Miracle-dz/UniFAR-FTUs huggingface dataset:
A dual‐process exploration of binge drinking: Evidence through behavioral and electrophysiological findings

The dual‐process model, describing addictive disorders as resulting from an imbalance between increased automatic approach behaviors towards the substance and reduced abilities to control these behaviors, constitutes a sound theoretical framework to understand alcohol‐use disorders. The present study aimed at exploring this imbalance at behavioral and cerebral levels in binge drinking, a pattern of excessive alcohol consumption frequently observed in youth, by assessing both reflective control abilities and automatic processing of alcohol‐related stimuli. For this purpose, 25 binge drinkers and 25 comparison participants performed a Go/No‐Go task during electrophysiological recording. Inhibition abilities were investigated during explicit (ie, distinguishing alcoholic versus nonalcoholic drinks) and implicit (ie, distinguishing sparkling versus nonsparkling drinks, independently of their alcohol content) processing of beverage cues. Binge drinkers presented poorer inhibition for the explicit processing of beverage cues, as well as reduced N200 amplitude for the specific processing of alcohol‐related stimuli. As a whole, these findings indicated inhibition impairments in binge drinkers, particularly for alcohol cues processing and at the attentional stage of the cognitive stream. In line with the dual‐process model, these results support that binge drinking is already characterized by an underactivation of the reflective system combined with an overactivation of the automatic system. Results also underlined the influence of explicit processing compared with implicit ones. At the clinical level, our findings reinforce the need to develop intervention methods focusing on the inhibition of approach behaviors towards alcohol‐related stimuli.
EmoSense: Automatically Sensing Emotions From Speech By Multi-way Classification

Reliably detecting emotions is a topic of current research in understanding mental health. Among the many modes of detecting emotion, audio has a prominent place. In this paper, we propose a two-level, multi-way classifier applied to classification of seven emotions from the standard Emo-DB database. The multi-way classifier is an automated methodology of analyzing a confusion matrix of a first-level classifier to build more classifiers at the next level. A random forest classifier is used on state-of-the-art features for analyzing affective speech. The confusion matrix from this classification level is analyzed to decide, for each class, which other classes are most confused by using a threshold on the misclassification rate. For the chosen pairs, second level classifiers are built and trained on the same data. Its performance on the training-set (73.3{\%) as well as a non-intersecting training set (72.9{\%) are both better than state-of-the-art performance. We initiate a possible explanation of the performance improvement by considering the confusion among emotions placed on Russel’s circumplex model.
Contract Based Energy Blockchain for Secure Electric Vehicles Charging in Smart Community

The smart community (SC), as an important part of the Internet of energy (IoE), can facilitate integration of distributed renewable energy sources (RES) and electric vehicles (EVs) in the smart grid. However, due to the potential security and privacy issues caused by untrusted and opaque energy markets, it becomes a great challenge to optimally schedule the charging behaviors of EVs with distinct energy consumption preferences in SC. In this paper, we propose a contract based energy blockchain for secure EV charging in SC. Firstly, a permissioned energy blockchain system is introduced to implement secure charging services for EVs with the execution of smart contracts. Secondly, a reputation based delegated Byzantine fault tolerance (DBFT) consensus algorithm is proposed to eficiently achieve the consensus in the permissioned blockchain. Furthermore, based on the contract theory, the optimal contracts are analyzed and designed to satisfy EVs' individual needs from energy sources while maximizing the operator's utility. Finally, numerical results validate the effectiveness and ef? ciency of the proposed scheme through comparison with other conventional schemes.
The Causes of Leaf Hydraulic Vulnerability and Its Influence on Gas Exchange in Arabidopsis thaliana1[OPEN]

Declines in leaf outside-xylem hydraulic conductance prior to turgor loss point contribute strongly to stomatal closure, and improve performance, survival and efficient water use during drought. The influence of the dynamics of leaf hydraulic conductance (Kleaf) diurnally and during dehydration on stomatal conductance and photosynthesis remains unclear. Using the model species Arabidopsis (Arabidopsis thaliana ecotype Columbia-0), we applied a multitiered approach including physiological measurements, high-resolution x-ray microcomputed tomography, and modeling at a range of scales to characterize (1) Kleaf decline during dehydration; (2) its basis in the hydraulic conductances of leaf xylem and outside-xylem pathways (Kox); (3) the dependence of its dynamics on irradiance; (4) its impact on diurnal patterns of stomatal conductance and photosynthetic rate; and (5) its influence on gas exchange and survival under simulated drought regimes. Arabidopsis leaves showed strong vulnerability to dehydration diurnally in both gas exchange and hydraulic conductance, despite lack of xylem embolism or conduit collapse above the turgor loss point, indicating a pronounced sensitivity of Kox to dehydration. Kleaf increased under higher irradiance in well-hydrated leaves across the full range of water potential, but no shift in Kleaf vulnerability was observed. Modeling indicated that responses to dehydration and irradiance are likely attributable to changes in membrane permeability and that a dynamic Kox would contribute strongly to stomatal closure, improving performance, survival, and efficient water use during drought. These findings for Columbia-0 provide a baseline for assessing variation across genotypes in hydraulic traits and their influence on gas exchange during dehydration.
Identification of Plasmonic Absorption Profile in Surface Plasmon Microscopy Using Morphology

Surface plasmon microscopy (SPM) provides the capability of measuring surface properties of subnanometer layers within the diffraction limited order. In a typical SPM, small changes correspond to surface plasmons (SPs) absorption profile variations on a reflecting back focal plane (BFP), which can be monitored in real-time. However, the lack of fast and high-accurate identification method on SPs profiles has posed significant challenges on objective-coupled SPM instruments, and also limited their practical applications in fast phenomenon sensing and image batch processing. Here we propose a morphological method to identify the SPs absorption profiles. It can extract the SPs profile information from experimentally recorded BFP images with low quality properly and automatically. Experimental verification and further discussions are included.
Pacemaker-Based Global Synchronization of Kuramoto Oscillators via Distributed Control

This brief targets the pacemaker-based global synchronization of a Kuramoto-oscillator network via distributed control, where “global” means that there is no constraint on the initial phases. A pacemaker-based distributed controller, that contains information from not only its neighbors but also the pacemaker, is proposed to achieve global synchronization. Both bounded and frequency synchronization have been considered with sufficient criteria established. A state-monitoring control scheme is also proposed, where oscillators with arbitrary initial phases will be driven into a pre-given region in need of the distributed controller, and then synchronization is achieved via the physical coupling and the influence of the pacemaker only.
Bifurcations and multi-stability in a model of cytokine-mediated autoimmunity

This paper investigates the dynamics of immune response and autoimmunity with particular emphasis on the role of regulatory T cells (Tregs), T cells with different activation thresholds, and cytokines in mediating T cell activity. Analysis of the steady states yields parameter regions corresponding to regimes of normal clearance of viral infection, chronic infection, or autoimmune behaviour, and the boundaries of stability and bifurcations of relevant steady states are found in terms of system parameters. Numerical simulations are performed to illustrate different dynamical scenarios, and to identify basins of attraction of different steady states and periodic solutions, highlighting the important role played by the initial conditions in determining the outcome of immune interactions.
Patch-level Tumor Classification in Digital Histopathology Images with Domain Adapted Deep Learning

Tumor histopathology is a crucial step in cancer diagnosis which involves visual inspection of imaging data to detect the presence of tumor cells among healthy tissues. This manual process can be time-consuming, error-prone, and influenced by the expertise of the pathologist. Recent deep learning methods for image classification and detection using convolutional neural networks (CNNs) have demonstrated marked improvements in the accuracy of a variety of medical imaging analysis tasks. However, most well-established deep learning methods require large annotated training datasets that are specific to the particular problem domain; such datasets are difficult to acquire for histopathology data where visual characteristics differ between different tissue types, in addition to the need for precise annotations. In this study, we overcome the lack of annotated training dataset in histopathology images of a particular domain by adapting annotated histopathology images from different domains (tissue types). The data from other tissue types are used to pre-train CNNs into a shared histopathology domain (e.g., stains, cellular structures) such that it can be further tuned/optimized for a specific tissue type. We evaluated our classification method on publically available datasets of histopathology images; the accuracy and area under the receiver operating characteristic curve (AUC) of our method was higher than CNNs trained from scratch on limited data (accuracy: 84.3% vs. 78.3%; AUC: 0.918 vs. 0.867), suggesting that domain adaptation can be a valuable approach to histopathological images classification.
Adaptive PI-Control of Superheat in a Refrigeration System

This paper describes adaptive control of the superheat in a refrigeration system. Traditionally, the superheat is PI controlled using constant gain and integration time, which often will have a conservative tuning. On-line update of the controller parameters in self-tuning scheme by identification in open loop or by using external signals in closed loop is cumbersome. In this work a method using PI controllers with shifting parameters in a bumpless set-up has been used for finding better parameters. Each controller is used in a certain time interval and ISE (Integrated Squared Error) is calculated as a measure for the quality. This is used in a gradient method to increase the performance. The improved PI controller gives the possibility to optimise the superheat reference via an additional control loop. The controllers are designed and tested in a laboratory set-up. The performance of the shifting adaptive controller turns out to have a better performance than a fixed parameter PI controller.
Data-Driven Active Disturbance Rejection Control of Pendulum Cart Systems

This paper proposes the discrete-time data-driven Active Disturbance Rejection Control (ADRC) algorithm for nonlinear Single Input-Multi Output pendulum cart systems (PCSs). The PCS is controlled using a Single Input-Single Output control system structure in cart position control loop. The performance of the control structure with data-driven ADRC controllers is compared with that of a control structure with a discrete-time data-driven Model-Free Control-intelligent Proportional Integral Derivative (MFC-iPID) controller and with linear controller having the same number of tunable parameters as ADRC and MFC-iPID through real-time experimental results. In order to have a fair comparison, the controller parameters are tuned in model-based term using a metaheuristic Gravitational Search Algorithm (GSA) optimizer.
Lumen Segmentation in Optical Coherence Tomography Images using Convolutional Neural Network

Lumen segmentation in Optical Coherence Tomography (OCT) images is a very important step to analyze points of interest that may help on atherosclerosis diagnostic and treatment. Past studies use many different methods to segment the lumen in IVOCT images, like level set, morphological reconstruction, Markov random fields, and Otsu binarization. Despite Convolutional Neural Networks (CNN) have shown promising results in the image processing area, we did not identify, in the literature, works applying CNN in IVOCT images. In this paper, we present the lumen segmentation using CNN. We evaluated three different CNN architectures. The CNNs were evaluated using three versions from the image dataset, differing from each other by image size (768x768 pixels and 192x192 pixels), and by coordinate system representation (Cartesian and polar). The best results, Accuracy, Dice index and Jaccard index of over 99%, 98% and 97%, respectively, were obtained with the smallest size images represented by polar coordinate system.
SALMA: UWB-based Single-Anchor Localization System using Multipath Assistance

Setting up indoor localization systems is often excessively time-consuming and labor-intensive, because of the high amount of anchors to be carefully deployed or the burdensome collection of fingerprints. In this paper, we present SALMA, a novel low-cost UWB-based indoor localization system that makes use of only one anchor and that does neither require prior calibration nor training. By using only a crude floor plan and by exploiting multipath reflections, SALMA can accurately determine the position of a mobile tag using a single anchor, hence minimizing the infrastructure costs, as well as the setup time. We implement SALMA on off-the-shelf UWB devices based on the Decawave DW1000 transceiver and show that, by making use of multiple directional antennas, SALMA can also resolve ambiguities due to overlapping multipath components. An experimental evaluation in an office environment with clear line-of-sight has shown that 90% of the position estimates obtained using SALMA exhibit less than 20 cm error, with a median below 8 cm. We further study the performance of SALMA in the presence of obstructed line-of-sight conditions, moving objects and furniture, as well as in highly dynamic environments with several people moving around, showing that the system can sustain decimeter-level accuracy with a worst-case average error below 34 cm.
DeepMI: Deep Learning for Multiclass Motor Imagery Classification

In Brain-Computer Interface (BCI) Research,Electroencephalography (EEG) has obtained great attention for biomedical applications. In BCI system, feature representation and classification are important tasks as the accuracy of classification highly depends on these stages. In this paper, we propose a model in which Common Spatial Pattern (CSP) is used to discriminate inter-class data using co-variance maximization and Fast Fourier Transform Energy Map (FFTEM) is used for feature selection and mapping of 1D data into 2D data (energy maps). Convolutional Neural Network is used for classification of multi-class Motor Imagery (MI) signals. Further, this paper investigates near-optimal parameter selection for feature mapping, frequency bands selection, and temporal segmentation. It is shown that our proposed method outperformed the reported methods by achieving 0.61 mean kappa value.
Adsorption of Fibronectin Fragment on Surfaces Using Fully Atomistic Molecular Dynamics Simulations

The effect of surface chemistry on the adsorption characteristics of a fibronectin fragment (FNIII8–10) was investigated using fully atomistic molecular dynamics simulations. Model surfaces were constructed to replicate self-assembled monolayers terminated with methyl, hydroxyl, amine, and carboxyl moieties. It was found that adsorption of FNIII8–10 on charged surfaces is rapid, specific, and driven by electrostatic interactions, and that the anchoring residues are either polar uncharged or of opposing charge to that of the targeted surfaces. On charged surfaces the presence of a strongly bound layer of water molecules and ions hinders FNIII8–10 adsorption. In contrast, adsorption kinetics on uncharged surfaces are slow and non-specific, as they are driven by van der Waals interactions, and the anchoring residues are polar uncharged. Due to existence of a positively charged area around its cell-binding region, FNIII8–10 is available for subsequent cell binding when adsorbed on a positively charged surface, but not when adsorbed on a negatively charged surface. On uncharged surfaces, the availability of the fibronectin fragment’s cell-binding region is not clearly distinguished because adsorption is much less specific.
Annealing of Gadolinium-Doped Ceria (GDC) Films Produced by the Aerosol Deposition Method

Solid oxide fuel cells need a diffusion barrier layer to protect the zirconia-based electrolyte if a cobalt-containing cathode material like lanthanum strontium cobalt ferrite (LSCF) is used. This protective layer must prevent the direct contact and interdiffusion of both components while still retaining the oxygen ion transport. Gadolinium-doped ceria (GDC) meets these requirements. However, for a favorable cell performance, oxide ion conducting films that are thin yet dense are required. Films with a thickness in the sub-micrometer to micrometer range were produced by the dry room temperature spray-coating technique, aerosol deposition. Since commercially available GDC powders are usually optimized for the sintering of screen printed films or pressed bulk samples, their particle morphology is nanocrystalline with a high surface area that is not suitable for aerosol deposition. Therefore, different thermal and mechanical powder pretreatment procedures were investigated and linked to the morphology and integrity of the sprayed films. Only if a suitable pretreatment was conducted, dense and well-adhering GDC films were deposited. Otherwise, low-strength films were formed. The ionic conductivity of the resulting dense films was characterized by impedance spectroscopy between 300 °C and 1000 °C upon heating and cooling. A mild annealing occurred up to 900 °C during first heating that slightly increased the electric conductivity of GDC films formed by aerosol deposition.
Design and Evaluation of Vivado HLS-Based Compressive Sensing for ECG Signal Analysis

This paper proposes an efficient implementation of the compressed sensing technique. Specifically, we present an architecture for implementing the Matching pursuit algorithm using the Vivado High-Level Synthesis Tool. The proposed architecture consists of compressing and recovering the electrocardiogram signal on a hardware. For the healthcare application, there is an obvious need to get the algorithm implementation up and running in real-time. The proposed implementation is applied on two different blocks sizes of the input data. Our results suggest that this difference in the size of input data produces consistent performance at the speed of execution. Specifically, according to our simulation, the blocks size of 256 samples achieve the speed by 8X as compare of a block of 2000 samples. Therefore, a calculation time of 2000 samples was 598ms and the computing time of 256 samples was 8.61ms and the necessary time to compute 2000 samples using a block of input 256 data was 67.2ms, which brings back to use a small vector to expect the best performance in terms of speed and area consumption. The hardware implementation results were compared with software implementation and the error between these two implementations was negligible of an order of 10^(-15).
Identification of Decentralized System with Common Parameters

This paper addresses a system identification problem for multiple isolated systems whose parameters are partially identical. An approach to the problem is a centralized identification; a single estimator collects all the input-output data from all of the systems to estimate their parameters at once. However, the approach can be practically infeasible when it is applied to a large number of systems due to the limitation of computational and communication resources. To solve the issue, we propose a method of a networked identification composed of two stages: 1) Multiple estimators collect the data from their own target systems to independently derive temporary estimates of their parameters and a covariance matrix of the estimates. 2) Then, with communicating the temporary estimates and the covariance matrix, they update their estimates. We also show the optimality of the proposed networked identification method with respect to the modeling accuracy, which is equivalently achieved by the centralized identification. Finally, we show the effectiveness of the proposed method in a numerical simulation.
A safety checking algorithm with multi-swarm particle swarm optimization

Model checking is a formal verification technique that automatically decides whether a software system conforms to its specification or not. One of the important features of model checking is to output a counterexample, if a violative execution is detected. Traditional exhaustive techniques fail to complete the verification in large-scale systems because of the shortage of computational resources. This is an efficiency problem called State Explosion Problem. On the other hand, the user of model checking hopes to obtain comprehensible and short outputs. However, balancing efficiency and comprehensibility is not easy, since the exhaustive investigation of the system executions is necessary to find a short counterexample. In this paper, we tackle the balancing problem with an approach of Search-Based Software Engineering. We propose a novel verification algorithm using multi-swarm Particle Swarm Optimization. Our technical breakthrough is to use the specification to decompose the verification problem into the small-sized subproblems dynamically. Each swarm solves one of the subproblems efficiently and the aggregation of the partial solutions found by the swarms provides us with sufficiently short counterexamples. Our numerical experiments show that the proposed algorithm outperforms traditional exhaustive techniques and state-of-the-art non-exhaustive techniques in terms of both efficiency and comprehensibility.