Miracle-dz/UniFAR-FTUs huggingface dataset:
Sign Inversion in Photopharmacology: Incorporation of Cyclic Azobenzenes in Photoswitchable Potassium Channel Blockers and Openers.

Photopharmacology relies on ligands that change their pharmacodynamics upon photoisomerization. Many of these ligands are azobenzenes that are thermodynamically more stable in their elongated trans-configuration, which predominates in the dark. Often, they are biologically active in this form and lose activity upon irradiation and photoisomerization to their cis-isomer. Recently, cyclic azobenzenes, so-called diazocines, have emerged. They are thermodynamically more stable in their bent cis-form than in their elongated trans-form. Incorporation of these switches into a variety of photopharmaceuticals could convert dark-active ligands into dark-inactive ligands, which is preferred in most biological applications. This "pharmacological sign-inversion" is demonstrated for a photochromic blocker of voltage-gated potassium channels, termed CAL, and a photochromic opener of G protein-coupled inwardly rectifying potassium (GIRK) channels, termed CLOGO.
The Effect of Beta-Alanine versus Alkaline Agent Supplementation Combined with Branched-Chain Amino Acids and Creatine Malate in Highly-Trained Sprinters and Endurance Athletes: A Randomized Double-Blind Crossover Study

The study aimed to verify the effect of intra- (beta-alanine—BA) versus extra- (alkaline agents—ALK) cellular buffering agent supplementation, combined with customarily used branched-chain amino acids (BCAAs) and creatine malate (TCM) treatment in natural training conditions. Thirty-one elite athletes (11 sprinters and 20 endurance athletes) participated in the study. Eight-week randomized double-blind, crossover, combined supplementation with BA-ALKplaBCAA&TCM and ALK-BAplaBCAA&TCM was implemented. In the course of the experiment, body composition, aerobic capacity, and selected blood markers were assayed. After BA-ALKplaBCAA&TCM supplementation, total fat-free mass increased in sprinters (p = 0.009). No other differences were found in body composition, respiratory parameters, aerobic capacity, blood lactate concentration, and hematological indices after BA-ALKplaBCAA&TCM/ALK-BAplaBCAA&TCM supplementation. The maximum post-exercise blood ammonia (NH3) concentration decreased in both groups after BA-ALKplaBCAA&TCM supplementation (endurance, p = 0.002; sprint, p < 0.0001). Also, lower NH3 concentrations were observed in endurance athletes in the post-exercise recovery period. The results of our study indicate that combined BCAA, TCM, and BA supplementation is more effective than combined BCAA, TCM and ALK supplementation for an increase in fat-free mass and exercise adaptation, but not for aerobic capacity improvement. Besides, it seems that specific exercise stimuli and the training status are key factors affecting exercise performance, even in athletes using efficient supplementation.
Cardiovascular effects of psychotic illnesses and antipsychotic therapy

Mortality from cardiovascular disease is increased in people with mental health disorders in general and schizophrenia in particular. The causes are multifactorial, but it is known that antipsychotic medication can cause cardiac side-effects beyond the traditional coronary risk factors. Schizophrenia itself is a contributor to an increased risk of cardiovascular mortality via cardiac autonomic dysfunction and a higher prevalence of metabolic syndrome, both contributing to a reduced life expectancy. The pro-arrhythmic impact of traditional antipsychotics, especially via the hERG-potassium channel, has been known for several years. Newer antipsychotics have a reduced pro-arrhythmic profile but might contribute to higher cardiac death rates by worsening the metabolic profile. Clozapine-induced cardiomyopathy, which is dose independent, is a further concern and continuous monitoring of these patients is required. Prophylaxis with angiotensin-converting enzyme inhibitors is currently under review. Overall, management of cardiovascular risk within this population group must be multifaceted and nuanced to allow the most effective treatment of serious mental illness to be conducted within acceptable parameters of cardiovascular risk; some practical measures are presented for the clinical cardiologist.
Inhibition Effect of Triglyceride Accumulation by Large Yellow Croaker Roe DHA-PC in HepG2 Cells

The phospholipids (PLs) of large yellow croaker (Pseudosciaena crocea, P. crocea) roe contain a high level of polyunsaturated fatty acids, especially docosahexaenoic acid (DHA), which can lower blood lipid levels. In previous research, PLs of P. crocea roe were found able to regulate the accumulation of triglycerides. However, none of these involve the function of DHA-containing phosphatidylcholine (DHA-PC), which is the main component of PLs derived from P. crocea roe. The function by which DHA-PC from P. crocea roe exerts its effects has not yet been clarified. Herein, we used purified DHA-PC and oleic acid (OA) induced HepG2 cells to establish a high-fat model, and the cell activity and intracellular lipid levels were then measured. The mRNA and protein expression of Fatty Acid Synthase (FAS), Carnitine Palmitoyl Transferase 1A (CPT1A) and Peroxisome Proliferator-Activated Receptor α (PPARα) in HepG2 cells were detected via RT-qPCR and western blot as well. It was found that DHA-PC can significantly regulate triglyceride accumulation in HepG2 cells, the effect of which was related to the activation of PPARα receptor activity, upregulation of CPT1A, and downregulation of FAS expression. These results can improve the understanding of the biofunction of hyperlipidemia mediated by DHA-PC from P. crocea roe, as well as provide a theoretical basis for the utilization of DHA-PC from P. crocea roe as a functional food additive.
A Framework for Applied AI in Healthcare

Significant efforts are being made to develop artificial intelligence technologies for health settings. In a health system that has been notoriously slow to adopt innovative technologies, it is important to consider the implementation of a new technology early in the development stage, especially one that will have added challenges of trust and transparency. To facilitate this process, an implementation framework for artificial intelligence technologies in clinical settings has been created.
Incremental Deployment of Programmable Switches for Network-wide Heavy-hitter Detection

The advent of Software-Defined Networking with OpenFlow first, and subsequently the emergence of programmable data planes, has boosted lot of research around many networking aspects: monitoring, security, traffic engineering. In the context of network monitoring, most of the proposed solutions show the benefits of data plane programmability by simplifying the complexity of the network with a one big-switch abstraction. Only few papers look at network-wide solutions, but consider the network as non heterogeneous: only composed by programmable devices. In this paper, we argue that the primary challenge for a successful adoption of those solutions is the deployment problem: how to compose and monitor a network consisting of both legacy and programmable switches? We propose an approach for incrementally deploy programmable devices in an ISP network with the goal of monitoring as many distinct network flows as possible. While assessing the benefits of our solution, we realized that proposed network-wide monitoring algorithms might not be optimized for a partial deployment scenario. We then also developed a novel strategy capable of detecting network-wide heavy flows with the same accuracy of state-of-the-art solutions but by relying on less information from the data plane.
Aperture-Coupled Microstrip Resonator for Millimeter-Wave Passive Pressure Sensors

In this paper, a millimeter-wave passive pressure sensor based on an aperture-coupled microstrip resonator is proposed. The aperture-coupling technique is applied here for improving the airtightness of pressure sensors and for avoiding unwanted electromagnetic radiation occurring when using the widely used probe-fed technique. The measured additional insertion loss due to the aperture-coupling is found to be of 1.4dB only. Both 3D full-wave electromagnetic simulation results and experimental data are reported for the validation of the novel millimeter-wave sensor design.
A Comparative Study of S-S and LCC-S Compensation Topology of Inductive Power Transfer Systems for EV Chargers

In inductive power transfer (IPT) systems, the structures and parameters of the resonance compensation topology are the main factors which determine the system characteristics. As the representative of conventional classical compensation topology and high-order composite compensation topology, series-series (S-S) and inductance-double capacitances-series (LCC-S) have been widely studied and applied to wireless charging for electric vehicles (EVs). In this paper, the properties of the two resonance topologies are compared comprehensively under the same conditions, the comparisons include influences of zero phase angle (ZPA) frequency and load variation on transmission performance, the characteristics of system voltage conversion ratio and input impedance angle under the condition of ZPA frequency and load variation. To complement the comparison and analysis, a 2kW level comparative experiments were carried out, it can be concluded that LCC-S is suitable for low power applications and S-S is suitable for high power applications. S-S has higher load sensitivity to the output characteristics than LCC-S.
Model Checking Longitudinal Control in Vehicle Platoon Systems

With a steadily growing number of vehicles, our roads are getting more and more crowded. As a consequence, traffic jams are becoming common. Vehicle platoon systems form a possible solution in the short term. It consists of a number of vehicles automatically following a leader vehicle, in-line, one after another at a short but safe distance. Ideally, all vehicles have to maintain the same speed, so as to have a better usage of the road by minimizing the distance between two vehicles. In this paper we present a timed automata model of a vehicle platoon system with the goal of finding a minimal but guaranteed safe distance between two vehicles under variable speed. Contrary to other models based on cooperative adaptive cruise control, we assume no (Internet) communication among different vehicles or road system. Instead of such global perspective we rather take a local point of view: each vehicle relies on its own sensors to dynamically calculate and maintain a safe distance with the preceding member of the platoon. We use the model checker UPPAAL to verify that the system does not deadlock, and most importantly, that it is safe, avoiding crashes at all time.
Requirements of an Energy Data Ecosystem

Digitalization is strongly entering new application domains around the world, thus also entering people’s everyday lives. Digitalization enables creating new kinds of businesses, but it also changes the way services, applications and data are provided and consumed. We carried out interviews with company representatives acting in the energy domain to receive up-to-date information about the companies’ awareness of digitalization, and the required changes they have identified in their future business scenarios. The interviewed companies were aware of the change pressure caused by digitalization, and especially the data exchange with other domain actors, predictions using data, new data-based services and data-based decision making were identified as key issues. However, currently there is a lack of digital infrastructure enabling the actors in the energy domain to perform data-based co-operation. The purpose of this paper is to identify the requirements for an energy data ecosystem that enables the companies in the energy domain to reach their business goals and to co-operate in data-based business. The initial goal is to identify the future tasks and goals of energy domain actors and define the type of co-operation environment, including knowledge management models and supporting services, required to support them. The research was implemented with industry actors in the energy domain that represent several different roles in energy markets, and the results of the research were also validated among the industry.
Sentiment Analysis of Weibo Comments Based on Deep Neural Network

with the rapid development of network information technology, social media has become a new information dissemination platform, and has the characteristics of rapid dissemination and wide audience. This paper proposes a micro-blog commentary sentiment analysis method based on deep neural network. By constructing multi-level pooling layer and convolution layer, the network complexity is increased, and then the data features can be effectively extracted, the sentiment analysis accuracy rate can be improved, and the network can be avoided. Locally optimal. Firstly, the microblog commentary is preprocessed, then the word2vec algorithm is used for text representation, and the feature matrix is input into the convolutional neural network to extract the key features in the text. Finally, the softmax logistic regression network is used to classify the features and obtain the sentiment analysis results. Through experimental verification, the accuracy of this method is significantly improved compared with the traditional machine learning method.
Alphanumeric Glyphs Transformation Based on Shape Morphing: Context of Text

In this paper, we present a new application of shape morphing in font generation. This method is adapted to the text tattooing context. The advantages of using this method for font generation are: the ability to generate all font characters in one step; all characters are changed; the user must only provide a shape towards which the characters will be distorted and define the percentage of deformation. In the context of text tattooing, the characters in the original text must be modified to embed information without disturbing the reading. Therefore, each character of the new font must be at the same time different and similar to the one of the font of the original document (Times New Roman, Arial, etc). In the results section, we show the visual similarity of the obtained characters as well as their ability to distinguish themselves in a classification process.
Enhancing AFB_BJ+-AC* algorithm

Several problems in multi-agent coordination can be modeled using a Distributed Constraint Optimization Problem (DCOP) paradigm, which can be solved using one of the state-of-the-art algorithms that solve DCOPs. The AFB_BJ+-AC* algorithm is one of the newest of these algorithms. It uses soft arc consistency techniques (AC*) to rapidly reach the optimal solution of a DCOP by deleting non-optimal values from each agent domain. This paper enhances the AFB_BJ+-AC* algorithm to surpass its inefficiency in some problems, especially those where the number of deletions is too limited or remains zero, by increasing the ability of soft arc consistency techniques (AC*) to generate deletions. The idea is centered on increasing the value of the zero-arity constraint, used as a bound to delete values, by redistributing the constraint costs between an agent and its neighbors via extension operations, then via the execution of AC*. Our experiments on different benchmarks show that the new improvements make AFB_BJ+-AC* better in terms of communication load and computation effort.
Benchmark of Visual SLAM Algorithms: ORB-SLAM2 vs RTAB-Map*

This works deals with a benchmark of two well-known visual Simultaneous Localization and Mapping (vSLAM) algorithms: ORB-SLAM2 proposed by Mur-Atal & al in 2015 [7] and RTAB-Map proposed by [8]. The benchmark has been carried out with an Intel real-sense camera 435D mounted on top of a robotics electrical powered wheelchair running a ROS platform. The ORB SLAM has been implemented taking into account a monocular, stereo and RGB-D camera. RTAB SLAM, meanwhile, has only implemented with monocular and RGB-D camera. Several experiments have been carried out in a controlled indoor environment at the ESIGELEC's Autonomous Navigation Laboratory. These experiments are supported by the use of the VICON motion capture system used as a ground-truth to validate our results [1]. Different motion scenarios are used to test and benchmark the SLAM algorithms in various configurations: straight-line, straight-line and back, circular path with loop closure, etc.
Construction of TEC Maps Using Compressive Sensing

The ionosphere has a time-varying electron content. The electron content has a significant effect on the signals emitted in the ionosphere. For this effect to be examined, it is important to estimate and map the total electron content. In addition, historical archiving is important in terms of the typical behaviors shown by the ionosphere with long-term examinations and the extraction of instant disturbances at certain days and times. Although compressive sensing has application in many areas, the studies on the ionosphere are negligible. In addition to the studies for estimating the total electron content, compressive sensing method was used in this study. It has been shown that the ionosphere maps are sparsely structured and are estimated by compressive sensing methods. The estimation performances of random and clustered observation methods is compared.
LW-DEM: Designing a Low Power Digital-to-Analog Converter Using Lightweight Dynamic Element Matching Technique

The need for low-power wireless sensor networks (WSNs) continues to grow. Based on the fact that digital-to-analog converters (DACs) are essential elements in the WSN and consumes a lot of power, this paper presents a new low-power DAC design to realize the low-power WSN. To do that, this paper exploits the dynamic element matching (DEM) technique, one of the well-known techniques for high performance DACs, proposes a lightweight DEM (LW-DEM) technique that minimizes power and area overhead of the DEM technique. More specifically, this paper is motivated from the observation that input data of DACs tends to be sufficiently random that the input data can be used for the random selection of the current source instead of a pseudo-random number generator (PRNG) for the traditional DEM. Because the PRNG consumes a lot of power and occupies a large area in a DAC, elimination of the PRNG from a DAC and utilizing input data result in significant power saving and area reduction in the DAC while meeting the required performance of the low-power WSNs. This paper provides a detailed LW-DEM architecture and its operation principle. To demonstrate the efficacy of the proposed method, a prototype 12-bit DAC using the LW-DEM is implemented. The 12-bit DAC is fabricated in 65 nm CMOS technology and occupies only $0.065~{mm}^{2}$ area. Measurement results with the prototype verify that the DAC using LW-DEM accomplishes 39% power saving and 52% area reduction in the randomizer, compared to a DAC using the conventional DEM. At the same time, the measured spurious-free dynamic range (SFDR) of the DAC using LW-DEM is better than 55 dB, demonstrating that the proposed DAC achieves almost same performance as a DAC using the conventional DEM.
A Deep Learning Approach to VNF Resource Prediction using Correlation between VNFs

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) greatly facilitate network service management. Specifically, these new network paradigms help manage the network environment dynamically and cost-efficiently. Virtual Network Function (VNF) and Service Function Chaining (SFC) are important aspects of the NFV environment. In terms of NFV management, resource demand of VNFs can be predicted at a future time to handle Quality of Service (QoS) and resource allocation problems efficiently. Hence, researchers study and build a management system where machine-learning-based predictions of VNF information are used to handle auto-scaling, deployment and migration of VNFs. In addition, in recent studies, these systems have involved SFC to obtain useful information, not just a lone VNF. However, not many of studies explain clearly how chaining dependency among VNFs in a SFC can be used to predict future resource demand of a VNF. In this paper, we introduce VNF resource prediction machine learning model that maximizes the benefits of using SFC. Then, we compare several machine learning models and analyze how SFC data can help predict resource usage patterns of VNFs. We also show benefits of Attention model to improve prediction accuracy and convergence time through experiments.
High-Throughput Interdigitated Electrode Array on Microgroove-Patterned Cantilever to Measure Electro-Mechanical Properties of Cardiomyocytes

This study reports a new MEMS-based cantilever device that simultaneously measures the mechanical force and electrophysiology of cardiac cells. On an interdigitated electrode (IDE) integrated with the cantilever, microgroove patterns are formed that positively influence the maturation and alignment of cardiac cells. The fabricated cantilever device can directly measure impedance of the cardiac cells as well as the displacement that occurs because of the contractions of cardiac cells. After the preliminary experiments, the cardiac cells were subjected to Verapamil drug in order to measure the drug-induced cardiac toxicity. Displacement of the cantilever in presence of Verapamil was lower than that in its absence, as were the impedance recordings.
Virtual Network Function Migration Based on Dynamic Resource Requirements Prediction

Network function virtualization (NFV) enables flexible deployment of virtual network function (VNF) in 5G mobile communication network. Due to the inherent dynamics of network flows, fluctuated resources are required to embedding VNFs. VNF migration has become a critical issue because of the time-varying resource requirements. In this paper, we propose a real-time VNF migration algorithm based on the deep belief network (DBN) to predict future resource requirements, which resolves the problem of lacking effective prediction in the existing methods. Firstly, we propose optimizing bandwidth utilization and migration overhead simultaneously in VNF migration. Then, to model the resource utilization that evolves over time, we adopt online learning with the assistant of offline training in the prediction mechanism, and further introduce multi-task learning (MTL) in our deep architecture in order to improve the prediction accuracy. Moreover, we utilize adaptive learning rate to speed up the convergence speed of DBN. For the migration, we design a topology-aware greedy algorithm with the goal to optimize system cost by taking full advantage of the prediction result. In addition, based on tabu search, the proposed migration mechanism is further optimized. Simulation results show that the proposed scheme can achieve a good performance in reducing system cost and improving the service level agreements (SLA) of service.
P4AIG: Circuit-Level Verification of P4 Programs

In this work, we set out to develop P4 AIG, a tool for the static verification of programmable data planes using sequential circuit analysis. P4 AIG targets P4 programs by treating them as hardware pipelines rather than software programs. P4 AIG allows for the circuit-level treatment of P4 programs, a feature not available for traditional software verification techniques. We believe that P4 AIG will exploit the nature of P4 programs to achieve higher scalability in verification.