-catalyzed sol–gel approach combined with a solvent-driven self-assembly process at low temperature is augmented to make manganese oxide (Mn3O4), copper oxide (CuO), and magnesium hydroxide (Mg(OH)2) nanostructures with size- and shape-controlled morphologies. Nanostructures of Mn3O4 with either hexagonal, irregular particle, or ribbon shape morphologies with an average diameter ranged from 100 to 200 nm have been prepared in four different solvent types. In all morphologies of Mn3O4, the experimental XRD patterns have indexed the nanocrystal unit cell structure to triclinic. The hexagonal nanoparticles of Mn3O4 exhibit high mesoporocity with a BET surface area of 91.68 m2 g−1 and BJH desorption average pore diameter of ∼28 nm. In the preparation of CuO nanostructures, highly nanoporous thin sheets have been produced in water and water/toluene solvent systems. The simulated XRD pattern matches the experimental XRD patterns of CuO nanostructures and indexes the nanocrystal unit cell structure to monoclinic. With the smallest desorption total pore volume of 0.09 cm3 g−1, CuO nanosheets have yielded the lowest BET surface area of 18.31 m2 g−1 and a BHJ desorption average pore diameter of ∼16 nm. The sol of magnesium hydroxide nanocrystals produces highly nanoporous hexagonal nanoplates in water and water/toluene solvent systems. The wide angle powder XRD patterns show well-defined Bragg's peaks, indexing to a hexagonal unit cell structure. The hexagonal plates show a significantly high BET surface area (72.31 m2 g−1), which is slightly lower than the surface area of Mn3O4 hexagonal nanoparticles. The non-template driven sol–gel synthesis process demonstrated herein provides a facile method to prepare highly mesoporous and nanoporous nanostructures of binary (II–IV) metal oxides and their hydroxide derivatives, enabling potential nanostructure platforms with high activities and selectivities for catalysis applications.
Equivalence classes of coherent projectors in a Hilbert space with prime dimension: Q functions and their Gini index

Coherent subspaces spanned by a finite number of coherent states are introduced, in a quantum system with Hilbert space that has odd prime dimension d. The set of all coherent subspaces is partitioned into equivalence classes, with d 2 subspaces in each class. The corresponding coherent projectors within an equivalence class, have the ‘closure under displacements property’ and also resolve the identity. Different equivalence classes provide different granularisation of the Hilbert space, and they form a partial order ‘coarser’ (and ‘finer’). In the case of a two-dimensional coherent subspace spanned by two coherent states, the corresponding projector (of rank 2) is different than the sum of the two projectors to the subspaces related to each of the two coherent states. We quantify this with ‘non-addditivity operators’ which are a measure of quantum interference in phase space, and also of the non-commutativity of the projectors. Generalized Q and P functions of density matrices, which are based on coherent projectors in a given equivalence class, are introduced. Analogues of the Lorenz values and the Gini index (which are popular quantities in mathematical economics) are used here to quantify the inequality in the distribution of the Q function of a quantum state, within the granular structure of the Hilbert space. A comparison is made between Lorenz values and the Gini index for the cases of coarse and also fine granularisation of the Hilbert space. Lorenz values require an ordering of the d 2 values of the Q function of a density matrix, and this leads to the ranking permutation of a density matrix, and to comonotonic density matrices (which have the same ranking permutation). The Lorenz values are a superadditive function and the Gini index is a subadditive function (they are both additive quantities for comonotonic density matrices). Various examples demonstrate these ideas.
Isolation, screening and identification of mannanase producer microorganisms.

Palm kernel cake (PKC) is used widely in the animal feed industry but has limited use in poultry feed due to its high fibre and low protein contents. The major component of the fibre is mannan which is insoluble and difficult to digest. Therefore, isolation of local potential microbes capable of degrading mannan was explored from various sources such as peat soil, rotten oil palm trunks and raw PKC. The isolates were screened based on the clearing zone method on selective agar media containing Azo-carob-galactomannan as substrate. A total of 36 isolates were screened and only 13 had a clear zone ranging from 3.42 ± 0.02 mm to 5.44 ± 0.06 mm which were used for further analysis. These isolates were incubated in shake flasks at 35 °C for 48 h with 10 g/litre PKC as substrate. The best enzymes producer was isolate IBRL F16.A4 with specific mannanase enzyme acitivity of 17.82 ± 0.05 U/mg, production of glucosamine at 9.84 ± 0.11 mg/g and mannose at 9.54 ± 0.06 mg/g. The isolate IBRL F16.A4 was identified as Aspergillus niger IBRL F16.A4 using a scanning electron microscope. Keywords: palm kernel cake; mannan; mannose; glucosamine; animal feed; Aspergillus niger
Cooperative scheduling of multi-core and cloud resources: fine-grained offloading strategy for multithreaded applications

Nowadays, advanced smart mobile devices equipped with multi-core central processing units for handling multithreaded (MT) applications. However, existing research mainly uses single-thread (ST) computing to deal with applications, which limits the performance of mobile computing. To make full use of multi-core resources, this study proposes a fine-grained MT offloading strategy to solve the offloading problem of MT application. The strategy jointly schedules cloud computing resources, as well as local multi-core computing and communication resources. Precisely, the authors first formulate the minimum energy consumption problem for ST offloading. Then, they prove that the problem is convex and solve it by standard convex optimisation technique. Thirdly, they extend the optimisation goals from ST applications to MT applications, and design calculation rules for MT applications to reduce computing costs. Finally, based on these calculation rules and the optimal solution for ST offloading, they develop a MT offloading strategy to solve the computation offloading problem of MT applications. Simulation results show that the proposed fine-grained MT offloading strategy effectively reduces the minimum delay requirement of mobile computing.
Cu-Doped ZnO Nanoparticles as an Electrochemical Sensing Electrode for Cardiac Biomarker Myoglobin Detection

Cu-doped ZnO nanoparticles were used in the form of a screen-printed electrode on a prefabricated gold-plated electrode for electrochemically determining the concentration of nanomolar myoglobin (Mb), a cardiac biomarker. Initially, Cu-doped ZnO nanoparticles were synthesised through the sol gel method by using three Cu-doping concentrations (varying number of Cu atoms). Then, the synthesised material was analysed using X-ray diffraction (XRD), Fourier transform infrared spectroscopy, and field emission secondary electron microscopy. Analysis revealed a mixed phase of ZnO and CuO, whereas the vibration band in the range of 650–700 cm−1 indicated metal oxide formation. Morphological observation showed uniform-sized spherical particles of approximately 15–20 nm (as nanoparticles) that were evenly distributed, which were also confirmed based on the average particle size estimated using XRD data. The developed electrodes were tested for biomarker concentration from 3 to 15 nM based on cyclic voltammogram and impedance spectroscopic curves, where the redox potential/current/charge transfer resistance changed linearly with Mb and dopant (Cu) concentration. The sensitivity was estimated as 2.13– $10.14~\mu $ AnM-1 cm−2 with dopant concentration of approximately 0.46 nM as the limit of detection.
Validation of a Questionnaire Developed to Evaluate a Pediatric eHealth Website for Parents.

There is a need for health professionals to provide parents with not only evidence-based child health websites but also instruments to evaluate them. The main aim of this research was to develop a questionnaire for measuring users’ evaluation of the usability, utility, confidence, the well-child section, and the accessibility of a Spanish pediatric eHealth website for parents. We further sought to evaluate the content validity and psychometric reliability of the instrument. A content validation study by expert review was performed, and the questionnaire was pilot tested. Psychometric analyses were used to establish scales through exploratory and confirmatory factor analyses. Reliability studies were performed using Cronbach’s alpha and two split-half methods. The content validation of the questionnaire by experts was considered as excellent. The pilot web survey was completed by 516 participants. The exploratory factor analysis excluded 27 of the 41 qualitative initial items. The confirmatory factor analysis of the resultant 14-item questionnaire confirmed the five initial domains detected in the exploratory confirmatory analysis. The goodness of fit for the competing models was established through fit indices and confirmed the previously established domains. Adequate internal consistency was found for each of the subscales as well as the overall scale.
Molecular phylogeny of treefrogs in the Rhacophorus dugritei species complex (Anura: Rhacophoridae), with descriptions of two new species

The taxonomy of the Asian treefrogs of the Rhacophorus dugritei complex, a group of widespread and morphologically similar species, is very controversial. To address many issues, we investigate the historical relationships among representative samples based on similar to 3 kbp of nucleotide sequences from both the mitochondrial (12S rRNA, tRNAvaline, and 16S rRNA) and nuclear (tyrosinase and rhodopsin) genomes. Maximum parsimony, maximum likelihood, and partitioned Bayesian inference analyses resolve three well-supported lineages. Lineage A, including Rhacophorus dugritei and Rhacophorus hui, is sister to lineage B, which contains Rhacophorus hungfuensis, Rhacophorus minimus, R. hui from Hubei, and R. dugritei from Chongqing. Together, these lineages are the sister group of lineage C, which is composed of Rhacophorus puerensis from China and Vietnam, and R. dugritei from Vietnam. The phylogeny validates the previously contested species R. puerensis, which is distributed northwards from northern Vietnam to north-western Yunnan, China. Rhacophorus hui is also considered to be a valid species, the range of which is restricted to Zhaojue County, Sichuan, China. Specimens formerly identified as R. dugritei from Chongqing and R. hui from Lichuan, Hubei, are described as new species Rhacophorus hongchibaensis sp. nov. and Rhacophorus wui sp. nov., respectively. Diagnoses, detailed descriptions, illustrations, data on distribution and ecology are provided for the two new species.
Observability Analysis of IMU Intrinsic Parameters in Stereo Visual–Inertial Odometry

In this article, we present self-calibrated visual–inertial odometry (VIO) that estimates inertial measurement unit (IMU) intrinsic parameters (scale factor and misalignment) using a stereo camera without any calibration boards. Most of the visual–inertial navigation algorithms assume that the visual and inertial sensors are well-calibrated before operating. However, this could degrade the navigation performance due to unmodeled errors. Specifically, we employ an extended Kalman filter (EKF)-based pose estimator in which the filter state is augmented by the IMU intrinsic parameter to model the egomotion more precisely. Since raw IMU readings are transformed by the intrinsic parameter, these are key factors that determine the performance of the egomotion tracking. The main contribution of this article is an analytic observability analysis of the self-calibrated VIO that is a nonlinear time-varying system. We inspect the rank of the observability matrix formed by Lie derivatives of the nonlinear system. Our theoretical result reveals that the IMU intrinsic parameter is fully observable when all six axes of an IMU are excited. This is further confirmed by our simulation experiments by examining state uncertainties. Moreover, the real-world experiment using a publicly available and author-collected data set reveals that the pose tracking performance is improved by modeling IMU intrinsic parameters.
Acu-Net: A 3D Attention Context U-Net for Multiple Sclerosis Lesion Segmentation

Multiple Sclerosis (MS) lesion segmentation from MR images is important for neuroimaging analysis. MS is diffuse, multifocal, and tend to involve peripheral brain structures such as the white matter, corpus callosum, and brainstem. Recently, U-Net has made great achievements in medical image segmentation area. However, the insufficiently use of context information and feature representation, makes it fail to achieve segmentation of MS lesions accurately. To solve the problem, 3D attention context U-Net (ACU-Net) is proposed for MS lesion segmentation in this paper. The proposed ACU-Net includes 3D spatial attention block, which is used to enrich spatial details and feature representation of lesion in the decoding stage. Furthermore, in the encoding and decoding stage of the network, 3D context guided module is designed for guiding local information and surrounding information. The proposed ACU-Net was evaluated on the ISBI 2015 longitudinal MS lesion segmentation challenge dataset, and it achieved superior performance compared to latest approaches.