Miracle-dz/UniFAR-FTUs huggingface dataset:
Divergent versus Nondivergent Instabilities of Piecewise Uniform Shear Flows on the f Plane

Abstract The linear instability of a piecewise uniform shear flow is classically formulated for nondivergent perturbations on a 2D barotropic mean flow with linear shear, bounded on both sides by semi-infinite half-planes where the mean flows are uniform. The problem remains unchanged on the f plane because for nondivergent perturbations the instability is driven by vorticity gradient at the edges of the inner, linear shear region, whereas the vorticity itself does not affect it. The instability of the unbounded case is recovered when the outer regions of uniform velocity are bounded, provided that these regions are at least twice as wide as the inner region of nonzero shear. The numerical calculations demonstrate that this simple scenario is greatly modified when the perturbations’ divergence and the variation of the mean height (which geostrophically balances the mean flow) are retained in the governing equations. Although a finite deformation radius exists on the shallow water f plane, the mean vortici.
Evidence for prey preference partitioning in the middle Eocene high-diversity crocodylian assemblage of the Geiseltal-Fossillagerstätte, Germany utilizing skull shape analysis

Abstract The Geiseltal fossil collection from southern Sachsen-Anhalt Germany contains remarkably well-preserved fossils of middle Eocene age. These include several crocodylian skulls, representing at least four different genera with a fifth genus represented by two mandibular rami. As sites with this many crocodylian genera are unknown in modern ecosystems, it has been hypothesized that these crocodylians may have differences in habit as compared to living crocodylians. In order to test similarities between the Geiseltal crocodylians and extant species, an analysis was conducted using geometric morphometrics to quantify shape in crocodylian skulls of all living species (n = 218) and all well-preserved crocodylian skulls of the Geiseltal fauna (n = 28). A relative warps analysis was used to quantify and compare skull shape, revealing Allognathosuchus and Boverisuchus to be very distinct from each other as well as from Asiatosuchus and Diplocynodon. Overlap in shape alone exists between some Diplocynodon and some Asiatosuchus, but there was significant difference in adult size. When compared with extant crocodylians, three Geiseltal genera occupied distinctly non-modern morphospace in the first two relative warps axes. Comparison of the diets of living crocodylians with similarly shaped skulls was used to reconstruct the prey preferences of the Geiseltal crocodylians, revealing differences in specialization. During the middle Eocene high global temperatures, partitioning of prey preference may have allowed this group to attain its higher than usual diversity, reducing the amount of direct competition.
An Overview on Multimodal Emergency Evacuation in an Urban Network

Emergency evacuation is important following natural disasters, terrorist attacks or other causes. This can put an enormous amount of pressure on our transportation networks as the generated traffic demand can significantly exceed those for routine travel conditions. Applying the best attributes of each mode of transportation is thus vital in such scenarios. Depending on the nature of the hazard and the population characteristics within the threat area, the implementation of evacuation models and simulations has the potential to save a vast array of lives by providing an effective path to safety for inhabitants. This paper aims to present an overview of approaches to emergency evacuation in an urban network, their features and findings. Key findings show a deficit of research in models incorporating other modes such as pedestrians and public transit, synergising different evacuation strategies for efficient evacuation and realistic travel behaviour.
Researchers eye-view of sarcasm detection in social media textual content

The enormous use of sarcastic text in all forms of communication in social media will have a physiological effect on target users. Each user has a different approach to misusing and recognising sarcasm. Sarcasm detection is difficult even for users, and this will depend on many things such as perspective, context, special symbols. So, that will be a challenging task for machines to differentiate sarcastic sentences from non-sarcastic sentences. There are no exact rules based on which model will accurately detect sarcasm from many text corpus in the current situation. So, one needs to focus on optimistic and forthcoming approaches in the sarcasm detection domain. This paper discusses various sarcasm detection techniques and concludes with some approaches, related datasets with optimal features, and the researcher's challenges.
UvA-DARE (Digital Academic Repository) Evaluating the feasibility of the steady-state visual evoked potential (SSVEP) to study temporal attention

Improvements in perceptual performance can be obtained when events in the environment are temporally predictable — and temporal predictability improves attention and sensory processing. The amplitude of the steady-state visual evoked potential (SSVEP) has been shown to correlate with attention paid to a flickering stimulus even if the flickering stimulus is irrelevant for the task. However, to our knowledge, the validity of the SSVEP to study temporal attention has not been established. Therefore, we designed an SSVEP temporal attention task to evaluate whether the SSVEP and its temporal dynamics can be used to study temporal attention. We used a forced-choice perceptual detection task while presenting task-irrelevant visual flicker at alpha (10 Hz) and two surrounding frequencies (6 or 15 Hz). Temporal predictability was manipulated by having the interstimulus intervals (ISI) be constant or variable. Behavioral results replicated previous studies confirming the benefits of temporal expectations on performance for trials with constant ISI. EEG analyses revealed robust SSVEP amplitudes for all flicker frequencies, although a main effect of temporal expectations on SSVEP amplitude was not significant. Additional analyses revealed temporal predictability-related modulations of SSVEP amplitude at 10 Hz and its second harmonic (20 Hz). The effect of temporal predictability was also observed for the 6 Hz flicker, but not for 15 Hz for any ISI condition. These results provide some evidence for the feasibility of the SSVEP technique to study temporal attention for stimuli with flicker frequencies around the alpha band.
Precipitation and net ecosystem exchange are the most important drivers of DOC flux in upland boreal catchments

According to recent studies, dissolved organic carbon (DOC) concentrations in rivers throughout the boreal zone are increasing. However, the mechanistic explanation of this phenomenon is not yet well known. We studied how the short and long‐term changes in precipitation, soil temperature, soil water content, and net ecosystem exchange (NEE) are reflected to DOC concentrations and runoff DOC fluxes in two small forested upland catchments in Southern Finland. We used continuous eddy covariance measurements above the forest and runoff flow measurements from the catchment areas conducted over a 15 year long time period to study the correlation between NEE, gross photosynthetic production, total ecosystem respiration, litter production, and runoff DOC. In addition, we looked for the most important environmental variables in explaining the interannual changes in runoff DOC by using multiple linear regression. Finally, we studied the temporal connection between runoff DOC concentrations, precipitation, soil water content, and NEE by using wavelet coherence analysis technique. Our results indicate that the DOC concentrations have increased over the last 15 years. The DOC flux was to a large extent determined by the amount of precipitation, but the previous year's NEE and litter production had also a small but significant effect on runoff DOC fluxes.
Suspicious Behavior Detection from Speech: A Proof of Concept

The goal of this study is to contribute on the development of human-machine interface that helps to identify individuals with suspicious behavior (of violence, terrorism,…) from speech.To do it, it was neceassry to define suspicious behavior through emotions according to valence and activation. Next, a scenario including background noise, security agent speech and vistor speech was defined. A dataset was constructed, speech processing and machine learning based algorithms are designed: after features extraction, audio stream is segmented into relevant content (noise, agent speech and visitor speech). The visitor speech is selected and a classification is carried to detect suspicious behavior. Simulation results show the effectiveness of the proposed solution.
Environmental control of deep convective clouds on Titan: The combined effect of CAPE and wind shear on storm dynamics, morphology, and lifetime

Titan has deep convective clouds driven by the release of latent from methane condensation. As on Earth, the presence of convective available potential energy (CAPE), which quantifies the amount of energy available through condensation, is required for storms to develop. While CAPE is a requirement for storms, the dynamics, morphology, and longevity of storms on Earth is controlled by both CAPE and wind shear, often expressed as a ratio in the form of the bulk Richardson Number. The impact of CAPE and wind shear on storms in a Titan‐like environment are explored through numerical simulation. Model results indicate that Titan storms should respond to changes in the Richardson Number in a manner similar to storms on Earth. Very long‐lived storms (>24 h) propagating for 1000 km or more might be possible on Titan when CAPE and wind shear are properly balanced. Some of the simulated storms exhibit dynamics similar to squall lines. Varying amounts of shear in the Titan environment might explain the variety of convective cloud expressions—varying from short‐lived single cell storms to longer‐lived linear features and large cloud bursts—identified in Cassini orbiter and ground‐based observations. The varying amounts and spatial distribution of precipitation, as well as surface winds associated with storms, should have implications on the formation of fluvial and aeolian features and on the exchange of methane with the surface and lakes.
Dynamique de population du doré jaune (Sander vitreus) du Lac Mistassini

Understanding the dynamics of wild populations is a major asset in concern with management and conservation of species. Récent literature largely supports the fact that some freshwater Systems are composed of many genetically distinct fish populations, instead of one large panmictic population. Being remote and pristine, Mistassini Lake, the largest natural lake in Québec's province, offers exceptional opportunities for the study of behavioral and ecological processes influencing the evolutionary pathway of aquatic species. This master's thesis specifically aims at determining genetic structure, identifying patterns of dispersai and migration as well as resulting spatial and temporal distribution of Mistassini Lake walleye (Sander vitreus) populations. Results underline the importance of using population assignment methods in order to elucidate parameters composing dynamics of wild populations. Behavioural processes such as asymmetrical dispersai and spatial partitioning between populations were identified as being partly responsible for the apparition and maintenance of stable genetic structuring within this species.
The plasticity of neuropeptide Y-Y1 receptor system on Tac2 neurons contributes to mechanical hyperknesis during chronic itch

Rationale: In the physiological states, the act of scratching protects the person from harmful substances, while in certain pathological conditions, the patient suffers from chronic itch, both physically and mentally. Chronic itch sufferers are more sensitive to mechanical stimuli, and mechanical hyperknesis relief is essential for chronic itch treatment. While neuropeptide Y-Y1 receptor (NPY-Y1R) system is known to play a crucial role in modulating mechanical itch in physiological conditions, it is elusive how they are altered during chronic itch. We hypothesize that the negative regulatory effect of Y1Rs on Tac2 neurons, the key neurons that transmit mechanical itch, declines during chronic itch. Methods: We combined transgenic mice, chemogenetic manipulation, immunofluorescence, rabies virus circuit tracing, and electrophysiology to investigate the plasticity of Y1Rs on Tac2 neurons during chronic itch. Results: We found that Tac2 neurons receive direct input from Npy neurons and that inhibition of Npy neurons induces activation of Tac2 neurons. Moreover, the expression of Y1Rs on Tac2 neurons is reduced, and the regulatory effect is also reduced during chronic itch. Conclusion: Our study clarifies the plasticity of Y1Rs on Tac2 neurons during chronic itch and further elucidates the mechanism by which NPY-Y1R system is responsible for modulating mechanical itch. We highlight Y1Rs as a promising therapeutic target for mechanical hyperknesis during chronic itch.
MIDDLE EOCENE TO EARLY OLIGOCENE CLIMATE: PRELIMINARY MAGNETOBIOSTRATIGRAPHIC DATA FROM MONTE CAGNERO SECTION, CENTRAL ITALY

Deep-sea foraminiferal stable isotope records (� 18 O) indicate a long-term cooling trend that culminated in the Oi-1 glaciation near the Eocene/Oligocene transition at ~34 Ma. In the late middle Eocene, this trend was interrupted by a strong, transient warming event at ~40 Ma, known as Middle Eocene Climatic Optimum (MECO). The Monte Cagnero sedimentary section, located in the northeastern Apennines near Urbania (Italy), in the Umbria-Marche Basin, is an important middle Eocene to lower Oligocene section. We use magnetobiostratigraphy to study the impact of the closure of the NeoTethyan gateway on subtropical Eocene circulation to better understand global climate changes during the switch from greenhouse to icehouse conditions. The studied sediments consist of alternating reddish and greenish limestones and marlstones. We undertook high-resolution paleomagnetic, foraminiferal and calcareous plankton biostratigraphic analyses to construct an integrated age model. The magnetic mineralogy is dominated by a mixture of high- and low-coercivity minerals (probably hematite and magnetite). Environmental magnetic data indicate long-term trends in the concentration, grain-size and composition of magnetic minerals. These new biostratigraphy and magnetic results from Monte Cagnero suggest that the section provides a continuous and well-preserved interval for analyzing middle Eocene to early Oligocene climate variability.
Asphalt Modification with LDPE Plastic Waste Treated with Glacidyl Methacrylate and Benzoyl Peroxide Initiator

The aim of this research is to determine the quality of the softening point test, penetration test and deactivation of modified asphalt from garfted LDPE plasticization waste. And for grafting treatment, use the internal mixer method where GMA and BPO are added to the grafting mixture. From the grafting results obtained, we carried out an FTIR test and identified a wave number of 1728/cm indicating the presence of the C-H or benzene functional group. This can be interpreted as a grafting process which can also be seen at the wave number 1730/cm. This can be interpreted as a grafting process from GMA to LDPE. And for the wave number at 2723/cm, there is a CH3 stetching from the LDPE chain backbone so that what is obtained is the LDPE-g-GMA spectrum. As for the TGA and DSC test results obtained, namely that in DSC the peak value of the number is at a temperature of 160.60 C and the value obtained from TGA is 373.13 C. And to find out the standard of modified asphalt from mixing asphalt with waste The value of the grafted plastic that we carried out was 65.23, for the penetration test it was 53.7 and for the deactivation test it was 55.46. This can be interpreted that all three are in accordance with the 2018 Bina Marga asphalt standards
Underwater Unmanned Vehicles Net-Breaking Detection and Recognition System based on YOLO

We proposed a deep learning-based net-breaking detection and recognition system for net-breaking detection and recognition of underwater unmanned vehicles. The system was developed to assist aquaculture farmers in checking the condition of offshore fish cages. Using a Remotely Operated underwater Vehicle (ROV), underwater images were captured to identify whether the fish cage was damaged or not. The system assisted in reducing fish losses and minimizing the cost and risk of employing divers. The ROV used a single RGB camera combined with deep learning for detection. The deep learning algorithm used in the system was You Only Look Once (YOLO), which allowed the machine to learn a large number of features of various categories first and then use the resulting model to detect net damage in the fish cages.
Development and Validation of a Deep Learning System for Diagnosing Glaucoma Using Optical Coherence Tomography

This study aimed to develop and validate a deep learning system for diagnosing glaucoma using optical coherence tomography (OCT). A training set of 1822 eyes (332 control, 1490 glaucoma) with 7288 OCT images, an internal validation set of 425 eyes (104 control, 321 glaucoma) with 1700 images, and an external validation set of 355 eyes (108 control, 247 glaucoma) with 1420 images were included. Deviation and thickness maps of retinal nerve fiber layer (RNFL) and ganglion cell–inner plexiform layer (GCIPL) analyses were used to develop the deep learning system for glaucoma diagnosis based on the visual geometry group deep convolutional neural network (VGG-19) model. The diagnostic abilities of deep learning models using different OCT maps were evaluated, and the best model was compared with the diagnostic results produced by two glaucoma specialists. The glaucoma-diagnostic ability was highest when the deep learning system used the RNFL thickness map alone (area under the receiver operating characteristic curve (AUROC) 0.987), followed by the RNFL deviation map (AUROC 0.974), the GCIPL thickness map (AUROC 0.966), and the GCIPL deviation map (AUROC 0.903). Among combination sets, use of the RNFL and GCIPL deviation map showed the highest diagnostic ability, showing similar results when tested via an external validation dataset. The inclusion of the axial length did not significantly affect the diagnostic performance of the deep learning system. The location of glaucomatous damage showed generally high level of agreement between the heatmap and the diagnosis of glaucoma specialists, with 90.0% agreement when using the RNFL thickness map and 88.0% when using the GCIPL thickness map. In conclusion, our deep learning system showed high glaucoma-diagnostic abilities using OCT thickness and deviation maps. It also showed detection patterns similar to those of glaucoma specialists, showing promising results for future clinical application as an interpretable computer-aided diagnosis.
Development and validation of a deep learning pipeline to measure pericardial effusion in echocardiography

Objectives The aim of this study was to develop a deep-learning pipeline for the measurement of pericardial effusion (PE) based on raw echocardiography clips, as current methods for PE measurement can be operator-dependent and present challenges in certain situations. Methods The proposed pipeline consisted of three distinct steps: moving window view selection (MWVS), automated segmentation, and width calculation from a segmented mask. The MWVS model utilized the ResNet architecture to classify each frame of the extracted raw echocardiography files into selected view types. The automated segmentation step then generated a mask for the PE area from the extracted echocardiography clip, and a computer vision technique was used to calculate the largest width of the PE from the segmented mask. The pipeline was applied to a total of 995 echocardiographic examinations. Results The proposed deep-learning pipeline exhibited high performance, as evidenced by intraclass correlation coefficient (ICC) values of 0.867 for internal validation and 0.801 for external validation. The pipeline demonstrated a high level of accuracy in detecting PE, with an area under the receiving operating characteristic curve (AUC) of 0.926 (95% CI: 0.902–0.951) for internal validation and 0.842 (95% CI: 0.794–0.889) for external validation. Conclusion The machine-learning pipeline developed in this study can automatically calculate the width of PE from raw ultrasound clips. The novel concepts of moving window view selection for image quality control and computer vision techniques for maximal PE width calculation seem useful in the field of ultrasound. This pipeline could potentially provide a standardized and objective approach to the measurement of PE, reducing operator-dependency and improving accuracy.
Enhancing Network Security in SDNIoT Environments Through CNN-Based Attack Detection

The rapid proliferation of Internet of Things (IoT) devices connected to the internet has given rise to an increase in IoT-based attacks, thereby necessitating the development of effective intrusion detection systems. This research addresses the challenges posed by the evolving threat landscape in Software Defined Network (SDN)-assisted IoT (SDNIoT) environments. Traditional intrusion detection solutions encounter difficulties in coping with the coexistence of conventional and IoT protocols in a centralized network architecture. To mitigate this issue, a novel approach is proposed, utilizing Convolutional Neural Networks (CNNs) for accurate attack detection within SDNIoT networks. The approach involves employing CNN architectures, which possess the capability to learn complex hierarchical patterns, for intrusion detection. The performance of the CNN model is extensively evaluated on two distinct SDNIoT-focused datasets, encompassing both binary and multiclass classification scenarios. Furthermore, a detailed analysis of the architectural components of the model is introduced, offering insights into the visualization of learned features. The proposed CNN-based framework demonstrates impressive capabilities in identifying and categorizing network attacks in SDNIoT networks. Proposing a novel approach employing Convolutional Neural Networks (CNNs) for precise attack detection, the model is evaluated on two SDNIoT datasets. Results showcase the CNN's remarkable capabilities, achieving an accuracy of 99.75% and 99.80% on “NetworkFlow_X” and “NetAttack_Y” datasets, respectively. This research contributes to the field by harnessing advanced deep learning techniques to enhance network security, thereby addressing the challenges posed by IoT-based threats within the SDN framework.