Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty in estuarine extreme water level predictions due to surge-tide interaction

Storm surge is often the greatest threat to life and critical infrastructures during hurricanes and violent storms. Millions of people living in low-lying coastal zones and critical infrastructure within this zone rely on accurate storm surge forecast for disaster prevention and flood hazard mitigation. However, variability in residual sea level up-estuary, defined here as observed sea level minus predicted tide, can enhance total water levels; variability in the surge thus needs to be captured accurately to reduce uncertainty in site specific hazard assessment. Delft3D-FLOW is used to investigate surge variability, and the influence of storm surge timing on barotropic tide-surge propagation in a tide-dominant estuary using the Severn Estuary, south-west England, as an example. Model results show maximum surge elevation increases exponentially up-estuary and, for a range of surge timings consistently occurs on the flood tide. In the Severn Estuary, over a distance of 40 km from the most upstream tide gauge at Oldbury, the maximum surge elevation increases by 255%. Up-estuary locations experience short duration, high magnitude surge elevations and greater variability due to shallow-water effects and channel convergence. The results show that surge predictions from forecasting systems at tide gauge locations could under-predict the magnitude and duration of surge contribution to up-estuary water levels. Due to the large tidal range and dynamic nature of hyper-tidal estuaries, local forecasting systems should consider changes in surge elevation and shape with distance up-estuary from nearby tide gauge sites to minimize uncertainties in flood hazard assessment.
Granular Jamming Based Controllable Organ Design for Abdominal Palpation

Medical manikins play an essential role in the training process of physicians. Currently, most available simulators for abdominal palpation training do not contain controllable organs for dynamic simulations. In this paper, we present a soft robotics controllable liver that can simulate various liver diseases and symptoms for effective and realistic palpation training. The tumors in the liver model are designed based on granular jamming with positive pressure, which converts the fluid-like impalpable particles to a solid-like tumor state by applying low positive pressure on the membrane. Through inflation, the tumor size, liver stiffness, and liver size can be controlled from normal liver state to various abnormalities including enlarged liver, cirrhotic liver, and multiple cancerous and malignant tumors. Mechanical tests have been conducted in the study to evaluate the liver design and the role of positive pressure granular jamming in tumor simulations.
Usability of cardiac magnetic resonance imaging for procedural myocardial infarction undergoing rotational atherectomy.

Rotational atherectomy (RA) is a safe and effective percutaneous technique utilized in a treatment of calcified lesions to modify as a primary therapy. Even though the procedural success rate with RA is 90%, RA is associated with higher rate of no flow/slow flow which occurs as a result of micro debris and/or bubbles than other coronary revascularization procedure (1).
Adhesive Interactions Delineate the Topography of the Immune Synapse

T cells form adhesive contacts with antigen-presenting cells (APCs) as part of the normal surveillance process that occurs in lymph nodes and other tissues. Most of these adhesive interactions are formed by integrins that interact with ligands expressed on the surface of the APC. The interactive strength of integrins depends on their degree of membrane proximity as well as intracellular signals that dictate the conformation of the integrin. Integrins appear in different conformations that endow them with different affinities for their ligand(s). Integrin conformation and thus adhesive strength between the T cell and the APC is tuned by intracellular signals that are turned on by ligation of the T cell receptor (TCR) and chemokine receptors. During the different stages of the process, integrins, the TCR and chemokine receptors may be interconnected by the actin cytoskeleton underneath the plasma membrane, forming a chemical and physical network that facilitates the spatiotemporal dynamics, positioning, and function of these receptors and supports cell-cell adhesion during T cell activation, allowing it to perform its effector function.
ESD study on a-IGZO TFT device architectures

The indium-gallium-zinc-oxide (IGZO) thin-film-transistor (TFT) technology offers to manufacture transistors on large and flexible substrates at low processing temperatures and at a low cost. However, optimization of ESD protection devices in this technology has not been thoroughly investigated. This paper analyzes the ESD performance of three different TFT device architectures.
Features of Referred Sensation Areas for Artificially Generated Sensory Feedback - A Case Study

Phantom limb pain (PLP) is a frequent consequence of amputation. Recent evidence suggests that the pathophysiological mechanisms of PLP are related to neuroplastic changes in the cortex, as consequence of the lost sensory feedback. Formation of referred sensation areas (RSAs) may follow amputation. Sensations may be evoked in the lost body part upon stimulation of RSAs that may be exploited as artificial sensory feedback. The RSAs, however, have also shown to change over time. Features of RSAs in the case of a 36 year old male with right arm amputation aiming to identify placement of electrodes for sensory feedback are reported in this paper. The arm was amputated at shoulder level following patient’s request five years after a vehicle accident that resulted in brachial plexus injury and consequent severe arm paralysis with residual sensory functionality up to 20 %. RSAs were characterized over five sessions within 27 days using mechanical stimuli (brushing over the area or applying light pressure). Tests of electrical stimuli were applied hrough two surface electrodes covering one or multiple RSAs to generate evoked sensations. Location and extent of RSAs as well as the type and location of sensations evoked in the phantom limb were stable within the session (tested up to 30 minutes) and dynamic between sessions. Partial overlapping of RSAs with associated evoked sensation of same or different type was observed for different sessions. Various painful and non-painful sensations were evoked by both mechanical and electrical stimuli dependent on location of the applied stimulus and assessment time. Mechanical andelectrical stimuli applied at the same location evoked the same or different types of sensation in the phantom limb. RSAs may be a promising pathway for delivering sensory feedback for PLP treatment. Features, however, of RSAs may highly influence the efficiency of the PLP treatment.
Real-Time Traffic Estimation of Unmonitored Roads

Due to growing cities, the real-time knowledge about the state of traffic is a critical problem for urban mobility and the road-segments traffic densities estimation influences the efficiency of fundamental smart city services as smart routing, smart planning for evacuations, planning of civil works on the city, etc. Nevertheless, the traffic-related data from navigator Apps (e.g., TomTom, Google, Bing) are too expensive to be acquired. Also, the traditional sensors for the traffic flow detection are very expensive, and they are usually not dense enough for a correct traffic monitoring. In order to overcome such problems, there is a space for low cost and fast solutions for dense traffic flow reconstruction. We propose a real-time visual self-adaptive solution to reconstruct the traffic density at every location of a wide urban area leveraging the detections from a few fixed traffic sensors deployed within the area of interest. A such method is based on fluid dynamic models to simulate macroscopic phenomena as shocks formation and propagation of waves backwards along roads. Such physical constraints are applied to a detailed street graph which is enriched by specific parameters representing a weight in terms of traffic road capacity. The weight's assignment has been estimated by a stochastic learning approach at each time slot of the day. The accuracy of the proposed model comes from the error between the reconstructed traffic density and the measured values at the sensor position by excluding each sensor iteratively and reconstructing the flow without it. The proposed reconstruction model has been created by exploiting open and real-time data in the context of Sii-Mobility research project by using Km4City infrastructure in the area of Florence, Italy, for its corresponding Smart City solution.
A Multichannel EEG Acquisition System With Novel Ag NWs/PDMS Flexible Dry Electrodes

In this paper, a multichannel reconfigurable EEG acquisition system with novel flexible dry electrodes is proposed. The novel electrode is designed to overcome the limitations of conventional wet electrodes such as skin irritation, skin preparation, and conductive gel requirements. It is based on the conductive and stretchable Ag NWs/PDMS composite material and produced by 3D printing technology. Meanwhile, a portable reconfigurable 8-channel EEG acquisition system based on the analog front end ADS1299 is proposed to overcome the drawbacks of traditional EEG acquisition system such as, large in size, difficult to configure, and complicated to use. It can be reconfigured by adjusting the gain of system and sampling rate. To verify the performance of proposed electrodes, a comprehensive test including electrode characterization and signal quality measurement is performed in comparison with Ag/AgCl electrode and Gold Cup electrode. Experiments reveal that proposed electrode achieves favorably results with wet electrodes. Furthermore, the proposed EEG acquisition system with novel dry electrodes is evaluated and compared with the commercial product. The evoked EEG signals (the steady-state visual evoked potentials, SSVEP) acquisition tasks of the proposed system are also conducted. Experimental results exhibit that proposed system satisfies the requirements of multi-channel EEG acquisition and provides a portable and comfortable way for EEG acquisition. With the high-quality sensing ability of the novel electrodes and the programmable gain amplifier of the proposed system, it can be expected to acquire the physiological signals like the electrocardiogram (ECG) and electromyogram (EMG) in the future.
Evaluation of Different Types of Filters in Magnetic Resonance Imaging Using Compressive Sensing with Pre-Filtering

Magnetic resonance imaging (MRI) machines allow one to acquire medical images based on static and variable magnetic fields, in such a way as to reveal the interior of human organs. To acquire images with good quality, MRI machines often demand a high number of measurements, which often require long acquisition times. Therefore, an important topic of research consists of developing acquisition methods that reduce the number of measurements and, consequently, the time required to acquire an MRI image. In this work, we use compressive sensing techniques and pre-filtering strategies to reduce the number of MRI measurements. We empirically tested a large set of filter banks to determine which filter settings provide the best image quality. When compared with state-of-the-art filters and to the non-uniform Fourier transform reconstruction, we have been able to increase the quality of the generated images while reducing the required number of radial lines and, therefore, of acquisition samples.
Deep Neural Networks in High Frequency Trading

The ability to give precise and fast prediction for the price movement of stocks is the key to profitability in High Frequency Trading. The main objective of this paper is to propose a novel way of modeling the high frequency trading problem using Deep Neural Networks at its heart and to argue why Deep Learning methods can have a lot of potential in the field of High Frequency Trading. The paper goes on to analyze the model's performance based on it's prediction accuracy as well as prediction speed across full-day trading simulations.
A hybrid EEG-EMG BMI improves the detection of movement intention in cortical stroke patients with complete hand paralysis

Motor rehabilitation based on brain-machine interfaces (BMI) has been shown as a feasible option for stroke patients with complete paralysis. However, the pathologic EEG activity after a stroke makes the detection of movement intentions in these patients challenging, especially in those with damages involving the motor cortex. Residual electromyographic activity in those patients has been shown to be decodable, even in cases when the movement is not possible. Hybrid BMIs combining EEG and EMG activity have been recently proposed, although there is little evidence about how they work for completely paralyzed stroke patients. In this study we propose a neural interface, relying on EEG, EMG or EEG+EMG features, to detect movement attempts. Twenty patients with a chronic stroke affecting their motor cortex were recruited, and asked to open and close their paralyzed hand while their electrophysiological signals were recorded. We show how EEG and EMG activities provide complementary information for detecting the movement intentions, being the accuracy of the hybrid BMI significantly higher than the EEG-based system. The obtained results encourage the integration of hybrid BMI systems for motor rehabilitation of patients with paralysis due to stroke.
Genus-Wide Assessment of Antibiotic Resistance in Lactobacillus spp

Lactobacillus species are generally considered to be nonpathogenic and are used in a wide variety of foods and products for humans and animals. However, many of the species examined in this study have antibiotic resistance levels which exceed those recommended by the EFSA, suggesting that these cutoff values should be reexamined in light of the genetic basis for resistance discussed here. Our data provide evidence for rationally revising the regulatory guidelines for safety assessment of lactobacilli entering the food chain as starter cultures, food preservatives, or probiotics and will facilitate comprehensive genotype-based assessment of strains for safety screening. ABSTRACT Lactobacillus species are widely used as probiotics and starter cultures for a variety of foods, supported by a long history of safe usage. Although more than 35 species meet the European Food Safety Authority (EFSA) criteria for qualified presumption of safety status, the safety of Lactobacillus species and their carriage of antibiotic resistance (AR) genes is under continuing ad hoc review. To comprehensively update the identification of AR in the genus Lactobacillus, we determined the antibiotic susceptibility patterns of 182 Lactobacillus type strains and compared these phenotypes to their genotypes based on genome-wide annotations of AR genes. Resistances to trimethoprim, vancomycin, and kanamycin were the most common phenotypes. A combination of homology-based screening and manual annotation identified genes encoding resistance to aminoglycosides (20 sequences), tetracycline (18), erythromycin (6), clindamycin (60), and chloramphenicol (42). In particular, the genes aac(3) and lsa, involved in resistance to aminoglycosides and clindamycin, respectively, were found in Lactobacillus spp. Acquired determinants predicted to code for tetracycline and erythromycin resistance were detected in Lactobacillus ingluviei, Lactobacillus amylophilus, and Lactobacillus amylotrophicus, flanked in the genome by mobile genetic elements with potential for horizontal transfer. IMPORTANCE Lactobacillus species are generally considered to be nonpathogenic and are used in a wide variety of foods and products for humans and animals. However, many of the species examined in this study have antibiotic resistance levels which exceed those recommended by the EFSA, suggesting that these cutoff values should be reexamined in light of the genetic basis for resistance discussed here. Our data provide evidence for rationally revising the regulatory guidelines for safety assessment of lactobacilli entering the food chain as starter cultures, food preservatives, or probiotics and will facilitate comprehensive genotype-based assessment of strains for safety screening.
Adverse family life events during pregnancy and ADHD symptoms in five‐year‐old offspring

Background Prenatal exposure to maternal adverse life events has been associated with offspring ADHD, but the role of familial confounding is unclear. We aimed to clarify if adverse life events during pregnancy are related to ADHD symptoms in offspring, taking shared familial factors into account. Method Data were collected on 34,751 children (including 6,427 siblings) participating in the population‐based Norwegian Mother and Child Cohort Study. During pregnancy, mothers reported whether they had experienced specific life events. We assessed ADHD symptoms in five‐year‐old children with the Conners’ Parent Rating Scale–Revised: short form. We modeled the associations between life events and mean ADHD scores with ordinary linear regression in the full cohort, and with fixed‐effect linear regression in sibling comparisons to adjust for familial confounding. Results Children exposed to adverse life events had higher ADHD scores at age 5, with the strongest effect observed for financial problems (mean differences 0.10 [95% CI: 0.09, 0.11] in adjusted model), and the weakest for having lost someone close (0.02 [95% CI 0.01, 0.04] in adjusted model). Comparing exposure‐discordant siblings resulted in attenuated estimates that were no longer statistically significant (e.g. mean difference for financial problems −0.03 [95% CI −0.07, 0.02]). ADHD scores increased if the mother had experienced the event as painful or difficult, and with the number of events, whereas sibling‐comparison analyses resulted in estimates attenuated toward the null. Conclusions These results suggest that the association between adverse life events during pregnancy and offspring ADHD symptoms is largely explained by familial factors.
Evaluation of a Sliding Mode Fault Tolerant Controller on the MuPAL-$\alpha$ Research Aircraft

This paper presents piloted flight test results of a sliding mode fault tolerant control scheme implemented on the Japan Aerospace Exploration Agency's MuPAL-$\alpha$ research aircraft. These results represent unique piloted validation tests of a sliding mode fault tolerant control allocation scheme on a full-scale aircraft operating in the presence of actuator faults. The control law used here does not require the presence of a fault detection and isolation unit and therefore in the event of faults/failures, the actuator effectiveness levels are unknown. In the absence of this information, a fixed control allocation mechanism has been used in order to retain nominal fault-free performance. The control scheme has been implemented on the lateral-directional motion and incorporated within the experimental fly-by-wire system. Piloted flight test results show that close to nominal tracking performance can be maintained despite the presence of unknown actuator faults as well as actuator uncertainties.
Accelerated Inference in Markov Random Fields via Smooth Riemannian Optimization

<italic>M</italic>arkov random fields (MRFs) are a popular model for several pattern recognition and reconstruction problems in robotics and computer vision. Inference in MRFs is intractable in general and related work resorts to approximation algorithms. Among those techniques, semidefinite programming (SDP) relaxations have been shown to provide accurate estimates, while scaling poorly with the problem size and being typically slow for practical applications. Our first contribution is to design a dual ascent method to solve standard SDP relaxations that takes advantage of the geometric structure of the problem to speed up computation. This technique, named <italic>dual ascent Riemannian staircase</italic> (<monospace>DARS</monospace>), is able to solve large problem instances <italic>in seconds</italic>. Our second contribution is to develop a second and faster approach. The backbone of this second approach is a novel SDP relaxation combined with a fast and scalable solver based on smooth Riemannian optimization. We show that this approach, named <italic>Fast Unconstrained SEmidefinite Solver</italic> (<monospace>FUSES</monospace>), can solve large problems <italic>in milliseconds</italic>. Contrarily to local MRF solvers, e.g., loopy belief propagation, our approaches do not require an initial guess. Moreover, we leverage recent results from optimization theory to provide per-instance sub-optimality guarantees. We demonstrate the proposed approaches in multi-class image segmentation problems. Extensive experimental evidence shows that: 1) <monospace>FUSES</monospace> and <monospace>DARS</monospace> produce near-optimal solutions, attaining an objective within 0.1% of the optimum; 2) <monospace>FUSES</monospace> and <monospace>DARS</monospace> are remarkably faster than general-purpose SDP solvers, and <monospace>FUSES</monospace> is more than two orders of magnitude faster than <monospace>DARS</monospace> while attaining similar solution quality; and 3) <monospace>FUSES</monospace> is faster than local search methods while being a global solver.
Logic Design on Mirrored Memristive Crossbars

This brief presents a hybrid CMOS-memristor logic design and implementation method on a novel mirrored crossbar architecture. The proposed structure supports in-memory computation and needs only one computational step to perform basic Boolean expressions. This logic can provide multiple fanins and/or fanouts, and does not have a destructive effect on input devices’ logical states. Various logic gates have been designed using the proposed structure. Simulation results and practical design constraints for different logic functions are presented, which confirms functionality of the method and its capability for large-scale memristive computations.
Estimating the Intensity and Anisotropy of Tumor Treating Fields Jsing Singular Value Decomposition. Towards a More Comprehensive Estimation of Anti-tumor Efficacy

Tumor treating fields (TTFields) is an anticancer treatment that inhibits tumor growth with alternating electrical fields. Finite element (FE) methods have been used to estimate the TTFields intensity as a measure of treatment “dose”. However, TTFields efficacy also depends on field direction and exposure time. Here we propose a new FE based approach, which uses all these parameters to quantify the average field intensity and the amount of unwanted directional field correlation (fractional anisotropy, FA). The method is based on principal component decomposition of the sequential TTFields over one duty cycle. Using a realistic head model of a glioblastoma patient, we observed significant unwanted FA in many regions of the brain, which may potentially affect therapeutic efficacy. FA varied between different array layouts and indicated a different order of array performance than predicted from the field intensity. Tumor resection nullified differences in field distributions between layouts and increased FA considerably. Our results question the rationale for the use of macroscopically orthogonal array layouts to reduce field correlation and rather indicate that arrays should be placed to maximize pathology coverage and field intensity. The proposed calculation framework has several potential applications, incl. improved treatment planning, technology development, and accurate prognostication models. Future studies are required to validate the method.