Miracle-dz/UniFAR-FTUs huggingface dataset:
Remarkable analytic relations among greybody parameters

In this paper we derive and discuss several implications of the analytic form of a modified blackbody, also called greybody, which is widely used in Astrophysics, and in particular in the study of star formation in the far-infrared/sub-millimeter domain. The research in this area has been greatly improved thanks to recent observations taken with the Herschel satellite, so that it became important to clarify the sense of the greybody approximation, to suggest possible further uses, and to delimi its intervals of validity. First, we discuss the position of the greybody peak, making difference between the optically thin and thick regimes. Second, we analyze the behavior of bolometric quantities as a function of the different greybody parameters. The ratio between the bolometric luminosity and the mass of a source, the ratio between the so-called "sub-millimeter luminosity" and the bolometric one, and the bolometric temperature are observables used to characterize the evolutionary stage of a source, and it is of primary importance to have analytic equations describing the dependence of such quantities on the greybody parameters. Here we discuss all these aspects, providing analytic relations, illustrating particular cases and providing graphical examples. Some equations reported here are well-known in Astrophysics, but are often spread over different publications. Some of them, instead, are brand new and represent a novelty in Astrophysics literature. Finally we indicate an alternative way to obtain, under some conditions, the greybody temperature and dust emissivity directly from an observing spectral energy distribution, avoiding a best-fit procedure.
Utility and challenges of using whole‐genome resequencing to detect emerging insect and mite resistance in agroecosystems

Arthropods that invade agricultural ecosystems systematically evolve resistance to the control measures used against them, and this remains a significant and ongoing challenge for sustainable food production systems. Early detection of resistance evolution could prompt remedial action to slow the spread of resistance alleles in the landscape. Historical approaches used to detect emerging resistance included phenotypic monitoring of agricultural pest populations, as well as monitoring of allele frequency changes at one or a few candidate pesticide resistance genes. In this article, I discuss the successes and limitations of these traditional monitoring approaches and then consider whether whole‐genome scanning could be applied to samples collected from agroecosystems over time for resistance monitoring. I examine the qualities of agroecosystems that could impact application of this approach to pesticide resistance monitoring and describe a recent retrospective analysis where genome scanning successfully detected an oligogenic response to selection by pesticides years prior to pest management failure. I conclude by considering areas of further study that will shed light on the feasibility of applying whole‐genome scanning for resistance risk monitoring in agricultural pest species.
Sabine: Self-Adaptive BlockchaIn coNsEnsus

The Practical Bizantine Fault Tolerance (PBFT) consensus is a well adopted consensus protocol in private and consortium blockchains, the reason being that it is a light protocol, it is not computational intensive (particularly when compared to proof-based consensus protocols), and it allows to reach high transaction throughput. Security in this consensus depends on the number of nodes (aka validators) involved in the consensus: the higher the number of nodes, the higher the security level. However, increasing the number of validators goes along with an increase in the latency, and consequently a decrease in the throughput. The present research proposes a new SelfAdaptive BlockchaIn coNsEnsus protocol (Sabine), which tries to optimally address this trade-off by continuously adapting the size of the pool of validators with the prime objective that the output throughput (i.e., number of transactions validated at a given time) meets –to the best extent possible - the input throughput (i.e., requested by the application). Sabine is evaluated and validated in real-life settings, whose results show that Sabine has a relative error of 7.79% between the requested and committed transaction throughput, compared to 39.5% for a classic chain with all nodes participating in the consensus, implying a more reactive chain, with less latency.
CORRIGENDUM: Momentum-space formalism for the total energy of solids

A momentum-space formalism for calculating the total energy of solids is derived. This formalism is designed particularly for application with the self-consistent pseudo- potential method. In the present formalism, the total energy is obtained through band- structure calculations without involving additional integrations. The Hellman-Feynman theorem is derived, as is a modified virial relation for the pseiidopotential Hamiltonian which provides an alternative way of calculating forces and total energies. The calculation of the total energy of solids and related derivatives with respect to structural degrees of freedom has been an ongoing problem since the early days of solid state physics (Wigner and Seitz 1933, 1934, Fuchs 1935). Quantum-mechanical calcu- lations on molecules suggest that correlation effects might sometimes be responsible for most of their binding energy (Schaefer 1972). The solid state approaches have concentrated on efforts to include most of these effects through an effective potential I/corr(p(r, Y')) (Hohenberg and Kohn 1964, Kohn and Sham 1965), rather than by complicated wave- function-related configuration interactions or many-electron perturbation techniques. Besides the problem of considering correlation effects, the self-consistent solution of the Schrodinger equation within a desired accuracy is quite difficult ; typically, the experi- mental binding energy of elemental solids is 10-4-10-5 times the total energy. These difficulties have inspired a large set of total-energy calculations that circumvents the complete solution of the Schrodinger equation (Harrison 1966, Heine and Weaire 1970). It is based on various approximations to the nearly-free-electron representation and may include the effect of more localised electrons (e.g. d states in transition metals) through specific interaction models (Moriarty 1974, 1977). As the variational self-consistent charge density remains unspecified in this approach, various forms of linear dielectric screening of the basic Coulomb interactions are introduced (Harrison 1966, Heine and Weaire 1970). Another set oftotal-energy calculations is based on direct solutions ofthe Schrodinger equation within a given interaction model : for example, Hartree-Fock (Harris and Monkhorst 1971, Wepfer et al 1974), density-functional (Ching and Callaway 1974,
Probabilistic reasoning for repeatability detection from urban facade image

In this paper, we propose a novel method of automatic detecting urban façade repeatability by using probabilistic reasoning. The main advantage of this algorithm is that all the occluded building components can be detected as well as components that appear in façade image. First, we use the image entropy theory to derive qualified closed regions in image as the candidate repetitive components. Moreover, we introduce a repetitive characteristic surface which can be used to easily determine accurate locations, shapes and sizes of derived components. Finally, we detect all the occluded repetitive components with accurate locations, shapes and sizes by using the Bayesian probability network which obtained through an offline train from a façade images database. Experiment result demonstrates that the proposed algorithm improves the accuracy, robustness and efficiency on façades databases compared with the state-of-the-art methods.
EEG-Validated Photobiomodulation Treatment of Dementia—Case Study

In this article, we perform a case study of the impact of photobiomodulation (PBM) on brain power spectrum and connectivity in an elderly person with a Self Administered Gerocognitive Exam (SAGE) score indicating probable memory and thinking disorder. First, we designed and realized the prototype of a near-infrared (NIR) device for PBM. Analysing the alpha band of the power spectrum, we found a positive long-term effect in nine out of sixteen electrodes in the eyes-open condition (OE) and in twelve out of sixteen electrodes in the eyes-closed condition (CE), while in the theta band, a positive long-term effect was found in nine out of sixteen electrodes for OE and seven out of sixteen electrodes for CE. When considering the theta-alpha ratio (TAR), the positive long-term effect is found on thirteen of sixteen electrodes for OE and on fourteen of sixteen electrodes for CE. A connectivity analysis using the imaginary component of the complex Pearson correlation coefficient (imCPCC) was also performed, and a global efficiency measure based on connectivity matrices with thresholds was calculated. The global efficiency calculated for the long-term effect was higher than before stimulation by a factor of 5.24 for the OE condition and by a factor of 1.25 for the CE condition. This case study suggests that PBM could have positive effects on improving desired brain activity, measured as improvement in power spectrum and connectivity measures in theta and alpha bands, for elderly people with memory and thinking disorders.
Intensity and polarization of the atmospheric emission at millimetric wavelengths at Dome Concordia

Atmospheric emission is a dominant source of disturbance in ground-based astronomy at millimetric wavelengths. The Antarctic plateau is recognized as an ideal site for millimetric and submillimetric observations, and the French/Italian base of Dome Concordia (Dome C) is among the best sites on Earth for these observations. In this paper, we present measurements at Dome C of the atmospheric emission in intensity and polarization at a 2-mm wavelength. This is one of the best observational frequencies for cosmic microwave background (CMB) observations when considering cosmic signal intensity, atmospheric transmission, detector sensitivity and foreground removal. Using the B-mode radiation interferometer (BRAIN)-pathfinder experiment, we have performed measurements of the atmospheric emission at 150 GHz. Careful characterization of the airmass synchronous emission has been performed, acquiring more than 380 elevation scans (i.e. 'skydip') during the third BRAIN-pathfinder summer campaign in 2009 December/2010 January. The extremely high transparency of the Antarctic atmosphere over Dome C is proven by the very low measured optical depth, = 0.050 ± 0.003 ± 0.011, where the first error is statistical and the second is the systematic error. Mid-term stability, over the summer campaign, of the atmosphere emission has also been studied. Adapting the radiative transfer atmosphere emission model am to the particular conditions found at Dome C, we also infer the level of the precipitable water vapor (PWV) content of the atmosphere, which is notoriously the main source of disturbance in millimetric astronomy (? mm). Upper limits on the airmass correlated polarized signal are also placed for the first time. The degree of circular polarization of atmospheric emission is found to be lower than 0.2 per cent [95 per cent confidence level (CL)], while the degree of linear polarization is found to be lower than 0.1 per cent (95 per cent CL). These limits include signal-correlated instrumental spurious polarization.
Deep Transfer Learning Model for Semantic Address Matching

Address matching, which aims to match an input descriptive address with a standard address in an address database, is a key technology for achieving data spatialization. The construction of today’s smart cities depends heavily on the precise matching of Chinese addresses. Existing methods that rely on rules or text similarity struggle when dealing with nonstandard address data. Deep-learning-based methods often require extracting address semantics for embedded representation, which not only complicates the matching process, but also affects the understanding of address semantics. Inspired by deep transfer learning, we introduce an address matching approach based on a pretraining fine-tuning model to identify semantic similarities between various addresses. We first pretrain the address corpus to enable the address semantic model (abbreviated as ASM) to learn address contexts unsupervised. We then build a labelled address matching dataset using an address-specific geographical feature, allowing the matching problem to be converted into a binary classification prediction problem. Finally, we fine-tune the ASM using the address matching dataset and compare the output with several popular address matching methods. The results demonstrate that our model achieves the best performance, with precision, recall, and an F1 score above 0.98.
Trends in 3D Point Cloud Contents Sampling in Mobile AR/VR Platforms

In recent years, point clouds have attracted increasing attention in a variety of industries, such as autonomous driving and augmented reality. The point clouds offer a denser and more specific representation than the traditional meshes. However, processing the large point cloud contents for rendering and computation is challenging as the data size of point cloud contents is much larger than the traditional mesh contents. Hence, sampling algorithm can be a major role in these industries by reducing both computation time and storage. In this paper, a comprehensive overview of the state-of-the-art point cloud sampling methods is proposed. Different concepts of sampling approaches are identified and described in detail including algorithm based sampling, geometry based sampling and finally, mesh based sampling.
MAGNETOHYDRODYNAMIC KINK WAVES IN NONUNIFORM SOLAR FLUX TUBES: PHASE MIXING AND ENERGY CASCADE TO SMALL SCALES

Magnetohydrodynamic (MHD) kink waves are ubiquitously observed in the solar atmosphere. The propagation and damping of these waves may play relevant roles in the transport and dissipation of energy in the solar atmospheric medium. However, in the atmospheric plasma dissipation of transverse MHD wave energy by viscosity or resistivity needs very small spatial scales to be efficient. Here, we theoretically investigate the generation of small scales in nonuniform solar magnetic flux tubes due to phase mixing of MHD kink waves. We go beyond the usual approach based on the existence of a global quasi-mode that is damped in time due to resonant absorption. Instead, we use a modal expansion to express the MHD kink wave as a superposition of Alfvén continuum modes that are phase mixed as time evolves. The comparison of the two techniques evidences that the modal analysis is more physically transparent and describes both the damping of global kink motions and the building up of small scales due to phase mixing. In addition, we discuss that the processes of resonant absorption and phase mixing are closely linked. They represent two aspects of the same underlying physical mechanism: the energy cascade from large scales to small scales due to naturally occurring plasma and/or magnetic field inhomogeneities. This process may provide the necessary scenario for efficient dissipation of transverse MHD wave energy in the solar atmospheric plasma.
Fixed Orthodontic Treatment Increases Cariogenicity and Virulence Gene Expression in Dental Biofilm

Background: Dental caries commonly occurs during orthodontic treatment because fixed appliances can impede effective oral hygiene practices. This study investigated the effects of fixed orthodontic treatment on dental biofilm maturity and virulence gene (gtfB, ldh, brpA, spaP, luxS, and gbpB) expression. Methods: Dental biofilms and virulence gene expression were determined in 24 orthodontic patients before and after treatment of ≥6 months. A three-tone disclosing gel was used to stain dental biofilm and assess its maturity by its color change—pink (new dental biofilm), purple (mature dental biofilm), and light blue (cariogenic dental biofilm). Gene expression levels were determined using real-time PCR. Results: After fixed orthodontic appliance insertion, the percentage of new dental biofilm decreased, whereas that of cariogenic dental biofilm significantly increased (p < 0.05). There was no significant difference in the percentage of mature dental biofilm (p > 0.05). Fixed orthodontic appliances increased gtfB, ldh, brpA, and gbpB gene expression above 1.5-fold in dental biofilm. In contrast, there was no change in spaP or luxS gene expression after treatment. Conclusions: Fixed orthodontic appliance insertion induced ecological changes and cariogenic virulence gene expression in dental biofilm.
Design and Assessment of an Interoperable Wireless Power Transfer System Using an Impedance-Based Method

With the recently published standard, wireless charging systems for electric vehicles (EVs) are at the stage of preparing for mass market production. In order to have success, safe, and efficient wireless power transfer (WPT) public charging needs to be ensured and therefore interoperability is a key element in the design of such systems. In this article, a design methodology based on impedance planes is presented, which enables consideration of many interoperability aspects at an early stage in the design and has a high degree of flexibility. Four impedance interfaces are presented and operational boundaries and system requirements are added at each interface to establish capability ranges, which can be transferred to any other interface and used to define and assess the design space. Furthermore, this article shows how magnetic field leakage limitations can be considered in the design process. Experimental verification of the simulations is then undertaken and an example presented of how the primary electronics for a public charging use case scenario could be assessed or designed.
A Deep-Learning Extraction Method for Orchard Visual Navigation Lines

Orchard machinery autonomous navigation is helpful for improving the efficiency of fruit production and reducing labor costs. Path planning is one of the core technologies of autonomous navigation for orchard machinery. As normally planted in straight and parallel rows, fruit trees are natural landmarks that can provide suitable cues for orchard intelligent machinery. This paper presents a novel method to realize path planning based on computer vision technologies. We combine deep learning and the least-square (DL-LS) algorithm to carry out a new navigation line extraction algorithm for orchard scenarios. First, a large number of actual orchard images are collected and processed for training the YOLO V3 model. After the training, the mean average precision (MAP) of the model for trunk and tree detection can reach 92.11%. Secondly, the reference point coordinates of the fruit trees are calculated with the coordinates of the bounding box of trunks. Thirdly, the reference lines of fruit trees growing on both sides are fitted by the least-square method and the navigation line for the orchard machinery is determined by the two reference lines. Experimental results show that the trained YOLO V3 network can identify the tree trunk and the fruit tree accurately and that the new navigation line of fruit tree rows can be extracted effectively. The accuracy of orchard centerline extraction is 90.00%.
Magnetic induction measurements using an all-optical $^{87}$Rb atomic magnetometer

In this work we propose, and experimentally demonstrate, the use of a self-oscillating all-optical atomic magnetometer for magnetic induction measurements. Given the potential for miniaturization of atomic magnetometers, and their extreme sensitivity, the present work shows that atomic magnetometers may play a key role in the development of instrumentation for magnetic induction tomography.
Simulated Gastric and Intestinal Fluid Electrolyte Solutions as an Environment for the Adsorption of Apple Polyphenols onto β-Glucan

Interactions with dietary fibers in the gastrointestinal tract might affect the potential bioactivities of phenolic compounds. In this study, the interactions between apple phenolic compounds and β-glucan (a dietary fiber) were studied by studying the adsorption process in simulated gastric and intestinal fluid electrolyte solutions. Phenolic compounds were extracted from apples, adsorbed onto β-glucan (2 h, 37 °C, in gastric or intestinal fluid electrolyte solutions), and determined using high performance liquid chromatography. Phenolic compounds (flavan-3-ols, flavonols, phenolic acids, and dihydrochalcone) were stable in the gastric fluid (pH 3). In the intestinal fluid (pH 7), flavan-3-ols were not found and chlorogenic acid isomerized. Polyphenols from the apple peel (up to 182 and 897 mg g−1) and flesh (up to 28 and 7 mg g−1) were adsorbed onto β-glucan in the gastric and intestinal fluids, respectively. The adsorption was affected by the initial concentration of the polyphenols and β-glucan and by the environment (either gastric or intestinal fluid electrolyte solution). By increasing the initial polyphenol amount, the quantity of adsorbed polyphenols increased. Increasing the amount of β-glucan decreased the amount adsorbed. The results can be helpful in explaining the fate of phenolic compounds in the gastrointestinal tract.
4D Multiscale Origami Soft Robots: A Review

Time-dependent shape-transferable soft robots are important for various intelligent applications in flexible electronics and bionics. Four-dimensional (4D) shape changes can offer versatile functional advantages during operations to soft robots that respond to external environmental stimuli, including heat, pH, light, electric, or pneumatic triggers. This review investigates the current advances in multiscale soft robots that can display 4D shape transformations. This review first focuses on material selection to demonstrate 4D origami-driven shape transformations. Second, this review investigates versatile fabrication strategies to form the 4D mechanical structures of soft robots. Third, this review surveys the folding, rolling, bending, and wrinkling mechanisms of soft robots during operation. Fourth, this review highlights the diverse applications of 4D origami-driven soft robots in actuators, sensors, and bionics. Finally, perspectives on future directions and challenges in the development of intelligent soft robots in real operational environments are discussed.