Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of a Fault-Controlled, Deep-Water Sub-Basin, Tabernas, SE Spain

The Neogene Tabernas Basin, SE Spain, provides important evidence at outcrop for the interplay between tectonic deformation of the sea floor, slope instability and turbidity current behaviour. Dextral-oblique strike-slip faults and associated folds propagated along the basin axis to deform the palaeo-sea floor, creating structurally-controlled depressions in which turbidity currents were trapped and ponded. EW-trending syn-depositional faults define a narrow sub-basin that subsided asymmetrically as a negative flower structure. The sub-basin contains an expanded succession (>300 m of ponded turbidite sheets, debrites and slumps) along its northern margin flanked by the principal fault strand defined by a wide zone of sheared and calcite-veined marl. A narrower fault zone with a smaller displacement marks the southern margin of the sub-basin and the fill close to it is thin with internal discordances, evidence of local failure and southward thinning of sandstone sheets. Both northern and southern faults ‘died’ at the same stratigraphic level and were overstepped by basin floor turbidites showing evidence of weaker and longer-range topographic confinement. As turbidites healed and aggraded out of the sub-basin to progressively onlap the southern margin of the basin, major gravity failures occurred emplacing thick (>100 m) mass-transport complexes. The first initially reworked the southern part of the sub-basin fill together with the early onlap wedge, the second remobilised the onlap wedge, and the third records failure of the upper part of the slope well above the wedge. The first two were toe-confined failures, the third and furthest travelled was confined by basin axis topography. All three failures are lateral to or directly overlain by ‘megabed’ sheets on the basin floor, implying either a common trigger (earthquakes) or slope instability following reflection of large volume flows. Tabernas turbidites highlight the role of basin tectonics (as opposed to up-dip supply and sea level fluctuations) in directly impacting on deep-water processes and stratigraphy. Small deep-water transtensional sub-basins opened up along long transfer faults accommodating regional extension.
“Brain Fog” by COVID-19 or Alzheimer’s Disease? A Case Report

Cognitive symptoms after COVID-19 have been increasingly recognized several months after the acute infection and have been designated as “brain fog. ” We report a patient with cognitive symptoms that started immediately after COVID-19, in which cerebrospinal fluid biomarkers were highly suggestive of Alzheimer’s disease. Our case highlights the need to examine patients with cognitive symptoms following COVID-19 comprehensively. A detailed assessment combining clinical, cognitive, and biomarker studies may help disentangle the underlying mechanisms associated with cognitive dysfunction in each case. The investigation of neurodegenerative processes in an early stage, especially in older patients, is probably warranted.
Restricted Kinematics in Children With Autism in the Execution of Complex Oscillatory Arm Movements

Restricted and repetitive behavior is a core symptom of autism spectrum disorder (ASD) characterized by features of restrictedness, repetition, rigidity, and invariance. Few studies have investigated how restrictedness is manifested in motor behavior. This study aimed to address this question by instructing participants to perform the utmost complex movement. Twenty children with ASD and 23 children with typical development (TD) performed one-dimensional, left-right arm oscillations by demonstrating varying amplitudes and frequencies. The entropy of amplitude and velocity was calculated as an index of kinematic complexity. Results showed that the velocity entropy, but not the amplitude entropy, was significantly lower in ASD than in TD (p < 0.01), suggesting restricted kinematics. Further analysis demonstrated that a significantly higher proportion of the velocity values was allocated at a low-speed level in the children with ASD (p < 0.01). A qualitative comparison of the complex movement with movement at preferred frequency suggested that the children with ASD might be less likely to shift away from the preferred movement. However, our study can be improved in terms of recruiting a larger sample of participants, measuring the level of motivation, and collecting both complex and preferred movements of the same participant.
The Role of Continuous Theta Burst TMS in the Neurorehabilitation of Subacute Stroke Patients: A Placebo-Controlled Study

Objectives: Transcranial magnetic stimulation, in particular continuous theta burst (cTBS), has been proposed for stroke rehabilitation, based on the concept that inhibition of the healthy hemisphere helps promote the recovery of the lesioned one. We aimed to study its effects on cortical excitability, oscillatory patterns, and motor function, the main aim being to identify potentially beneficial neurophysiological effects. Materials and Methods: We applied randomized real or placebo stimulation over the unaffected primary motor cortex of 10 subacute (7 ± 3 days) post-stroke patients. Neurophysiological measurements were performed using electroencephalography and electromyography. Motor function was assessed with the Wolf Motor Function Test. We performed a repeated measure study with the recordings taken pre-, post-cTBS, and at 3 months' follow-up. Results: We investigated changes in motor rhythms during arm elevation and thumb opposition tasks and found significant changes in beta power of the affected thumb's opposition, specifically after real cTBS. Our results are consistent with an excitatory response (increase in event-related desynchronization) in the sensorimotor cortical areas of the affected hemisphere, after stimulation. Neither peak-to-peak amplitude of motor-evoked potentials nor motor performance were significantly altered. Conclusions: Consistently with the theoretical prediction, this contralateral inhibitory stimulation paradigm changes neurophysiology, leading to a significant excitatory impact on the cortical oscillatory patterns of the contralateral hemisphere. These proof-of-concept results provide evidence for the potential role of continuous TBS in the neurorehabilitation of post-stroke patients. We suggest that these changes in ERS/ERD patterns should be further explored in future phase IIb/phase III clinical trials, in larger samples of poststroke patients.
MET Receptor Tyrosine Kinase Regulates Lifespan Ultrasonic Vocalization and Vagal Motor Neuron Development

The intrinsic muscles of the larynx are innervated by the vagal motor nucleus ambiguus (nAmb), which provides direct motor control over vocal production in humans and rodents. Here, we demonstrate in mice using the Phox2bCre line, that conditional embryonic deletion of the gene encoding the MET receptor tyrosine kinase (MET) in the developing brainstem (cKO) results in highly penetrant, severe deficits in ultrasonic vocalization in early postnatal life. Major deficits and abnormal vocalization patterns persist into adulthood in more than 70% of mice, with the remaining recovering the ability to vocalize, reflecting heterogeneity in circuit restitution. We show that underlying the functional deficits, conditional deletion of Met results in a loss of approximately one-third of MET+ nAmb motor neurons, which begins as early as embryonic day 14.5. The loss of motor neurons is specific to the nAmb, as other brainstem motor and sensory nuclei are unaffected. In the recurrent laryngeal nerve, through which nAmb motor neurons project to innervate the larynx, there is a one-third loss of axons in cKO mice. Together, the data reveal a novel, heterogenous MET-dependence, for which MET differentially affects survival of a subset of nAmb motor neurons necessary for lifespan ultrasonic vocal capacity.
Physical Exercise Promotes a Reduction in Cardiac Fibrosis in the Chronic Indeterminate Form of Experimental Chagas Disease

Chagas disease (CD), caused by the protozoan Trypanosoma cruzi, is a neglected tropical disease and a health problem in Latin America. Etiological treatment has limited effectiveness in chronic CD; thus, new therapeutic strategies are required. The practice of physical exercises has been widely advocated to improve the quality of life of CD patients. The most frequent clinical CD manifestation is the chronic indeterminate form (CIF), and the effect of physical exercises on disease progression remains unknown. Here, in a CIF model, we aimed to evaluate the effect of physical exercises on cardiac histological, parasitological, mitochondrial, and oxidative metabolism, electro and echocardiographic profiles, and immunological features. To establish a CIF model, BALB/c and C57BL/6 mice were infected with 100 and 500 trypomastigotes of the Y T. cruzi strain. At 120 days postinfection (dpi), all mouse groups showed normal PR and corrected QT intervals and QRS complexes. Compared to BALB/c mice, C57BL/6 mice showed a lower parasitemia peak, mortality rate, and less intense myocarditis. Thus, C57BL/6 mice infected with 500 parasites were used for subsequent analyses. At 120 dpi, a decrease in cardiac mitochondrial oxygen consumption and an increase in reactive oxygen species (ROS) were detected. When we increased the number of analyzed mice, a reduced heart rate and slightly prolonged corrected QT intervals were detected, at 120 and 150 dpi, which were then normalized at 180 dpi, thus characterizing the CIF. Y-infected mice were subjected to an exercise program on a treadmill for 4 weeks (from 150 to 180 dpi), five times per week in a 30–60-min daily training session. At 180 dpi, no alterations were detected in cardiac mitochondrial and oxidative metabolism, which were not affected by physical exercises, although ROS production increased. At 120 and 180 dpi, comparing infected and non-infected mice, no differences were observed in the levels of plasma cytokines, indicating that a crucial biomarker of the systemic inflammatory profile was absent and not affected by exercise. Compared with sedentary mice, trained Y-infected mice showed similar parasite loads and inflammatory cells but reduced cardiac fibrosis. Therefore, our data show that physical exercises promote beneficial changes that may prevent CD progression.
Multimodal and Multiview Wound Monitoring with Mobile Devices

Along with geometric and color indicators, thermography is another valuable source of information for wound monitoring. The interaction of geometry with thermography can provide predictive indicators of wound evolution; however, existing processes are focused on the use of high-cost devices with a static configuration, which restricts the scanning of large surfaces. In this study, we propose the use of commercial devices, such as mobile devices and portable thermography, to integrate information from different wavelengths onto the surface of a 3D model. A handheld acquisition is proposed in which color images are used to create a 3D model by using Structure from Motion (SfM), and thermography is incorporated into the 3D surface through a pose estimation refinement based on optimizing the temperature correlation between multiple views. Thermal and color 3D models were successfully created for six patients with multiple views from a low-cost commercial device. The results show the successful application of the proposed methodology where thermal mapping on 3D models is not limited in the scanning area and can provide consistent information between multiple thermal camera views. Further work will focus on studying the quantitative metrics obtained by the multi-view 3D models created with the proposed methodology.
Consistent aeroelastic linearisation and reduced-order modelling in the dynamics of manoeuvring flexible aircraft

This work proposes a novel reduced-order modelling approach in time domain for the coupled flight dynamics and aeroelastic response of manoeuvring very flexible aircraft. The starting point is the coupling of a displacement-based, geometrically-nonlinear flexiblebody dynamics formulation with a 3-D unsteady vortex-lattice method. This is followed by a linearisation of the structural degrees of freedom, which are assumed to be small in a body-fixed reference frame. The translations and rotations of that reference frame and their time derivatives, which describe the vehicle flight dynamics, can be arbitrarily large. As a result, all couplings between the rigid and elastic motions are introduced without the a priori assumptions of the mean axes approximation, traditionally used to decouple the equations in flexible-aircraft dynamics. The resulting system can be projected onto a few vibration modes of the unconstrained aircraft with geometrically-nonlinear static deflections at a trim condition. Equally, the unsteady aerodynamics are approximated on a fixed lattice defined by the deformed static geometry. The resulting high-order aerodynamic system, which defines the mapping between the small number of generalised coordinates and unsteady aerodynamic loads, is then reduced through balanced truncation. This unified description of the flexible aircraft dynamics provides a hierarchy of aeroelastic model fidelities, which will be illustrated on a representative high-altitude, long-endurance aircraft to identify the importance of geometrically-nonlinear wing deformations on the vehicle dynamics. Application of the reduced-order modelling approach further shows a very substantial reduction in model size that leads to model orders (and computational cost) similar to those in conventional frequency-based methods but with higher modelling fidelity to compute manoeuvre loads. Closed-loop results for the Goland wing finally demonstrate the application of this approach in the synthesis of a robust flutter suppression controller.
Digital-To-Analog Converter for FSK

This thesis is one part of a overall task of designing a module for frequency shift keying (FSK) to be used in an Ultra Wide Band (UWB) system. The FSK system has a Direct Digital Synthesizer (DDS) and Digital-to-Analog (DAC). The DACs differential current signals are directly fed to a RF (Radio Frequency) unit that generates the UWB RF signal.The focus of this thesis is on DAC while the DDS is developed in VHDL as another thesis work. This thesis demonstrates a low-power, ultra wide band 10 bit DAC with an update frequency of 24 MSPS(Mega Samples Per Second). The DAC uses a L-fold linear interpolation architecture. It includes a 16-tap voltage controlled delay line and a 10 bit binary-weighted DAC with a time interleaved structure. The linear interpolation technique improves the attenuation of mirror components and also reduces the glitch. This helps to relax the analog filter requirements and sometimes an off chip capacitor is enough as low pass filter. The attenuation of image components is doubled in decibels(dB) compared with that of conventional DAC.In this work various DAC architectures are studied. The current-steering DAC is chosen due to its high speed and high resolution. A binary weighted architecture is chosen to reduce the digital circuits. This helped in reducing the power consumption. The design and simulation is done with help of Cadence. The layout is done in Cadence Virtuoso and the DDS is integrated with the DAC. The chip is to be manufactured in 130 nm CMOS process.
Adaptive Aggregation Networks for Class-Incremental Learning

Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase. An inherent problem in CIL is the stability-plasticity dilemma between the learning of old and new classes, i.e., high-plasticity models easily forget old classes, but high-stability models are weak to learn new classes. We alleviate this issue by proposing a novel network architecture called Adaptive Aggregation Networks (AANets) in which we explicitly build two types of residual blocks at each residual level (taking ResNet as the baseline architecture): a stable block and a plastic block. We aggregate the output feature maps from these two blocks and then feed the results to the next-level blocks. We adapt the aggregation weights in order to balance these two types of blocks, i.e., to balance stability and plasticity, dynamically. We conduct extensive experiments on three CIL benchmarks: CIFAR-100, ImageNet-Subset, and ImageNet, and show that many existing CIL methods can be straightforwardly incorporated into the architecture of AANets to boost their performances1.
HVAC Cooling-Heating-Power Demand Strategy Optimization Based on DDPG Control

Electric energy and hot and cold energy are the basic energy needs of human life and the two main energy forms of integrated energy system. As a typical demand response resource, Heating Ventilation Air Conditioning (HVAC) can be introduced into the demand response problem of cooling-heating-power synergy. Aiming at the difficulties of traditional modeling such as self-learning ability and slow optimization speed, a model free deep reinforcement learning based HVAC cooling-heating-power demand optimization strategy was proposed. Considering the complex observation state and the prediction of continuous action space, a deep deterministic policy gradient (DDPG) was chosen to solve the demand response problem. Firstly, the thermal demand response model on the demand side was defined, and the HVAC environment model was established. Secondly, HVAC is described as a Markov decision process. Appropriate state actions, control signals, and reward functions are designed to speed up the optimization process. Finally, in order to verify the feasibility of the optimization strategy based on DDPG, the simulation results of cold and hot power under different constraints were introduced respectively. The results show that the typical HVAC system with multiple energy flows of cold and hot power has good demand-side response characteristics
A Novel Multitemporal Deep Fusion Network (MDFN) for Short-Term Multitemporal HR Images Classification

High-resolution (HR) satellite images, due to the technical constraints on spectral and spatial resolutions, usually contain only several broad spectral bands but with a very high spatial resolution. This provides rich spatial details of the objects on the Earth surface, while their spectral discrimination is relatively low. Recently, the increase of the satellite revisit times made it possible to acquire more frequent data coverage for finer classification. In this article, we proposed a novel multitemporal deep fusion network (MDFN) for short-term multitemporal HR images classification. Specifically, a two-branch structure of MDFN is designed, which includes a long short-term memory (LSTM) and a convolutional neural network (CNN). The LSTM branch is mainly used to learn the joint expression of different temporal-spectral features. For the CNN branch, the three-dimensional (3-D) convolution is firstly applied along the temporal and spectral dimensions to jointly learn the temporal-spatial and spectral-spatial information, respectively, and then the 2-D convolution is performed along the spatial dimension to further extract the spatial context information. Finally, features generated from the two different branches are fused to obtain the discriminative high-level semantic information for classification. Experimental results carried on two real multitemporal HR remote sensing datasets demonstrate that the proposed MDFN provides better classification performance over the state-of-the-art methods, and it also shows the potentiality to use short-term multitemporal HR images for more accurate land use/land cover mapping.
ICon: How Can We Intelligently Predict TCP Congestion?

To enhance the wireless network performance, several new wireless standards, such as IEEE 802.11ac/ax, are introduced, where the objective is to increase the throughput in the network. However, Transmission Control Protocol (TCP) congestion degrades the network performance due to limited network capacity. The standard congestion control mechanism focuses primarily on acknowledgment (ACK) packets, which may result in inappropriate handling of congestion, and lead to the increase in packet drop rate and packet transmission delay. As a consequence, overall network efficiency is degraded. In this paper, we propose Intelligent Congestion control (ICon), a learning-based congestion control mechanism, that uses Deep Neural Network (DNN) to smartly predict the occurrence of congestion based on the present channel condition in the network and the last congestion window size (old window size). We use Signal-to-Noise Ratio (SNR) as the measurement of channel condition, and thus the impact of signal quality on the volume of transmitted packet is implicitly introduced in the prediction of congestion in ICon. For the generation of the dataset to train the proposed DNN, an intensive simulation is used. Through simulation, we analyze the performance of ICon, where it is noted that the proposed scheme significantly improves overall network performance.
Research on the Classification of Dendrobium Images Based on Channel Shuffle

Image classification techniques are very important in our daily life. In this paper, we propose a new network framework (CS_ResNet18) which is an image classification model more suitable for dendrobium classification. In this new model, the shuffle channel technique is mainly used to enhance the information exchange between channels and improve classification accuracy. To overcome the difficulty of sparse Dendrobium dataset, the data augmentation method is used to expand the dataset and further improve the model classification accuracy. Meanwhile, we explored the effect of group parameters on model accuracy. Through comparison experiments, it was found that CS_ResNet18 obtain higher classification accuracy and the data augmentation technique exaggerate the dataset which improves the classification performance of the network model.
Fake News Prediction On COVID Dataset Using Machine Learning

Fake news is false information, nowadays these are big challenges in all types of media, especially social media. In this covid-19 pandemic situation, people are facing more problems and struggling every day. One among those problems, is fake news or false information about covid. To tackle this, we have made an attempt and created a dataset with 4200 records from social media. We analyze the outbreak of covid information and visualize them using charts and graphs and predict the fake news using three classifier machine learning models. They are passive aggressive classifiers, Naïve Bayes classifiers and Support Vector Machines.
Higher Order Optimization Based Control of Grid-Tied DFIG Wind Energy Conversion System

This paper proposes a higher order optimization based adaptive filter for grid side converter (GSC) of doubly fed induction generator (DFIG) based wind energy conversion system (WECS). The proposed control algorithm along with battery energy storage system (BESS) is responsible for operating the system in fixed and variable mode in addition to power quality enhancement of the system. It determines precise weight of control parameters and eliminates the use of additional phase locked loop (PLL) computational blocks in GSC control thereby reducing system complexity. The optimal wind power extraction is ensured by adopting tip speed ratio control in rotor side converter control (RSC) of the system. The presented control is found to have better performance than the conventional control under various operating conditions. The effectiveness of the control is verified by studying behaviour of system and control parameters in MATLAB/Simulink.