Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of ursolic acid on sub-lesional muscle pathology in a contusion model of spinal cord injury

Spinal Cord Injury (SCI) results in severe sub-lesional muscle atrophy and fiber type transformation from slow oxidative to fast glycolytic, both contributing to functional deficits and maladaptive metabolic profiles. Therapeutic countermeasures have had limited success and muscle-related pathology remains a clinical priority. mTOR signaling is known to play a critical role in skeletal muscle growth and metabolism, and signal integration of anabolic and catabolic pathways. Recent studies show that the natural compound ursolic acid (UA) enhances mTOR signaling intermediates, independently inhibiting atrophy and inducing hypertrophy. Here, we examine the effects of UA treatment on sub-lesional muscle mTOR signaling, catabolic genes, and functional deficits following severe SCI in mice. We observe that UA treatment significantly attenuates SCI induced decreases in activated forms of mTOR, and signaling intermediates PI3K, AKT, and S6K, and the upregulation of catabolic genes including FOXO1, MAFbx, MURF-1, and PSMD11. In addition, UA treatment improves SCI induced deficits in body and sub-lesional muscle mass, as well as functional outcomes related to muscle function, motor coordination, and strength. These findings provide evidence that UA treatment may be a potential therapeutic strategy to improve muscle-specific pathological consequences of SCI.
Analytical Capacitance Model for In-Ga-Zn-O Thin-Film Transistors Including Degeneration

In this paper, a capacitance model of amorphous In-Zn-Ga-O thin-film transistors is proposed based on terms of surface potential. The carrier degeneracy is considered due to the device characteristics. Based on the symmetric quadrature version of Charge-Sheet Model, a new expression of terminal charge is provided. Therefore, the capacitance model can be obtained. The validity of the model is verified by comparisons with measured data and simulation results. It is very useful to circuit simulators.
Blood Pressure Prediction Based on Pattern Classification

With the development of wearable medical care, some researchers pay more attention to wearable blood pressure estimation. In general, the photoplethysmography(PPG) signal is used to extract the featuresfor regression analysis. However, although many regression methods (e.g., Linear regression, ANN, SVR, etc.) can be applied in this field, how to improve the accuracy of blood pressure prediction is a big problem. In this article, based on pattern classification, a method of blood pressure estimation is firstly proposed. Compared with the previous regression analysis, the different ranges of blood pressure are divided into different classes. Then the signals are classified into the corresponding category according to the extracted features. Blood pressure value could be obtained by finding the interval median. Experiments have shown that the idea of classification can be able to achieve the higheraccuracy of blood pressure prediction.
Grade of Service in UDWDM-PON with Statistical Wavelength Subscribing

The Grade of Service (GoS) or ONU rejection probability in coherent UDWDM-PONs with ONU lasers under limited tunability is evaluated. Each active ONU is assigned an exclusive wavelength channel, which is managed by heuristic Wavelength Assignment (WA) schemes. With a take rate of 90%, GoS values below 0.1% are achieved, but when statistical subscribing is considered, the GoS is dramatically improved.
A Multi-source Machine Learning Approach to Predict Defect Prone Components

Software code life cycle is characterized by continuous changes requiring a great effort to perform the testing of all the components involved in the changes. Given the limited number of resources, the identification of the defect proneness of the software components becomes a critical issue allowing to improve the resources allocation and distributions. In the last years several approaches to evaluating the defect proneness of software components are proposed: these approaches exploit products metrics (like the Chidamber and Kemerer metrics suite) or process metrics (measuring specific aspect of the development process). In this paper, a multisource machine learning approach based on a selection of both products and process metrics to predict defect proneness is proposed. With respect to the existing approaches, the proposed classifier allows predicting the defect proneness basing on the evolution of these features across the project development. The approach is tested on a real dataset composed of two well-known open source software systems on a total of 183 releases. The obtained results show that the proposed features have effective defect proneness prediction ability.
Performance Investigation of Automotive SAR Imaging

This paper investigates the potential of applying synthetic aperture radar (SAR) to automotive applications using a short range vehicle-mounted Frequency Modulation Continuous Wave (FMCW) radar. The Range Migration Algorithm (RMA) is adopted in this work for SAR image formation as it is ideal for focusing range curvatures of targets at short ranges. The first part of this paper gives a brief overview of RMA based strip map SAR processing of FMCW radar signals. In the latter part, collected data of real world stationary targets such as cars, poles and buildings are processed providing high resolution SAR images of different synthetic aperture lengths. The obtained images are presented, discussed and compared in terms of cross range accuracy and resolution to show that high resolution target imaging can be achieved using automotive SAR.
Cdt1 stabilizes kinetochore–microtubule attachments via an Aurora B kinase–dependent mechanism

Robust kinetochore–microtubule (kMT) attachment is critical for accurate chromosome segregation. G2/M-specific depletion of human Cdt1 that localizes to kinetochores in an Ndc80 complex–dependent manner leads to abnormal kMT attachments and mitotic arrest. This indicates an independent mitotic role for Cdt1 in addition to its prototypic function in DNA replication origin licensing. Here, we show that Cdt1 directly binds to microtubules (MTs). Endogenous or transiently expressed Cdt1 localizes to both mitotic spindle MTs and kinetochores. Deletion mapping of Cdt1 revealed that the regions comprising the middle and C-terminal winged-helix domains but lacking the N-terminal unstructured region were required for efficient MT binding. Mitotic kinase Aurora B interacts with and phosphorylates Cdt1. Aurora B–phosphomimetic Cdt1 exhibited attenuated MT binding, and its cellular expression induced defective kMT attachments with a concomitant delay in mitotic progression. Thus we provide mechanistic insight into how Cdt1 affects overall kMT stability in an Aurora B kinase phosphorylation-dependent manner; which is envisioned to augment the MT-binding of the Ndc80 complex.
Performance Improvement of Orthogonal Matching Pursuit Based on Wilkinson Matrix for Block Compressive Sensing

Compressed sensing is a new paradigm to sample compressible signals below the Nyquist rate, whilst still allowing near optimal reconstruction of the signal. In this paper, a new structured Wilkinson matrix is proposed as a measurement matrix to improve the performance of the block compressive sensing technique which is based on the orthogonal matching pursuit (OMP) algorithm. OMP is simpler to implement and potentially faster than linear programming algorithms but it suffers from two problems: the lack of reconstruction quality, and the need for a large number of measurements. The use of the new structured Wilkinson matrix has improved the OMP reconstruction quality, increased the compression ratio and reduced the computational time. In our experiments, the proposed method was applied to three different test images: Lenna, Barbara and heart MRI. Compared to conventional CS algorithms, the proposed method improves the reconstruction quality by approximately 10 dB with less computational time.
Column Scan Optimization by Increasing Intra-Instruction Parallelism

The key objective of database systems is to reliably manage data, whereby high query throughput and low query latency are core requirements. To satisfy these requirements for analytical query workloads, in-memory column store database systems are state-of-the-art. In these systems, relational tables are organized by column rather than by row, so that a full column scan is a fundamental key operation and thus, the optimization of the key operation is very crucial. For this reason, we investigated the optimization of a well-known scan technique using SIMD (Single Instruction Multiple Data) vectorization as well as using Field Programmable Gate Arrays (FPGA). In this paper, we present both optimization approaches with the goal to increase the intra-instruction execution parallelism to process more columns values in a single instruction simultaneously. For both, we present selective results of our exhaustive evaluation. Based on this evaluation, we draw some lessons learned for our ongoing research activities.
Identifying Topology of Low Voltage Distribution Networks Based on Smart Meter Data

In a power distribution network, the network topology information is essential for an efficient operation. This network connectivity information is often not available at the low voltage (LV) level due to uninformed changes that happen from time to time. In this paper, we propose a novel data-driven approach to identify the underlying network topology for LV distribution networks including the load phase connectivity from time series of energy measurements. The proposed method involves the application of principal component analysis and its graph-theoretic interpretation to infer the steady state network topology from smart meter energy measurements. The method is demonstrated through simulation on randomly generated networks and also on IEEE recognized Roy Billinton distribution test system.
$\mathsf{Hap-SliceR}$: A Radio Resource Slicing Framework for 5G Networks With Haptic Communications

It is expected that the emerging 5G networks will not only support diverse use cases, but also enable unprecedented applications such as <italic>haptic communications</italic>. Therefore, <italic>network slicing</italic> will provide the required design flexibility. Radio resource slicing would be an indispensable component of any network slicing solution. This paper proposes <inline-formula><tex-math notation="LaTeX">$\mathsf{Hap-SliceR}$</tex-math> </inline-formula>, which is a novel radio resource slicing framework for 5G networks with haptic communications. First, <inline-formula><tex-math notation="LaTeX">$\mathsf{Hap-SliceR}$</tex-math></inline-formula> derives a <italic> network-wide</italic> radio resource slicing strategy for 5G networks. The optimal slicing strategy, which is based on a <italic>reinforcement learning</italic> approach, allocates radio resources to different slices while accounting for the dynamics and utility requirements of different slices. Second, <inline-formula><tex-math notation="LaTeX"> $\mathsf{Hap-SliceR}$</tex-math></inline-formula> provides customization of radio resources for haptic communications over 5G networks. The radio resource allocation requirements of haptic communications have been translated into a unique radio resource allocation problem. A low-complexity heuristic algorithm has been developed for resource allocation. Finally, a comprehensive performance evaluation of <inline-formula><tex-math notation="LaTeX"> $\mathsf{Hap-SliceR}$</tex-math></inline-formula> has been conducted based on a recently proposed 5G air-interface design.
Fisher Vector Coding for Covariance Matrix Descriptors Based on the Log-Euclidean and Affine Invariant Riemannian Metrics

This paper presents an overview of coding methods used to encode a set of covariance matrices. Starting from a Gaussian mixture model (GMM) adapted to the Log-Euclidean (LE) or affine invariant Riemannian metric, we propose a Fisher Vector (FV) descriptor adapted to each of these metrics: the Log-Euclidean Fisher Vectors (LE FV) and the Riemannian Fisher Vectors (RFV). Some experiments on texture and head pose image classification are conducted to compare these two metrics and to illustrate the potential of these FV-based descriptors compared to state-of-the-art BoW and VLAD-based descriptors. A focus is also applied to illustrate the advantage of using the Fisher information matrix during the derivation of the FV. In addition, finally, some experiments are conducted in order to provide fairer comparison between the different coding strategies. This includes some comparisons between anisotropic and isotropic models, and a estimation performance analysis of the GMM dispersion parameter for covariance matrices of large dimension.
Data Driven Prognostics for Failure of Power Semiconductor Packages

Power chips such as Metal Oxide Field Effect Transistors (MOSFETs) are widely used and can be found in many electronics and electrical products. The ability to predict the degradation of such power electronic devices can minimise the risk of their failure during operation and support maintenance planning operations. In this study, a data driven prognostics approach using system identification and machine learning modelling technique is developed and used to predict the time-to-failure of MOSFET TO-220 packages associated with delamination failure mode of the die attachment. Run-to-failure data under thermal overstress loading conditions for power chip devices, available from the NASA Prognostics Centre data repository, is used to develop a data-driven prognostic model that can be used to predict the time-to-failure (TtF) of power MOSFETs under accelerated test loads. An increment in ON-state resistance of the MOSFET is used as precursor for device failure through die-attach degradation. Results from this research show that when monitored data from a damage indicator for a particular failure mode of an electronic package changes dynamically, data-driven modelling using engineering control techniques such as State-Space representation is capable of producing reliable, multi-step ahead predictions for the time-to-failure of the device.
UAV-Based Ground Penetrating Synthetic Aperture Radar

A novel approach for anti-personnel landmine detection using an unmanned aerial vehicle (UAV) in combination with a ground penetrating synthetic aperture radar (GPSAR) is presented. The objective of the system is to accelerate the process of land release in humanitarian demining. Suspicious objects shall be detected by the radar and marked for further investigations using different sensor principles.The ground penetrating radar (GPR) module consists of a 1 GHz to 4 GHz side-looking frequency modulated continuous wave (FMCW) radar, a radar and lidar altimeter, and a real time kinematic global navigation satellite system (RTK GNSS). The image processing is done offline using a back-projection algorithm. In the theoretical part of this paper the system partitioning, the sensor module, and the position accuracy requirements are briefly described. In the experimental part of this paper synthetic aperture radar (SAR) measurements are presented.
Energy Efficient Path Planning of Hybrid Fly-Drive Robot (HyFDR) using A* Algorithm

Hovering flight is agile and energy expansive, but driving on ground is slow and energy efficient method of locomotion. To get the benefits of these two methods of locomotion, we combined them in a single platform named as HyFDR. It is a Quadcopter with powered wheels, it can fly in air and drive on ground. Autonomous navigation of HyFDR creates new challenges in the field of path planning. The goal is to simulate the navigation of HyFDR with minimum energy consumption using A* algorithm. Depending upon the terrain, obstacles, energy constraints, and desired flight time, HyFDR can autonomously switch between flight mode, drive mode and hybrid mode. We showed that in some cases HyFDR is energy efficient than Quadcopter.
A Novel Simulation System Oriented Description Language of Worm Propagation

Software simulation is one of the most common methods to study worm propagation. However, simulation process and software interface, which lack unified description, cause inefficiencies of worm propagation simulation. In order to achieve a unified description for worm propagation simulation process, we propose a Worm Propagation Simulation Description Language (WPSDL), which characterizes the software interfaces and parameters of each module in simulation process in a unified way. Meanwhile, we develop a simulation system of worm propagation oriented our presented language. In this system, we use related commands of the description language as interactive instructions, and then implement worm propagation simulation for different types of network worms. In addition, we conduct a series of experiments to analyse the impacts of worm propagation mechanism and network topology structure. The experiments show that increasing infection ability and scanning rate can improve the propagation scale significantly, and the propagation delay can influence the outbreak time and explosion intensity of worm propagation. The experiments not only provide support for the study of worm propagation mechanisms, but also verify the practicability of the description language and the simulation system.
The Dynamic Sensor Data Description and Data Format Conversion Language

Nowadays the proliferation of IoT (Internet of Things) devices results in heterogeneous and proprietary sensor data formats which makes challenging the processing and interpretation of sensor data across IoT domains. To achieve syntactic interoperability (the ability to exchange uniformly structured data) is still an issue under research. In this paper, we introduce our purpose developed new script language called Language for Sensor Data Description (L4SDD) to achieve cross-domain syntactic interoperability. L4SDD defines a unified output data format and specifies how the data is to be converted into this format from the various sensor inputs. Besides the language itself, we also present the main features of the workbench created to edit and maintain the scripts and introduce the IoT framework around the solution. The approach provides a high performant, secure and easy-to-use solution to transform their data to an easily processed, self-describing, universal data structure. Although the paper contains implementation details, the solution can be used in other, similar projects as well. As a practical validation, we also illustrate our solution via a real-life case study.
A Human–Machine Methodology for Investigating Systems Thinking in a Complex Corpus

Systems thinking characterizes the paradigm needed to effectively design, maintain, and utilize systems. Prior work has shown that there is a language of systems thinking and that its presence can be quantified within text using supervised learning methods. Building on this foundation, we present a human-in-the-loop methodology that utilizes topic models to facilitate the identification of systems thinking within a corpus of documents. Though explorative, it requires no manual grading of documents, which makes it significantly faster than previous methods. The methodology uses each document's topic proportion within a systems thinking topic as a proxy measure for the potential of strong systems thinking. The novel aspect of the methodology is in the seeding of the corpus; the user encourages the emergence of the systems thinking topic by adding several documents that demonstrate strong systems thinking to the corpus. Additionally, seeding could be used with concepts other than systems thinking. A Tukey test on a graded corpus reveals that the top echelons of strong systems thinking papers have significantly higher mean topic proportions in the systems thinking topic than lower graded papers. Additionally, a case study on a corpus of Army documents related to the development, character, and management of soldiers demonstrates the methodology's effectiveness in overviewing a system and in providing research direction. The definition of strong systems thinking and the interpretation of topics are subjective, but the methodology overcomes this hurdle by leveraging human intuition and keeping a human in the loop.
Evolutionnary Fixed-Structure LPV/LFT Controller Synthesis for Multiple Plants

Abstract: This paper proposes to address the problem of fixed-structure gain-scheduled LPV/LFT controllers for plants with time-varying measurable and time invariant unmeasurable uncertainties. Due to the complexity of merging μ-technics with LPV/LFT approach, an alternative presented here consists in computing robust fixed-structure LPV/LFT controllers using the multiple plants framework instead of μ-technics. The complexity of this optimization problem is tackled with global evolutionary optimization. This paper shows that this approach is quite efficient and very simple to implement. The algorithm has been tested on the pendulum in the cart academic example.
Optical and electrical properties of TiO2/Pt/TiO2 nanolaminate structures

We report the results of a study on the optical and electrical properties of nanolaminate TiO2/Pt/TiO2 structures fabricated using RF magnetron sputtering. The effect was investigated of the discontinuous Pt layer on the optical transmittance, electrical conductivity and morphology of the TiO2/Pt/TiO2 structure. On the basis of the theory of electronic tunneling transport between metal granulates in a dielectric matrix, a new technological approach is proposed of preparing high-sensitivity strain sensors based on resistors with a nanolaminate structure containing Pt granulates.
Framework for the Recognition of Activities of Daily Living and Their Environments in the Development of a Personal Digital Life Coach

Due to the commodity of the use of the off-the-shelf mobile devices and technological devices by ageing people, the automatic recognition of the Activities of Daily Living (ADL) and their environments using these devices is a research topic were studied in the last years, but this project consists in the creation of an automatic method that recognizes a defined dataset of ADL using a large set of sensors available in these devices, such as the accelerometer, the gyroscope, the magnetometer, the microphone and the Global Positioning System (GPS) receiver. The fusion of the data acquired from the selected sensors allows the recognition of an increasing number of ADL and environments, where the ADL are mainly recognized with motion, magnetic and location sensors, but the environments are mainly recognized with acoustic sensors. During this project, several methods have been researched in the literature, implementing three types of neural networks, these are Multilayer Perceptron (MLP) with Backpropagation, Feedforward neural network (FNN) with Backpropagation and Deep Neural Networks (DNN), verifying that the neural networks that report highest results are the DNN method for the recognition of ADL and standing activities, and the FNN method for the recognition of environments.
Integração de imagem infravermelha e visual para vistoria de equipamentos

— In this work, computer vision techniques were used to build three-dimensional (3D) visual and thermal models of a small portion of a train track. To accomplish this, visual and thermal images were simultaneously captured, using cameras and the software LabVIEW. Then, VisualSFM was utilized to apply the Structure from Motion (SfM) technique to the visual images, which generated the visual 3D model. Afterwards, the 3D thermal model was obtained by using the geometric relationships between the thermal and visual cameras and reprojecting the 3D points on the thermal images. The results show that the developed system is capable of generating qualitatively satisfactory three-dimensional models, both in the visual and the infrared spectrum.