Miracle-dz/UniFAR-FTUs huggingface dataset:
Different Generations of Type-B Monoamine Oxidase Inhibitors in Parkinson’s Disease: From Bench to Bedside

Three inhibitors of type-B monoamine oxidase (MAOB), selegiline, rasagiline, and safinamide, are used for the treatment of Parkinson’s disease (PD). All three drugs improve motor signs of PD, and are effective in reducing motor fluc-tuations in patients undergoing long-term L-DOPA treatment. The effect of MAOB inhibitors on non-motor symptoms is not uniform and may not be class-related. Selegiline and rasagiline are irreversible inhibitors forming a covalent bond within the active site of MAOB. In contrast, safinamide is a reversible MAOB inhibitor, and also inhibits voltage-sensitive sodium channels and glutamate release. Safinamide is the prototype of a new generation of multi-active MAOB inhibitors, which in-cludes the antiepileptic drug, zonisamide. Inhibition of MAOB-mediated dopamine metabolism largely accounts for the an-tiparkinsonian effect of the three drugs. Dopamine metabolism by MAOB generates reactive oxygen species, which contrib-ute to nigro-striatal degeneration. Among all antiparkinsonian agents, MAOB inhibitors are those with the greatest neuropro-tective potential because of inhibition of dopamine metabolism, induction of neurotrophic factors, and, in the case of safina-mide, inhibition of glutamate release. The recent development of new experimental animal models that more closely mimic the progressive neurodegeneration associated with PD will allow to test the hypothesis that MAOB inhibitors may slow the progression of PD.
LA INFLUENCIA DE LAS REDES SOCIALES VIRTUALES EN LA DIFUS IÓN DE INFORMAC IÓN Y CONOCIMIENTO: ESTUDIO DE PYMES

The aim of the work is to analyze the influence of virtual social networks in the dissemination of information and knowledge. The methodology is based on case study research and includes a literature review, the use of questionnaires and social network analysis. The case studies revealed a limited use of social networks, namely LinkedIn is used in institutional form to a little extent and does not contribute to the dissemination of institutional information. In the case of those who use LinkedIn as a tool for work links, they do not evidenced that the network is an important channel of transfer and absorption of information, and it does not reflect conviction regarding the contribution of social networks in the future success of the organization.
Diffusion Tensor Imaging of Healthy Skeletal Muscles: A Comparison Between 7 T and 3 T

Objective The aim of this study was to assess and compare the overall performance, reliability, variability, as well as the accuracy of diffusion tensor imaging (DTI) and fiber tracking metrics, for the muscles of the calf at 3 T and 7 T. Materials and Methods Ten volunteers (5 males; mean age, 29.1 ± 4.7 years), with no history of muscle disease, were examined twice at 3 T and 7 T, using a stimulated-echo acquisition mode DTI sequence. Signal-to-noise ratio (SNR) and DTI metrics (track numbers [trn], length [trl], volume [trv], fractional anisotropy [FA], mean [MD], axial [AD], and radial diffusivity [RD]) of the whole-calf muscles, the tibialis anterior, the gastrocnemius medialis, the gastrocnemius lateralis, and the soleus were collected. The Student t test was used to compare SNR and DTI metrics obtained at 3 T and 7 T. The coefficients of variation and the intraclass correlation coefficients were derived to assess the variability and the reliability of the DTI measurements at 3 T and 7 T. To further assess the accuracy of the measurements, the absolute difference was computed for each DTI metric at 3 T and 7 T and then compared (Student t test). The applied level of significance for all the statistical analyses was P < 0.05. Results As expected, the SNR was higher at 7 T than at 3 T (+111%; P < 0.001). At 7 T, the tracked fibers of the whole calf muscles, the gastrocnemii, and the soleus were more numerous (trn +5.5%, +3.1%, +8.5%, and +15.1%, respectively), longer (trl +13.1%, +18.8%, +19.3%, and +33.3%, respectively), and showed a greater volume (trv +12.1%, +12.2%, +14.7%, and +15.7%, respectively) than at 3 T (P < 0.05 each). The soleus demonstrated higher FA (+14.3%), lower MD (−1.7%), AD (−1.9%), and RD (−2%) at 7 T than at 3 T (P < 0.05 each), whereas the other muscles showed more heterogeneous results. The coefficients of variation were good (ie, <10%) for all DTI metrics at both 3 T and 7 T. The intraclass correlation coefficient was excellent (>0.750) at 7 T and 3 T for several DTI metrics, such as the trn of the gastrocnemii and the soleus, the trv of the gastrocnemii, the FA of the whole-calf muscles, gastrocnemius medialis, tibialis anterior, and soleus, and the RD of all investigated muscles (ie, whole-calf muscles, gastrocnemii, tibialis anterior, and soleus). There were no significant differences between the 2 consecutive measurements with each device, except for the trn of the whole-calf muscles and the FA of the gastrocnemius lateralis (higher mean absolute difference at 3 T and 7 T, respectively; P < 0.05 each). Conclusions Despite the numerous challenges associated with DTI of the muscles, both 3 T and 7 T demonstrated reliable and precise results.
A Procedure to Generate Discrete MIMO Closed-loop Benchmark Via LFT with Application to State Space Identification

In this paper we use the conformal transformation known as linear fractional transformation (LFT), with the purpose of generating a discrete multivariable closed-loop benchmark from continuous multivariable closedloop control system, having in mind state space identification. To reach this objective we propose a procedure based on the general framework representation (GFR) and on the multi input multi output (MIMO) LFT bilinear discretization process. We first use the LFT tool to obtain the continuous joint control-output (augmented) system form for representing the canonical closed-loop continuous system. Afterwards, we discretize the augmented continuous closed-loop system in order to obtain an augmented discrete model, then, we calculate the discrete plant and controller in the state space form. An application to the multivariable control of a continuous chemical reactor is presented and also we use the discrete benchmark generated to identify a state space model an example of the potential of the our proposal.
Capacity and SNR Analysis of a Millimeter Wave Cellular Network with Phased Antenna Array

Channel capacity of a Millimeter(MM) wave cellular network employing phased antenna array (uniform linear antenna array) and the array output signal to noise ratio is analyzed. Millimeter wave as the name indicates working on a very high frequency band 28, 38, and 72 GHz bands results in antenna size in millimeter. Therefore the antennas are integrated on chip and they are also steerable to achieve very high capacity. In millimeter wave directional antennas are used with beam steering to achieve high data rate and high capacity. The data rate increases 40 times more than the off chip or external antennas. In this paper channel capacity is analyzed for a uniform linear array and a planar array antenna structure with beamforming and beamsteering.
Role and Aplications of Iot in Online Transactions using Blockchain Technology

IoT is one of the leading technologies apart from blockchain that is being worked on to develop more consumer friendly devices which can help in the daily chores to perform more important and necessary actions, but as we know all the malware and security concerns which can hamper the growth of IoT based devices, we need blockchain technology to reinforce the devices as blockchain has proved it's worth as a security door which is unbreachable. The use of peer to peer communication between the IoT devices will make transactions more secure and user friendly while being fully precise. With these things in place we can see automation in household work or reduction in piracy via internet, the proper integrated use of IoT with our household devices or phones and digital devices will make our lives better and more convenient while being able to sustain more resources for the future. In current scenario, the biggest challenge is that how will Online Transactions get better and automated if we implement IoT using blockchain technology to improve the efficiency, transparency and security? In this paper we are trying to make our online transactions much more accessible and autonomous to interact whole being highly secure so as they can be integrated in the new gen smart homes systems and the home security systems, blockchain is the key to this need that we need to integrate with IoT to make our transactions more objective centered and have our payment system autonomous to make interactions with the daily services monitored by a IoT based smart device ecosystem more hassle free and fully secured. For this we will be going through the mining of blocks and their suggested applicability in the IoT ecosystem.
A Lightweight Authentication Protocol for V2G Networks in Smart Grid

: In a Smart Grid, a Vehicle to Grid (V2G) network aims to provide mobile distributed capacity of battery storage, helping to minimize the dependency of non-renewable energy sources. In this network, the privacy and anonymity of users’ identity, confidentiality of the transmitted data and location of the Electric Vehicle (EV) must be guaranteed. This article proposes a lightweight authentication protocol devoted to the protection of EV identities, prevention against the tracking of the vehicle and confidentiality of transmitted data. In a performance comparison with other protocols, the results from computing and communications performance analyses are better. The lower consumption of resources allows contributing for reducing signalling congestion and saving bandwidth. The protocol protects from various known attacks and security analysis shows that the protocol achieves the security goals.
Human-Robot Interaction Control Through Demonstration

In human-robot interaction (HRI) tasks, robots are required to change the behaviours according to different applications. As different interaction behaviours require different task requirements, it is difficult to formulate a HRI control problem in a general or unified way that enables robots to learn a set of task requirements or behaviours through human's demonstrations and then execute the tasks by using one controller. This paper aims to solve this problem by using a dynamic potential energy function to describe a set of different task requirements so that the motion behaviours demonstrated by human can be acquired or learned by the robot systems in a unified way. A control strategy is proposed to enable the robots to perform various tasks demonstrated by human and also change the behaviours during HRI according to different applications in a stable manner. The stability of overall system is shown using Lyapunov-like analysis and experimental results are presented to illustrate the performance of the proposed control strategy.
A Distributionally Robust Optimization Model for Unit Commitment Based on Kullback–Leibler Divergence

This paper proposes a new distance-based distributionally robust unit commitment (DB-DRUC) model via Kullback–Leibler (KL) divergence, considering volatile wind power generation. The objective function of the DB-DRUC model is to minimize the expected cost under the worst case wind distributions restricted in an ambiguity set. The ambiguity set is a family of distributions within a fixed distance from a nominal distribution. The distance between two distributions is measured by KL divergence. The DB-DRUC model is a “min-max-min” programming model; thus, it is intractable to solve. Applying reformulation methods and stochastic programming technologies, we reformulate this “min-max-min” DB-DRUC model into a one-level model, referred to as the reformulated DB-DRUC (RDB-DRUC) model. Using the generalized Benders decomposition, we then propose a two-level decomposition method and an iterative algorithm to address the RDB-DRUC model. The iterative algorithm for the RDB-DRUC model guarantees global convergence within finite iterations. Case studies are carried out to demonstrate the effectiveness, global optimality, and finite convergence of a proposed solution strategy.
Combined Design of Adaptive Sliding Mode Control and Disturbance Observer with Friction Compensation for a Feed Drive System

Mechanical friction and disturbance are the key issues in precision control of mechanical systems especially in computer numerical controlled (CNC) feed drives. The friction does not only deteriorate the motion accuracy but also increases consumed energy of machining process. Feed drives have diverse applications and operate for a long time over the world, and therefore energy saving is highly expected. This paper proposes a combined design of adaptive sliding mode control and disturbance observer with a nonlinear static friction compensation to improve the motion accuracy and save consumed energy of a biaxial feed drive system. The friction compensator is designed based on a nonlinear friction model. Contour error, which is defined as the shortest distance between the actual position and the reference trajectory, is more important on the machining accuracy than the tracking error in each axis, and therefore contouring controllers are designed by feeding back contour errors. The disturbance observer and friction compensator are integrated into sliding mode contouring control and adaptive sliding mode contouring control. In order to verify the effectiveness of the combined design, comparative experiment was carried out with a biaxial feed drive system. The proposed approach effectively improved the contouring performance and achieved a significant reduction of the consumed energy.
Design and Simulation of ARTD Controller for Chemical Process Industry PMDC Motor

Recently an alternative of proportional - integral-derivative controller (PID) has been developed which is alternative RTD (ARTD) controller for single input single output system. It is a four-mode control scheme whose tuning parameters are directly related to controller performance (Robustness, set point Tracking, Disturbance rejection and controller Aggressiveness). It is a combination of simple PID controller and MPC (Model Predictive Control). Implementation of ARTD is direct and transparent. The ARTD tuning parameters lie between 0 and 1. The proposed controller is applied on a permanent magnet DC motor according to the proposed tuning rules. ARTD controller provides better performance and is demonstrated via MATLAB/Simulink simulation tool. The comparison is made with conventional PID controller.
Calculating Similarity of Curriculum Lattes

This paper implements a similarity algorithm that calculates the percentage of adhesion between academic profile of the University of Brasilia. The supplier base field of data that makes possible the calculation will be the Lattes Platform. This offers the information of academic literature production registered members. The calculation shows the classification of the individuals and the similarities between their academic productions. This is achieved by considering a profile basis compared to one or more destination profiles. To perform this procedure, the information is extracted from each curriculum Lattes, created ontology of concepts that contain data pertaining to production to provide subsidy to comparisons. These comparisons are performed in each term of the names of all the literature production of both profiles compared. Each term can contain a set of synonyms which are also considered in the comparison. The results are compiled and presented in a spreadsheet that contains the summaries of all the percentage of adhesion compared. The application of the algorithm which determines a set of people has more or less proximity and semantic link according to their academic productions when compared with other individuals, resulting in the percentage of similarity.
Vulnerable open source dependencies: counting those that matter

Background: Vulnerable dependencies are a known problem in today's open-source software ecosystems because OSS libraries are highly interconnected and developers do not always update their dependencies. Aim: Our paper addresses the over-inflation problem of academic and industrial approaches for reporting vulnerable dependencies in OSS software, and therefore, caters to the needs of industrial practice for correct allocation of development and audit resources. Method: Careful analysis of deployed dependencies, aggregation of dependencies by their projects, and distinction of halted dependencies allow us to obtain a counting method that avoids over-inflation. To understand the industrial impact of a more precise approach, we considered the 200 most popular OSS Java libraries used by SAP in its own software. Our analysis included 10905 distinct GAVs (group, artifact, version) in Maven when considering all the library versions. Results: We found that about 20% of the dependencies affected by a known vulnerability are not deployed, and therefore, they do not represent a danger to the analyzed library because they cannot be exploited in practice. Developers of the analyzed libraries are able to fix (and actually responsible for) 82% of the deployed vulnerable dependencies. The vast majority (81%) of vulnerable dependencies may be fixed by simply updating to a new version, while 1% of the vulnerable dependencies in our sample are halted, and therefore, potentially require a costly mitigation strategy. Conclusions: Our case study shows that the correct counting allows software development companies to receive actionable information about their library dependencies, and therefore, correctly allocate costly development and audit resources, which is spent inefficiently in case of distorted measurements.
Global Environmental Nontuberculous Mycobacteria and Their Contemporaneous Man-Made and Natural Niches

Seminal microbiological work of environmental nontuberculous mycobacteria (NTM) includes the discovery that NTM inhabit water distribution systems and soil, and that the species of NTM found are geographically diverse. It is likely that patients acquire their infections from repeated exposures to their environments, based on the well-accepted paradigm that water and soil bioaerosols – enriched for NTM – can be inhaled into the lungs. Support comes from reports demonstrating NTM isolated from the lungs of patients are genetically identical to NTM found in their environment. Well documented sources of NTM include peat-rich soils, natural waters, drinking water, hot water heaters, refrigerator taps, catheters, and environmental amoeba. However, NTM have also been recovered in biofilms from ice machines, heated nebulizers, and heater-cooler units, as well as seat dust from theaters, vacuum cleaners, and cobwebs. New studies on the horizon aim to significantly expand the current knowledge of environmental NTM niches in order to improve our current understanding of the specific ecological factors driving the emergence of NTM lung disease. Specifically, the Hawaiian Island environment is currently being studied as a model to identify other point sources of exposure as it is the U.S. state with the highest number of NTM lung disease cases. Because of its geographic isolation and unique ecosystem, the Hawaiian environment is being probed for correlative factors that may promote environmental NTM colonization.
Control for MIMO Systems with No Relative Degree: Output Redefinition versus Dynamic Extension

Output redefinition-based dynamic inversion (ORDI) control is applied to solve the control problem for multi-input-multi-output (MIMO) systems with no relative degree and is compared with the traditional dynamic extension-based dynamic inversion (DEDI) control. A MIMO system has no relative degree if its control matrix is singular, preventing the direct use of the powerful nonlinear control method, dynamic inversion. For this problem, dynamic extension is a traditional solution, which makes dynamic extension at the input side to achieve a relative degree. DEDI results in a fully linearized system of higher order. But the requirement to calculate the higher order derivatives of the output makes it difficult to apply to complex systems. ORDI provides a new solution for the existing problem. It achieves a relative degree by redefinition of a new output, leading to a partially linearized system cascaded with stable zero dynamics. ORDI is much easier to implement for complex systems and reduces the computational burden, though it has some performance limitations. A linear system example along with the application to a hypersonic flight vehicle are provided to illustrate the concept of ORDI and its differences with DEDI.
Regulation of septin phosphorylation: SEPT12 phosphorylation in sperm septin assembly

The sperm annulus, a septin‐based ring structure, is important for reproductive physiology. It is composed of SEPT12‐based septin core complex followed by assembling as octameric filament. In clinical examinations, mutations of Septin12 result in male infertility, immotile sperm, as well as sperm with defective annuli. The dynamic assembly of septin filaments is regulated by several post‐translational modifications, including sumoylation, acetylation, and phosphorylation. Here, we briefly review the biological significance and the regulation of SEPT12 phosphorylation in the mammalian sperm physiology. During mammalian spermiogenesis, the phosphorylation of SEPT12 on Ser198 residue is important in regulating mammalian annulus architectures. SEPT12 phosphomimetic knock‐in mice displayed poor male fertility due to weak sperm motility and loss of the sperm annulus. SEPT12 is phosphorylated via Protein kinase A (PKA), and its phosphorylation interfered with SEPT12 polymerization into complexes and filaments. Taken together, the phosphorylation status of SEPT12 is crucial for its function in regulating the mammalian sperm physiology.
Prediction of Binary Labels for Edges in Signed Networks: A Random-Walk Based Approach

Mining of signed networks where the links/edges between nodes have a positive or negative sign/label, is getting the attention of researchers and practitioners due to its wide realworld applicability in various domains. Label prediction for nodes in a network is a well-known and explored problem. However, the prediction of labels for edges in a network is relatively less explored, and very challenging and interesting problem. In this paper, we consider the problem of binary label prediction for edges in undirected and unweighted networks. The prediction of binary labels has a number of applications in realworld like friend/foe prediction, recommendation, trust/distrust prediction in social networks, and categorization. In this work, a semi-supervised approach is proposed that uses the concept of random-walk for prediction of binary labels for edges. In this paper, we demonstrate the viability and the effectiveness of the proposed approach using a real-world network.