Miracle-dz/UniFAR-FTUs huggingface dataset:
Unravelling vulture avoidance tactic of wind turbines combining empirical and simulation data

The increase of wind turbine installations to limit climate change may affect bird populations because of collisions with rotor blades. Birds may respond to wind turbine presence along a gradient of behavioural changes: avoiding the wind farm (macro-scale) or only the wind turbines either by anticipating wind turbine locations (meso-scale) or engaging into last-minute flee attempts after late perception (micro-scale). We investigated the flight response at these three spatial scales of 25 adult griffon vultures (Gyps fulvus) equipped with GPS tags over three years when flying in an area including ten wind farms in the Causses, France. At macro-scale, the population foraging range and habitat use revealed that vultures did not avoid wind farms. To investigate avoidance at meso- and micro-scales we focused on the four mostly visited wind farms. We compared vulture flights to null movement models, based on a method allowing us to keep the correlation between flights and topography while creating movement independent of wind turbine locations. At most sites, vultures did not show avoidance behaviour. Yet, simulations from our agent-based model highlighted that the avoidance pattern detected at one wind farm matched with an anticipated avoidance of turbines, probably linked to the presence of a ridge nearby. Overall, our results suggest wind farm-specific responses by soaring birds as a function of the landscape topography. Thus, stakeholders should carefully consider the wind farm location for siting and designing preventive measures (e.g. improve detection of species not able to avoid turbines in switching off on-demand technologies) to reduce collision risk of soaring birds.
Deep estimation of the intensity and timing of selection from ancient genomes

Leveraging past allele frequencies has proven to be key to identify the impact of natural selection across time. However, this approach often suffers from imprecise estimations of the intensity (s) and timing (T) of selection particularly when ancient samples are scarce in specific epochs. Here, we aimed at bypassing the computation of past allele frequencies by implementing new convolutional neural networks (CNNs) algorithms that directly use ancient genotypes sampled across time to refine the estimations of selection parameters. Using computer simulations, we first show that genotype-based CNNs consistently outperform an approximate Bayesian computation (ABC) approach based on past allele frequency trajectories, regardless of the selection model assumed and of the amount of ancient genotypes available. When applying this method to empirical data from modern and ancient Europeans, we confirmed the reported excess of selection events in post-Neolithic Europe, independently of the continental subregion studied. Furthermore, we substantially refined the ABC-based estimations of s and T for a set of positively-and negatively-selected variants recently identified, including iconic cases of positive selection and experimentally validated disease-risk variants. Thanks to our CNN predictions we provide support to the history of recent and strong selection in northern Europe associated to the Black Death pandemic and confirm the heavy burden recently imposed by tuberculosis in Europe. These findings collectively support that detecting the imprints of natural selection on ancient genomes are crucial for unraveling the past history of severe human diseases.
Parallel EEG assessment of different sound predictability levels in tinnitus

Objective Tinnitus denotes perception of a non-environmental sound and might result from aberrant auditory prediction. Successful prediction of formal (e.g. type) and temporal sound characteristics facilitates the filtering of irrelevant information (“sensory gating”, SG). Here, we explored if and how parallel manipulations of formal and temporal predictability affect sensory gating in persons with and without tinnitus. Methods Age-, education- and sex-matched persons with and without tinnitus (N = 52) participated and listened to paired-tone “oddball” sequences, varying in formal (standard vs. deviant pitch) and temporal predictability (isochronous vs. random timing). EEG was recorded from 128 channels and data were analyzed by means of temporal spatial principal component analysis (tsPCA). Results SG was observed in P50- and N100-like activity (amplitude suppression for the 2nd tone in the pair) in both timing conditions and groups. Correspondingly, deviants elicited overall larger amplitudes than standards. However, only in persons without tinnitus N100-like activity in response to deviants was enhanced with isochronous relative to random timing. Conclusions Persons with tinnitus do not benefit similarly as persons without tinnitus from temporally predictable context in deviance processing. Significance The current results indicate altered temporal sensitivity and selective attention allocation in persons with tinnitus. Highlights - Persons with tinnitus display altered auditory predictions affecting the processing of unexpected auditory input - Position predictions did not differ between persons with tinnitus and without - Temporal predictability facilitated deviance processing for P50-like activity in persons with tinnitus and without
Effects of an Adventure Therapy Mountain Bike Program on Middle School Students’ Resiliency

ABSTRACT Early adolescence is an important developmental period marked by educational transitions and significant physical, mental, social, emotional, and cognitive changes. During this time, the prevalence of mental health issues begins to accelerate, and perceived support declines. Adventure therapy (AT), a group-based approach to counseling that uses kinesthetic activities to promote therapeutic progress, has the potential for increasing resiliency, thus protecting adolescents from these mental health issues and their potential long-term effects. The purpose of this study was to examine the effects of mountain biking on middle school students’ resiliency. Specifically, we were interested in comparing a mountain biking program with integrated AT concepts (AT-MTB) to the same mountain biking program without integrated AT concepts (MTB). Using a randomized-controlled design, we randomly assigned participants (n = 30) into a treatment group (AT-MTB) or a comparison group (MTB). Results of a multilevel growth curve analysis indicated that participants in the AT-MTB group experienced an increase in resiliency, while those in the MTB group experienced a decrease. Implications for practice and future research are discussed.
Enhanced cell segmentation with limited annotated data using generative adversarial networks

The application of deep learning is rapidly transforming the field of bioimage analysis. While deep learning has shown great promise in complex microscopy tasks such as single-cell segmentation, the development of generalizable foundation deep learning segmentation models is hampered by the scarcity of large and diverse annotated datasets of cell images for training purposes. Generative Adversarial Networks (GANs) can generate realistic images that can potentially be easily used to train deep learning models without the generation of large manually annotated microscopy images. Here, we propose a customized CycleGAN architecture to train an enhanced cell segmentation model with limited annotated cell images, effectively addressing the challenge of paucity of annotated data in microscopy imaging. Our customized CycleGAN model can generate realistic synthetic images of cells with morphological details and nuances very similar to that of real images. This method not only increases the variability seen during training but also enhances the authenticity of synthetic samples, thereby enhancing the overall predictive accuracy and robustness of the cell segmentation model. Our experimental results show that our CycleGAN-based method significantly improves the performance of the segmentation model compared to conventional training techniques. Interestingly, we demonstrate that our model can extrapolate its knowledge by synthesizing imaging scenarios that were not seen during the training process. Our proposed customized CycleGAN method will accelerate the development of foundation models for cell segmentation in microscopy images.
Burstprop for Learning in Spiking Neuromorphic Hardware

The need for energy-efficient solutions in Deep Neural Network (DNN) applications has led to a growing interest in Spiking Neural Networks (SNNs) implemented in neuromorphic hardware. The Burstprop algorithm enables online and local learning in hier-archical networks, and therefore can potentially be implemented in neuromorphic hardware. This work presents an adaptation of the algorithm for training hierarchical SNNs on MNIST. Our implementation requires an order of magnitude fewer neurons than the previous ones. While Burstprop outper-forms Spike-timing dependent plasticity (STDP), it falls short compared to training with backpropagation through time (BPTT). This work establishes a foundation for further improvements in the Burst-prop algorithm, developing such algorithms is essential for achieving energy-efficient machine learning in neuromorphic hardware.
The use of social information in vulture flight decisions

Animals rely on a balance of personal and social information to decide when and where to move next in order to access a desired resource, such as food. The benefits from cueing on conspecifics to reduce uncertainty about resources availability can be rapidly overcome by the risks of within-group competition, often exacerbated toward low-ranked individuals. Being obligate soarers, relying on thermal updrafts to search for carcasses around which competition can be fierce, vultures represent ideal models to investigate the balance between personal and social information during foraging movements. Linking dominance hierarchy, social affinities and meteorological conditions to movement decisions of eight captive vultures, Gyps spp. , released for free flights in natural-like soaring conditions, we found that they relied on social information (i.e. other vultures using/having used the thermals) to find the next thermal updraft, especially in unfavourable flight conditions. Low-ranked individuals were more likely to disregard social cues when deciding where to go next, possibly to minimise the competitive risk of social aggregation. These results exemplify the architecture of decision-making during flight in social birds. It suggests that the environmental context, the context of risk and the social system as a whole calibrate the balance between personal and social information use.
Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex

Brain signal irreversibility has been shown to be a promising approach to study neural dynamics. Nevertheless, the relation with cortical hierarchy and the influence of different electrophysiological features is not completely understood. In this study, we recorded local field potentials (LFPs) during spontaneous behavior, including awake and sleep periods, using custom micro-electrocorticographic (μECoG) arrays implanted in ferrets. In contrast to humans, ferrets remain less time in each state across the sleep-wake cycle. We deployed a diverse set of metrics in order to measure the levels of complexity of the different behavioral states. In particular, brain irreversibility, which allows us to quantify the level of non-equilibrium captured by the arrow of time of the signal, revealed the hierarchical organization of the ferret’s cortex. We found different signatures of irreversibility and functional hierarchy of large-scale dynamics in three different brain states (active awake, quiet awake, and deep sleep), showing a lower level of irreversibility in the deep sleep stage, compared to the other. Irreversibility also allowed us to disentangle the influence of different brain regions and frequency bands in this process, showing a predominance of the parietal area and the theta band. Furthermore, when inspecting the embedded dynamic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate and lower entropy production. These results suggest functional hierarchies in organization that can be revealed through thermodynamic features and information theory metrics.
Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep

Learning and consolidation of motor skills dynamically evolve both online during practice and offline after training. We investigated, using magnetoencephalography, the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods shortly after the end of learning (short-term boost window, within 30 min) and at more delayed time scales (silent 4h and next day 24h windows) with an intermediate nap or wakefulness after the boost window. Resting-state neural dynamics in brain networks were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and resting-state functional connectivity (rsFC), respectively, as well as their relationship with the evolution of motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance achievements, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training re-exposure to the task modulated fast and slow network characteristics during the boost but not in the silent window. These short practice-related induction effects were observed again on the next day, to a reduced extent as compared to the boost window. Daytime naps did not significantly modulate memory consolidation both at behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and subsequent memory consolidation processes and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.
Fundamentals of General Linear Acoustics

Acoustics deals with the production, control, transmission, reception, and effects of sound. Owing to acoustics being an interdisciplinary field, this book is intended to be equally accessible to readers from a range of backgrounds including electrical engineering, physics and mechanical engineering. This book introduces the fundamentals of acoustic wave motion. It addresses in a clear and systematic way some of the most difficult parts of acoustics for beginners, such as the widely different approximations due to the wide frequency range, the apparently arbitrary choice between the use of analytical solutions to the wave equation with boundary conditions, and the fundamentally different energybased considerations used in noise control. As a result, it provides readers with a self-contained source of information on acoustics which can be used for self-study or as a graduate course text.
Predictors of Attention Deficit/Hyperactivity Disorder Behaviors in Chinese Adoptees: A 10-Year Longitudinal Study

ABSTRACT This longitudinal study examined predictors of ADHD behaviors in Chinese adoptees. We collected the data over 10 years from 173 adoptees and their adoptive mothers. We analyzed the data to explore the associations between age at adoption, family type, internalizing problems, externalizing problems, adverse life events, and the adoptees’ ADHD outcomes. Results showed that internalizing problems, externalizing problems, and adverse life events significantly predicted later ADHD behaviors, explaining 13% variance of the outcomes. The study shed light on contributing factors of ADHD behaviors in Chinese adoptees and offered implications for counselors working with Chinese adoptees.
Single bright NV centers in aggregates of detonation nanodiamonds

In this paper, we experimentally investigate optical and spin properties of NV centers in aggregates of detonation nanodiamonds. We show that despite the small size of nanodiamonds forming the aggregate, the NV centers in these aggregates exhibit spin properties comparable to similar size nanodiamonds grown by other methods, but with brightness enhanced by a factor of 2.
Performance Analysis of Gravitational Water Vortex Turbine using OpenFOAM

A Gravitational Water Vortex Turbine is a new development that extracts power from artiﬁcially formed water vortex when the water from open ﬂow channel directed tangentially to a round basin and discharges through the hole provided at the bottom of the basin. This can harness hydro power in application range of low to ultralow head and median to low ﬂows and has the unique feature of leaving positive environmental impact through the aeration of water as it passes through the turbine. However, the presence of free–surface vortex in the ﬂow ﬁeld complicates its design from conventional impulse and reaction turbines perspective due to vortex-blade interaction. Recent investigations have indicated that curved blade proﬁles are more eﬃcient to harness the kinetic energy of the vortex. However, little amount of work has been done on the optimization of suitable runner for the free–surface vortex proﬁle considering the vortex-blade interaction, the ratio of runner diameter to basin diameter (blade area) and its eﬀect on torque and power output. In this study, the performance of Gravitational Water Vortex Turbine has been investigated numerically using OpenFOAM. The eﬀect of design parameters such as vortex-blade interaction, the ratio of runner diameter to basin diameter on the performance parameters such as eﬀective head, torque, power and eﬃciency has been characterized. As a result, it is found out that a small blade size runner has higher rotational speed due to the strength of vortex was higher in the vicinity of air core but produces less torque. However, a large blade size runner has less rotational speed and high torque due to the nature of vortex tangential velocity ﬁeld decrease with increasing radius and large blade area respectively. In addition, this work identiﬁed extending the size of runner blades to the far–ﬁeld region resulting in reduction of power output. Therefore, the outcomes from this study will be helpful to design and establish performance analysis for the Gravitational Water Vortex Power Plant for a given ﬂow and head of particular hydropower site. Moreover, it can also be used as pointer for the future generations of Gravitational Water Vortex Turbine technology.
Integral representation and relaxation of convex local functionals on $BV(\Omega )$

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A quantitative evaluation of �ood damage methodology under a portfolio of adaptation scenarios

Flood risk is anticipated to increase, driven by change in climate and socio-economic development. Flood impact assessments rely heavily on models, hence understanding the effect of uncertainties encompassed in the modelling chain is critical for the adequate interpretation of �ood risk and for proposing effective �ood adaptations. Previous researches focused on understanding the effect of processes embedded in models, or �ood frequency analysis onto �ood risk. However, there is yet no study evaluating the cascading uncertainty effects of �ood damage methodology on estimating annual damage, optimal �ood protection, and residual �ood damage. Here, using an updated global river and inundation model forced by the latest climate data and employing a standard �ood methodology we found that, during 2020–2100, expected annual damage (EAD) increase by US$16.2 and US$44.5 billion yr − 1 under a low and high emission scenario, respectively. During the same period and despite the adoption of optimal levels of �ood protections, global total RFD remained high under both low and high emission scenarios, US$25.8 and US$36.2 billion yr − 1 , respectively. Our results demonstrate that, under current levels of �ood protection, EAD approximately double by switching methodologies. Aggregating data at the regional scale revealed con�icting trends between methodologies for developing and high-income countries driven by i) existing levels of �ood protection and ii) the intensity, evolution, and distribution of GDP at the administrative level. Flood damage methodology is the dominant source of uncertainty, followed by unit construction cost and discount rates, respectively.
Fighting Genre Design Guidelines

This paper is intended to identify how fighting games can be better designed. It will clarify what user exists, and what they generally want. The paper will establish a categorization of fighting game characters, arch types . In the end guidelines for designing fighting games will be established, many of the guidelines (if not all) are also reasonable to use for designing modern beat-em up games.
Noxa mediates hepatic stellate cell apoptosis by proteasome inhibition

Aim: Induction of hepatic stellate cell (HSC) apoptosis is a viable therapeutic strategy to reduce liver fibrogenesis. Although BH3‐only proteins of the Bcl‐2 family trigger pro‐apoptotic pathways, the BH3‐only proteins mediating HSC apoptosis have not been well defined. Our aim, using proteasome inhibition as a model to induce HSC apoptosis, was to examine the BH3‐only proteins contributing to cell death of this key liver cell subtype.
Employee Attrition Classification Model Based on Stacking Algorithm

This paper aims to build an employee attrition classification model based on the Stacking algorithm. Oversampling algorithm is applied to address the issue of data imbalance and the Randomforest feature importance ranking method is used to resolve the overfitting problem after data cleaning and preprocessing. Then, different algorithms are used to establish classification models as control experiments, and R-squared indicators are used to compare. Finally, the Stacking algorithm is used to establish the final classification model. This model has practical and significant implications for both human resource management and employee attrition analysis.
The detection of 11th of March 2011 Tohoku's TEC seismo-ionospheric anomalies using the Singular Value Thresholding (SVT) method

The Total Electron Content (TEC) measured by the Global Positioning System (GPS) is useful for registering the pre-earthquake ionospheric anomalies appearing before a large earthquake. In this paper the TEC value was predicted using the singular value thresholding (SVT) method. Also, the anomaly is detected utilizing this predicted value and the definition of the threshold value, leading to the use of the anomaly as a precursor. The SVT is used in the matrix completion problem, namely the accurate recovery of a matrix from a nearly minimal set of entries. In this study, the SVT has been applied to the ionospheric TEC of the global ionosphere maps(GIM) data on a powerful earthquake in Tohoku on the 11th of March in 2011. In this method, the two-hour TEC observations of this region are converted into a matrix for several consecutive days before and after the occurrence of an earthquake. In this matrix the rows and the columns represent the days and the sequential hours, respectively. The prediction of the non-linear time series is formulated as a method for solving the low-rank recovery problem. Results indicate that under suitable conditions the TEC values can be estimated properly in the aforementioned days and hours by solving a simple optimization problem. In order to show the efficiency of this method in predicting the time series, the results obtained from this research were compared with those from other researches.