Miracle-dz/UniFAR-FTUs huggingface dataset:
CoRoT photometry and STELLA spectroscopy of an eccentric, eclipsing, and spotted HgMn binary with sub-synchronized rotation

Context. We report the discovery and analysis of very narrow transits in the eccentric spectroscopic binary HSS 348 (IC 4756). Aims. The aim is to characterize the full HSS 348 system. Methods. We obtained high-precision CoRoT photometry over two long runs and multi-epoch high-resolution echelle spectroscopy and imaging with STELLA. Standard radial-velocity extraction, spectrum synthesis, Fourier analysis, and light-curve inversions are applied to the data. Results. HSS 348 is found to be an eccentric ( e = 0.18) double-lined spectroscopic binary with a period of 12.47 d in which at least the primary component is a peculiar B star of the HgMn class. The orbital elements are such that the system undergoes a grazing eclipse with the primary in front but no secondary eclipse. The out-of-eclipse light variations show four nearly equidistant but unequal minima stable in shape and amplitude throughout our observations. Their individual photometric periods are all harmonics of the same fundamental period which happens to agree with the transit period to within the errors. We interpret the fundamental period to be the rotation period of at least one if not both stars due to surface inhomogeneities. Due to the non-zero eccentricity of the orbit the two components are rotating sub-synchronously. Conclusions. It appears that HSS 348 is not a member of the IC 4756 cluster but a background B8+B8.5 binary system. Its sharp eclipses every 12.47 days just mimic a small-body transit but are in reality the grazing eclipses of a B-star binary and thus a classical false positive. The system seems to be pre-main sequence with the primary possibly just arrived on the ZAMS. The light curve with four unequal minima can be explained with four cool spots of different size equidistantly positioned in longitude. Our data do not allow to uniquely assign the spots to either of the two stars.
Research on the Design of Crop Disease Recognition System Based on Deep Learning

As an important economic pillar, agriculture plays a vital role in the development of China. However, Crop diseases and insect pests have always been the most common agricultural problems in agricultural production, which seriously affect the efficiency and quality of agricultural production. Therefore, how to identify effectively and control pests and diseases, so as to make the agricultural output increase well and quickly for farmers, is a topic of particular concern for farmers and researches at present. At present, there are still many deficiencies in the detection and control of agricultural pests and diseases in China. It is very important to strengthen the research on the detection and control of agricultural pests and diseases, improve the detection and control level of pests and diseases, and maintain the agricultural ecological balance. In view of this situation, this paper abandons traditional methods such as manual recognition and instrument recognition, then introduces a new generation of disease recognition methods based on deep learning, combined with image processing to the identification of crop pathological conditions, and obtains images based on the Internet of Things technology, which is an inevitable trend for the development of precision agriculture in the future. The results of this system design will be visualized, predictable and platformized to improve the utilization rate and usability of farmers.
Reflecting on the Reflections Driving Variations in Heat Transfer Teaching

Reflecting on teaching is a powerful habit of mind that can improve one’s own teaching practice. Reflective practitioners make changes in the moment of teaching (reflection-in-action) and also look back on past teaching experiences (reflection-on-action) to inform planning of future instruction (Schön, 1983). This paper describes an engineering professor’s journey through the Reflective Cycle of Course Development, and how his reflections-in-action and reflections-onaction caused him to implement instructional variations within a senior level heat transfer course over three semesters. Looking back, the authors reflect on the reflections driving course variation and discuss considerations for future reflective course development.
Social Plasticity Enhances Signal-Preference Codivergence

The social environment is often the most dynamic and fitness-relevant environment animals experience. Here we tested whether plasticity arising from variation in social environments can promote signal-preference divergence—a key prediction of recent speciation theory but one that has proven difficult to test in natural systems. Interactions in mixed social aggregations could reduce, create, or enhance signal-preference differences. In the latter case, social plasticity could establish or increase assortative mating. We tested this by rearing two recently diverged species of Enchenopa treehoppers—sap-feeding insects that communicate with plant-borne vibrational signals—in treatments consisting of mixed-species versus own-species aggregations. Social experience with heterospecifics (in the mixed-species treatment) resulted in enhanced signal-preference species differences. For one of the two species, we tested but found no differences in the plastic response between sympatric and allopatric sites, suggesting the absence of reinforcement in the signals and preferences and their plastic response. Our results support the hypothesis that social plasticity can create or enhance signal-preference differences and that this might occur in the absence of long-term selection against hybridization on plastic responses themselves. Such social plasticity may facilitate rapid bursts of diversification.
Temperature and light patterns at four reefs along the Great Barrier Reef during the 2015–2016 austral summer: understanding patterns of observed coral bleaching

ABSTRACT Data from real-time sensor networks along the Great Barrier Reef (GBR) over the 2015–2016 austral summer showed that reef water temperatures exceeded empirical coral bleaching thresholds at a number of sites. Temperatures in the southern GBR were within historically normal limits with temperatures below the empirical bleaching threshold. The central GBR just reached the empirical bleaching threshold while, in the north, Lizard Island recorded four consecutive days above the bleaching threshold. Thursday Island in the far northern GBR experienced 10 days above the bleaching threshold. The in situ data predicted only slight bleaching in the southern GBR, moderate bleaching in the central GBR, widespread bleaching in the north and severe bleaching in the far north, which compares well with the initial survey data. Peak temperatures occurred later in the year in the north (mid-March 2016) than in the south (early February 2015) with temperatures remaining above the long-term mean well into the austral autumn. Comparison against satellite sea surface temperature data highlighted issues of cloud cover with data only being available for 30–40% of days over the summer. While the agreement with the in situ data was good, the satellite data missed fine-scale events and under-estimated the event at Thursday Island.
Nonparametric Estimation of Time-Variant Parametric Models with Application to Cross-Sectional Data

In this article, two estimation approaches based on age-speciﬁc parametric model have been proposed and a comparative study between them has been studied. We assume that outcome variable follows a parametric model, but the parameters are smooth function of time (age). Our estimation is based on a two-step smoothing method, in which we ﬁrst obtain the raw estimators of the parameters at a set of disjoint time points, and then compute the ﬁnal estimators at any time by smoothing the raw estimators. We derived asymptotic properties such as asymptotic biases, variances and mean squared error (MSE) for the local polynomial smoothed estimator and kernel smoothing estimator for the parameter of the time-variant parametric model. A mathematical relationship is established between two asymptotic MSEs. Mathematical relationship between two smoothing estimators has also been established. Applications of our two-step estimation method have been demonstrated through a large demographic study to estimate fecundability. Theoretical results on coverage of bootstrap conﬁdence intervals for these smoothing estimators have been derived. Finite sample properties of our procedures are investigated by a simulation study.
TimeCast: Real-Time Many-to-Many Data-Sharing in Low-Power Wireless Distributed Systems

Many-to-many/all-to-all sharing of data is one of the common requirements in any distributed/decentralized system. In low-power wireless distributed systems composed of highly resource-constrained devices, a many-to-many data-sharing strategy needs to be not only efficient but also sufficiently simple in nature. Advancements in the field of synchronous transmission (ST) have proliferated a number of efficient data-sharing strategies for low-power systems. In this work, we first identify the fundamental and simple possible approaches to accomplish the goal. Through extensive simulation-based comparison among them, we demonstrate the superiority of the approach adopted by a strategy called MiniCast where the desired task is achieved through pipelined execution of the one-to-many floods from the designated source nodes. However, through further in-depth study, we identify certain crucial flaws in MiniCast, which are mostly caused by its substandard realization through radio-event-triggered architecture. To circumvent the problems, we propose a new strategy TimeCast where the radio-event-triggered architecture of MiniCast is replaced by a time-triggered architecture. Through rigorous experimental studies in IoT-testbeds, we demonstrate that TimeCast substantially improves the robustness, and thus, due to lesser failures, it achieves all-to-all data-sharing up to 57.1% faster while consuming up to 63.6% lesser radio-on time compared to MiniCast.
Fingerprint generation and authentication though Adaptive convolution generative adversarial network (ADCGAN)

Fingerprints are crucial in identification of humans. The uniqueness of finger prints makes it an interesting subject. Fingerprints are termed as a technique used to define, assess, and quantify a person's physical and behavioral property. Deep learning has made its application in all the major fields such as natural language processing, computer vision and speech processing. Deep learning has also found its application in the important subject of fingerprint synthesis and biometric. The ever-growing complexity of fingerprint authentication issues, from cellphone authentication to airport security systems, seems to be best handled by these models. In recent years, deep learning-based models have been used more and more to raise the accuracy of various fingerprint recognition systems. The persuasive capacity of Generative Adversarial Networks (GANs) to generate believable instances can be credibly taken from an existing distribution of samples. GAN exhibits exceptional performance on data generation-based tasks and also encourages study in privacy and security. In this work, using Adaptive Deep Convolution Generative Adversarial Networks (ADCGAN), we develop a model that generates and authenticate the fingerprints. A Socofing dataset was trained on ADGAN model. The model gave 92% accuracy. The conduct of fingerprint research has been made possible due to ADGAN, without restrictions related to the confidential nature of biometric data.
Ionospheric Amplitude and Phase Fluctuations at the GPS Frequencies

The F-region of the ionosphere at times becomes turbulent and develops small scale (<500 m) irregu- larities of electron density. When sufficiently intense, these irregularities scatter radio waves at L-band fre- quencies and generate amplitude and phase scintilla- tion of GPS signals. Amplitude scintillation causes cycle slips and data losses to occur and phase scintil- lation generates fast variations of frequency with which the receiver has to cope. We have utilized the available data sets to establish the constraints imposed by scin- tillation on the operation of GPS receiving systems. This will help GPS users to correctly distinguish re- ceiver problems from ionospheric scintillation. The worst source of scintillation is the equatorial anomaly region. This region corresponds to two belts, each several degrees wide, of enhanced ionization in the F-region at approximately 15” north and 15O south of the magnetic equator. In this region, during the solar maximum periods, amplitude scintillations at 1.5 GHz may exceed 20 dB for several hours after sunset. Ascension Island in the Atlantic, Diego Garcia in the Indian Ocean, Hong Kong and Taiwan in the Pacific are some of the stations that fall directly under the anomaly region. However, areas within a few degrees of the magnetic equator may show only 5-7 dB fades. Occurrence of scintillation in the equatorial region is a function of longitude. For example the region between 0” and 30” West Longitude can have severe fading from September to May while the region from 110” to 130” East has maximum occurrence in the equinoxes with minimum occurrence from November to January (in solar maximum years). The other potentially active regions are at aurora1 and polar cap latitudes. In the central polar cap in years of solar maximum, GPS receivers may suffer > 10 dB fades during the solar maximum period. Relatively rare effects of magnetic storms noted in Japan will be outlined. GPS satellites offer a unique source for measure ments of amplitude and phase scintillation on a global scale. One receiver can record scintillation magnitudes and spectra at multiple propagation paths in the over- head sky. The data can be used to study ionospheric plasma structures, develop weather models of scintil- lation and can be scaled in frequency to support many operational systems. However the interpretation of the fading structure and scintillation spectra for GPS is not straightforward since both the ionospheric motion and the velocity of the satellite control the fading rate of scintillation. With the use of more data the problem of amplitude fading could be organized for the user’s benefit.
Power laws in the cluster sizes of group-crossing pedestrians: empirical evidence and modelling of causes

Pedestrian-vehicle mixed traffic at unsignalized crosswalks is a self-organizing system, because of the absence of external control. Consequently, pedestrian group-crossing behaviour may be expected to exhibit the power-law phenomenon, which is often an indicator of self-organized criticality. The assumption that the distribution of cluster sizes may obey power laws was theoretically supported by Xin et al. To test this claim empirically, we observed pedestrian collective motion under natural conditions, using video recordings. These real data confirmed the existence of the power-law phenomenon. A simulation study was then undertaken to uncover the mechanism that generated the observations. The observed patterns could be fully replicated if the behaviour of pedestrians and motorists were modelled, in particular the yielding behaviour of motorists, when approaching crossings and the adaptive behaviour of pedestrians as they increased in number. As well as providing empirical evidence, our findings also provide a deeper understanding of the patterns of the pedestrian collective motion. The results may have implications in the validation of mixed traffic simulation models.
Ensemble Learning Technique for Artificial Intelligence Assisted IVF Applications

In-vitro fertilization (IVF) is considered one of the most effective assisted reproductive approaches. However, it involves a series of complex and expensive procedures, that result in approximately 30% success rate. New technological concepts, like deep learning (DL), are required to increase the success rate while simplifying the procedure. DL techniques can automatically extract valuable features from the available data, can be flexible, and can work efficiently on multiple problems. Motivated by these characteristics, in this work, a DL ensemble model is trained on a private dataset to classify blastocysts’ images. Further analysis is conducted to provide insights for future research.
On the spectralization of affine and perfectly normal spaces

Abstract We show that for a field K and n ≥ 1 {n\geq 1} , the soberification 𝒮 ⁢ ( 𝔸 n ⁢ ( K ) ) {\mathcal{S}(\mathbb{A}^{n}(K))} of the affine n-space 𝔸 n ⁢ ( K ) {\mathbb{A}^{n}(K)} over K is homeomorphic to its spectralization ℬ ⁢ 𝒮 ⁢ ( 𝔸 n ⁢ ( K ) ) {\mathcal{BS}(\mathbb{A}^{n}(K))} , and it can be embedded into the spectrum Spec ⁢ ( K ⁢ [ X 1 , … , X n ] ) {\mathrm{Spec}(K[X_{1},\dots,X_{n}])} . Moreover, if the field K is algebraically closed, then there are homeomorphisms 𝒮 ⁢ ( 𝔸 n ⁢ ( K ) ) ≈ ℬ ⁢ 𝒮 ⁢ ( 𝔸 n ⁢ ( K ) ) ≈ Spec ⁢ ( K ⁢ [ X 1 , … , X n ] ) \mathcal{S}(\mathbb{A}^{n}(K))\approx\mathcal{BS}(\mathbb{A}^{n}(K))\approx% \mathrm{Spec}(K[X_{1},\dots,X_{n}]) . We also show that for a space X, the subspace z ⁢ Spec ⁢ ( C ⁢ ( X ) ) ⊆ Spec ⁢ ( C ⁢ ( X ) ) z\mathrm{Spec}(C(X))\subseteq\mathrm{Spec}(C(X)) of prime z-ideals of the ring C ⁢ ( X ) {C(X)} of real-valued continuous functions on X is homeomorphic to the space z ⁢ 𝒮 ⁢ ℛ ⁢ ( X ) {z\mathcal{SR}(X)} of prime z-filters with an appropriate topology and there is a homeomorphism ℬ ⁢ 𝒮 ⁢ ( X ) ≈ z ⁢ Spec ⁢ ( C ⁢ ( X ) ) {\mathcal{BS}(X)\approx z\mathrm{Spec}(C(X))} provided X is perfectly normal.
Unravelling social barriers in ecological restoration decision‐making: A network analysis approach

Ecological restoration (ER hereafter) is situated within a complex socio‐political landscape, characterized by competing interests that can hinder effective decision‐making and implementation of ER initiatives. Despite its importance, these social factors influencing ER decision‐making processes have not been comprehensively examined or interconnected in a systemic manner. This research aims to identify and systematically analyse the social barriers that obstruct ER decision‐making processes in the case of the Valencian Autonomous Region, Spain. Employing a qualitative methodological approach, this study utilized causal maps and network analysis to create a systemic representation of social barriers impacting ER, as identified by the key actors involved in the decision‐making process. The findings establish a relational model of social barriers that influence ER decision‐making. Central nodes of the process, as revealed by this network analysis, include the lack of private sector interest in ER initiatives, the excessive electoral motivation of political decisions and the inefficient civil service structure. This research contributes to a better understanding of the socio‐political dimensions of ER and provides valuable insights for overcoming barriers in the decision‐making process.
Structuring of the Main Measurement Zones of Acoustic Tracks for Diagnose of the Internal Combustion Engine Components

The article describes the prerequisites for creating a methodology for diagnosing an internal combustion engine by the noise emitted. The methodology used by diagnosticians at car services is taken as a basis. An overview of the works in which noise is analyzed and modeled, the source of which is technical means. The classification of noise emitted by an internal combustion engine is proposed. The analysis of the main cyclic processes in the internal combustion engine, which should form the basis of the methodology of its diagnosis, is given. Based on the engineering layout of the engine, zones are proposed in which it is possible to record characteristic engine noises. An experiment was conducted to fix sound tracks at various points above the engine of a real car. A preliminary method of signal processing is proposed and the results of such processing are presented. Conclusions are drawn about the validity of deterministic approaches to the analysis of noise produced by the engine.
Statistical Tests for Analysing Particle Trajectories: Application to Intracellular Imaging

In this thesis, we are interested in quantifying the dynamics of intracellular particles as proteins, inside living cells. In fact, inference on the modes of mobility of molecules is central in cell biology since it reflects the interactions between the structures of the cell. We model the particle trajectories with stochastic processes as the interior of a living cell is a fluctuating environment. Diffusions are stochastic processes with continuous paths and can model a large range of intracellular movements. Biophysicists distinguish three main types of diffusions, namely Brownian motion, superdiffusion and subdiffusion. These different diffusion processes correspond to distinct biological scenarios. A particle evolving freely inside the cytosol or along the plasma membrane is modelled by Brownian motion; the particle does not travel along any particular direction and can take a very long time to go to a precise area in the cell. Active intracellular transport can overcome this difficulty so that motion is faster and direct specific. In this case, particles are carried by molecular motors along microtubular filament networks and their motion is modelled with superdiffusions. Subdiffusion can be observed in two cases i/ when the particle is confined in a microdomain, ii/ when the particle is hindered by molecular crowding and encounters dynamic or fixed obstacles. We develop a statistical test for classifying the observed trajectories into the three groups of diffusion of interest namely Brownian motion, super-diffusion and subdiffusion. We also design an algorithm to detect the changes of dynamics along a single trajectory. We define the change points as the times at which the particle switches from one diffusion type (Brownian motion, superdiffusion or subdiffusion) to another. Finally, we combine a clustering algorithm with our test procedure to identify micro domains that is zones where the particles are confined. Molecular interactions of great importance for the functioning of the cell take place in such areas.
The integration between insect biomonitoring study and GIS study to assess the pollution impacts as oxidative stress parameters within Abu-Zaabal industrial area

Sulphates, phosphates and heavy metals such as lead, cadmium and ferrous are considered as a serious environmental pollutant due to their ability to persistent within the environment and therefore case a serious damage to various ecosystem components. This study aimed to develop and assess the integration models between biomonitoring process in form of oxidative stress components and a Geographic Information System (GIS), to evaluate the pollutants exposure and their effects on surrounding biomonitoring agent Crocothemis erthrea with respect to using a control study area . The biomonitoring processes were done using colorimetric analysis in form of oxidative stress parameters (including the activity of glutathione reductase (GR), glutathione peroxidase (GPx), and glutathione-s-transferase (GST)) in brain, thoracic muscles, and gut of male and female C. erthrea collected from different studied areas. The results showed that the activity of GR was increased in female than male and the increasing levels occurred along decreasing the distance from pollution source in male and female insects. However, there were a fluctuation in the activity of GPx in male insects, and GST in male and female insect along increasing the distance from pollution source with the highest value of 3x-fold and 4x fold, respectively, with respect to control. Our study demonstrated the ability of to combine the biomonitoring-GIS modeling to assess the environmental pollutants and predict the deleterious effect of these pollutants on living organisms inform of oxidative stress issues.