Miracle-dz/UniFAR-FTUs huggingface dataset:
Groupoid models for the C*-algebras of topological higher-rank graphs

We provide groupoid models for Toeplitz and Cuntz-Krieger algebras of topological higher-rank graphs. Extending the groupoid models used in the theory of graph algebras and topological dynamical systems to our setting, we prove results on essential freeness and amenability of the groupoids which capture the existing theory, and extend results involving group crossed products of graph algebras.
LECTURES ON LINEAR SERIES 1

The past decade has witnessed two important new developments in the study of linear series on algebraic varieties. First, vector bundles have emerged as powerful tools for analyzing linear series on curves and surfaces. More recently, the viewpoints and techniques of higher dimensional geometry have started to play a central role. The purpose of these lectures is to provide a down-to-earth introduction to some of these new ideas and methods. The present notes, written with the assistance of G. Fernandez del Busto, are a considerably expanded version of a course given at the 1993 Regional Geometry Institute on Higher Dimensional Complex Geometry at Park City, Utah. While I hope they may be of interest to any geometers wishing to learn about recent work on linear series, I have particularly attempted to aim the discusion at a novice audience. My hope is that with a little faith and effort, these notes should be accessible to anyone having finished the standard introductory texts in algebraic geometry. The underlying theme of the lectures is the search for higher dimensional generalizations of the most basic facts about linear series on curves. But in an attempt to keep things elementary we work more or less exclusively on surfaces. Thus we end up discussing one central result -- viz. Reider's theorem -- from many points of view. Sticking to the case of surfaces allows one to eliminate many technical complexities, and I hope that parts of these lectures might therefore serve as a useful first introduction to the powerful cohomological techniques (involving $Q$-divisors) of contemporary
Aspects of type IIB theory on asymptotically locally Euclidean spaces

D-brane technology and strong/weak coupling duality supplement traditional orbifold techniques by making certain background geometries more accessible. In this spirit, we consider some of the geometric properties of the type IIB theory on R{sup 6}{times}M, where M is an {open_quotes}asymptotically locally Euclidean{close_quotes} (ALE) gravitational instanton. Given the self-duality of the theory, we can extract the geometry (both singular and resolved) seen by the weakly coupled IIB string by studying the physics of a D1-brane probe. The construction is both amusing and instructive, as the physics of the probe completely captures the mathematics of the construction of ALE instantons via {open_quotes}hyper-Kaehler quotients,{close_quotes} as presented by Kronheimer. This relation has been noted by Douglas and Moore for the A series. We extend the explicit construction to the case of the D and E series{emdash}uncovering a quite beautiful structure{emdash}and highlight how all of the elements of the mathematical construction find their counterparts in the physics of the type IIB D-string. We discuss the explicit ALE metrics which may be obtained using these techniques, and comment on the role duality plays in relating gauged linear {sigma} models to conformal field theories.
Effect of Teachers' Health Behaviors on Students' Health; A Social Cognitive Viewpoint of Role-Modeling

Teachers as role models teach and set an example through their actions. Students routinely observe and are directly influenced by the teacher's health behavior. Unfortunately, school teachers are vulnerable to many health issues that affect student success. Teachers are often absent, showing examples of poor health. This study sought information on the impact of teachers' health behaviors on student health from a cross-sectional descriptive study of mixed methods in secondary schools of Bagmati Province, Nepal. The total sample size of the students in the quantitative interview was 412 from 46 randomly selected schools in the Rasuwa, Bhaktapur, and Chitwan districts. Another 30 students from different schools were selected for in-depth study. It focused on finding out how students identify teachers as role models. Since the concept of role models appears in Albert Bandura's social learning/cognitive theory, which focuses on cognitive concepts, this study examined student cognition to connect to the role modeling structure of the theory. This theory focuses on how children and adults cognitively process social experiences and how these cognitions affect behavior and development. In the light of this theory, it was explained the effect of teacher health behaviors on student's health. Observable health behaviors of teachers are proved to have positive and negative impacts on students' health.
Physical and chemical evolution of an electron track in liquid water

We present a model to determine the position and identity of each species about the track of an electron in liquid water at any time from the passage of the primary electron (which occurs in approx. 10/sup -15/ s) to times of the order of a microsecond at which chemical evolution of the track is essentially complete.
Network Control System Applications for Manager Decision-Making

Many categories of manager decision-making (human organisations, environmental systems, supply chains) are in some way or another related to systems. However, system dynamics do not always lend themselves well to superficial, or intuitive, interpretation. This can inadvertently result in suboptimal managerial decision-making. The application of control science concepts for guiding managerial decision-making has the potential to improve results. The contemporary manager is typically resource-constrained and time-stressed. Control-science-based decision-making guidance that does not accommodate the reality of the manager’s time constraints may have limited affect in practice. A manager’s attention-scheduling behaviour is more analogous to that of a networked control system (NCS) than to that of a singularly focused control loop.There is an opportunity to apply NCS aspects of control science to identify the minimum attention/frequency requirements of key decision-making realms. This paper acknowledges the time-poor reality of the contemporary manager. It considers how learnings from NCS theory can be applied to add resilience and efficiency to control-science-inspired improvements to manager decision-making. Just as regulatory control systems don’t perform well when subjected to unexpected network or input/output delays, the application of control theory concepts to manager decision-making will be challenged if the time-poor aspects of the manager are not catered for.
Analysis of the Covid-19 Process in Terms of Health Managers

In this study, sentiment analysis was conducted on the data of the Covid-19 epidemic process from the official twitter account of the Republic of Turkey Fahrettin Koca, Minister of Health, @drfahrettinkoca (SO) and the Twitter account of the @WHO (World Health Organization). First of all, twitter data was obtained and necessary arrangements were made for analysis. Then, tweets were shown with a word cloud and it was determined which words were used more frequently. Afterwards, sentiment analysis was performed on the data using the TextBlob library. In addition, it has been found out which subjects are focused on tweets sent from SO and @WHO (World Health Organization) accounts with the LDA algorithm. It has been seen that positive tweets were sent from both accounts, giving positive messages to the society.
Physics-Informed Learning: Distributed Parameter Systems, Hidden Markov Models, and the Viterbi Algorithm

We present an approach for a physics-informed Hidden Markov Model with a special emphasis on including prior physical knowledge in the Viterbi algorithm. To this end, we develop a covariate-dependent (forward) hidden Markov model (HMM) with two emission or observed variables that represent two observable time series y and u. The goal is to predict the time series u based on the observations y. The hidden Markov chain serves as a forward model with the two observed quantities, assumed to be described by a bivariate normal distribution which is a function of the state of the hidden Markov chain. In addition, the bivariate normal and hidden Markov chain parameters are allowed to depend on various covariates via built-in regression models. The model can then be trained using the Baum-Welch/Expectation Maximization (EM) algorithm and a training set of the time series y and u and covariate measurements. The resulting fit model, together with observations of time series y only and the Viterbi algorithm, are then used to find the most likely path through the hidden Markov chain and the corresponding emissions with the intent of solving the inverse problem of obtaining an estimate of u from measurements of y alone. At this point we propose to augment the Viterbi algorithm to also include prior physical knowledge in the form of a forward model y=ℒu, creating a physics-informed Viterbi algorithm. We illustrate the proposed method in the context of a problem involving a transdermal alcohol biosensor and provide numerical examples.
Adaptive Event-Triggered Interval Type-2 T-S Fuzzy Control for Lateral Dynamic Stabilization of AEVs With Intermittent Measurements and Actuator Failure

This article presents an adaptive event-triggered dynamic output feedback interval type-2 (IT-2) T-S fuzzy control method for autonomous electric vehicle systems. First, the IT-2 T-S fuzzy model is introduced to describe the nonlinear lateral dynamics of the vehicle systems. Second, a novel adaptive event-triggered strategy is designed to reduce the computational burden. Third, the phenomenon of intermittent measurements is depicted by the Bernoulli random distributed process, and the failure of the actuator is also included. According to the Lyapunov stability theory, an IT-2 T-S fuzzy dynamic output feedback controller (DOFC) is presented to ensure the random stability and performance of the vehicle systems. Finally, the effectiveness and real-time performance of the proposed control strategy are verified via numerical simulation and a hardware-in-the-loop (HIL) test platform.
Social studies teachers' experiences with in-class foreign students

In this study, which aims to reveal the experiences of social studies teachers in Turkey with foreign students, a literature review was carried out regarding the subject. The phenomenology model, which is one of the qualitative research methods, was used in the research. The study group of the research consists of 12 social studies teachers. Eight participants were female and four were male. While determining the study group in the research, the easily accessible sampling method, which is one of the purposeful sampling methods, was used. Looking at the results of the research, it was stated that foreign students were more interested in the subjects related to Turkey and culture in the social studies course. Regarding the problems encountered by foreign students, the participants mostly focused on language and culture problems. Keywords: Turkey, social studies, foreign students, culture;
Modeling Fabric-Type Actuator Using Point Clouds by Deep Learning

Flexible actuators are popular in the consumer and medical fields because of their flexibility and compliance. However, they are typically difficult to model because of their viscoelasticity and nonlinearity. This letter proposes a method for correcting the deformation of the simulated flexible robots to make it similar to the deformation of real robots using point clouds by deep learning. Long short-term memory (LSTM) can simulate the next frame of actuator deformation from the previous frames of deformations. In this study, we presented the robots with four different muscle structures. We found that using an encoder–LSTM–decoder network can improve the similarity between the deformation of a learned muscle structure and the real deformation and is also effective in correcting the deformation of the unlearned structures. Our correction method reduced the average Chamfer distance of the simulated point clouds of the basic-type structure actuator from 15.89 to 7.81. This research can provide a new concept for future flexible robot modeling using point clouds.
Perceived Stress, Grit, and Self-Care Behaviors in First-Year Medical Students

Medical students experience more stress than the general population, which over time can cause mental and physical disease, including burnout. Identifying factors impacting stress during early medical training could inform strategies to minimize its impacts throughout training and in clinical practice. This study surveyed 238 first-year osteopathic medical students to assess stress (Perceived Stress Scale; PSS), grit, sleep quality (Pittsburgh Sleep Quality Index; PSQI), physical activity (Godin-Shephard Leisure-Time Physical Activity Score; LTPA), and nutrition habits (Rapid Eating Assessment for Participants; REAP) within the first 2 weeks of starting medical school and again 10 weeks later. Incomplete responses were removed, leaving 204 study participants. We observed statistically significant decreases in grittiness (∆grit = −2.230%, P = .002) and physical activity (∆LTPA = −22.147%, P < .0001), while perceived stress (∆PSS = 34.548%, P < .0001) and poor sleep quality (∆PSQI% = 19.853, P < .0001) increased. Correlation analyses identified the strongest relationships were between ∆PSS vs ∆PSQI (r = .47, P < .0001) and ∆PSS vs ∆LTPA (r = −.20, P < .01). Multivariable linear regression analysis isolated ∆PSQI (P < .0001) and ∆LTPA (P = .012) as statistically significant predictors of ∆PSS. These results suggest early, repeated curricular interventions focused on physical activity and sleep hygiene may help students better manage stress during medical education.
Efficient Artificial Intelligence-based Models for COVID-19 Disease Detection and Diagnosis from CT-Scans

COVID-19 is contagious virus that first emerged in China in 2019’s last month. It mainly infects the both the lungs and the respiratory system. The virus has severely impacted life and the economy, which exposed threats to governments worldwide to manage it. Early diagnosis of COVID-19 could help with treatment planning and disease prevention strategies. In this study, we use CT-Scanned images of the lungs to show how COVID-19 may be identified using transfer learning model and investigate which model achieved the best and fastest results. Our primary focus was to detect structural anomalies to distinguish among COVID-19 positive, negative, and normal cases with deep learning methods. Every model received training with and without transfer learning and results were compared for various versions of DenseNet and EfficientNet. Optimal results were obtained using DenseNet201 (99.75%). When transfer learning was applied, all models produced almost similar results.
Farmland Aerial Images Fast-Stitching Method and Application Based on Improved SIFT Algorithm

The main black land conservation measure in China is the straw return to the fields. The processing of high-resolution images collected by aerial photography of UAVs through image stitching technology can provide image information for achieving fast and accurate detection of straw cover over large areas. The classical SIFT algorithm has many drawbacks, such as high dimensionality of feature descriptors, high computational effort, and low matching efficiency. To solve the problems above, this study proposes an improved algorithm. First, the method down sampled the high-resolution images before detecting the features to reduce the number of feature points and improve the efficiency of feature detection. Then, matching among feature points is achieved by gradient normalization-based feature descriptors to improve the matching accuracy. Next, the Progressive Sample Consistency algorithm eliminates the mismatch points and optimizes the transformation model. Finally, the images are fused with optimal stitching combined with fade-in and fade-out to achieve high-quality stitching. The comparative experimental results show that compared with the traditional SIFT and the speed-up robust feature algorithms, the algorithm has the advantage of the speed and good robustness to angle rotation, and makes full use of the texture information and the detail information, so it has higher accuracy. Compared with the traditional methods, the panoramic stitching image quality herein is excellent and can be applied to subsequent straw cover detection, the straw cover error is ≤3%, meeting the demand for large-area straw cover detection. Overall, the method proposed herein achieves an ideal balance between accuracy and efficiency; and outperforms other widely used and superior methods.
Probabilistically Safe Mobile Manipulation in an Unmodeled Environment with Automated Feedback Tuning

As manufacturing process reconfiguration increases in frequency to support more customized products, manual programming of industrial robots has become less feasible. Although sensor feedback can be used to handle new environments and tasks, continuous sensing is unnecessary for repetitive tasks if a safe trajectory can be learned from prior executions. This paper addresses the problem of developing a controller for a mobile manipulator to safely reach goals without collision while estimating its open-loop safety. To handle novel environments, an iterative feedback tuning scheme is developed based on a fast collision-checking metric and a carefully selected cost function. The open-loop collision probability with the tuned controller is explicitly quantified by propagating uncertainty in the mobile base pose to the sensed environment.
Initial Alignment and Position Aiding Time Delay Compensation for SDINS of Deep Sea Underwater Vehicles

In the case of a vehicle descending to the deep sea floor, DVL (Doppler velocity log) usually loses the bottom lock for quite a long time. Therefore, various initial alignment methods available on the surface cannot guarantee its accuracy down to the sea floor and therefore cannot be directly applicable to this deep sea case. Besides, near the seafloor the vehicle’s USBL (Ultra-short baseline) aiding position usually includes range-related time delay which can be up to several seconds. This paper proposes an simple but effective initial alignment scheme for deep sea underwater vehicles combining with position aiding time delay compensation method. Some practical issues in the construction of SDINS (Strapdown inertial navigation system), such as lever arm effects compensation, are also discussed. Experimental tests are also carried out in the water tank environment to verify the effectiveness of proposed schemes.
Achieving Automated Vehicle Path Following with Blend Path Curvature Control

This paper presents a new lateral control structure for path following control in an autonomous vehicle. The proposed method transforms a conventional feedback path following controller to a control architecture that relies exclusively on feedforward control of the curvature at a lookahead point of a blend path that connects the current vehicle kinematics with the desired target trajectory at some merge point. This approach provides a practical method for ensuring tracking performance and passenger comfort. Mathematical proofs and experiment results are provided to demonstrate that the proposed controller guarantees feedback stability of the lateral error dynamics, provides a systematic way of setting the corresponding feedback gains for accurate path tracking, and delivers desirable transient characteristics and smooth steering actuation effort.
Autoimmune Pulmonary Alveolar Proteinosis with Suspected Exacerbation after Osimertinib Administration for Lung Cancer

A 46-year-old woman with lung cancer who received chemotherapy was admitted to our hospital for lower-lobe bilateral ground-glass opacity (GGO). GGO developed after the lung cancer diagnosis, deteriorated after the initiation of osimertinib, and incompletely decreased after interrupting osimertinib; therefore, flexible bronchoscopy was performed. Transbronchial lung biopsy histology and anti-granulocyte/macrophage colony-stimulating factor autoantibody positivity revealed autoimmune pulmonary alveolar proteinosis (aPAP) that did not require treatment. This rare case of aPAP comorbid with lung cancer suggested that using PAP findings to differentiate from drug-induced lung injury or lymphangitis is difficult and that osimertinib was suspected to exacerbate aPAP.
Smartphone metal‐casing integrated frequency‐reconfigurable multiple‐input multiple‐output slot antenna

A novel four‐element frequency‐reconfigurable multiple‐input multiple‐output (MIMO) slot antenna for a smartphone with metal‐casing is presented. The inherent disadvantage of ultra‐narrow bandwidth of thin slots is overcome with the help of frequency reconfiguration using varactor diodes. Antenna operation within the frequency range of 5.1–6 GHz was achieved. MIMO performance, smartphone components, and user effects were investigated. Simulated and experimental results demonstrate excellent performance.
Whole Body Control for Haptic Interaction with VR

In this paper, we present a Quadratic Programming (QP) based Whole-Body Control (WBC) approach to generating Haptic Interaction with Virtual Reality. The primary challenge of controlling haptic feedback devices is the ability to create realistic contact forces for static or dynamic contact scenarios at any time, while otherwise not impeding the motion of a user. The combination of four control tasks in a WBC solves this problem by providing a general solution for generating haptic feedback for any interaction with a virtual object. We present a method to decouple the real and virtual dynamics of the system through the introduction of a Haptic Contact Object (HCO), which simplifies the representation of virtual interactions for feedback to the high-level controller. We implement our controller on a 10-DOF upper-body powered exoskeleton and demonstrate its effectiveness at generating sufficient haptic feedback for static, dynamic, and no contact scenarios.
Active Fault-Tolerant Control Integrated with Reinforcement Learning Application to Robotic Manipulator

In this paper, we propose an active fault-tolerant control framework for robotic manipulators subjected to joint actuator faults. The proposed active fault-tolerant control scheme includes a neural network-based fault diagnosis module and a reinforcement learning-based fault-tolerant control module. Once the actuator fault is detected and diagnosed, an additive reinforcement learning controller will produce compensation torques to guarantee the system safety and maintain the control performance. Compared to traditional methods, our strategy avoids overly relying on the exact system models, which has potential for wider applications. The scheme is evaluated on a 7-DOF Panda manipulator for tracking control tasks in the MuJoCo simulator. Simulation results demonstrate the effectiveness of the proposed framework in dealing with single and multiple actuator faults.