Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-Layer Detector Cone-Beam CT Angiography for Stroke Assessment: First-in-Human Results (the Next Generation X-ray Imaging System Trial)

Dual-layer detector cone-beam CTA virtual monoenergetic images are noninferior to CTA under certain conditions. Notably, the prototype is hampered by a long scan time and is not capable of contrast media bolus tracking. BACKGROUND AND PURPOSE: In patients with stroke, IV cone-beam CTA in the angiography suite could be an alternative to CTA to shorten the door-to-thrombectomy time. However, image quality in cone-beam CTA is typically limited by artifacts. This study evaluated a prototype dual-layer detector cone-beam CT angiography versus CTA in patients with stroke. MATERIALS AND METHODS: A prospective, single-center trial enrolled consecutive patients with ischemic or hemorrhagic stroke on initial CT. Intracranial arterial segment vessel conspicuity and artifact presence were evaluated on dual-layer cone-beam CTA 70-keV virtual monoenergetic images and CTA. Eleven predetermined vessel segments were matched for every patient. Twelve patients were necessary to show noninferiority to CTA. Noninferiority was determined by the exact binomial test; the 1-sided lower performance boundary was prospectively set to 80% (98.75% CI). RESULTS: Twenty-one patients had matched image sets (mean age, 72 years). After excluding examinations with movement or contrast media injection issues, all readers individually considered dual-layer cone-beam CT angiography noninferior to CTA (CI boundary, 93%, 84%, 80%, respectively) when evaluating arteries relevant in candidates for intracranial thrombectomy. Artifacts were more prevalent compared with CTA. The majority assessment rated each individual segment except M1 as having noninferior conspicuity compared with CTA. CONCLUSIONS: In a single-center stroke setting, dual-layer detector cone-beam CTA virtual monoenergetic images are noninferior to CTA under certain conditions. Notably, the prototype is hampered by a long scan time and is not capable of contrast media bolus tracking. After excluding examinations with such scan issues, readers considered dual-layer detector cone-beam CTA noninferior to CTA, despite more artifacts.
The Effect of Virtual Presence and Self-Efficacy on Nursing Students’ Evaluation of Virtual Simulation Training

ABSTRACT The study aims to determine the effect of virtual simulation practice in nursing students using the Virtual Patient Learning System Evaluation (VPLSE) tool. The study uses descriptive research, centering on correlation and regression analysis. 295 nursing students in Grades 3 and 4 who have experienced virtual simulation practice within the past year were included. The main variables of VPLSE comprise four sub-domains: nursing knowledge improvement, clinical competency development, confidence in nursing performance, and nursing care plan application. In addition to the VPLSE, two other tools, measuring virtual presence and self-efficacy, were utilized. The VPLSE was significantly positively correlated with virtual presence and self-efficacy, respectively. The VPLSE subdomain of “clinical competency development” had a strong positive correlation with virtual presence. To increase the effect of virtual simulation education, it is necessary to maintain the advantages of virtual simulation practice, such as freedom from space and time, repeated learning, psychological stability and application of nursing plans.
Cognitive and Noncognitive Factors Influencing Nursing Students’ Academic Success: Structural Equation Model Analysis

Abstract AIM The study aim was to identify and explore relationships among cognitive and noncognitive factors that may contribute to prelicensure baccalaureate nursing students’ academic success across their program of study. BACKGROUND Nurse educators are challenged to improve students’ academic success. With limited evidence, cognitive and noncognitive factors have been identified in the literature as potential factors that influence academic success and may support students’ readiness for practice as new graduate nurses. METHOD Data sets from 1,937 BSN students at multiple campuses were analyzed using an exploratory design and structural equation modeling. CONCLUSION Six factors were conceptualized as contributing equally to the initial cognitive model. The final noncognitive model, with deletion of two factors, yielded the best fit for the four-factor model. Cognitive and noncognitive factors were not significantly correlated. This study provides a beginning understanding of cognitive and noncognitive factors associated with academic success that may support readiness for practice.
SLEEP-SEE-THROUGH: Explainable Deep Learning for Sleep Event Detection and Quantification From Wearable Somnography

Evidence is rapidly accumulating that multifactorial nocturnal monitoring, through the coupling of wearable devices and deep learning, may be disruptive for early diagnosis and assessment of sleep disorders. In this work, optical, differential air-pressure and acceleration signals, acquired by a chest-worn sensor, are elaborated into five somnographic-like signals, which are then used to feed a deep network. This addresses a three-fold classification problem to predict the overall signal quality (normal, corrupted), three breathing-related patterns (normal, apnea, irregular) and three sleep-related patterns (normal, snoring, noise). In order to promote explainability, the developed architecture generates additional information in the form of qualitative (saliency maps) and quantitative (confidence indices) data, which helps to improve the interpretation of the predictions. Twenty healthy subjects enrolled in this study were monitored overnight for approximately ten hours during sleep. Somnographic-like signals were manually labeled according to the three class sets to build the training dataset. Both record- and subject-wise analyses were performed to evaluate the prediction performance and the coherence of the results. The network was accurate (0.96) in distinguishing normal from corrupted signals. Breathing patterns were predicted with higher accuracy (0.93) than sleep patterns (0.76). The prediction of irregular breathing was less accurate (0.88) than that of apnea (0.97). In the sleep pattern set, the distinction between snoring (0.73) and noise events (0.61) was less effective. The confidence index associated with the prediction allowed us to elucidate ambiguous predictions better. The saliency map analysis provided useful insights to relate predictions to the input signal content. While preliminary, this work supported the recent perspective on the use of deep learning to detect particular sleep events in multiple somnographic signals, thus representing a step towards bringing the use of AI-based tools for sleep disorder detection incrementally closer to clinical translation.
Sexualität im Maßregelvollzug. Ergebnisse einer qualitativen Befragung von Mitarbeitenden und Handlungsempfehlungen für die klinische Praxis

Zusammenfassung Ziel der Studie Untersuchung der Perspektive von Mitarbeitenden auf Sexualität und sexuelle Gesundheit bei stationär untergebrachten Patient*innen in zwei geschlechtergetrennten bayrischen Maßregelvollzügen. Methoden 19 teilstrukturierte Interviews wurden inhaltsanalytisch ausgewertet. Ergebnisse wurden mit Mitarbeitenden diskutiert und eine Handlungsempfehlung ausgearbeitet. Ergebnisse Mitarbeitende beschreiben eine ungenügende und nicht systematische Berücksichtigung von Sexualität in beiden Einrichtungen. Regelungen zu erlaubten und nicht erlaubten Verhaltensweisen sind entweder nicht vorhanden, nicht bekannt oder verbleiben für viele der Mitarbeitenden und Patient*innen auf impliziter Ebene. Schlussfolgerung Die Berücksichtigung von Sexualität und sexuellen Bedürfnissen von Patient*innen sollte nachvollziehbar und transparent gestaltet werden. Eine beigefügte Handlungsempfehlung zum Umgang mit Sexualität kann Einrichtungen dabei unterstützen, Sexualität im Maßregelvollzug stärker zu berücksichtigen.
Disparities in Timing of Alveolar Bone Grafting and Dental Reconstruction in Patients With Clefts

Objective This study sought to identify disparities in the timing of alveolar bone grafting (ABG) surgery and the replacement strategy for missing maxillary lateral incisors for patients with clefts. Design A retrospective record review identified patients who underwent ABG. Multivariable regression analyzed the independent contribution of each variable. Setting This institutional study was performed at the University of California, San Francisco. Participants Patients who presented under age 12 and underwent secondary ABG between 2012 and 2020 (n = 160). Main Outcome Measures The age at secondary ABG and the recommended dental replacement treatment for each patient, either dental implantation or canine substitution. Results The average age at ABG was 10.8 ± 2.1 years, 106 (66.3%) patients were not White, and 80 (50.0%) had private insurance. Independent predictors of older age at ABG included an income below $ 50 000 as estimated from ZIP code (β = 15.0 months, 95% CI, 5.7-24.3, P = .002) and identifying as a race other than White (β = 10.1 months, 95% CI, 2.1-18.0, P = .01). After ABG, patients were more likely to undergo dental implantation over canine substitution if they were female (odds ratio [OR] = 4.3, 95% CI, 1.3-17.1, P = .02) or had private insurance (OR = 12.5, 95% CI, 2.2-143.2, P = .01). Conclusions Patients who were low-income or not White experienced delays in ABG, whereas dental implantation was more likely to be recommended for patients with private insurance. Understanding the sources of disparities in dental reconstruction of cleft deformities may reveal opportunities to improve equity.
Data-I – Interactive Experience of IoT Data: A Practical Tool for IoT Sensor Data in Interdisciplinary Human-Computer Interaction Education

In this teachable moment we present our approach and tools for an ongoing teaching-learning project that brings the focus of human-data interaction in classical human-computer education, namely a human-centered design process. We explain our motivation, coming from an HCI course open not only for computer science students but for multiple disciplines aiming at integrating an interdisciplinary perspective. We motivate why we chose to focus on simple IoT sensor data and why a hands-on approach is useful for students. We discuss our Sensorkit, which is designed to support students in all relevant stages of the HCD process, from initial exploration, gaining insights and understanding, to prototyping, to evaluating a prototype that collects and visualizes sensor data in a small study with participants.
On Controller Design for Unknown Nonlinear Systems with Prescribed Performance and Input Constraints

This paper considers the tracking control problem for an unknown control-affine nonlinear system with time-varying bounded disturbance subjected to a prescribed performance and input constraints. For such a problem, we cannot arbitrarily specify any restriction on the input. For the same, we have given a feasibility condition for prescribing input constraints. In addition, we cannot prescribe arbitrary performance constraints when input constraints are present. There is always a trade-off when performance and input constraints are specified simultaneously. To deal with such arbitrary prescription issues, we also devised a feasibility condition for prescribing performance constraints. Furthermore, considering the feasibility condition, we have proposed a low-complexity approximation-free controller, which guarantees that the tracking performance will never violate its prescribed performance and input constraints. Simulation results, including case studies, show the effectiveness of the proposed controller and support the feasibility condition by examining the failure of the tracking performance under unfeasible conditions.
A Gait Recognition Network With Attention-GaitNet

In this paper, we propose a novel network structure GaitNet, which treats gait as a set of disordered sequences, compares different sequences to extract features, and introduces attention to gait recognition, thereby achieving a good recognition score under different angles and carrying situations of different clothes/bags. Experiments show that our method shows its advantages when the cross-view angle is larger than thirty-six angle. The average rank-1 accuracy rate on the CASIA-B gait dataset is greater than ninety-five angle, and it is in the OU-MVLP gait. The accuracy of the average rank-1 on the data set is greater than eighty-six angle, and our method shows excellent robustness in the case of missing frames and incomplete silhouettes of some images. At the same time, a gait database based on the OU-MVLP gait data structure was established. The video includes long-distance and short-distance from zero angle to two hundred and seventy angle, recording a video every fifteen angle, with two sequences, the average recognition rate of rank-1 is over seventy-eight angle.
Smart Navigation System Assistance for Visually Impaired People

In this paper, we have proposed a system for visually impaired people using an ESP32 microcontroller, Radar sensor, Ultrasonic sensor, and GPS. This proposed system detects the object and converts it into an audio signal. We have used different types of sensors. It also contains Bluetooth for audio navigation. This smart stick is user-friendly, light in weight, low power consumption, easy to use, and easy to understand for the user. This project aims towards helping visually impaired people so they can roam around independently. It is portable in use in any environment. It is a smart stick that guides the user by sensing the obstacles in the range of the stick on the path of the user. The vibration and buzzer sensor which is placed on the stick notifies the user about the hurdles or objects which are on the path of the user.
Observer-Based Input-to-State Stabilizing Control for Multiple Transmission Channels under Denial-of-Service*

Interests in strengthening the security of cyber-physical systems have grown exponentially in the last decade. The control research community has perceived that the feed-back loop embedded in integrated systems can be affected once attackers accomplish violations of the cyber-physical protection and detection systems. The resilient control problem for cyber-physical systems with numerous transmission channels under Denial-of-Service (DoS) is investigated in this article. First, a set of Luenberger-like observers are designed considering different transmission channels under DoS. A switching topology is developed to switch observers according to the situation of Multi-Channel DoS (MCDoS). Then a resilient control strategy is designed for the Full-Scale DoS (FSDoS). We characterize the frequency of switching observers for MCDoS and the frequency and duration of FSDoS, retaining the closed-loop system's Input-to-State Stability (ISS) in the presence of disturbance. A proper schedule of the transmission periods is developed in order to accomplish ISS.
Fractional-order Composite Adaptive Attitude Control of Satellite using Double Gimbal Variable Speed Control Moment Gyroscope

Satellites need constant attitude control for maintaining precise pointing while going through several external disturbance torques. In satellites, uncertainty in the inertia matrix is common due to fuel depletion and mass displacement. The estimation of these uncertain parameters while assuring the satellite attitude control is addressed in this work using a fractional-order composite adaptive control algorithm which has the attributes of the composite adaptive controller and is more robust because of its extra degree of freedom in terms of non-integer differentiation and (or) integration. A composite adaptive controller estimates the uncertain inertia parameters by utilizing both tracking and prediction errors resulting in faster convergence and reduced tracking error. A Double Gimbal Variable Speed Control Moment Gyroscope (DGVSCMG), which can produce torques in all three axes with its single unit, is used as the momentum exchange device on board. The system with the controller is stable and exponentially convergent. The efficacy of the designed controller is shown by comparing it with a direct adaptive controller and an integer order composite adaptive controller. Finally, the simulation results validate the claims made.
Deep Learning Approach for Sound Signal Processing

In recent years, environmental sound classification has been a burgeoning subject of study. The unstructured nature of environmental sounds makes analysis challenging. However, sound signals have spectro-temporal patterns makes analysis easier using deep learning algorithms. Based on created spectrogram images and several feature extraction techniques Mel Spectrogram and Mel Frequency Cepstral Coefficients, we shall analyze sound signals using Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) in this paper. All the three models use UrbanSound8k based modified dataset and compared for the accuracy achieved on train and test datasets are: 98% and 88% for CNN, 95% and 86% for LSTM and 81% and 76% for ANN
Trustworthiness of Data in IoT Crowd Sensing Environments

In the context of mobile crowd sensing, an input data that is collected from multiple people may result to be incorrect or malicious and ultimately alter predictions, data reliability is one of the crucial factors taken into account. In this research, the reliability of the data is assessed using crowdsensing data from the transportation and traffic domain, where numerous people contribute data on the flow of traffic that may be used to calculate the shortest route and other traffic-related metrics. This project attempts to build a voting-based trustworthiness model. Game theory is utilized to encourage users to contribute to crowdsensing data, and metrics including voting capacity, user reputation, user pay-off, and trustworthiness are determined depending on the user’s votes. Another approach used to assess user data trust is based on the concepts of experience and reputation, with experience being determined by how frequently a user interacts with other users. Based on their reputation scores, each user receives rewards in the form of badges. By repeatedly running both algorithms, the stability of each is examined.
Identification of Potholes using Yolov4 and Darknet

Potholes and other pavement distresses are a matter of concern. Potholes not only cause harm to the vehicles but also endanger the lives of the people driving the vehicles. These potholes not only cause accidents that take lives but also cause minor back injuries that can become serious over a period of time. In India, as reported by the Ministry of Road Transport and Highways (MoRTH) around 5000 passengers are killed every year in road accidents caused by potholes. Pothole identification has been dependent upon manual detection, which is neither cost-effective nor time efficient. In this research study, we aim to accurately detect potholes with the help of two open-source datasets. We also intend to explore and compare various deep learning models to determine the optimum approach to detecting potholes in this work. After randomly mixing datasets to produce a combined dataset, we implemented variations of the YOLO model to improve efficiency.
Cluster Consensus of Multi-agent Systems with Second Order Dynamics Over Matrix-weighted Graphs

Distributed cluster consensus problem for a set of agents with second order dynamics is considered here. In cluster consensus, agents within one cluster converge to a common value, and agents within different clusters converge to a different final value. It is shown that when the agents interact over a matrix weighted graph that is multi-partitioned and structurally balanced, cluster consensus is achieved. An additional cluster consensus scheme where agents update their states with respect to a dynamic common leader, is also developed. Under appropriate connectedness conditions, agent positions are shown to achieve cluster consensus around the leader position and the agent velocities are shown to converge to the leader velocity, asymptotically. Results are analytically shown using Lyapunov theory and are illustrated by numerical simulations.
Stock Index Probability Prediction using the FB Prophet Model

Given how unpredictable the stock market has recently become, it is now crucial to be able to accurately predict the future trend of equities. Because of the financial crisis, it is now essential to estimate stock values accurately in order to make money. Non-linear signal forecasting requires sophisticated statistical models and machine learning methods. We must identify the stock’s purchasing and selling points for a specific stock market history in order to make a profit. The Dow Theory, MACD, the Relative Strength Index (RSI), the Exponential Moving Average, and others have all been used to forecast stock market performance. It could be challenging to make a specific judgement because these models could provide a variety of indications. The aforementioned issue was attempted to be solved by a wave of machine learning algorithms in artificial intelligence. Some strategies have yet to provide promising results, while others have not reacted as well on the stock market exchange, due to the non-linear structure of the stock market signals. We intend to employ the Prophet model, which is reliable and generates findings with minimal data. The Prophet refers to the method of forecasting time series data based on an additive model with non-linear trends suited with holiday impacts, daily seasonality, weekly, and yearly. Strong seasonal impacts in time-series data are ideal for it. The prophet is resilient to missing data and adept at handling outliers. Facebook has made available the prophet, an open-source Python package. Predictions of stock prices are shown by indicators like RSI, MACD, Super Trend and others.
Novel Approach to Capture Fake News Classification Using LSTM and GRU Networks

Before releasing an item, every news website or-ganizes it into categories so that users may quickly select the categories of news that interest them. For instance, I frequently visited news websites and click on the technology section because I want to read about the most recent technological developments. You might prefer to read about politics, business, entertainment, or even sports if you don ‘t enjoy reading about technology. The content administrators of news websites currently classify the news stories by hand. However, in order to save time, they can also incorporate a machine learning model on their websites that reads the news headline or the news’s content and categorizes it. Information from high-profile sources as well as inadvertent and unconscious mechanisms, people to apply to any news that is detrimental to their viewpoints. In this project, we ‘ll create an LSTM model to determine whether a particular news item is authentic or not. Based on information in the dataset. Data will be gathered using the ” pandas-datareader ” package with an API to ensure that the data is current and we are not left behind. The LSTM module is subsequently trained using the preprocessed data. The output of the test data will then be predicted and plotted. The authors main aim is to do a comparison between the LSTM method and the GRU method and find which performs better.