Miracle-dz/UniFAR-FTUs huggingface dataset:
Transmissive optical imaging device with micromirror array

We propose a new imaging optics called the Transmissive Mirror Device (TMD). It consists of numerous micromirrors placed perpendicular to the surface of a flat, thin metal plate. The micro-mirror array is implemented by the inner walls of minute square holes, which are densely pitted on the device. The basic mode of operation is based on two reflections by a pair of adjacent mutually perpendicular mirrors, i.e., a dihedral corner reflector. Although the principal of operation is based on reflection by mirrors, the device is also transmissive and deflects light. Since this imaging system forms a real image at a plane symmetric point, the depth of the 3D image is inverted. Its optical defects are low optical transmittance and stray light caused by non-reflected light and that reflected once. We manufactured the device experimentally with nano-precision machining technology and also evaluated it.
Gestational age and cancer risk up to young adulthood in Swedish population born 1974 to 2013: A population‐based cohort study

We examined the association between gestational age and risk of any primary cancer and observed whether the risk patterns differed by sex, birth weight for gestational age categories, cancer site and age of onset. All people live‐born in Sweden 1974 to 2013 were prospectively followed up from birth until 2016 using national registers. Gestational age was extracted from the Medical Birth Register and primary malignant cancer diagnoses were from the Swedish cancer register. The adjusted hazard ratios (aHR) for any primary cancer according to weekly gestational age and gestational age categories were determined using cox proportional hazards models adjusted for birth year and parental age. The study included 3 137 691 people; 180 363 (5.8%) born preterm and 254 790 (8.1%) born postterm. They were followed up for 71 691 112 person‐years, to a maximum of 43 years and recorded 22 604 new cancers. Although aHRs for the predefined GA categories were only increased for moderate to late preterm delivery (aHR 1.07, 95% CI 1.01‐1.14), gestational week‐specific aHRs were increased for gestational weeks 30 to 35, with greatest aHR observed for 31 weeks (aHR 1.18, 95% CI 1.05‐1.32). Increased cancer risk related to shorter gestational ages were observed particularly for women, those born small for gestational age, childhood cancers and for cancers originating at certain sites (eg, testicular and liver cancer). We provide the first evidence that those born between 30 and 35 weeks gestation may have increased risk of any primary malignant cancer up to young adulthood. Additionally, increasing gestational ages may reduce the risk of testicular and liver cancer.
Breaking the Convergence Barrier: Optimization via Fixed-Time Convergent Flows

Accelerated gradient methods are the cornerstones of large-scale, data-driven optimization problems that arise naturally in machine learning and other fields concerning data analysis. We introduce a gradient-based optimization framework for achieving acceleration, based on the recently introduced notion of fixed-time stability of dynamical systems. The method presents itself as a generalization of simple gradient-based methods suitably scaled to achieve convergence to the optimizer in a fixed-time, independent of the initialization. We achieve this by first leveraging a continuous-time framework for designing fixed-time stable dynamical systems, and later providing a consistent discretization strategy, such that the equivalent discrete-time algorithm tracks the optimizer in a practically fixed number of iterations. We also provide a theoretical analysis of the convergence behavior of the proposed gradient flows, and their robustness to additive disturbances for a range of functions obeying strong convexity, strict convexity, and possibly nonconvexity but satisfying the Polyak-Łojasiewicz inequality. We also show that the regret bound on the convergence rate is constant by virtue of the fixed-time convergence. The hyperparameters have intuitive interpretations and can be tuned to fit the requirements on the desired convergence rates. We validate the accelerated convergence properties of the proposed schemes on a range of numerical examples against the state-of-the-art optimization algorithms. Our work provides insights on developing novel optimization algorithms via discretization of continuous-time flows.
Estimating Optimum Timing for Preventive Maintenance Treatment to Mitigate Pavement Roughness

The evaluation of pavement preservation interventions is the most important component of a pavement management system. A rational methodology is needed to evaluate pavement preservation alternatives to maximize project- and network-level benefits. A lack of adequate guidance on the timing of preventive maintenance treatments was the main motivation for this study. On the basis of different evaluation criteria, several mathematical models to estimate optimum timing (OT) of various maintenance treatments were developed. On the basis of long-term benefits, the Area2T model was found to be a rational and practical tool to estimate OT for various maintenance treatments by adjusting the model's parameters. The sensitivity and practical use of the models are demonstrated in the paper. Although the modeling approach uses the existing understanding, the model can be used to estimate OT of a fix on the basis of the pretreatment pavement performance, jump in pavement condition just after treatment, and expected rate of deterioration after treatment application. The required data for the model can be obtained for various treatments from the existing historical performance data collected for pavement management purposes. The model can be calibrated to local needs by matching actual practice and calculated OT for different fix types.
PrivateSNN: Privacy-Preserving Spiking Neural Networks

How can we bring both privacy and energy-efficiency to a neural system? In this paper, we propose PrivateSNN, which aims to build low-power Spiking Neural Networks (SNNs) from a pre-trained ANN model without leaking sensitive information contained in a dataset. Here, we tackle two types of leakage problems: 1) Data leakage is caused when the networks access real training data during an ANN-SNN conversion process. 2) Class leakage is caused when class-related features can be reconstructed from network parameters. In order to address the data leakage issue, we generate synthetic images from the pre-trained ANNs and convert ANNs to SNNs using the generated images. However, converted SNNs remain vulnerable to class leakage since the weight parameters have the same (or scaled) value with respect to ANN parameters. Therefore, we encrypt SNN weights by training SNNs with a temporal spike-based learning rule. Updating weight parameters with temporal data makes SNNs difficult to be interpreted in the spatial domain. We observe that the encrypted PrivateSNN eliminates data and class leakage issues with a slight performance drop (less than ~2%) and significant energy-efficiency gain (about 55x) compared to the standard ANN. We conduct extensive experiments on various datasets including CIFAR10, CIFAR100, and TinyImageNet, highlighting the importance of privacy-preserving SNN training.
Early life infection and proinflammatory, atherogenic metabolomic and lipidomic profiles at 12 months of age: a population-based cohort study

Background: The risk of adult onset cardiovascular and metabolic (cardiometabolic) disease accrues from early life. Infection is ubiquitous in infancy and induces inflammation, a key cardiometabolic risk factor, but the relationship between infection, inflammation, and metabolic profiles in early childhood remains unexplored. We investigated relationships between infection and plasma metabolomic and lipidomic profiles at age 12 months, and mediation of theseassociations by inflammation.Methods: Matched infection, metabolomics and lipidomics data were generated from 555 infants in a pre-birth longitudinal cohort. Infection data from birth to 12 months were parent-reported (total infections at age 1, 3, 6, 9, and 12 months), inflammation markers (high-sensitivity C-reactive protein, hsCRP); glycoprotein acetyls GlycA) were quantified at 12 months. Metabolic profiles were 12-month plasma nuclear magnetic resonance metabolomics (228 metabolites) and liquid-chromatography/mass-spectrometry lipidomics (776 lipids). Associations were evaluated with multivariable linear regression models. Results: Frequent infant infections were associated with adverse metabolomic (elevated inflammation markers, triglycerides, phenylalanine, and lower HDL cholesterol, apolipoprotein A1, and omega-3 fatty acids) and lipidomic profiles (elevated phosphatidylethanolamines and lower hexosylceramides, trihexosylceramides, and cholesteryl esters). Similar, more marked, profiles were observed with higher GlycA, but not hsCRP. GlycA, but not hsCRP, mediated a substantial proportion of the relationship between infection and metabolome/lipidome. Conclusions: Infants with a greater infection burden from birth to 12 months had pro-inflammatory and pro-atherogenic plasma metabolomic/lipid profiles, indicative of heightened risk of cardiovascular disease, obesity, and type 2 diabetes in adults. These findings suggest potentially modifiable pathways linking early life infection and inflammation with subsequent cardiometabolic risk.Funding: The establishment work and infrastructure for the BIS was provided by the Murdoch Childrens Research Institute, Deakin University and Barwon Health. Subsequent funding was secured from the National Health and Medical Research Council of Australia, The Jack Brockhoff Foundation, the Scobie Trust, the Shane OBrien Memorial Asthma Foundation, the Our Womens Our Childrens Fund- Raising Committee Barwon Health, The Shepherd Foundation, the Rotary Club of Geelong, the Ilhan Food Allergy Foundation, GMHBA Limited and the Percy Baxter Charitable Trust, Perpetual Trustees. In-kind support was provided by the Cotton On Foundation and CreativeForce. Research at Murdoch Childrens Research Institute is supported by the Victorian Government's Operational Infrastructure Support Program. This work was also supported by NHMRC Senior Research Fellowships (1008396 to ALP; 1064629 to DB; 1045161 to RS) and NHMRC Investigator Grants to ALP (1110200) and DB (1175744). SB is supported by the Dutch Research Council (452173113).
Assessing Open Science practices in physical activity behaviour change intervention evaluations

Objectives Concerns on the lack of reproducibility and transparency in science have led to a range of research practice reforms, broadly referred to as ‘Open Science’. The extent that physical activity interventions are embedding Open Science practices is currently unknown. In this study, we randomly sampled 100 reports of recent physical activity randomised controlled trial behaviour change interventions to estimate the prevalence of Open Science practices. Methods One hundred reports of randomised controlled trial physical activity behaviour change interventions published between 2018 and 2021 were identified, as used within the Human Behaviour-Change Project. Open Science practices were coded in identified reports, including: study pre-registration, protocol sharing, data, materials and analysis scripts sharing, replication of a previous study, open access publication, funding sources and conflict of interest statements. Coding was performed by two independent researchers, with inter-rater reliability calculated using Krippendorff’s alpha. Results 78 of the 100 reports provided details of study pre-registration and 41% provided evidence of a published protocol. 4% provided accessible open data, 8% provided open materials and 1% provided open analysis scripts. 73% of reports were published as open access and no studies were described as replication attempts. 93% of reports declared their sources of funding and 88% provided conflicts of interest statements. A Krippendorff’s alpha of 0.73 was obtained across all coding. Conclusion Open data, materials, analysis and replication attempts are currently rare in physical activity behaviour change intervention reports, whereas funding source and conflict of interest declarations are common. Future physical activity research should increase the reproducibility of their methods and results by incorporating more Open Science practices.
Numerical analysis of low-velocity rigid-body impact response of composite panels

This paper investigates various modelling strategies to identify the most suitable approach for modelling the low-velocity impact response of laminated composite panels. The purpose of this paper is to thoroughly investigate a dropped tool scenario or a ground vehicle impact on an aircraft fuselage panel using detailed numerical models. Three-dimensional meso-scale finite element models have been developed and implemented with user-defined material subroutines in ABAQUS/Standard. The models predict the simultaneous evolution of inter-laminar and intra-laminar damage mechanisms that occur in composite panels during impact. The paper describes the implementation of the combined inter/intra-laminar models and assesses their performance. User-defined material models developed in previous work for quasi-static problems have been further developed in this paper for damage analysis under impact loading. Experimental drop-weight impact tests, representative of low-velocity high-energy rigid-body impacts, have been carried out for model validation. Impact energy levels were varied from 10 to 40 J to evaluate the damage threshold and damage area that develops within the laminate. The results of the combined inter/intra-laminar model are in excellent agreement with experimental data, especially in terms of energy absorbed during impact. Numerical results provide an accurate description of the threshold at which a significant change in laminate stiffness occurs. It is shown conclusively that the combined inter/intra-laminar damage model developed in this work can be employed as an accurate predictive tool for low-velocity impact events.
Temperature dependence of hot carrier effects in short-channel Si-MOSFETs

Full-band Monte Carlo simulations were carried out to investigate hot carrier effects associated with impact ionization under the lateral electric field profiles typical of submicrometer Si-MOSFETs. It is shown that the temperature dependence of the band-gap energy of Si plays an important role for hot carrier suppression at low temperature in submicrometer devices. On the other hand, as the device size shrinks into the sub-0.1 regime, in which the high-field region is comparable in size to or smaller than the energy relaxation length, the number of electrons with energy below the supply drain voltage becomes less sensitive to temperature. As a result, the suppression of impact ionization at low temperature in sub-0.1 /spl mu/m devices could be ascribed to both quasi-ballistic transport characteristics and temperature-dependent band-gap energy.
Review of Evidence for Temporal Transferability of Mode-Destination Models

One main motivation for developing travel behavior models is to use them to forecast future levels of transport demand. Given that the interest in transport planning is often in long-term forecasts, with forecast horizons of up to 30 years, it is important to consider the transferability of travel behavior models over time. The importance of model transferability has been recognized since disaggregate models were first applied in the late 1970s and early 1980s, but seems to have been largely forgotten recently, because the focus has been on the development of ever more advanced models that better explain current behavior, with a particular focus on the representation of taste heterogeneity. However, there are sufficient grounds to suspect that the model that best explains current behavior may not necessarily be the best tool for forecasting, not least because of the risk of overfitting to the base data. This paper aims to return the crucial issue of temporal transferability of travel demand models to the research agenda. First, the notion of transferability is discussed, highlighting the potential impacts of violations of the assumption of transferability, and the way transferability can be assessed is also described. Next, the most complete review of existing work investigating the temporal transferability of mode and mode-destination models to date is presented. Finally, a number of areas in which future research should be directed are identified.
Comparison of evaluation methods of the small current breaking performance for SF/sub 6/ gas circuit breakers

Circuit breakers have to possess both the small current and the large current interruption capabilities. The need has been constantly increased to evaluate the current interruption performance without resort to the short-circuit tests to save time and expenses during the development of gas circuit breakers. The increasing reliability of the analysis schemes and the dramatically increased computing speed have made it possible to evaluate them from the computer simulation results to some extent. The objective of this paper is to estimate the small current interruption performance of SF/sub 6/ gas circuit breakers and to compare the results from various methods. In order to evaluate the dielectric recovery strength of gas circuit breakers for small current interruption, two methods have been usually utilized. One is the empirical formula obtained from a series of tests and the other is the theoretical formula obtained from streamer theory. In this paper, both methods were applied to predict the small current interruption capability of model circuit breakers and were investigated in terms of the accuracy by comparing the simulation results with test ones.
Effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality: systematic review and meta-analysis

OBJECTIVE To review the evidence on the effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality. DESIGN Systematic review and meta-analysis. Observational and interventional studies that assessed the effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality.
Voltage control stability and dynamic interaction phenomena of static VAr compensators

This paper describes the results of a comprehensive investigation on: (1) small-signal dynamic interactions of voltage control loops of multiple static VAR compensators (SVCs); (2) the phenomena of SVC-network interactions; and (3) torsional interactions with SVCs. This paper is concerned with small-signal dynamics in the high frequency (HF) range, i.e. above 4 Hz. The studies are conducted on various power system configurations, based on the use of a newly developed eigen analysis software tool. The eigen analysis tool provides a systematic approach to include the dynamic mathematical model of the transmission network in the overall system model, which is necessary for HF dynamic studies. The investigations identify undesirable small-signal oscillations associated with SVC-SVC and/or SVC-network interactions, and provide fundamental information for design/coordination of SVCs. >
Large deviations, asymptotic equipartition property for super-critical SINR random networks

Abstract In this article, we obtain large deviation asymptotic for supercritical social or communication networks modelled as signal-interference-noise ratio (SINR) graphs. To do this, we define the empirical power measure and the empirical connectivity measure, and prove joint large deviation principles (LDPs) for the two empirical measures on two different scales, i.e. λ and λ 2 aλ , where λ is the intensity measure of the Poisson point process (PPP) which defines the SINR random network and aλ real-valued sequence such that λ 2 aλ → ∞ as λ → ∞Using these joint LDPs we prove an asymptotic equipartition property for the super-critical networks modelled as the SINR random networks. Furthermore, we prove a local large deviation principle (LLDP) for the SINR random network. From the LLDP we prove a large deviation principle, and a classical McMillian theorem for the SINR random network processes. Note that, for a given empirical power measure and typical empirical connectivity measure, qπ ⊗ π, we can deduce from the LLDP a bound on the cardinality of the space of SINR networks to be approximately equal to , where the connectivity probability of the network, , satisfies and qπ ⊗ π is the typical behavior of the empirical connectivity measure. Observe, the LDPs for the empirical measures of SINR random networks were obtained on spaces of measures equipped with the τ-topology, and the LLDPs were obtained in the space of SINR network process without any topological restrictions.
A CMOS-compatible high-voltage IC process

A CMOS-compatible high-voltage IC process is proposed and implemented. An important feature of this process is that the extra processing steps do not affect the performance of the low-voltage devices. High-voltage lateral DMOS transistors with breakdown voltages of 400 V, as well as merged MOS-bipolar devices such as lateral insulated-gate transistors (LIGTs) and insulated-base transistors (IBTs) have been fabricated using this process. The IBTs, which have breakdown voltages of 400 V, high current-handling capabilities, and high switching speeds, offer better performance than the LIGTs. In addition the IBT, because it does not latchup, is a more reliable structure than the LIGT. >
“Know Your Noise” Helps Young People Understand Risks Associated with Recreational Noise Exposure and Take Action to Prevent Hearing Damage

Abstract The Know Your Noise website was developed to provide accurate and personalized information about hearing health to increase users’ awareness of the risk for noise-induced hearing loss and increase their likelihood to undertake behavior change to reduce their exposure. This study aimed to examine the effectiveness of the site in meeting these goals. User engagement via google analytics showed good engagement with the site itself across a range of demographics. Results from an online evaluation survey indicated improvement in hearing health engagement following participation, particularly for those shown to be most at risk.
Mechanical and thermal behavior of plantain peel powder filled recycled polyethylene composites

Abstract In the arduous search for ways to clean up the environment and produce viable materials from waste plastics; plantain peel powder filled low density polyethylene wastes (wLDPE) were developed through melt mixing and compression moulding techniques. Optimum properties were determined at 15, 55 and 30 % formulation of plantain peel powder, and Kankara clay respectively. Composition with optimum properties has tensile strength of 55.5 MPa, flexural strength of 50.45 MPa and elastic modulus of 2.30 GPa with corresponding minimal water absorption of 0.95% after 30 days immersion period at room temperature. The thermal properties investigated by Dynamic Mechanical Analysis showed that the composite has better thermal stability at higher temperature than wLDPE. Similarly, through creep analysis, the composite was observed to have better load bearing capability at elevated temperature than waste low density polyethylene material. These results indicate that incorporation of treated plantain peel powder and Kankara clay into wLDPE enhanced the mechanical, thermal and creep resistance of wLDPE. This implies load bearing capability and potential suitability for different wall tiles applications.