Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Neural Networks for Traffic Forecasting

The significant increase in world population and urbanisation has brought several important challenges, in particular regarding the sustainability, maintenance and planning of urban mobility. At the same time, the exponential increase of computing capability and of available sensor and location data have offered the potential for innovative solutions to these challenges. In this work, we focus on the challenge of traffic forecasting and review the recent development and application of graph neural networks (GNN) to this problem. GNNs are a class of deep learning methods that directly process the input as graph data. This leverages more directly the spatial dependencies of traffic data and makes use of the advantages of deep learning producing state-of-the-art results. We introduce and review the emerging topic of GNNs, including their most common variants, with a focus on its application to traffic forecasting. We address the different ways of modelling traffic forecasting as a (temporal) graph, the different approaches developed so far to combine the graph and temporal learning components, as well as current limitations and research opportunities.
Reproductive dynamics of three amphibian species in Mediterranean wetlands: the role of local precipitation and hydrological regimes

SUMMARY 1. Although the influence of water availability and precipitation regimes on amphibians has been studied at large scales, whether and how interannual rainfall and hydrological variations affect amphibians dynamics at a local scale have rarely been addressed. In this respect, accounting for variations in species detectability in space and time has also been overlooked. 2. We assessed the effects of rainfall and hydrological variations on the breeding dynamics of three amphibian taxa: Pelodytes punctatus, Hyla meridionalis and Pelophylax spp. in 20 ponds of the Camargue region (southern France) over a 7-year study period. 3. We used multiple season occupancy models to test the effect of winter‐spring rainfall and interannual variations in hydroperiod, mean water depth and drought events on tadpole presence in spring (March‐June), a proxy for breeding dynamics. 4. We used an independent survey with spatial replicates (dipnet sweeps) to disentangle the relative contributions of phenology and detectability to the absence of records in a given month. For the three taxa considered, the probability of missing a species when that species was actually present in a pond was most often negligible. Hence, we could consider that multiseason models properly tracked changes in species phenology. 5. Pelodytes punctatus was first detected in March, while the two other taxa appeared later in April. Hyla meridionalis appeared as a mid-season species with much more synchronous pond occupancy than Pelodytes punctatus. The detection peak of Pelophylax spp. was short and unexpectedly early for this taxon. 6. Seasonal winter‐spring rainfall was associated with a decrease in extinction rates and even more strongly with an increase in colonisation rates at individual ponds. 7. Colonisation rate increased following an annual drought and was best modelled as a negative quadratic effect of the variance of pond hydroperiod. Extinction probability was best modelled by a negative quadratic effect of mean water level. Hence, breeding was more stochastic (i) in unpredictable and shallow ponds because of yearly drying up and (ii) in highly predictable and deep ponds, possibly due to the presence of predators such as fish and crayfish. 8. Overall, we show that ponds with intermediate rather than extreme variations in environmental conditions currently correspond to optimal breeding sites. Our study demonstrates that amphibian monitoring coupled with fine-scale analysis of environmental conditions is necessary to understand species dynamics in the long run and to inform conservation efforts for these species.
Striped Bass, Temperature, and Dissolved Oxygen: A Speculative Hypothesis for Environmental Risk

Abstract Striped bass Morone saxatilis has a paradoxical record of distribution and abundance, including population declines in coastal waters and variable success of freshwater introductions. This record is analyzed for consistency with a hypothesis that striped bass are “squeezed” between their thermal and dissolved oxygen preferences or requirements. A commonality among diverse field and laboratory observations supports an inherent thermal niche for the species that changes to lower temperatures as fish age. This shift can cause local conditions, especially warm surface strata and deoxygenated deep water, to be incompatible with the success of large fish. Crowding due to temperature preferences alone or coupled with avoidance of low oxygen concentrations can lead to pathology and overfishing, which may contribute to population declines. Through a mixture of evidence and conjecture, the thermal niche-dissolved oxygen hypothesis is proposed as a unified perspective of the habitat requirements of the spec.
Spiking Neural Network on Curve Fitting

Currently, curve fitting has been widely used in data processing. Spiking Neural Network is used to provide fast and accurate curve fitting with discrete data in this paper. First, the principle of Spiking Neural Network is introduced. Second, the Spiking Neural Network for curve fitting based on the MATLAB simulation platform is established. Finally, curve fitting of a linear function and an exponential function are made. The average error values are 0 and 0.3, the results show that Spiking Neural Network can be used in curve fitting effectively. Introduction Curve fitting is a method of processing data that uses a continuous curve approximation or matches a set of discrete points on the plane to represent a function between coordinates. Applications of curve fitting have important theoretical and practical significance for revealing the inherent law of the data. However, the traditional methods of fitting curves have some limitations, such as polynomial fitting with low accuracy and cubic spline interpolation with high precision but long computation time. In recent years, artificial neural networks are widely used in information science, brain discipline, neuron psychology and other disciplines. Artificial neural networks [1,2] (ANN) are a model that processes distributed parallel information using characteristics of biological nervous system behavior. As a third-generation artificial neural network, Spiking neural network (SNN) represents the latest research results in the field of biological neuron science and ANN. SNN with better biological properties and powerful computing capabilities can simulate a variety of neuronal signals and arbitrary continuous functions [3] . SNN SNN is a new network proposed by Mass [4] . SNN uses the accurate time of pulse to code information, compared with the traditional way that uses firing frequency of pulses to code information. Therefore, SNN is closer to the processing mechanisms of biological information. The Model of the Spiking Neuron The spiking neuron is a mathematical model that is closer to the biological neurons. The traditional Sigmoid neuron model changes a real number input into a real number output signal by passing a transfer function. The essence of the model is, when the spiking neuron receives an external stimulus, its membrane potential will increase. When the membrane potential reaches the threshold, the neuron will generate a spike and send an output signal [5] . There are several common neuron models such as the integrate and fire (I&F) model, (leaky integrate and fire (LIF) model, Hodgkin-Huxley (H-H) model, spike response model (SRM) model. The I & F neuron model is used in this paper, because it can obtain better dynamic characteristics of the nervous system with comprehensive comparison and is relatively simple to implement. The basic circuit of the I&F neuron model is shown in Figure 1 [6] .
The Makam Metalanguage Technical Overview

Programming language design is challenging. The language designer is faced with an immense spectrum of choices, whose advantages and disadvantages become apparent only once the alternatives are tried out in practice. Furthermore, producing an actual implementation is hard work, especially when the language supports an advanced type system (e.g. dependent types). Important aspects of the implementation that greatly determine whether it can be put to use in practice, such as performance, error messages presented to the user and interoperability with existing languages, frameworks and libraries, require substantial amounts of additional work. The extensibility of a design is also of utmost importance, yet it is often at odds with producing a realistic implementation of the current design – basic assumptions might change that invalidate the implementation effort already invested. Because of these challenges, a lot of research on programming languages and type systems tends to stay at the prototype level.
Efficient Regularization Paths for Generalized Thresholding Operators

In this paper we look at the problem of constructing regularization paths in the context of penalized least-squares with concave penalties. We propose coordinate wise methods based on generalized univariate thresholding operators, for this purpose. We introduce a formal statistical framework of properly calibrating the two tuning parameters (λ, γ) (controlling the degrees of regularization and concavity of the penalty) via the notion of “effective degrees of freedom”. This plays a crucial part in the construction of the doubly regularized paths of coefficients. We see that our optimization methods have guaranteed convergence properties and perform better than state-of-the-art convex relaxation techniques. We formally explore the reasons behind this sub-optimality.
Impact of investment on GDP and non-oil GDP in Azerbaijan

The causal relationship among, investment and growth is mixed and controversial both the oretically and empirically. There is large empirical literature which examines the investment-growth nexus. This paper examines the causal relationship among, investment and economic growth in Azerbaijan using months’ time series data from 2010-2019. Results for Augmented Dickey‒Fuller (ADF), Phillips−Perron (PP) and Kwiatkowski−Phillips−Schmidt−Shin (KPSS) unit root tests show that all variables under consideration are I(1). Result from the Auto Regressive Distributed Lag Bounds Testing (ARDLBT) indicates that there exists cointegration among gross domestic investment, gross domestic product. Investment have significant positive effect on economic growth of Azerbaijan both in the short-run and in the long-run.
Identifying Anomalies in past en-route Trajectories with Clustering and Anomaly Detection Methods

—This paper presents a framework to identify and characterise anomalies in past en-route Mode S trajectories. The technique builds upon two previous contributions introduced in 2018: it combines a trajectory-clustering method to obtain the main ﬂows in an airspace with autoencoding artiﬁcial neural networks to perform anomaly detection in ﬂown trajectories. The combination of these two well-known Machine Learning techniques (ML) provides a useful reading grid associating cluster analysis with quantiﬁed level of abnormality. The methodology is applied to a sector of the French Bordeaux Area Control Center (ACC) during its 385 hours of operation over seven months of ADS-B trafﬁc. The results provide a good taxonomy of deconﬂiction measures and weather-related ATC actions. The application of this work is manyfold, ranging from safety studies estimating risks of midair collision, to complexity and workload assessments of trafﬁc when a sector is operated, or to the constitution of a database of ATC actions ensuring aircraft separation. This database could be used to train further ML techniques aimed at improving the state of the art of deconﬂiction algorithms.
Continuous Monoculture of Alfalfa and Annual Crops Influence Soil Organic Matter and Microbial Communities in the Rainfed Loess Plateau of China

Cropping systems are structured to maximize crop yields and increase sustainability in agricultural production. A field study was conducted to investigate different long-term cropping systems on soil organic matter and microbial communities. The cropping systems studied were: (i) a 14-year continuous alfalfa (Medicago sativa L.) (CA), (ii) a 9-year alfalfa removed and rotated with 4–5 years continuous annual crops (spring wheat (Triticum aestivum L.), maize (Zea mays L.), potato (Solanum tuberosum L.), and millet (Panicum miliaceum L.)), and (iii) a 5-year field fallow after alfalfa. Results showed that continued annual crops decreased total organic C and labile organic C by 10 to 20% and 17 to 34% in the topsoil (0–30 cm), and by 15 to 35% and 20 to 46% in the subsoil (30–60 cm), respectively, compared with CA. Similar trends were found in soil total N concentration, which decreased by 7 to 20% in the topsoil. Highest microbial biomass C was found in CA. Shannon-Wiener diversity and substrate richness of soil microbes measured by Biolog EcoPlates was significantly affected by cropping system with CA exhibiting a higher degree of soil microbial functional diversity in the topsoil, while the lowest values were found in the alfalfa-potato system. The higher soil organic matter content and functional diversity of soil microbe in CA indicates that soil nutrition and microbial activity did not limit alfalfa development and growth in the dryland area. The lower microbial activity and functional diversity observed in the potato field indicates the importance of crop selection in cropping systems.
Regulation of plant plasma membrane H+‐ATPase activity

The plant plasma membrane H+-ATPase plays a central role in plant physiology. This enzyme belongs to the P type family of cation-translocating pumps and generates the proton-motive force that drives nutrient uptake across the plasma membrane. It also determines the extracellular acidification associated with elongation growth. The activity of the plasma membrane H+-ATPase is rapidly altered after exposure of plant tissues to plant growth factors such as plant hormones, light and pathogens. However, very little is known about the mechanisms that regulate plasma membrane H+-ATPase activity in the intact cell. The recent identification of an auto-inhibitory domain in the C-terminus of the plant plasma membrane H+-ATPase implies that there are several possible means by which the enzyme could be regulated. The inhibitory interaction between the inhibitory domain and the catalytic site and/or a proton binding site may thus be regulated by a variety of means, such as the binding of effector molecules, phosphorylation, partial proteolysis, or removal of the inhibitory domain at the gene level. In addition, proton pumping across the plasma membrane could be regulated by changes in the transcriptional activity of H+-ATPase genes or by differential expression of pump isoforms varying in their C-terminal domain.
Three Dimensional Digital Image Correlation for Dynamic Measurements

Structural inspections have traditionally been completed by inspectors using simple tools to determine the health of a structure. The limitations using these techniques led to the development of quantitative sensors such as strain gauges, displacement transducers, and accelerometers. These sensors are still limited by requiring physical access to the structure and extensive cabling and setup. Optical techniques, such as laser Doppler vibrometry and photogrammetry, have been developed to allow technicians to inspect the structure with little or no physical access to the structure and minimal cabling. Digital Image Correlation (DIC) is a photogrammetry technique that involves tracking multiple subsets through a series of images to determine full field displacements. DIC can take the form of 2D DIC using one camera or 3D DIC using more than one camera. DIC use has typically focused on strain analysis, although researchers have recently begun to use DIC for full field dynamic analysis. Commercial packages have focused on strain analysis, with few software packages having any modal analysis capabilities built in. Commercial packages are also quite expensive. The goal of this work is to provide an open-source 3D DIC software package with built in modal analysis functionality. The software package will allow for use of various interpolation functions, shape functions, and de-noising options working within an Inverse Compositional Gauss-Newton (IC-GN) registration algorithm. A numeric study was undertaken to determine optimal 2D and 3D DIC analysis parameters using the IC-GN algorithm. The numeric study made use of simulated images to provide improved control over image quality and to eliminate or control error sources. Images were simulated using 8px diameter speckles and a subpixel displacement from 0 to 1px. Left and right images were generated with a known horizontal displacement to simulate a classical stereo vision setup. For 2D analysis, a bicubic interpolation function with a 5x5 Gaussian blur is recommended. If the displacement field is known, then a matching shape function should be used. Otherwise, a 2 order shape function should be used to ensure the displacements are captured. A subset size between 41x41px and 61x61px is recommended for 2D analysis. For 3D analysis, a bicubic interpolation function with no Gaussian blur is recommended. A higher order interpolation function was found to not improve error levels, and the Gaussian blur increased the random errors without improving the bias errors. A 2 order shape function is recommended when matching between left/right images to capture deformation with imperfectly aligned cameras. A matching shape function is recommended for analyzing the sequential images if the displacement field is known, otherwise a 2 order shape function should be used for analyzing sequential images as well. A 41x41px to 61x61px subset size is also recommended for 3D DIC
Assistência de ar em barra de pulverização no controle da ferrugem asiática da soja

AIR-ASSISTANCE IN SLEEVE BOOM SPRAY IN THE CONTROL OF SOYBEAN RUST The experiment was set in Botucatu, Sao Paulo State, Brazil, in the soybean crop, harvest 2006/07. Two application technologies have been compared: conventional spraying (without air); spraying with air assistance in the sleeve boom at 9, 11 and 29 km h -1 of the air speed on artificial targets as well as in the control of soybean rust in different parts of the plants. For the adaxial surface of leaves in the top part of the plants, the air assistance with 11 km h -1 , provided greaters spray deposits. In the bottom part of the plants it was not observed significative differences between the treatments. The deposits in the abaxial surface of leaves in the top part of the plant, with maximum air speed generated by the fan (29 km h -1 ), provided greaters levels of deposits. The air assistance provided a higher productivity, in relation
Detection of cervical cancer from evoked tissue fluorescence images using 2- and 3-way methods

In-vivo examinations of cervical tissue were performed using Evoked Tissue Fluorescence (ETF) as part of a clinical trial. These examinations were performed in conjunction with conventional colposcopy, along with of biopsies of suspect cancerous or pre-cancerous tissue. The ETF data consisted of 22 images of the cervix taken at combinations of 3 excitation wavelengths and 9 emission wavelengths. The ultimate goal was to use these images to accurately classify tissue between normal states (normal squamous and normal columnar) and pre-cancerous and cancerous states (squamous metaplasia, and high and low Squamous Intraepithelial Lesion, HSIL and LSIL). Several chemometric methods were applied to the data for the purpose of preprocessing (e.g. image alignment), eliminating patient to patient variability (e.g. GLS and specialized centering), classification of tissues (hierarchical PLS-DA) and elucidating the underlying signatures of the tissues and background components (PARAFAC).
Building an Ecodesign Transition Framework toward Sustainable Product Innovation

In order to effectively integrate environmental aspects into product development processes, companies have to significantly change some of the practices and habits of all involved stakeholders and organisation. To complement earlier research and the (technical) “hard side of ecodesign”, this article explores the promising “soft side” that considers company culture and human factors, through a multiple steps literature review. Whereas a consistent prescriptive change model is still lacking in ecodesign literature, a strong convergence and synergy is shown with the emerging Transition Management approach designed for sustainability issues facing organisations. The principles of an “ecodesign transition framework” are proposed, combining a three-level systemic approach, complementary top-down planning and bottom-up innovation, through new types of interaction and cycles of action and learning, with a deeper stakeholder management. This new combination could be capable to address change management issues and help companies evolve toward a more effective sustainable product innovation process, in the context of evolving business management practices.
Isolation and Characterization of Paraeforce Tolerance and Utilizing Fungi in Soil Samples Collected From an Exposed Arable Farmland

The aim of this study is to isolate and characterize paraeforce tolerance and utilizing fungi from an arable farmland previously exposed to the herbicide paraeforce. Soil samples were collected in sterile containers and aseptically cultured on potato dextrose agar using a streak plate culture technique. The fungal isolates were screened for tolerance and utilization of paraeforce using 50mg/l concentration. The screened fungal species were further characterized using molecular method. The presumptive identification of fungal isolates based on colonial and cell morphology showed the presence of Saccharomyces, Aspergillus, Pichia, Candida, Rhizopus, Penicillium, Trichoderma and Fasurium species. The screening test done showed that of these isolates, only Saccharomyces, Aspergillus and Candida were able to grow on paraeforce medium. The molecular characterization proved these screened fumgal species to be Pichiakrudiavzevii, Pichiacecembensis, Hanseniasporaopuntiae and Aspergillusspinosus. However, a more vigorous growth was observed in the mold (Aspergillusspinosus) than the yeast counterparts. This is due to their filamentous nature and their ability to produce extracellular enzymes.