Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructions of some families of smooth Cauchy transforms

Abstract For a given Beurling–Carleson subset E of the unit circle $\mathbb {T}$ which has positive Lebesgue measure, we give explicit formulas for measurable functions supported on E such that their Cauchy transforms have smooth extensions from $\mathbb {D}$ to $\mathbb {T}$ . The existence of such functions has been previously established by Khrushchev in 1978, in non-constructive ways by the use of duality arguments. We construct several families of such smooth Cauchy transforms and apply them in a few related problems in analysis: an irreducibility problem for the shift operator, an inner factor permanence problem. Our development leads to a self-contained duality proof of the density of smooth functions in a very large class of de Branges–Rovnyak spaces. This extends the previously known approximation results.
Applied Exoskeleton Technology: A Comprehensive Review of Physical and Cognitive Human–Robot Interaction

Exoskeletons and orthoses are wearable mobile systems providing mechanical benefits to users. Despite significant improvements in the last decades, the technology is not fully mature to be adopted for strenuous and nonprogrammed tasks. To accommodate this insufficiency, different aspects of this technology need to be analyzed and improved. Numerous studies have tried to address some aspects of exoskeletons, e.g., mechanism design, intent prediction, and control scheme. However, most works have focused on a specific element of design or application without providing a comprehensive review framework. This study aims to analyze and survey the contributing aspects to this technology’s improvement and broad adoption. To address this, after introducing assistive devices and exoskeletons, the main design criteria will be investigated from both physical human–robot interaction (HRI) perspectives. In order to establish an intelligent HRI strategy and enable intuitive control for users, cognitive HRI will be investigated after a brief introduction to various approaches to their control strategies. The study will be further developed by outlining several examples of known assistive devices in different categories. Some guidelines for exoskeleton selection and possible mitigation of current limitations will be discussed.
Coronavirus and incomes: the COVID-19 pandemic dynamics in Africa in February 2022

The relative accumulated and daily characteristics of the COVID-19 pandemic dynamics in Africa were used to find links with the gross domestic product per capita (GDP), percentages of fully vaccinated people and daily numbers of tests per case. A simple statistical analysis of datasets corresponding to February 1, 2022 showed that accumulated and daily numbers of cases per capita, daily numbers of deaths per capita and vaccination levels increase with the increase of GDP. As in the case of Europe, the smoothed daily numbers of new cases per capita in Africa increase with the increasing of the vaccination level. But the increase of the accumulated numbers of cases and daily number of deaths with increasing the vaccination level was revealed in Africa. In comparison with Europe, no significant correlation was revealed between the vaccination level and the number of deaths per case. As in the case of Europe, African countries demonstrate no statistically significant links between the pandemic dynamics characteristics and the daily number of tests per case. It looks that countries with very small GDP are less affected by the COVID-19 pandemic. The cause of this phenomenon requires further research, but it is possible that low incomes limit the mobility of the population and reduce the number of contacts with infected people. In order to overcome the pandemic, quarantine measures and social distance should not be neglected (this also applies to countries with a high level of income and vaccination).
Advancing Point-of-Care Ultrasound Training in Medical Schools: Ultrasound-Guided Subclavian Vein Access Training Using Formalin-Embalmed Cadavers

OBJECTIVES: This brief report examines the feasibility of using formalin-embalmed cadavers in training medical students to use ultrasound guidance to access the subclavian. This novel educational approach is discussed in the context of the ongoing integration of point-of-care ultrasound training into medical education. Additionally, this report explores how cadavers can provide practical, effective, and hands-on skills training opportunities for medical students to learn to perform common clinical procedures under ultrasound guidance. DESIGN: This report presents subjective and objective data evaluating the utility of teaching medical students to perform ultrasound-guided subclavian vein access on formalin-embalmed cadavers. SETTING: Rocky Vista University College of Osteopathic Medicine in Ivins, UT. SUBJECTS: Twenty-five first-year medical students at Rocky Vista University. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Pre and posttraining questionnaires were administered to assess each participant’s self-confidence in using ultrasonography to access the subclavian vein of a cadaver. A statistically significant increase in participant self-confidence was observed across all questionnaire items from pre to posttraining. Objective evaluation consisted of a supervised skills test. Participants were evaluated on their ability to visualize the subclavian vein with ultrasound and achieve flashback of blood/embalming fluid into a syringe. During skills testing, the number of needle sticks and the time taken to achieve flashback were recorded for each participant. Twenty-three of the 25 participants were able to successfully complete the skills testing assessment. CONCLUSIONS: The formalin-embalmed cadaver can be a readily available and effective learning tool for medical education programs seeking to provide training opportunities in ultrasound-guided clinical procedures. The use of cadavers allows learners to train in a low stress and anatomically authentic environment without risk of patient discomfort.
COVID-19 Face Mask Detection

The present scenario of COVID-19 demands an efficient face mask detection application. The main goal of the project is to implement this system at entrances of colleges, airports, hospitals, and offices where chances of spread of COVID-19 through contagion are relatively higher. Reports indicate that wearing face masks while at work clearly reduces the risk of transmission. It is an object detection and classification problem with two different classes (Mask and Without Mask). A hybrid model using deep and classical machine learning for detecting face mask will be presented. A dataset is used to build this face mask detector using Python, OpenCV, and TensorFlow and Keras. While entering the place everyone should scan their face and then enter ensuring they have a mask with them. If anyone is found to be without a face mask, beep alert will be generated. As all the workplaces are opening. The number of cases of COVID-19 are still getting registered throughout the country. If everyone follows the safety measures, then it can come to an end. Hence to ensure that people wear masks while coming to work we hope this module will help in detecting it.
Cliques within Clusters – Multi-dimensional Network Integration and Innovation Activities

Regional industrial clusters have certainly been one of the most prominent loci where competition meets cooperation. Not surprisingly, the coexistence of these two phenomena is a constitutive feature of most cluster definitions. Nevertheless, the micro-dynamics of clusters with respect to these and other processes are still little understood, especially regarding one of the most important outcomes of clusters – innovation. The present paper aims at enhancing our understanding of how clusters are conducive to innovation by investigating the micro-structures and -processes of an emerging optics/photonics cluster in Germany using a broad range of research methodologies, including clique analysis and participant observation. The results confirm the expectation that the micro-structures that have actually emerged in the cluster do not entirely correspond to the formal cluster governance and that multiplex overlapping cliques provide not only for a fair amount of network integration, but also a social context conducive for turning complex knowledge of research organizations into marketable products. While questioning such unidirectional effects, the generation of innovation can also be understood as a medium for generating and stabilizing such relational structures – even between competitors within clusters.
Destination Port Detection for Vessels: An Analytic Tool for Optimizing Port Authorities Resources

—Port authorities have many challenges in congested ports to allocate their resources to provide a safe and secure loading/unloading procedure for cargo vessels. Selecting a destination port is the decision of a vessel master based on many factors such as weather, wavelength and changes of priorities. Having access to a tool which leverages Automatic Identiﬁcation System (AIS) messages to monitor vessel’s movements and accurately predict their next destination port promotes an effective resource allocation process for port authorities. In this research, we propose a method, namely, Reference Route of Trajectory (RRoT) to assist port authorities in predicting inﬂow and outﬂow trafﬁc in their local environment by monitoring AIS messages. Our RRo method creates a reference route based on historical AIS messages. It utilizes some of the best trajectory similarity measures to identify the destination of a vessel using their recent movement. We evaluated ﬁve different similarity measures such as Discrete Fr´echet Distance (DFD), Dynamic Time Warping (DTW), Partial Curve Mapping (PCM), Area between two curves (Area) and Curve length (CL). Our experiments show that our method identiﬁes the destination port with an accuracy of 98.97% and an f-measure of 99.08% using Dynamic Time Warping (DTW) similarity measure.
FatigueAlert: A real-time fatigue detection system using hybrid features and Pre-train mCNN model

Summary Several computer-vision based applications are developed to detect of driver fatigue (DDF) and to decrease road accidents in a real-time environment. Those DDF systems were more focused on extracting visual-features. However, it is very much difficult to extract visual-features for defining PERCLOS measure due to different factors such as night-time driving, head is not centered-aligned and occlusion of faces. Due to these reasons, it is very much difficult to detect driver eyes, mouth and ears. As a result, some authors suggested using non-visual features combined with visual features to get accurate results. Accordingly, a hybrid and novel DDF system is developed in this paper by combining both visual and non-visual features through multi-cams stream approach and electrocardiography (ECG) sensors to measure heart rate variability (HRV). Those ECG sensors are mounted on driver’s steering. This DDF system is known as FatigueAlert and developed through deep architecture especially transfer-learning method. The proposed FatigueAlert system pre-trained many convolutional neural network (mCNN) models on different driver’s eyes, ears and mouths datasets. Three online datasets such as closed eyes in the wild (CEW), yawing dataset (YAWDD) and Columbia gaze dataset (CAVE-DB) were utilized to train and evaluate the proposed FatigueAlert system. On average, the FatigueAlert DDF system achieved 93.4% detection accuracy on different real-time driver’s datasets. To perform comparisons, different deep-learning models were used to compare with proposed pre-trained mCNN multi-layer architecture model. The obtained results indicate that the FatigueAlert system is outperformed compared to other state-of-the-art DDF systems.
How Bad Is Labor Market Concentration? Evidence From Soviet (Urban) Satellites

Absent exogenous variation, the casual eﬀect of labor market concen-tration on wages is hard to identify. A quasi-natural experiment rooted in the practice of urban planning of the Soviet Union, however, provides us with such a variation. Soviet planners developed green-ﬁeld urban satellites and industrial plants hand-in-hand as independent and closed, large, lumpy units. However, sometimes they happened to be spatially close to each other. Today, with labor mobility, such close-by satellites form com-mon labor markets and the number of nearby satellites creates concentration variations in a quasi-random fashion. We ﬁnd a 10% increase in the number of ﬁrms improves wages by 3.4%. Abstract Absent exogenous variation, the casual eﬀect of labor market concentration on wages is hard to identify. A quasi-natural experiment rooted in the practice of urban planning of the Soviet Union, however, provides us with such a variation. Soviet planners developed green-ﬁeld urban satellites and industrial plants hand-in-hand as independent and closed, large, lumpy units. However, sometimes they happened to be spatially close to each other. Today, with labor mobility, such close-by satellites form com-mon labor markets and the number of nearby satellites creates concentration variations in a quasi-random fashion. We ﬁnd a 10% increase in the number of ﬁrms improves wages by 3.4%.
Virome of Three Termite Species from Southern Vietnam

Modern metagenomic approaches enable the effective discovery of novel viruses in previously unexplored organisms. Termites are significant ecosystem converters and influencers. As with the majority of tropical forest insects, termites are studied insufficiently, and termite virome remains especially understudied. Here, we studied the virome of lichenophagous and mycophagous termites (Hospitalitermes bicolor, Macrotermes carbonarius and Odontotermes wallonensis) collected in the Cat Tien National Park (Vietnam). We assembled four full genomes of novel viruses related to Solemoviridae, Lispiviridae, Polycipiviridae and Kolmioviridae. We also found several contigs with relation to Chuviridae and Deltaflexiviridae that did not correspond to complete virus genomes. All the novel viruses clustered phylogenetically with previously identified viruses of the termites. Deltaflexi-like contigs were identified in the fungi-cultivating M. carbonarius and showed homology with viruses recently discovered in the edible basidiomycete mushrooms.
Modelling, Dynamic Analysis and Robotic Control Strategies for the Deorbiting Operations of the ESA Satellite Envisat

This master thesis was developed in the context of the e.Deorbit project. Firstly, the force for the deorbiting manoeuvre was analysed in order to find a profile that does not induce detachment between the Servicer module and Envisat when a clamping mechanism is not considered. Then, the proposed configurations with a four points clamping connection and a clamping to the payload adapter of Envisat were modelled and analysed. A special focus was posed on the flexibility of the 16 meter long solar panel of Envisat: this was considered divided in rigid sections and flexible joints. Additionally, the detumbling manoeuvre was analysed in order to validate the feasibility of this manoeuvre with the robotic arm grasped to the target. The main part of the work was to analyse a closed loop configuration: the aim was to reduce the oscillation of the solar panel during the deorbiting operations. In order to simulate that configuration, a the Loop Joint method by Featherstone was implemented and included in the SpaceDyn library. After testing the written algorithms, a damping control was simulated.
Simplified Super Twisting Sliding Mode Approaches of the Double-Powered Induction Generator-Based Multi-Rotor Wind Turbine System

This work proposes a new indirect filed-oriented control (IFOC) scheme for double-powered induction generators (DPIGs) in multi-rotor wind turbine systems (MRWTS). The IFOC strategy is characterized by its simplicity, ease of use, and fast dynamic speed. However, there are drawbacks to this method. Among its disadvantages is the presence of ripples in the level of torque, active power, and current. In addition, the total harmonic distortion (THD) value of the electric current is higher compared to the direct torque control method. In order to overcome these shortcomings and in terms of improving the effectiveness and performance of this method, a new algorithm is proposed for the super twisting algorithm (STA). In this work, a new STA method called simplified STA (SSTA) algorithm is proposed and applied to the traditional IFOC strategy in order to reduce the ripples of torque, current, and active power. On the other hand, the inverter of the DPIG is controlled by using a five-level fuzzy simplified space vector modulation (FSSVM) technique to obtain a signal at the inverter output of a fixed frequency. The results obtained from this proposed IFOC-SSTA method with FSSVM strategy are compared with the classical IFOC method which uses the classical controller based on a proportional-integral (PI) controller. The proposed method is achieved using the Matlab/Simulink software, where a generator with a large capacity of 1.5 megawatts is used. The generator is placed in a multi-rotor electric power generation system. On the other hand, the two methods are compared in terms of ripple ratio, dynamic response, durability, and total harmonic distortion (THD) value of the electric current. Through the results obtained from this work, the proposed method based on SSTA provided better results in terms of ripple ratio, response dynamic, and even THD value compared to the classical method, and this shows the robustness of the proposed method in improving the performance and efficiency of the generator in the multi-rotor wind system.
The Role of Power Doppler Ultrasonography in Caudal Epidural Injection

Background and Objectives: Although the ultrasound-guided technique is used in caudal epidural injections, severe complications can happen if ultrasound cannot identify the occurrence of intravascular injection. To determine intraepidural and intravascular injection during caudal epidural injections, we used power Doppler ultrasonography (PDU) when injecting medications into the epidural space. Materials and Methods: This is a retrospective study that enrolled a total of 277 patients with refractory low back pain or degenerative disc from January 2019 to December 2019. The injectate flow of caudal epidural injections was examined with the assistance of PDU and confirmed by fluoroscopy. Four flow patterns were identified by PDU in our study: the “Earthworm sign,” the “Patch sign,” the ”Tubular sign” and the “Absent flow sign. ” The accuracy of PDU in identifying intraepidural and intravascular injections was determined by fluoroscopy images recorded during each injection. Results: We evaluated 277 patients (mean age, 68.6 ± 13.2 years; 106 men). The “Patch sign” showed a sensitivity of 88.76% and a specificity of 80% in predicting epidural injection without intravascular injection. The “Earthworm sign” demonstrated a sensitivity of 70% and a specificity of 100% in detecting intravascular injection. The “Tubular sign” showed a specificity of 100% and a sensitivity of 9.4% in predicting successful epidural injection. The absence of a flow signal showed a sensitivity of 1.87% and a specificity of 90% in predicting successful epidural injection. Conclusions: Ultrasound-guided caudal epidural injection can accurately determine intraepidural and intravascular injections with the assistance of PDU and is thus a good alternative technique to fluoroscopy-guided caudal epidural injection.
Saturation Dynamics, Fine Spectrum, and Chirp Control in a CW FEL Oscillator

Here we report in brief the results of an experimental study of the saturation dynamics and the optimal conditions for maximal radiation power extraction in a Free Electron Laser (FEL) oscillator. The Israeli Electrostatic Accelerator Free Electron Laser (EA-FEL) is capable of providing lasing pulses at frequencies between 95-110 GHz (depending on the electron beam energy). A critical parameter affecting the performance of the laser is the reflectivity and transmission of the out-coupling element of the resonator. By attaching a variable reflectivity out-coupling element (based on a series of wire-grid polarisers) to the resonator of our EA-FEL we demonstrate the ability to optimise performance depending upon the desired output. For maximum lasing time the out-coupling from the resonator must be minimized, although sufficient for some radiation to leave. For maximum peak-power the reflectivity must be set differently depending upon the energy of the electron-beam (in MeV), which relates to the frequency emitted, and to the magnitude of the electron-beam current (in A). Mode competition ceases and a single longitudinal resonator mode is established more quickly the higher the reflectivity (important for short pulses). The variable out-coupling allows us to operate optimally over a large range of frequencies and beam currents which would be impossible with an element with fixed reflectivity.
Predicting Soil Properties and Interpreting Vis-NIR Models from across Continental United States

The United States NRCS has a soil database that has data collected from across the country over the last several decades. This also includes soil spectral scans. This data is available, but it may not be used to its full potential. For this study, pedon, horizon and spectral data was extracted from the database for samples collected from 2011 to 2015. Only sites that had been fully described and horizons that had been analyzed for the full suite of desired properties were used. This resulted in over 14,000 samples that were used for modeling and eight soil properties: soil organic carbon (SOC); total nitrogen (TN); total sulfur (TS); clay; sand; exchangeable calcium (Caex); cation exchange capacity (CEC); and pH. Four chemometric methods were employed for soil property prediction: partial least squares (PLSR); Random Forest (RF); Cubist; and multivariable adaptive regression splines (MARS). The dataset was sufficiently large that only random subsetting was used to create calibration (70%) and validation (30%) sets. SOC, TN, and TS had the strongest prediction results, with an R2 value of over 0.9. Caex, CEC and pH were predicted moderately well. Clay and sand models had slightly lower performance. Of the four methods, Cubist produced the strongest models, while PLSR produced the weakest. This may be due to the complex relationships between soil properties and spectra that PLSR could not capture. The only drawback of Cubist is the difficult interpretability of variable importance. Future research should include the use of environmental variables to improve prediction results. Future work may also avoid the use of PLSR when dealing with large datasets that cover large areas and have high degrees of variability.
Automatic Implementation Algorithm of Pressure Relief Drilling Depth Based on an Innovative Monitoring-While-Drilling Method

An innovative monitoring-while-drilling method of pressure relief drilling was proposed in a previous study, and the periodic appearance of amplitude concentrated enlargement zone in vibration signals can represent the drilling depth. However, there is a lack of a high accuracy model to automatically identify the amplitude concentrated enlargement zone. So, in this study, a neural network model is put forward based on single-sensor and multi-sensor prediction results. The neural network model consists of one Deep Neural Network (DNN) and four Long Short-Term Memory (LSTM) networks. The accuracy is only 92.72% when only using single-sensor data for identification, while the proposed multiple neural network model could improve the accuracy to being greater than 97.00%. In addition, an optimization method was supplemented to eliminate some misjudgment due to data anomalies, which improved the final accuracy to the level of manual recognition. Finally, the research results solved the difficult problem of identifying the amplitude concentrated enlargement zone and provided the foundation for automatically identifying the drilling depth.