Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of a Multi-Strategy Behavioral Intervention to Increase Vegetable Sales in Primary School Canteens: A Randomized Controlled Trial

Children’s vegetable intake remains inadequate and school canteens may provide opportunities to address this public health concern. This study aimed to determine the effectiveness of an 8-week multi-strategy behavioral intervention that included vegetable provisioning and online menu architecture on vegetable sales in primary school canteens. A randomized controlled trial was undertaken in 16 Australian primary schools (n = 4302 students). The control arm kept their regular canteen menu. The primary outcome was vegetable sales measured by assessing vegetable content (in grams) from all menu items and using canteen sales (ordered online and over-the-counter) to calculate vegetable sales (in grams/week) at baseline (3 weeks) and during intervention implementation (8 weeks). Secondary outcomes were vegetable sales in subcategories, intervention acceptability among canteen managers and vegetable waste (four schools). Linear mixed model analysis showed that from baseline to follow-up, the intervention group had significantly higher weekly vegetable sales overall compared with the control group (2707 g/week, 95% CI 1276 to 4137 g/week; p < 0.001), with increased vegetable sales in the subcategories of burgers, hot foods and snacks, but not in sandwiches and pasta/rice dishes. The intervention did not lead to more vegetable waste, nor to a decrease in canteen revenue. The canteen managers found the intervention easy to implement and felt children responded favorably to three of the seven strategies. In conclusion, a multi-strategy behavioral canteen intervention increased vegetable sales amongst primary school students.
A Mixed Calibration Method of Time-to-Digital Converter for LiDAR Applications

This brief presents a mixed calibration method of time-to-digital converter (TDC) for Light Detection and Ranging (LiDAR) applications, which is primarily implemented by a feedback loop consisting of VCRO-based TDC, double-digital-to-analog converter (DDAC), encoder, sampling circuit, LSB-first algorithm (LSBFA) block, and analog buffer. Based on the external input frequency control code (FCC), the method automatically adjusts the DDAC output to control TDC. To ensure that the DDAC output meets the accuracy requirements, a voltage coverage strategy is proposed, which is implemented by an external code controlling the R-2R array. Furthermore, after the chip operation is stable, a voltage trimming approach is presented to avoid introducing drastic switching activity, which is implemented using an error comparator and an LSBFA block. The peak-to-peak INL and DNL of TDC are 1.81 LSB and 0.44 LSB, respectively.
Values and the making of standards in ‘sustainable’ coffee networks: The case of 4C and Nestlé in México

The ‘sustainable’ coffee industry has experienced steady expansion in recent decades, due to the emerging demand and values of consumers. Coffee corporations have invested in this market segment, participating in multi-stakeholder initiatives through which they seek to control the definitions of sustainability and to secure the supply chain. After highlighting the ambiguity of the concept of sustainability, the article analyzes the impacts of the 4C code of conduct adopted by Nestlé, in the context of Mexico’s growing coffee crisis. It highlights the existing dissonance between the sustainability proclaimed by the code of conduct and the reality in the coffee producing regions of Mexico where Nestlé sources 4C certified coffee. It concludes that the application of this code is far from adhering to the values of economic, social, and environmental sustainability that it claims to defend.
Probing hot electron transport across an epitaxial Schottky interface of SrRuO3/Nb:SrTiO3

SrRuO3 (SRO), a conducting transition metal oxide, is commonly used for engineering domains in BiFeO3. Oxide devices can be envisioned by integrating SRO with an oxide semiconductor as Nb doped SrTiO3 (Nb:STO). Using a three-terminal device configuration, we study vertical transport in a SRO/Nb:STO device at the nanoscale and find local differences in transport which originate due to the high selectivity of SRO growth on the underlying surface terminations in Nb:STO. This causes a change in the interface energy band characteristics and is explained by the differences in the spatial distribution of the interface-dipoles at the local Schottky interface.
Neuroprotective and neurorestorative actions of mesenchymal stromal cell-derived small extracellular vesicles in the ischemic brain

Ischemic stroke is a highly prevalent condition that frequently results in life-long disability and death. Considerable efforts have been made to establish treatments that prevent secondary ischemic damage and promote stroke recovery. Until now, the recanalization of occluded blood vessels via thrombolysis and thrombectomy, although highly potent, remains the only treatment in humans that enhances stroke outcome. Small extracellular vesicles are non-replicating, nano-sized (70–150 nm) lipid bilayer-enclosed vesicles, which have shown remarkable biological activities in various physiological and pathophysiological contexts. When administered post-stroke, mesenchymal stromal cell-derived extracellular vesicles (MSC-EVs) induce neuroprotection, promote brain remodeling and plasticity, and enhance neurological recovery in rodents and non-human primates via mechanisms that involve immunomodulation and anti-inflammation. In this review, experimental studies on the therapeutic actions of MSC-EVs in animal stroke models are summarized and perspectives for clinical translation are outlined.
Asymptotic convergence of the partial averaging technique

We study the asymptotic convergence of the partial averaging method, a technique used in conjunction with the random series implementation of the Feynman-Kac formula. We prove asymptotic bounds valid for most series representations in the case when the potential has first order Sobolev derivatives. If the potential has also second order Sobolev derivatives, we prove a sharper theorem which gives the exact asymptotic behavior of the density matrices. The results are then specialized for the Wiener-Fourier series representation. It is found that the asymptotic behavior is o(1/n^2) if the potential has first order Sobolev derivatives. If the potential has second order Sobolev derivatives, the convergence is shown to be O(1/n^3) and we give the exact expressions for the convergence constants of the density matrices.
Flexible resources for quantum metrology

Quantum metrology offers a quadratic advantage over classical approaches to parameter estimation problems by utilising entanglement and nonclassicality. However, the hurdle of actually implementing the necessary quantum probe states and measurements, which vary drastically for different metrological scenarios, is usually not taken into account. We show that for a wide range of tasks in metrology, 2D cluster states (a particular family of states useful for measurement-based quantum computation) can serve as flexible resources that allow one to efficiently prepare any required state for sensing, and perform appropriate (entangled) measurements using only single qubit operations. Crucially, the overhead in the number of qubits is less than quadratic, thus preserving the quantum scaling advantage. This is ensured by using a compression to a logarithmically sized space that contains all relevant information for sensing. We specifically demonstrate how our method can be used to obtain optimal scaling for phase and frequency estimation in local estimation problems, as well as for the Bayesian equivalents with Gaussian priors of varying widths. Furthermore, we show that in the paradigmatic case of local phase estimation 1D cluster states are sufficient for optimal state preparation and measurement.
A Multi-Temporal Network for Improving Semantic Segmentation of Large-Scale Landsat Imagery

With the development of deep learning, semantic segmentation technology has gradually become the mainstream technical method in large-scale multi-temporal landcover classification. Large-scale and multi-temporal are the two significant characteristics of Landsat imagery. However, the mainstream single-temporal semantic segmentation network lacks the constraints and assistance of pre-temporal information, resulting in unstable results, poor generalization ability, and inconsistency with the actual situation in the multi-temporal classification results. In this paper, we propose a multi-temporal network that introduces pre-temporal information as prior constrained auxiliary knowledge. We propose an element-wise weighting block module to improve the fine-grainedness of feature optimization. We propose a chained deduced classification strategy to improve multi-temporal classification’s stability and generalization ability. We label the large-scale multi-temporal Landsat landcover classification dataset with an overall classification accuracy of over 90%. Through extensive experiments, compared with the mainstream semantic segmentation methods, our proposed multi-temporal network achieves state-of-the-art performance with good robustness and generalization ability.
Peering beyond the horizon with standard sirens and redshift drift

An interesting test on the nature of the Universe is to measure the global spatial curvature of the metric in a model independent way, at a level of |Ωk|<10−4, or, if possible, at the cosmic variance level of the amplitude of the CMB fluctuations |Ωk|≈10−5. A limit of |Ωk|<10−4 would yield stringent tests on several models of inflation. Further, improving the constraint by an order of magnitude would help in reducing “model confusion” in standard parameter estimation. Moreover, if the curvature is measured to be at the value of the amplitude of the CMB fluctuations, it would offer a powerful test on the inflationary paradigm and would indicate that our Universe must be significantly larger than the current horizon. On the contrary, in the context of standard inflation, measuring a value above CMB fluctuations will lead us to conclude that the Universe is not much larger than the current observed horizon; this can also be interpreted as the presence of large fluctuations outside the horizon. However, it has proven difficult, so far, to find observables that can achieve such level of accuracy, and, most of all, be model-independent. Here we propose a method that can in principle achieve that; this is done by making minimal assumptions and using distance probes that are cosmology-independent: gravitational waves, redshift drift and cosmic chronometers. We discuss what kind of observations are needed in principle to achieve the desired accuracy.
Molecular outflows entrained by jet bowshocks

Recent high-resolution observations of CO outflows often reveal well-collimated emitting regions with radial velocities that increase away from the outflow source (accelerating outflows), surrounded by limb brightened low-velocity emission. There is growing evidence that molecular flows exhibiting this structure are driven by high-velocity, possibly time-variable, narrow jets. In order to better understand the dynamical interaction between such jets and the large-scale outflow, we present a simple, analytic model in which the molecular outflow corresponds to environmental gas entrained in the wake of a bowshock created by an internal jet working surface. This model results in flows with velocity and density structures that appear to be similar to the observed accelerating molecular flows
Hysteresis in permeability evolution simulated for a sandstone by mineral precipitation and dissolution

Abstract. Mineral dissolution and precipitation can substantially affect rock permeability, which is a critical parameter for a broad range of geological subsurface applications. Virtual experiments on digital pore-scale samples represent a powerful and flexible approach to understand the impact of microstructural alterations on evolving hydraulic rock behaviour and quantify trends in permeability. In the present study, porosity-permeability relations are simulated for a precipitation-dissolution cycle within a typical reservoir sandstone. A hysteresis in permeability is observed depending on the geochemical process and dominating reaction regime, whereby permeability of the six investigated reaction paths varies by more than two orders of magnitude at a porosity of 17 %. Controlling parameters for this hysteresis phenomenon are the closure and re-opening of micro-scale flow channels, derived from changes in pore throat diameter and connectivity of the pore network. In general, a transport-limited regime exhibits a stronger impact on permeability than a reaction-limited regime, which uniformly alters the pore space. In case of mineral precipitation, higher permeability reduction results from successive clogging of pore throats, whereas in case of dissolution, permeability significantly increases due to a widening of existing flow paths. Both, the geochemical process and dominating reaction regime govern characteristic microstructural alterations, which cannot be simply reversed by the inversion of the geochemical processes itself. Hence, permeability evolution clearly depends on the hydrogeochemical history of the sample.
Maximum Efficient Power Performance Analysis and Multi-Objective Optimization of Two-Stage Thermoelectric Generators

Two-stage thermoelectric generators have been widely used in the aerospace, military, industrial and daily life fields. Based on the established two-stage thermoelectric generator model, this paper further studies its performance. Applying the theory of finite-time thermodynamics, the efficient power expression of the two-stage thermoelectric generator is deduced firstly. The maximum efficient power is obtained secondly by optimizing the distribution of the heat exchanger area, distribution of thermoelectric elements and working current. Using the NSGA-II algorithm, multi-objective optimizations of the two-stage thermoelectric generator are performed thirdly by taking the dimensionless output power, thermal efficiency and dimensionless efficient power as objective functions, and taking the distribution of the heat exchanger area, distribution of thermoelectric elements and output current as optimization variables. The Pareto frontiers with the optimal solution set are obtained. The results show that when the total number of thermoelectric elements is increased from 40 to 100, the maximum efficient power is decreased from 0.308W to 0.2381W. When the total heat exchanger area is increased from 0.03m2 to 0.09m2, the maximum efficient power is increased from 0.0603W to 0.3777W. The deviation indexes are 0.1866, 0.1866 and 0.1815 with LINMAP, TOPSIS and Shannon entropy decision-making approaches, respectively, when multi-objective optimization is performed on three-objective optimization. The deviation indexes are 0.2140, 0.9429 and 0.1815 for three single-objective optimizations of maximum dimensionless output power, thermal efficiency and dimensionless efficient power, respectively.
Carbon-enhanced metal-poor stars in dwarf galaxies

We investigate the frequency and origin of carbon-enhanced metal-poor (CEMP) stars in Local Group dwarf galaxies by means of a statistical, data-calibrated cosmological model for the hierarchical build-up of the Milky Way and its dwarf satellites. The model self-consistently explains the variation with dwarf galaxy luminosity of the observed: (i) frequency and [Fe/H] range of CEMP stars; (ii) metallicity distribution functions; (iii) star formation histories. We show that if primordial faint supernovae dominated the early metal-enrichment, then CEMP-no stars enriched by the first stellar generations should be present in all dwarf galaxies, with similar number of stars and CEMP fractions at [Fe/H] 107 L⊙. We present model predictions for the low-Fe tail and CEMP fraction of stars in dwarf galaxies, with particular emphasis on the Sculptor dSph, that can be used to shed light on the properties of the first stars.
A Short Survey On Memory Based Reinforcement Learning

Reinforcement learning (RL) is a branch of machine learning which is employed to solve various sequential decision making problems without proper supervision. Due to the recent advancement of deep learning, the newly proposed Deep-RL algorithms have been able to perform extremely well in sophisticated high-dimensional environments. However, even after successes in many domains, one of the major challenge in these approaches is the high magnitude of interactions with the environment required for efficient decision making. Seeking inspiration from the brain, this problem can be solved by incorporating instance based learning by biasing the decision making on the memories of high rewarding experiences. This paper reviews various recent reinforcement learning methods which incorporate external memory to solve decision making and a survey of them is presented. We provide an overview of the different methods - along with their advantages and disadvantages, applications and the standard experimentation settings used for memory based models. This review hopes to be a helpful resource to provide key insight of the recent advances in the field and provide help in further future development of it.
Waveform inversion of large datasets for radially anisotropic Earth structure

The amount of high-quality seismic data is expanding rapidly, and there is a need for algorithms that take advantage of classical methods to achieve high efficiency using widely available computing power. In this study we develop a novel waveform inversion method to retrieve radially anisotropic Earth models that can be used to investigate deformation and flow in the mantle. Our method is comprised of two parts: (1) extraction and fitting of the fundamental mode, and (2) fitting of the full synthetic waveform. The waveform inversion method results in path average model constraints with uniquely determined independent uncertainties. We demonstrate through synthetic testing that the method is able to retrieve radially anisotropic perturbations down to the mantle transition zone, and leakage effects due to ignoring P-wave anisotropy are minimal. We apply the method to ∼16,000 waveforms generated by earthquakes occuring in the East Sea (Sea of Japan) region, and we demonstrate that the subsequent linear inversion of radially anisotropic path constraints produces models that are similar to those resulting from full waveform adjoint tomography methods. We validate our model by predicting waveforms for earthquakes not included in our inversion, and we show that our method is able to extract structural information. Our results indicate low velocity anomalies and weak radial anisotropy in NE Japan, which may be due to competing influences from ascending fluids and/or melts and horizontal flow in the lower crust and upper mantle. In the southern East Sea we image low velocities and relatively high radial anisotropy, which may reflect high temperatures, shallow dehydration, and olivine LPO in the upper mantle.
Tuning Yu-Shiba-Rusinov States in a Quantum Dot

We present transport spectroscopy of sub-gap states in a bottom gated InAs nanowire coupled to a normal lead and a superconducting aluminium lead. The device shows clearly resolved sub-gap states which we can track as the coupling parameters of the system are tuned and as the gap is closed by means of a magnetic field. We systematically extract system parameters by using numerical renormalization group theory fits as a level of the quantum dot is tuned through a quantum phase transition electrostatically and magnetically. We also give an intuitive description of sub-gap excitations.
Kalman-filter control schemes for fringe tracking Development and application to VLTI/GRAVITY

Context. The implementation of fringe tracking for optical interferometers is inevitable when optimal exploitation of the instrumental capacities is desired. Fringe tracking allows continuous fringe observation, considerably increasing the sensitivity of the interferometric system. In addition to the correction of atmospheric path-length differences, a decent control algorithm should correct for disturbances introduced by instrumental vibrations, and deal with other errors propagating in the optical trains. Aims. In an effort to improve upon existing fringe-tracking control, especially with respect to vibrations, we attempt to construct control schemes based on Kalman filters. Kalman filtering is an optimal data processing algorithm for tracking and correcting a system on which observations are performed. As a direct application, control schemes are designed for GRAVITY, a future four-telescope near-infrared beam combiner for the Very Large Telescope Interferometer (VLTI). Methods. We base our study on recent work in adaptive-optics control. The technique is to describe perturbations of fringe phases in terms of an a priori model. The model allows us to optimize the tracking of fringes, in that it is adapted to the prevailing perturbations. Since the model is of a parametric nature, a parameter identification needs to be included. Different possibilities exist to generalize to the four-telescope fringe tracking that is useful for GRAVITY. Results. On the basis of a two-telescope Kalman-filtering control algorithm, a set of two properly working control algorithms for four-telescope fringe tracking is constructed. The control schemes are designed to take into account flux problems and low-signal baselines. First simulations of the fringe-tracking process indicate that the defined schemes meet the requirements for GRAVITY and allow us to distinguish in performance. In a future paper, we will compare the performances of classical fringe tracking to our Kalman-filter control. Conclusions. Kalman-filter based control schemes will likely become the next standard for fringe tracking, providing the possibility to maximize the tracking performance. The results of the present study are currently being incorporated into the final design of GRAVITY.
Fast and Non-Destructive Quail Egg Freshness Assessment Using a Thermal Camera and Deep Learning-Based Air Cell Detection Algorithms for the Revalidation of the Expiration Date of Eggs

Freshness is one of the most important parameters for assessing the quality of avian eggs. Available techniques to estimate the degradation of albumen and enlargement of the air cell are either destructive or not suitable for high-throughput applications. The aim of this research was to introduce a new approach to evaluate the air cell of quail eggs for freshness assessment as a fast, noninvasive, and nondestructive method. A new methodology was proposed by using a thermal microcamera and deep learning object detection algorithms. To evaluate the new method, we stored 174 quail eggs and collected thermal images 30, 50, and 60 days after the labeled expiration date. These data, 522 in total, were expanded to 3610 by image augmentation techniques and then split into training and validation samples to produce models of the deep learning algorithms, referred to as “You Only Look Once” version 4 and 5 (YOLOv4 and YOLOv5) and EfficientDet. We tested the models in a new dataset composed of 60 eggs that were kept for 15 days after the labeled expiration label date. The validation of our methodology was performed by measuring the air cell area highlighted in the thermal images at the pixel level; thus, we compared the difference in the weight of eggs between the first day of storage and after 10 days under accelerated aging conditions. The statistical significance showed that the two variables (air cell and weight) were negatively correlated (R2 = 0.676). The deep learning models could predict freshness with F1 scores of 0.69, 0.89, and 0.86 for the YOLOv4, YOLOv5, and EfficientDet models, respectively. The new methodology for freshness assessment demonstrated that the best model reclassified 48.33% of our testing dataset. Therefore, those expired eggs could have their expiration date extended for another 2 weeks from the original label date.