Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Influence Measure Algorithm for Social Networks

As the development of communication, social networks are becoming increasingly ubiquity nowadays. The reliable measure of the influence of each individual has a great significance. Better decisions can be made according to the influence ranking in a social network. In this paper, we introduce the time series into PageRank and propose an improved influence measure model. Experiments on the Erdos1 coauthors network and the bloggers network both achieved reliable results.
Environmentally Adaptive Control Including Variance Minimization Using Stochastic Predictive Network with Parametric Bias: Application to Mobile Robots

In this study, we propose a predictive model composed of a recurrent neural network including parametric bias and stochastic elements, and an environmentally adaptive robot control method including variance minimization using the model. Robots which have flexible bodies or whose states can only be partially observed are difficult to modelize, and their predictive models often have stochastic behaviors. In addition, the physical state of the robot and the surrounding environment change sequentially, and so the predictive model can change online. Therefore, in this study, we construct a learning-based stochastic predictive model implemented in a neural network embedded with such information from the experience of the robot, and develop a control method for the robot to avoid unstable motion with large variance while adapting to the current environment. This method is verified through a mobile robot in simulation and to the actual robot Fetch.
Second-Generation Tyrosine Kinase Inhibitor Discontinuation in Chronic Myeloid Leukemia Patients with Stable Deep Molecular Response: A Systematic Review and a Meta-Analysis

The treatment with 2nd-generation tyrosine kinase inhibitors (2G-TKIs), namely, dasatinib and nilotinib, has been reported to have faster and deeper responses in newly diagnosed chronic phase-chronic myeloid leukemia (CP-CML) patients as compared with imatinab. A number of studies on the discontinuation of 2G-TKIs have been conducted and recently published. A meta-analysis was conducted in this study to assess the rate of treatment-free remission (TFR) rate as well as the long-term safety of 2G-TKI discontinuation in CML patients with stable deep molecular response (DMR). 517 patients were recruited in 5 single-armed, prospective cohort studies. The overall weighted mean TFR rate at the follow-up of 12 months reached 57% (95% CI 51-64%; I2 = 56.4%). The weighted mean TFR rate at the 24-month follow-up was 53% (95% CI 47-60%; I2 = 47.1%). The loss of TFR was primarily concentrated in the first 12 months. 96.5% of patients, having restarted TKI therapy after a molecular relapse, achieved major molecular response (MMR) rapidly. There were four deaths at the two-year follow-up. As suggested from the results of the final study, 2G-TKI discontinuation in CML patients with stable DMR was reported to be feasible. Relapsed patients were retreated with 2G-TKI, and over 95% of patients could reach MMR. Almost no deaths occurred due to adverse events in two years after discontinuation, and more than half of the patients could maintain a TFR.
Emerging Community Noise Reduction Approaches

An overview of the current NASA research portfolio in the area of aircraft noise reduction is presented. The emphasis of the research described herein is on meeting the aggressive near- and mid-term national goals for reducing aircraft noise emissions, which NASA internal studies have shown to be feasible using noise reduction technologies currently being developed in-house or in partnership with NASA’s industry and academic partners. While NASA has an active research effort in airframe noise reduction, this overview focuses on propulsion noise reduction only.
Automatic Learning System for Object Function Points from Random Shape Generation and Physical Validation

In this paper, we aim to recognize function points of category-agnostic objects and perform object manipulation. To recognize function points of various shapes, it is necessary to train with a large amount of training data. Also, it is necessary to take into account not only visual information but also physics and interaction between objects. To solve these problems, we are working on the automatic generation of training data by detecting function points from a physical simulation. In the proposed system, we add simulation with target task operation and goal state, which allows a robot to acquire the target function point recognizer. We also use GAN to generate various random shapes and render them with random domains, and train Deep Neural Networks on these data. These enable the robot to recognize function points of unseen objects in the real world and realize manipulation.
Human perception evaluation system for urban streetscapes based on computer vision algorithms with attention mechanisms

Virtual 3D modeling is widely implemented in urban planning and design. To evaluate urban planning modeling, based on existing computer vision models, this article aims to improve performance in the field of human perception analysis for urban street views. In this study, the PSP module extracts detailed features from recognized objects of different sizes, an attention mechanism is applied to solve the problem of large information differences in pictures, and transfer learning technology is used to expand the model to the field of virtual 3D modeling to extract more representative and universal features, similar to how humans perceive street view information. Finally, we obtain a more objective, stable, and accurate neural network model that imitates human perception. This evaluation model converges within the correct interval on the training and validation datasets compared with an evaluation of virtual modeling by a large number of people.
Methodology for the generation and evaluation of safety system alternatives based on extended Hazop

Process safety is paramount in the secure and viable operation of any chemical process plant. Many techniques and methodologies have been defined over the years to undertake steps that will ensure a safe operating environment at a chemical facility. However, until now, process simulation has found very little or no use in safety‐related studies. This article introduces a systematic framework based on Extended Hazop (that is, Hazop supported by disturbance simulation related to process malfunctions.) The Extended Hazop differs from standard Hazop in documenting results, classification of frequency and consequences of process deviations, and application of a risk potential matrix (Hazop decision matrix). In this article, the Extended Hazop method is explained. Application of this method will be demonstrated in a separate article. © 2006 American Institute of Chemical Engineers Process Saf Prog, 2007
Yoga for the Management of Attention-Deficit/Hyperactivity Disorder

Yoga has been shown to play a role in reducing the symptoms associated with the inattentive and hyperactive-impulsive forms of attention-deficit/hyperactivity disorder (ADHD). The medical history and clinical findings for a nine-year-old patient presenting with difficulty paying attention and impulsive speech and actions at home and school are presented. After the diagnosis of combination type ADHD by assessment of DSM-5 criteria, both at home and school and through parent and teacher evaluations using National Institute for Children’s Health Quality (NICHQ) Vanderbilt Assessment Scales, the patient initiated a yoga training regimen. Six months after initiating the yoga training regimen, follow-up parent and teacher questionnaires revealed improvement in both the inattentive and hyperactive-impulsive symptoms. Literature sourced from the PubMed database to explore the efficacy of yoga for ADHD was used to support the research hypothesis that a structured yoga training regimen improves the symptoms associated with the inattentive and hyperactive-impulsive forms of ADHD, and thus, yoga is recommended as a management technique for individuals with ADHD.
The population dynamics of ecosystem engineers and habitat modification

We consider a native species whose environment is stressed due to the presence of an ecosystem engineer, a specific type of invasive species that has the ability to convert native habitat into one which is more conducive to its own survival. The re-engineering of the native habitat results in an increase in the intra-species competition among the resident species. A key aspect of ecosystem engineer dynamics is that their invasion is limited by the size of the native habitat and their ability to transform it. It has long been recognised that invasive species play a role in declining biodiversity and the degradation of native habitat. We explore the effect of a single engineer species on a resident species and its habitat. The invading engineer species does not prey on the resident species but does compete for available habitat. The engineer species converts, modifies or re-engineers native habitat. The rate at which this conversion is performed determines whether or not colonisation will be successful. Over time the converted habitat degrades forming a decayed habitat that is not suitable to either species. The decayed habitat returns to its native state through a process of recovery. Once in its native state the habitat can be occupied by either the resident species or modified again by the ecosystem engineer. This recycling of the habitat is an important and novel feature in the model. We investigate the dynamics of this model. We assume the dynamics of both species is governed by a logistictype differential equation whose carrying capacity is equal to the size of its habitat. In the logistic equation, it is the carrying capacity that determines the population size. A reduction in habitat will drive the population to a smaller size, similarly an increase in habitat will increase the population size. An analysis of the model reveals three different approaches that may be used to control the invading species. The first is based on reducing the ability for the engineer species to convert native habitat. The model shows the existence of a minimum conversion rate below which the engineer species can not colonise the native habitat. Therefore conservation strategies should focus on reducing the engineer’s ability to convert habitat. A second strategy is a well known strategy, the harvesting of the invading species. And the third is based on ‘quarantining’ the decayed habitat for a certain period of time. Although quarantining does not alter the conversion rate it does however favour the resident species by not permitting the engineer species to become the dominant species. However, large quarantine periods cause oscillations within the system which may not be desirable. Finally, we propose directions for future work.
Role of Colchicine in Management of COVID-19?

: CoV-2 disease 2019 (COVID-19) pandemic has exerted a great burden on the health and economic systems worldwide. One of the most important factors that affect the severity and prognosis of COVID-19 is the occurrence of hyperstimulation of the immune system resulting in “cytokine storm”. Similar to SARS -CoV, an intracellular complex called nod like receptor family pyrin domain containing 3 (NLRP3) inflammasome was found to be activated by SARS-CoV-2, then in turn stimulates several interleukins and initiates the inflammatory cascade. In addition, other inflammatory mediators such as interferons e.g., IFN- α, and IFN - γ, interleukins e.g., IL - 1β, IL-12, IL-18, tumor necrosis factor- α (TNF - α), and chemokines e.g., CCL2, CXCL10 have been reported in severely ill patients. These mediators represent the corner stone in developing cytokine storm that results in uncontrolled systemic inflammatory reaction with subsequent acute respiratory distress syndrome (ARDS), multiple organ failure and eventually death. Based on its an-ti-inflammatory effects, colchicine has also gained attention to be utilized in the management of COVID-19 patient. Colchicine exerts its anti-inflammatory effect through inhibition of formation of microtubules which is considered an essential step in several cellular processes such as division, signalling, and migration. Also, colchicine affects the cytokine cascade by inhibiting IL- 1β leading to reduction in neutrophils recruitment, free radicles production and inflammasome stimulation. This raises the concerns about the effectiveness of colchicine in COVID-19 treatment and the possibility of providing an improvement of the clinical course of the disease.
Analysis of efficiency and misalignment tolerances in laser diode pigtailing using single ball lens

In t his paper we present so me investigations and analysis on co upling ef ficiency and misalignment tolerances in coupling of laser diode to single mode fiber using single ball lens of (diameter = 1mm, and refractive index n = 1.8333). It has been found that there should be an optimization for p ositioning of the ball lens between laser d iode a nd the tip of the fiber. At some o ptimum positions, the cupping efficiency can r each max imum value over 90% when the ratio of the divergence angles xy (M = e / e ) i n bo th x and y d irections res pectively is (20/33) and the m ode m atching i s very effective as i t is illustrated by the ef fect of the l ateral and angular offsets in bo th x and y di rections on t he misalignment tolerances. But when the rat io is (M=8/33) t here is a reduc tion i n co upling ef ficiency and the t ransformation i s not effective. We al so st udied the effect of the t ransformed beam waist radii on the 1_dB axial, lateral and angular misalignment tolerances for the proposed coupling scheme and also noticed that the m ode m atching i s very effective f or ( M=20/33). Our experi mental results showed a sign ificant relaxation in misalignment tolerances using single ball lens for coupling with optimum coupling efficiency of ab out 5 5% at a relaxed working distance in the range of (0 .7 - 1.2 mm).
Physical Activity Monitoring using Accelerometers

In today’s society there are those who would like to keep track of how active they are. These situations span across a wide range of lifestyles such as those that are extremely active to those who are prescribed to be more active. Designing a system to monitor an individual’s physical activity would assist the desires or needs of these individuals. We have developed algorithm that can detect differences in activities (e.g. walking, running, and jumping) as well as inactivity. With these algorithms, we constructed an application that can monitor, store specific data of activities, and report various statistics of the user’s activity throughout the day. This system could be used in hospitals, nursing homes, or for one’s personal use. This system is an ongoing research project that allows for educational experience by developing methodologies and testing each one. The system is designed to read data coming from a 3 axes accelerometer sensor, filter the data, and then analyze aspects of the data to determine the current activity. The data is first filtered by summing the square of each axes and then taking the square root of that sum. This is called the magnitude. After, we take the average of every ten magnitude calculated. From here the derivative is calculated roughly every tenth of a second. Based on the results of the algorithm, the system displays the current activity that is detected, time active, total time recorded, and calories burned. Many devices today, for example smart phones, have an accelerometer sensor inside the device. This system was implemented into an Android application to function on Android smart phones. The development of this application took place within the Eclipse software environment. By combining this algorithm and more data analysis we can merge this design into medical devices or smart phones of individuals interested in the product.
Unified treatment of core losses and saturation in the orthogonal-axis model of electric machines

The paper treats simultaneously the core losses and the magnetic saturation of the main-flux path in the orthogonal-axis model of electric machines. Core losses are modelled by additional fictitious windings on the stator and on the rotor. Tests are suggested to determine the constants of these windings. A set of equations, expressed as a state equation, is derived for the general case. Synchronous, induction, and DC commutator machines are obtained as special cases by imposing constraints on the general equations. Simulation of the steady state and transients of a highly saturated induction machine is also presented with the implications of saturation and core losses shown and discussed in some detail.
Real-World Prescribing Pattern of Asthma Management in China: A Retrospective, Descriptive Analysis

Background: Asthma is a chronic inflammatory disorder of the airway that requires long-term medication management. Objective: To describe the real-world prescribing patterns for asthma management in the Chinese population. Methods: A retrospective analysis of 8,732 patients from January 2011 to September 2019 in 10 hospitals was conducted. Prescribing patterns of short-acting beta-agonists (SABA), long-acting beta-agonists (LABA), inhaled corticosteroids (ICS), intravenous corticosteroids, antihistamines, leukotriene receptor antagonists (LTRA), theophylline, antibiotics, and Chinese patent medicines were included in the analysis. Chi-square and logistic regression were calculated. A P value of <0.05 was considered as statistical significance.Results: ICS/LABA were prescribed for 2,940 (33.67%) patients with stable asthma and 141 (31.4%) patients with asthma exacerbations. LTRA was prescribed in 2,006 (22.97%) patients with stable asthma, and 86 (19.15%) patients with asthma exacerbations. Systemic antibiotics (46.1Z%), systemic corticosteroids (57.91%), and theophylline (51.45%) were frequently prescribed during exacerbations. A total of 5,766 patients (64.95%) were managed with traditional Chinese medication (TCM); 3237 patients (36.47%) received western medicine. Patients using ICS/LABA (P=0.019) and SABA (P=0.008) had a significantly lower rate of asthma exacerbations.Conclusions: This study provides valuable insight into clinical practices of asthma management in China. Poor adherence to clinical reports was identified. Efforts are required to improve the quality of asthma care.
Resistive micromegas for sampling calorimetry

Micromegas is an attractive option for a gaseous sampling calorimeter. It delivers proportional and fast signals, achieves high efficiency to minimum ionising particles with a compact design and shows well-uniform performance over meter-square areas. The current R&D focuses on large- size spark-protected Micromegas with integrated front-end electronics. It targets an application at future linear colliders (LC) and possible upgrades of LHC experiments for the running at high luminosity. In the later case, occasional sparking should be suppressed to avoid dead-time and technical solutions using resistive coatings are investigated. Small prototypes of resistive and non-resistive Micromegas were constructed and tested in a beam at DESY. Results are reported with emphasis on the impact of the resistive layer on the detector performance.
TUPPer-Map: Temporal and Unified Panoptic Perception for 3D Metric-Semantic Mapping

In this paper, we propose TUPPer-Map, a metric-semantic mapping framework based on the unified panoptic segmentation and temporal data association. In contrast to the previous mapping method, our framework integrates the data association stage into the holistic pixel-level segmentation stage in an end-to-end fashion, taking advantage of both intra-frame and inter-frame spatial and temporal knowledge. Firstly, we unify two-branch instance segmentation network and semantic segmentation network into a single network by sharing the backbone net, maximizing the 2D panoptic segmentation performance. Next, we leverage geometric segmentation to refine the segments predicted via deep learning. Then, we design a novel deep learning based data association module to track the object instances across different frames. Optical flow of consecutive frames and alignment of ROI (Region of Interest) candidates are learned to predict the frame-consistent instance label. At last, 2D semantics are integrated into 3D volume by TSDF raycasting to build the final map. We evaluated the performance of our framework extensively over the SceneNN, ScanNet v2 and Cityscapes-VPS datasets. Our experimental results demonstrate the superiority of TUPPer-Map over existing semantic mapping methods. Overall, our work illustrates that using learning based data association strategy can enable a more unified perception network for 3D mapping.
A Powered Prosthetic Ankle Designed for Task Variability – A Concept Validation

Ankle joints play key roles in everyday locomotion, such as walking, stair climbing, and sit-to-stand. Despite the achievement in designing powered prosthetic ankles, engineers still face challenges to duplicate the full mechanics of ankle joints, including high torque, large range of motion (ROM), low profile, backdrivability, and efficiency, using electric motors and related transmissions. In this study, our goal was to develop a new active prosthetic ankle, Variable Spring embedded Motor-ball screw (VSeM) ankle, to meet all these requirements at the same time. Using a manually adjustable elastic element, which is parallel with our motor actuator, we can readjust the ROM of VSeM to handle all normal locomotion tasks. VSeM’s capability to mimic human ankle was validated through both bench tests and human subject tests.