Miracle-dz/UniFAR-FTUs huggingface dataset:
PINT: A Modern Software Package for Pulsar Timing

Over the past few decades, the measurement precision of some pulsar timing experiments has advanced from ∼10 μs to ∼10 ns, revealing many subtle phenomena. Such high precision demands both careful data handling and sophisticated timing models to avoid systematic error. To achieve these goals, we present PINT (PINT Is Not T empo3), a high-precision Python pulsar timing data analysis package, which is hosted on GitHub and available on the Python Package Index (PyPI) as pint-pulsar. PINT is well tested, validated, object oriented, and modular, enabling interactive data analysis and providing an extensible and flexible development platform for timing applications. It utilizes well-debugged public Python packages (e.g., the NumPy and Astropy libraries) and modern software development schemes (e.g., version control and efficient development with git and GitHub) and a continually expanding test suite for improved reliability, accuracy, and reproducibility. PINT is developed and implemented without referring to, copying, or transcribing the code from other traditional pulsar timing software packages (e.g., Tempo/Tempo2) and therefore provides a robust tool for cross-checking timing analyses and simulating pulse arrival times. In this paper, we describe the design, use, and validation of PINT, and we compare timing results between it and Tempo and Tempo2.
Does adherence to workplace-based exercises alter physical capacity, pain intensity and productivity?

Abstract Study design: This is a prospective study. Objective: To evaluate the effects of adherence to a workplace-based exercise programme on physical capacity, pain intensity and productivity. Methods: Forty office workers were evaluated for shoulder strength, endurance and pain intensity before and both 12 and 24 weeks after the onset of the exercise programme. The workers were asked about their productivity. A 15-min programme was offered twice a week for 24 weeks. Each session consisted of warm-up, strengthening and stretching exercises. The participants were divided into three groups based on the level of adherence: low (0–33%), medium (34–67%) and high (>67%). Results: The shoulder strength increased from baseline to the end of the 12 weeks independently of the level of adherence, but the increase in strength was only maintained at the 24-week in the groups with medium and high adherence. Endurance was significantly longer in the high adherence group compared to the low adherence group. No differences were found regarding neck and shoulder pain intensity independently of the level of adherence to the exercise programme. According to the workers’ reports, the programme did not interfere with their productivity. Conclusions: The workplace-based exercises led to increases in shoulder strength and endurance, but did not reduce pain. The increases in strength and endurance were dependent on the level of adherence to the exercise programme. The break from work activities to participate in the exercise programme did not interfere with productivity.
Some Discrete Optimization Problems With Hamming and H-Comparability Graphs

Any H-comparability graph contains a Hamming graph as spanning subgraph. An acyclic orientation of an H-comparability graph contains an acyclic orientation of the spanning Hamming graph, called sequence graph in the classical open-shop scheduling problem. We formulate different discrete optimization problems on the Hamming graphs and on H-comparability graphs and consider their complexity and relationship. Moreover, we explore the structures of these graphs in the class of irreducible sequences for the open shop problem in this paper.
A Social Mobilized Inspection System against External Damage of Power Grid Based on Block Chain Technology

The operation and inspection of external damage of power grid is becoming more and more difficult, mainly reflected in the uncertainty of risk and insufficient manpower. At present, most of the on-site inspection rely on human inspection since new technology cannot respond to unexpected situations. In this paper, we proposed a social mobilized inspection system. By mobilized the social forces around power facilities, we propose an inspection framework to encourage people upload photos of external damage. To ensure the incentive system transparency, we use block chain technology as the decentralized database. This method provides a new insight against external damage of power grid and is hopefully to be used in inspection system.
Decoding auditory attention and neural language processing in adverse conditions and different listener groups

5 Impact statement 7 Chapter 1: General introduction . 15 1.1 Speech processing in adverse conditions . 15 1.1.1 Different types of degradation . 17 1.1.2 Brain bases of speech processing . 21 1.2 Decoding auditory attention . 28 1.2. 1 Bottom-up and top-down effects of auditory attention . 28 1.2. 2 Listening effort and its relationship with auditory attention . 32 1.2. 3 Measures to investigate auditory attention and language processing . 36 1.2. 4 Hearing impairment and its effects on auditory attention and speech processing . 38 1.2. 5 The role of motivation as a top-down factor in neural language processing . 45 1.2. 6 The current thesis . 48 Chapter 2: The effect of different maskers on neural speech processing . 51 2.
Handwritten Numerals Recognition using Hough Transformation Technique

Handwriting recognition has always been a exigent task in image processing and pattern recognition. In this paper, we have presented Hough transformation technique for extracting the features of handwritten numerals. SVM based approach has been used to classify the numerals based on the extracted features. In this study, we have taken the samples of handwritten numerals from 120 different writers. Each writer has written each numeral five times. We have collected total 6000 samples of handwritten numerals for training and testing. We have taken 75% data as training data and 25% data as testing data. The proposed system achieves a maximum recognition accuracy of about 91.75%. Keywords - SVM classifier, Hough transform, OCR.
An Analysis of Human Resource Information Systems impact on Employees

This research hypothesizes that automated collection, storage, and retrieval of information related to the human resource element in any organization helps to make more informed, lasting decisions about positioning, utilization, and retention of its human resources which aids the organization in its competitive efforts. A large-scale household appliance manufacturer that implemented an automated process for its human resource information gathering and now relies on a completely computerized storage, retrieval, and utilization of the human resource-related data was surveyed.
Optimization of the Circuit Parameter in Class-E Power Amplifier in the Two-coil Wireless Power Transfer System

In this paper, the circuit parameters of the Class-E power amplifier are investigated and optimized to achieve the maximum of output power and efficiency in a two-coil Wireless Power Transfer (WPT) system. The circuit is simulated with software named Saber. Working frequency, the value of capacitors and inductor, which has relationship with the output power and efficiency, are investigated during the simulation. With the simulation results, it is found that the frequency and the shunt capacitor need to be optimized for high performance of the wireless power transfer system.
Biocontrol of leopard moth, Zeuzera pyrina L. (Lep.: Cossidae) using entomopathogenic nematodes in Iran

Walnuts produced in the Juglans region is among Iran's strategic products and the Leopard moth, Zeuzera pyrina L. (Lep.: Cossidae) is the key pest of this tree. Difficulty in chemical application against this caterpillar and special habitats of its larvae encouraged us to address efficiency of some entomopathogenic nematodes (EPNs) against different larval stages. Native isolates of EPNs were isolated from soil habitats of this pest in Arak, Markazi province, Iran. Using ITS and D2/D3 expansions of 28S sequences they were identified as Heterorhabditis bacteriophora, Steinernema feltiae and S. carpocapsae. In laboratory assays,the susceptibility of second, third and fourth larval stages to commercial products of S. carpocapsae and H. bacteriophora, and native strain of H. bacteriophora were studied. For field application, both strains were used via injection of nematode suspensions into the galleries bored in tree stems or branches. This study showed that both tested nematodes at 2,000 IJs/larva proved to be effective on Z. pyrina. The results indicated the higher efficiency of S. carpocapsae as compared to H. bacteriophora on larvae of this moth. Key word: Insect pathogen, Heterorhabditis, Steinernema, Iran, microbial control, tiger moth
Induction of phr gene expression in Escherichia coli strain KY706/pPL-1 by UVA (365 nm) irradiation

We report the induction of phr gene expression in the Escherichia coli strain KY706 pPL-1 by UVA irradiation. The induction of phr gene expression was monitored by the activity of β-galactosidase enzyme and was found to depend on the UVA dose and the post irradiation incubation period in the nutrient medium. Mechanistic studies carried out using quenchers and enhancer for singlet oxygen and hydroxyl radicals showed the involvement of these reactive oxygen species in the induced enzyme induction.
Exploiting Linguistic Knowledge for Low-Resource Neural Machine Translation

: Exploiting the linguistic knowledge of the source language for neural machine translation (NMT) has recently achieved impressive performance on many large-scale language pairs. However, since the Turkish → English machine translation task is low-resource and the source-side Turkish is morphologically-rich, there are limited resources of bilingual corpora and linguistic information available to further improve the NMT performance. Focusing on the above issues, we propose a multi-source NMT approach that models the word feature in parallel to external linguistic features by using two separate encoders to explicitly incorporate linguistic knowledge into the NMT model. We extend the word embedding layer of the knowledge-based encoder to accommodate for each word’s linguistic annotations in the context. Moreover, we share all parameters across encoders to enhance the representation ability of the NMT model on the source language. Experimental results show that our proposed approach achieves substantial improvements of up to 2.4 and 1.1 BLEU scores in Turkish → English and English → Turkish machine translation tasks, respectively, which points to a promising way to utilize the source-side linguistic knowledge for the low-resource NMT.
Clinical Aspects of Overlap Syndrome - Case Report and Literature Review

We report a patient with overlap syndrome (systemic sclerosis (SSc) and polymyositis (PM)). The heterogeneous nature of systemic sclerosis may lead to a great diversity in the clinical presentation of the disease. With this case report we aim to demonstrate clinical manifestations of systemic sclerosis and polymyositis in an overlap-syndrome, with support from antibody profile and laboratory data.
Enhanced Extraction of Traversable Region by Combining Scene Clustering with 3D World Modeling based on CCD/IR Image

Accurate extraction of traversable region is a critical issue for autonomous navigation of unmanned ground vehicle(UGV). This paper introduces enhanced extraction of traversable region by combining scene clustering with 3D world modeling using CCD(Charge-Coupled Device)/IR(Infra Red) image. Scene clustering is developed with K-means algorithm based on CCD and IR image. 3D world modeling is developed by fusing CCD and IR stereo image. Enhanced extraction of traversable regions is obtained by combining feature of extraction with a clustering method and a geometric characteristic of terrain derived by 3D world modeling.
Design and Implementation of a Two Stage Controller for Ball and Beam System Using FPGA

- The ball and beam is, in fact, a standout amongst the essentially vital models, which are generally utilized for educating the control system because its simplicity to be built, modeled, and controlled. This system involve a ball roll on a beam and since the angle of the beam manipulate by servo motor, so the purposes of this paper is to design two stages controller to stabilize the ball position along the beam by varying the angle of beam. The First controller stage “is Proportional- Integral-Derivative” (PID) “for servo motor. The second stage of the controller is Proportional-Derivative (PD) to control the ball and beam plant. The Matlab simulation results illustrate the efficiency of this controller. Furthermore, this paper presents the complete hardware Field Programmable Gate Arrays (FPGA) design with real time implementation for the suggested controller. This controller utilized 1% of occupied slices when implement on Spartan-3A DSP 3400A Xilinx kit with 70.265 𝑛𝑠 minimum time required to complete the controller function. The experimental tests shows that the suggested two stages controller satisfy the functional requirements results.
Effectiveness of a question web-based learning model to improve students’ science process skills

This study aims to improve students’ science process skills (SPS) through the use of question web based learning model (QWBL) in physics learning. The QWBLis a learning model specifically designed to train students’ scientific questioning skills. Scientific questioning skills are related to SPS because every scientific question must be proven through experiments. This research employed educational design research (EDR) and was conducted at senior high schools in academic year of 2017/2018 involving 32 students as samples. This study used one group pre-test and post-test design and data was analyzed descriptively using the n-gain indicator. The results showed that only 3.125% of students completed pre-test and 87.5% of students completed at post-test. The increase of n-gain score was from 0.33 to 0.88 which fall into moderate and high category. The increase in N-gain indicated that the QWBL model was effective to improve SPS in physics learning especially in subject of static fluid. This criterion shows that the QWBL is effective for improving students’ science process skills in learning of physic.
Proposal of a method for systematic electrothermal analysis

It remains not so trivial to construct the equations of a given electrothermal problem. Various elements must be identified for their thermal influences while the electronic activities must be well modeled to take into account both effect of currents and heat. Mechanics interacts with these two physics through forces. We submit here a method combining Kron's method and branch fluxes under the tensorial analysis of networks to establish these equations. The objective is to deliver engineers a simple and efficient technique for electrothermal studies.
Design of D-STATCOM for Power Quality Improvement in Wind Power Systems

The study of problem is when high value reactive load adding or removing that time voltage sag and swell, we are trying to removing sag and swell we can say that stable for all time. The D-STATCOM ac current mode activities in active and reactive current are suppressed by mode shape; along these lines fortifying the receptive current exercises on the heap transport voltage guideline. It can be clearly seen from the obtained results that the voltage attains stability after a short surge even on sudden change in load conditions. The aforesaid conditions have been verified by both load addition and load removal. Application is all generation system used (wind, thermal, solar, etc.), D-STATCOM implement in wind power systems.
Research on News Video Multi-topic Extraction and Summarization

Comparing to other videos, news video contains more important information. Processing a news video with semantic extraction,nbspabstract and annotate its sub-theme can facilitate our operations such as reading and retrieval, make the news video more easy to management and application. In this paper, we use news video text area positioning and extraction technology to obtain text corpus, put these sentences in Chinese word segmentation and syntactic analysis system, then computation sentence weighting and ranking them, calculate the similarity with the sentences. Finally, conduct a study about multi-topic extraction and summarization. The experimental result show this method with high pertinence which can better achieve the targets. nbsp
Multi-source fault identification based on combined deep learning

This study establishes a multi-source fault identification method based on a combined deep learning strategy to identify a multi-source fault effectively in the fault diagnosis of complex industrial systems. This framework is composed of feature extraction and classifier design. In the first state, the signal is transformed to the time-frequency domain and the time-frequency feature is learned using stacked denoising autoencoders. A learning method that consists of unsupervised pre-learning and supervised fine-tuning is used to train this deep model. In the second state, a model for an ensemble multiple support vector machine classifier is created to recognize fault information. Ten types of rolling bearing signals were adopted in a simulation experiment to validate the effectiveness of the proposed framework. The results demonstrate that the joint model helps to obtain higher recognition accuracy.
An Environment-Aware Robot Arm Platform Using Low-Cost Sensors and Deep Learning

The main aim of the thesis is to use low-cost hardware components to create an environment-aware robot arm system capable of analysing its workspace using vision sensors and ensure collision-free operation. The objective is not only to build a physical system, but the focus is to develop algorithms to understand the environment by using image and depth information from multiple cameras and allow the robot to calculate and execute safe movement trajectories in dynamically changing environment. In this thesis, we have developed an automatic camera-to-robot calibration system to perform camera internal and Eye-to-Hand calibrations. Furthermore, a visually based reactive-reflexive robot behaviour method allows the robot to find safe trajectories throughout a dynamically changing environment with a capability of quickly reacting to unexpectedly appearing close obstacles. The robot was also used to charge electric vehicles by using vision-based guidance autonomously. Eventually, the work evolved to using deep learning approaches to recognise the robot and estimate its 3D position with a simple 2D colour image used as an input. Multi-objective convolutional neural networks and transfer learning techniques allowed to expand the method to more robot types when having a limited amount of training data. The thesis concludes that commercially-available hardware can be integrated using advanced algorithms to precisely model the workspace of the robot and allow path planning and quick reactions to unexpected situations. Many 3D cameras as well as robots, including Universal Robots, Kuka iiwa LBR and Franka Emika Panda, were used to perform all the experiments in real-world conditions.
Virtual High Throughput Screening Based Prediction of Potential Drugs for COVID-19

SARS-CoV-2 is a betacoronavirus that was first identified during the Wuhan COVID-19 epidemic in 2019. It was listed as a potential global health threat by WHO due to high mortality, high basic reproduction number and lack of clinically approved drugs and vaccines for COVID-19. The genomic sequence of the virus responsible for COVID-19, as well as the experimentally determined three dimensional structure of the Main protease (Mpro) are available. The reported structure of the target Mpro was utilized in this study to identify potential drugs for COVID-19 using virtual high throughput screening. The results of this study confirm earlier preliminary reports based on studies of homologs that some of the drugs approved for treatment of other viral infections also have the potential for treatment of COVID-19. Approved anti-viral drugs that target proteases were ranked for potential effectiveness against COVID-19 and novel candidates for drug repurposing were identified.
Three-dimensional laser radar modelling

Laser radars have the unique capability to give intensity and full 3-D images of an object. Doppler lidars can give velocity and vibration characteristics of an objects. These systems have many civilian and military applications such as terrain modelling, depth sounding, object detection and classification as well as object positioning. In order to derive the signal waveform from the object one has to account for the laser pulse time characteristics, media effects such as the atmospheric attenuation and turbulence effects or scattering properties, the target shape and reflection (BRDF), speckle noise together with the receiver and background noise. Finally the type of waveform processing (peak detection, leading edge etc.) is needed to model the sensor output to be compared with observations. We have developed a computer model which models performance of a 3-D laser radar. We will give examples of signal waveforms generated from model different targets calculated by integrating the laser beam profile in space and time over the target including reflection characteristics during different speckle and turbulence conditions. The result will be of help when designing and using new laser radar systems. The importance of different type of signal processing of the waveform in order to fulfil performance goals will be shown.
Detecting Embankment Instability Using Measurable Track Geometry Data

The British railway system is the oldest in the world. Most railway embankments are aged around 150 years old and the percentage of disruption reports that feature them is frequently higher than other types of railway infrastructure. Remarkable works have been done to understand embankment deterioration and develop asset modelling. Nevertheless, they do not represent a sufficient way of managing assets in detail. As a result, reactive approaches combined with proactive ones would improve the whole asset management scenario. To guarantee good system performance, geotechnical asset management (GAM) aims to reduce uncertainty through informed, data driven decisions and optimisation of resources. GAM approaches are cost sensitive. Thus, data driven approaches that utilize existing resources are highly prized. Track geometry data has been routinely collected by Network Rail, over many years, to identify track defects and subsequently plan track maintenance interventions. Additionally, in 2018 Network Rail commissioned AECOM to undertake a study, described in this paper, to investigate the use of track geometry data in the detection of embankment instabilities. In this study, track geometry data for over 1800 embankments were processed and parameters offering the best correlation with embankment movements were identified and used by an algorithm to generate an embankment instability metric. The study successfully demonstrated that the instability of railway embankments is clearly visible in track geometry data and the metric gives an indication of the worsening of track geometry, that is likely due to embankment instability.