Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome Compression based on Hilbert Space Filling Curve

The genome compression algorithm based on the Hilbert space- filling curve is proposed in this paper. In order to utilize the correlations among the bases in the genome sequence, our algorithm firstly use the Hilbert space filling curve to map the genome sequence into a new 2-D image. Then the image obtained is encoded by the context weighting modeling technology. For context weighting, the values of weights are corresponding to the description length of the context model which is corresponding to their weights respectively. When the receiver obtain the mapping image, the reverse Hilbert space filling matrix is used to help the decoding procedure. The experiments results indicate that the final compression results by our algorithm are better than the results by other similar algorithms, although the useless area which will lead to the reduce of the compression efficiency is resulted in the mapping image by our algorithm.
Near Infrared Monitoring of a Sample of Blazars with the Carlos Sanchez Telescope. I

We present results of a program of near infrared monitoring of a sample of blazars and miniblazars observed during 1990 and 1991 with the Carlos Sanchez Telescope, in Teide Observatory, Tenerife. All of the objects in the sample show blazar activity during the course of the observations. Several of the objects in the sample have been monitored for rapid variations on timescales of a few hours; only one of them has not, to date, shown such activity.
Adjustment of Monthly or Quarterly Series to Annual Totals: An Approach Based on Quadratic Minimization

Abstract This article considers the problem of adjusting monthly or quarterly time series to make them accord with independent annual totals or averages without introducing artificial discontinuities. A general approach and some specific procedures involving constrained minimization of a quadratic form in the differences between revised and unrevised series are proposed. Some computational advantages are noted. Attention is given to the relationships between the adjustment problem and earlier work by other authors on the creation of monthly or quarterly series when only annual figures are available. An example is provided to illustrate the application of the proposed adjustment procedures.
Broad-band CPWG to stripline feed-through transition and de-embedding method for millimeter wave measurement

A broadband coplanar waveguide with ground to stripline transition with high performance is designed and implemented, which can work at DC or a frequency up to 40 GHz. The ground-signal-ground probe test is a widely used method for accurate measurement of millimeter wave device. Because the probe pitch is smaller than the best dimension of the transition, a 50Ω connector fabricated by Gallium-Arsenic technology was introduced as the bridge between the transition and the probes. In order to acquire accurate measurement result, de-embedding method is adopted. The measurement results show that the insertion loss of the entire transition path is less than 0.5 dB and the return loss is better than −14.5 dB at a frequency up to 40 GHz. Besides, an equivalent circuit model of the transition is also proposed.
Simulation of Time-evolving Speckle Patterns Using Kolmogorov Statistics

Abstract We present a new approach for simulating time-evolving speckle patterns, by combining the shift of a rigid phase screen with its evolution. Thus, the two time-scales associated with speckle boiling and speckle motion can be adjusted independently. The statistical properties of the phase perturbation and the speckle pattern are investigated by using the temporal phase structure function and the temporal intensity correlation. We found a very good agreement with experimental results.
Knowledge-Guided Transfer Learning for Modeling Subsurface Phenomena Under Data Paucity

Knowledge transfer from machine learning (ML) models, pre-trained on large corpuses has been leveraged effectively in domains like natural language processing and computer vision to improve model generalization. The knowledge transfer prowess of ML and especially deep learning (DL) models has been demonstrated to be especially effective under data paucity of the target task. Many scientiﬁc phenomena require the execution of costly simulations to estimate the process of interest. Predicting molecular conﬁguration of ﬂuids conﬁned in porous media is one such problem of extreme relevance in many scientiﬁc applications, the study of which requires the execution of expensive Molecular Dynamics (MD) simulations. However, due to the cost of MD, large scale simulations become intractable. Hence, in this work, we develop a novel science-guided deep learning framework NanoNet-SG to emulate MD simulations. Our proposed NanoNet-SG model leverages scientiﬁc domain knowledge in conjunction with knowledge from pre-trained knowledge bases for estimating molecular conﬁguration of ﬂuid mixtures. Through rigorous experimentation, we demonstrate that our proposed NanoNet-SG model yields good generalization performance improvements (i.e., overall performance improvement of greater than 16 % over baselines) and yields predictions that are consistent with known scientiﬁc domain rules despite being trained on a low volume of MD simulation data (i.e., data paucity).
AutoRL-Bench 1.0

It is well established that Reinforcement Learning (RL) is very brittle and sensitive to the choice of hyperparameters. This prevents RL methods from being usable out of the box. The field of automated RL (AutoRL) aims at automatically configuring the RL pipeline, to both make RL usable by a broader audience, as well as reveal its full potential. Still, there has been little progress towards this goal as new AutoRL methods often are evaluated with incompatible experimental protocols. Furthermore, the typically high cost of experimentation prevents a thorough and meaningful comparison of different AutoRL methods or established hyperparameter optimization (HPO) methods from the automated Machine Learning (AutoML) community. To alleviate these issues, we propose the first tabular AutoRL Benchmark for studying the hyperparameters of RL algorithms. We consider the hyperparameter search spaces of five well established RL methods (PPO, DDPG, A2C, SAC, TD3) across 22 environments for which we compute and provide the reward curves. This enables HPO methods to simply query our benchmark as a lookup table, instead of actually training agents. Thus, our benchmark offers a testbed for very fast, fair, and reproducible experimental protocols for comparing future black-box, gray-box, and online HPO methods for RL.
Quantitative Estimation of COD Values from an Array of Metal Nanoparticle Modified Electrodes and Artificial Neural Networks

Water quality monitoring has become critical in modern societies in multiple areas and at different stages. In this regard, chemical oxygen demand (COD) has become a key index in water testing, as it readily allows the determination of its overall quality and the presence of organic contaminants. However, conventional COD determination presents several drawbacks in view of the use of toxic reagents and possible interferences. The electrochemical determination of COD can be an alternative with many advantages, especially if using an array of sensors. Herein, the use of an electronic tongue (ET) for the estimation of COD was explored. The proposed ET was formed by an array of five voltammetric electrodes modified with different metal nanoparticles. An artificial neural network (ANN) model was built based on the responses of the array towards glucose and glycine as standards. This model was then used with real and spiked water samples, and the results compared to the electrochemical calibration and the commercial COD colorimetric methods. While the COD values of the real samples were low and outside the range of the ANN model, a satisfactory prediction for the spiked samples was achieved, showing a good agreement with the reference colorimetric method, that was better than the performance of the conventional electrochemical calibration method.
Determining Current Medications Usage within a Cohort of Patients in the UK—A Descriptive Retrospective Study

Swansea University’s United Kingdom (UK) Multiple Sclerosis (MS) Register is a platform that contains information on more than 17,600 people with MS living in the UK. The register has been in operation since 2011 and represents comprehensive information about people living with MS in the UK. It is considered the first register of its kind that can link information from patients to clinical data and has been established to answer different information needs about MS. Aim: To elucidate the trends in patterns of medicines currently used by people with MS in the UK MS register. Methods: This study follows an exploratory descriptive design using the UK MS register as data resource. A number of 4516 people completed the EQ-5D survey out of 8736 people who have given their consent to answer online questionnaires which represents around 52% of the register total population. Descriptive analysis and tests were performed with SPSS to address the research objectives. Results: There are several medicine names entered by people with MS in their profiles. These medicines are used either to manage MS symptoms or to treat its associated complications. Among the medicine types revealed in this study, disease modifying drugs (DMDs), muscle relaxants, and anticonvulsants are the medicine types mainly used by people with MS followed by antidepressant and antianxiety medicines. Conclusions: From the antidepressants used most widely, amitriptyline was chosen as a subject medicine for further investigation in the remaining studies of this research due to its high frequency use, the elevated depression rates discovered among people with MS who seek information on it online, and the high online content noted on websites about this medicine.
Mitochondrial DNA Analysis of Awassi-Iq (Ovies aries) Sheep Breeds from Nineveh Province, Iraq

In Iraq, Awassi is the most widespread sheep breed of non-European origin. The breed is adapted to a wide range of environmental conditions. Sheep farming, mainly sheep of Awassi breed, represents an essential economic activity of Iraq; however, so far, only very few genetic data exist on this breed. The present study was performed in Biology Department, University of Zakho. A total of 32 blood samples were obtained from Awassi-Iq (Ovis aries) sheep breed in three farms located in Nineveh province, Iraq. Genomic DNA extraction was performed using spin column method. Specific marker (GenBank: NC001942) used for amplifying 550 bps fragments of mitochondrial DNA in PCR assay. The PCR products were separated by electrophoresis on 1.5% agarose gel, visualized by staining with RedSafe dye, and photographed by a digital camera. Ten PCR products were sequenced and compared to sequences from other countries available in NCB1.The main results indicate that Awassi- 1q (Ovis aries) sheep breed shares similarity to both Chines Tibetan sheep breed (Linzhou); Iranian sheep breed (91.37%), to both Iraqi sheep breeds Hamdani; Nima (91.62%) respectively and to Ovis aries AKK- 46 from Turkey (92.35%). However, Awassi-Iq was shown to be monophyletic, out group breed and diverged earlier than related breeds (Turkey, Iran, and China). This study highlights the importance of Mitochondrial DNA analysis in genetic study of sheep breeds.
Use of CGF in Oral and Implant Surgery: From Laboratory Evidence to Clinical Evaluation

Edentulism is the condition of having lost natural teeth, and has serious social, psychological, and emotional consequences. The need for implant services in edentulous patients has dramatically increased during the last decades. In this study, the effects of concentrated growth factor (CGF), an autologous blood-derived biomaterial, in improving the process of osseointegration of dental implants have been evaluated. Here, permeation of dental implants with CGF has been obtained by using a Round up device. These CGF-coated dental implants retained a complex internal structure capable of releasing growth factors (VEGF, TGF-β1, and BMP-2) and matrix metalloproteinases (MMP-2 and MMP-9) over time. The CGF-permeated implants induced the osteogenic differentiation of human bone marrow stem cells (hBMSC) as confirmed by matrix mineralization and the expression of osteogenic differentiation markers. Moreover, CGF provided dental implants with a biocompatible and biologically active surface that significantly improved adhesion of endothelial cells on CGF-coated implants compared to control implants (without CGF). Finally, data obtained from surgical interventions with CGF-permeated dental implants presented better results in terms of optimal osseointegration and reduced post-surgical complications. These data, taken together, highlight new and interesting perspectives in the use of CGF in the dental implantology field to improve osseointegration and promote the healing process.
Deep Learning based Approach for Photographs and Painting Classification using CNN Model

One of the most powerful technologies for dealing with a wide range of real-world difficulties in the fast-paced world of the twenty-first century is Machine Learning (ML). Both regular and differently-abled persons benefit from machine learning. The Convolutional Neural Network (CNN) has been proposed for a variety of applications such as multimedia processing and so on. Here in this research paper, we have described the way and created a Binary Classification model using CNN for identifying the Paintings and Photographs. Each painting and Photograph have been warped using various procedures such as a convolutional layer, dense layer, and Flatten layers. The model is used for Binary Classifications. The wrap has been done at random on a large dataset for CNN training. We explore the architecture of CNN affects the accuracy of the identification. The proposed Model aims to increase the efficiency and accuracy of the model.
Work-Related Musculoskeletal Disorders among Dentists in the United Arab Emirates: A Cross-Sectional Study

Background and Objectives: A dental environment possesses a serious ergonomic health issue on the dental team members which in turn contributes to the development of work-related musculoskeletal disorders (WMSDs). The present research is aimed at evaluating the frequency of musculoskeletal disorders and their associated sociodemographic and work environment risk factors among dentists in the United Arab Emirates. Material and Methods: In this cross-sectional study, a pretested and validated questionnaire was sent via email as well as on different social media platforms to a total of 497 dentists. A total of 179 dentists completed the study survey, with a total response rate of 36%. A binary logistic regression model was conducted to identify significant risk factors associated with WMSDs. Results: Dentists in the United Arab Emirates experience a high prevalence of work-related musculoskeletal disorders (90.4%) which were associated with high levels of stress in the work environment. Furthermore, dentists in the private sector worked more clinical hours per day (p = 0.000) and had more financial stress (p = 0.007) as compared to those in the governmental sector. Gender (p = 0.007), age (p = 0.01), monthly income (p = 0.073), work experience (p = 0.037), number of patients treated per day (p = 0.049), and the use of an ergonomic dental chair (p = 0.005) were all factors associated with a greater number of affected body regions by WMSDs. Multivariate binary logistic regression for the number of regions affected by WMSDs revealed that not using an ergonomic dental chair (OR 2.70, 95% CI, 1.14–6.36) and high stress in the work environment (OR 1.31, 95% CI 1.02 to1.67) were associated with more body regions being affected by WMSDs. Conclusions: This study highlights the high prevalence rate of WMSDs among dentists in the UAE. Future research should be directed towards reducing stress in the work environment, increasing awareness regarding the importance of an ergonomic dental chair, and reducing gaps between private and governmental practices.
Simple sensor system for measuring the dielectric properties of saline solutions

On-line measurements of dielectric properties can be used for quality determination of process streams. Measurements of the water cut are important in order to determine how much water and oil that are e.g. produced from an oil well or how much oil is discharged to the sea. This work describes the use of a single open-ended probe measurement system for determination of the dielectric properties of saline solutions, the salinity being an important parameter in the determination of the dielectric properties of the water. The frequency range of the measurements is from 500 MHz up to 40 GHz, the salinity varying from 0 to 3.5 wt% (sea water) at C. Models are developed under controlled laboratory conditions relating the static permittivity, relaxation time and conductivity to the salinity of the water. For model verification the same set of tests are performed with a well established precision laboratory system. The potential for on-line implementation of the open-ended probe has been verified by tests in a multiphase flow loop, and these results are also presented.
Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach

Artificial intelligence (AI) has been used in physical therapy diagnosis and management for various impairments. Physical therapists (PTs) need to be able to utilize the latest innovative treatment techniques to improve the quality of care. The study aimed to describe PTs’ views on AI and investigate multiple factors as indicators of AI knowledge, attitude, and adoption among PTs. Moreover, the study aimed to identify the barriers to using AI in rehabilitation. Two hundred and thirty-six PTs participated voluntarily in the study. A concurrent mixed-method design was used to document PTs’ opinions regarding AI deployment in rehabilitation. A self-administered survey consisting of several aspects, including demographic, knowledge, uses, advantages, impacts, and barriers limiting AI utilization in rehabilitation, was used. A total of 63.3% of PTs reported that they had not experienced any kind of AI applications at work. The major factors predicting a higher level of AI knowledge among PTs were being a non-academic worker (OR = 1.77 [95% CI; 1.01 to 3.12], p = 0.04), being a senior PT (OR = 2.44, [95%CI: 1.40 to 4.22], p = 0.002), and having a Master/Doctorate degree (OR = 1.97, [95%CI: 1.11 to 3.50], p = 0.02). However, the cost and resources of AI were the major reported barriers to adopting AI-based technologies. The study highlighted a remarkable dearth of AI knowledge among PTs. AI and advanced knowledge in technology need to be urgently transferred to PTs.
Preliminary Results of Angle-Resolved BTDF Characterization of Optical Transmissive Diffusers

Optical diffusers are used in many application areas related to illumination. With the purpose of characterizing the transmission properties of optical diffusers, we present preliminary results of BTDF (Bidirectional Transmittance Distribution Function) measurement with high angular resolution at two different wavelengths. The expanded total uncertainty of the measurement is estimated to be 0.5% in the best case. In this way, the scattering distribution of optical diffusers could be quantitatively described by their BTDF values with high precision, good repeatability and reproducibility. The diffusers’ angle-dependent spectral transmittances in the VIS (from 380 nm to 780 nm in the electromagnetic spectrum) are also measured using a commercially available spectrophotometer. Results are compared to simulation calculated from a simple model and a first-step explanation for the sample spectral dependency is proposed.
PhysioSense Controller: Self-Actuating Button Based on Player Physiology for Improved Avatar Control

Games generally require a certain level of skill to control the avatar. However, this can also potentially lead to frustration since there is no way for a game to truly adapt to the player’s skill during gameplay. We propose the PhysioSense controller, a custom designed gamepad controller that senses the player’s electrodermal activity (EDA), heart rate, and motion to compute their cognitive load level in real-time and trigger a haptic feedback during key events in the game. The haptic feedback is delivered via subtle actuation on the button, allowing the player to still retain their sense of agency. We performed an initial evaluation on the PhysioSense Controller using a platforming action game with three custom difficulty levels. We found that there was a clear physiological and motion response to the presented difficulties, and that the player’s behavior changes to adapt to them. We believe that our system can potentially make more players enjoy most games in the future regardless of presented difficulty.
Computational study of neuronal networks with spatial constraints by means of the Izhikevich model

: A huge effort has been made along the last 20 years to map the detailed structural organisation of neural networks. The main reason behind this effort is to understand the relationship between the structure of the network and its dynamics or functional traits. To advance in this quest, numerical simulations have emerged to help exploring the relation between structure and function. Here we used the Izhikevich model to simulate neuronal networks with spatial constraints. We launched numerical simulations of 1000 neurons in two different modular networks, and mimicking designs reported in experiments. We observed that some information about structure can be glimpsed when the spatial constraints are very strong. In general, however, the properties of the underlying structural network differ greatly from those obtained from the simulations, indicating that the assessment of structural connectivity from just dynamics is not possible. We also applied damage to the networks, and observed that targeted attacks strongly affects the activity and functional traits of the networks under study.
An Application of Analytical Target Cascading for a Hierarchical Multidisciplinary System: The Preliminary Design of a Launch Vehicle Powered by Hybrid Rocket Motors

Analytical target cascading (ATC) is a method for coordinating hierarchical system design optimization with a decomposition-based framework. Since a launch vehicle (LV) is usually powered by two or more stages of rocket motors, the overall design of the LV clearly has a hierarchical structure, including system level (conducted by the general design department) and subsystem level (conducted by the motor stage design department). In particular, the subsystem level contains stage-divided elements rather than discipline-divided elements. Therefore, ATC is inherently suitable for the overall design of the LV. This paper presents an ATC decomposition framework for LV design according to practical engineering. The feasibility of the multi-island genetic algorithm (MIGA) used in the ATC decomposition is verified by a mathematical programming test, in which non-linear programming with the quadratic Lagrangian (NLPQL) algorithm is set as a comparison. The multi-disciplinary analysis modules of a hybrid rocket motor (HRM) propelled LV, including propulsion, structure, aerodynamics and trajectory, are established. A hierarchical decomposition is proposed for this multi-level design with a multi-disciplinary model. The application and optimization results verify the feasibility of the ATC decomposition framework with MIGA in the preliminary design of the LV and the final orbit accuracy is better than that of the MDF method. In addition, the final design schemes also prove that HRMs can be considered as a feasible choice of propulsion system for a small payload at low earth orbit.