Search Results (572)

This expository paper provides a unified and pedagogical introduction to optimal quantization for probability measures supported on spherical curves and discrete subsets of the sphere, emphasizing both continuous and discrete settings. We first present a detailed geometric and analytical foundation for intrinsic quantization on the unit sphere, including definitions of great and small circles, spherical triangles, geodesic distance, Slerp interpolation, the Fréchet mean, spherical Voronoi regions, centroid conditions, and quantization dimensions. Building upon this framework, we develop explicit continuous and discrete quantization models on spherical curves, namely great circles, small circles, and great circular arcs—supported by rigorous derivations and pedagogical exposition. For uniform continuous distributions, we compute optimal sets of n-means and the associated quantization errors on these curves; for discrete distributions, we analyze antipodal, equatorial, tetrahedral, and finite uniform configurations, illustrating convergence to the continuous model. The central conclusion is that for a uniform probability distribution supported on a one-dimensional geodesic subset of total length L, the optimal n-means form a uniform partition and the quantization error satisfies $V_n=L^2/\left(12n^2\right)$.The exposition emphasizes geometric intuition, detailed derivations, and clear step-by-step reasoning, making it accessible to beginning graduate students and researchers entering the study of quantization on manifolds. This article is intended as an expository and tutorial contribution, with the main emphasis on geometric reformulation and pedagogical clarity of intrinsic quantization on spherical curves, rather than on the development of new asymptotic quantization theory. Full article

This review article is the third of a three-article introductory series on virtual cathode oscillators. The first article has laid the theoretical ground for understanding the physical properties of the virtual cathode, and the second article has provided a numerical tool for studying virtual cathode oscillation. This third article focuses on the interaction between the electron beam and electromagnetic field. The virtual cathode oscillator has been studied for decades with the aim of developing it as high-power microwave source. The beam-field interaction has been one of the core issues that always perplexes both experimentalists and theorists. Using the physical model established in the first article and the numerical method described in the second article, this article is an attempt to answer some of the key questions based on a more comprehensive description of the device and its interaction process. This article is expected to serve as a reference for young researchers and students working on high-power microwaves and pulsed particle beams. Full article

One difficulty that arises in tracking and forecasting real-time epidemics is reporting delays, which are defined as the inherent delay between the time of symptom onset and the time a case is reported. Reporting delays can be caused by delays in case detection, symptom onset after infection, seeking medical care, or diagnostics, and they distort the accurate forecasting of diseases during epidemics and pandemics. To address this, we introduce a practical nowcasting approach grounded in survival analysis and actuarial science, explicitly allowing for non-stationarity in reporting delay patterns to better capture real-world variability. Despite its relevance, no flexible and accessible toolbox currently exists for non-stationary delay adjustment. Here, we present Nowcast-It, a tutorial-based toolbox that includes two components: (1) an R code base for delay adjustment and (2) a user-friendly R-Shiny application to enable interactive visualization and reporting delay correction without prior coding expertise. The toolbox supports daily, weekly, or monthly resolution data and enables model performance assessment using metrics such as mean absolute error, mean squared error, and 95% prediction interval coverage. We demonstrate the utility of Nowcast-It toolbox using publicly available weekly Ebola case data from the Democratic Republic of Congo. We and others have adjusted for reporting delays in real-time analyses (e.g., Singapore) and produced early COVID-19 forecasts; here, we package those delay adjustment routines into an accessible toolbox. It is designed for researchers, students, and policymakers alike, offering a scalable and accessible solution for addressing reporting delays during outbreaks. Full article

The interpretability of seismic images of Earth’s interior properties relies on the quantifiable level of image fidelity. We recommend evaluating seismic image fidelity via three quantifiable elements: image resolution, artifact level, and position accuracy. Though the resolution of seismic images is routinely assessed, it is difficult to detect deceiving image artifacts or evaluate the accuracy of image position beyond the drilling limit. Many image artifacts, such as fake or distorted features, are generated by mistaking the signals in the imaging process or due to limitations in seismic illumination and imaging methods. Most image position errors are produced by erroneous velocity models used in seismic imaging. To ensure seismic image fidelity, we should establish practical evaluation standards during the processes of making and interpreting seismic images. For mitigating image artifacts and position errors, we should analyze their causes and follow practical rules, such as “from known to unknown”, in evaluating and interpreting seismic images. Full article

Simultaneous state and parameter estimation is essential for control system design and dynamic modeling of physical systems. This capability provides critical real-time insight into system behavior, supports the discovery of underlying mechanisms, and facilitates adaptive control strategies. Surveyed in this review paper are two classes of state and parameter estimation methods: Kalman Filters and Luenberger Observers. The Kalman Filter framework, including its major variants such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Cubature Kalman Filter (CKF), and Ensemble Kalman Filter (EnKF), has been widely applied for joint and dual estimation in linear and nonlinear systems under uncertainty. In parallel, Luenberger observers, typically used in deterministic settings, offer alternative approaches through high-gain, sliding mode, and adaptive observer structures. This review focuses on the theoretical foundations, algorithmic developments, and application domains of these methods and provides a comparative analysis of their advantages, limitations, and practical relevance across diverse engineering scenarios. Full article

Background/Objectives: BrdU (5′-bromo-2′-deoxyuridine), a synthetic thymidine (T) analog, is widely used to study cell proliferation and DNA synthesis. To precisely identify where and when DNA replication starts and terminates, it is essential to determine the BrdU incorporation rate and sites at a single-nucleotide resolution. Although several deep learning-based methods have been developed for detecting BrdU using Oxford nanopore sequencing data, there is a lack of accessible, easy-to-follow tutorials to guide researchers in preparing training data and implementing deep learning approaches as the nanopore sequencing technologies continue to evolve. Methods: Due to the lack of ground truth BrdU-positive data generated on the latest R10 flow cells, we prepared model training data from legacy R9 flow cells, consistent with existing tools. We processed publicly available synthetic and real nanopore DNA sequencing datasets, with and without BrdU incorporation, using a combination of open-source and custom software tools. Subsequently, we trained bidirectional gated recurrent unit (BiGRU)-based recurrent neural networks (RNNs) for BrdU detection using the TensorFlow library on the Google Colab platform. Results: We trained BiGRU-based RNNs for BrdU detection with a high specificity (>94%) but a moderate sensitivity due to limited BrdU-positive data. We detail the setup, training, testing, and fine-tuning of the model using both synthetic and real DNA sequencing data. Conclusions: Though the models were trained with data generated on legacy flow cells, we believe that this detailed protocol, covering both data preparation and model development, can be readily extended to R10 flow cells and basecallers for other base modifications. This work will facilitate the broader adoption of deep learning neural networks in biological research, particularly RNNs, which are well suited for modeling sequential and time-series data. Full article

Background: Maintaining daily optimal dental hygiene, especially in medically vulnerable patients with periodontitis, remains challenging in dental practice. Mobile apps and other digital tools might offer useful support alongside traditional advice. Objectives: This study aimed to develop a mobile health app, PerioSupportPro, that helps patients improve their daily plaque control habits. It also reports on the pilot testing of the app’s usability and users’ perception in a small patient group. Methods: The app was created by a mixed team including periodontists, psychologists, developers, and data protection specialists. The first version included reminders, gamified elements, video tutorials, and motivational messages. After internal testing, a group of 18 patients tested the app and completed a feedback questionnaire that assessed usability (Q3–Q5), educational impact (Q6–Q8), motivation (Q9–Q11), and overall satisfaction (Q12–Q14). Cronbach’s alpha was used to check internal consistency, and non-parametric tests were applied for basic statistical comparisons. Results: The motivation section of the questionnaire showed acceptable consistency (α = 0.784), while usability and educational impact had lower values (α = 0.418 and 0.438). No clear differences were found between age groups. Satisfaction was positively associated with reminders and motivational items. Most appreciated features included reminders, the simple interface, and short videos. Based on the input provided by the questionnaire, a few improvements were made, and a second version of the app was prepared. Conclusions: Early user responses show that PerioSupportPro may help motivate and guide patients in their oral hygiene routine. While still in an early phase, the app seems well-received and ready for future clinical validation with more users. Full article

Spiking neural networks (SNNs) provide a biologically inspired, event-driven alternative to artificial neural networks (ANNs), potentially delivering competitive accuracy at substantially lower energy. This tutorial-study offers a unified, practice-oriented assessment that combines critical review and standardized experiments. We benchmark a shallow fully connected network (FCN) on MNIST and a deeper VGG7 architecture on CIFAR-10 across multiple neuron models (leaky integrate-and-fire (LIF), sigma–delta, etc.) and input encodings (direct, rate, temporal, etc.), using supervised surrogate-gradient training implemented in Intel Lava, SLAYER, SpikingJelly, Norse, and PyTorch. Empirically, we observe a consistent but tunable trade-off between accuracy and energy. On MNIST, sigma–delta neurons with rate or sigma–delta encodings achieve 98.1% accuracy (ANN baseline: 98.23%). On CIFAR-10, sigma–delta neurons with direct input reach 83.0% accuracy at just two time steps (ANN baseline: 83.6%). A GPU-based operation-count energy proxy indicates that many SNN configurations operate below the ANN energy baseline; some frugal codes minimize energy at the cost of accuracy, whereas accuracy-oriented settings (e.g., sigma–delta with direct or rate coding) narrow the performance gap while remaining energy-conscious—yielding up to threefold efficiency compared with matched ANNs in our setup. Thresholds and the number of time steps are decisive factors: intermediate thresholds and the minimal time window that still meets accuracy targets typically maximize efficiency per joule. We distill actionable design rules—choose the neuron–encoding pair according to the application goal (accuracy-critical vs. energy-constrained) and co-tune thresholds and time steps. Finally, we outline how event-driven neuromorphic hardware can amplify these savings through sparse, local, asynchronous computation, providing a practical playbook for embedded, real-time, and sustainable AI deployments. Full article

Introduction: In-hospital cardiac arrest (IHCA) is still associated with high mortality. Introduction of multi-parameter early warning systems (EWS) could reduce the incidence of IHCA. However, data regarding prevention of IHCA remains conflicting. Moreover, an aging population and a shortage of healthcare workers strain Austrian acute care hospitals. Sicker patients and fewer staff could hinder the implementation of multi-parameter EWS in Austria. Therefore, we sought to identify such barriers by assessing local and national data. Furthermore, we investigated the incidence of in-hospital cardiac arrests at Medical University Innsbruck. Methods: In this perspective study, we retrospectively analyzed all patients experiencing an in-hospital cardiac arrest between 2017 and 2024. In the qualitative part, ten experts in in-hospital emergency medicine were interviewed using guided interviews. The main results from the interviews were identified using a structured content analysis according to Mayring. Quantitative and qualitative data were integrated through narrative. Using the Consolidated Framework for Implementation Research, we stratified our data into five domains. Finally, we applied the “eight steps for leading change” to develop a practice guideline. Results: In six years, 1356 patients were treated by an emergency medical team; 1317 emergencies were included, with 365 of them being resuscitated. Overall, 114 survived for 24 h. The incidence rate of in-hospital cardiac arrests was 0.86 cases/1000 admissions per year. The guided interviews demonstrated a nearly complete absence of EWS using multiple parameters in Austria. Strained human resources after the pandemic, the fear of an increased workload and the lack of robust data regarding the benefit of survival were mentioned as main reasons. The best practice tutorial considers the challenges identified and provides guidance for structured implementation in hospitals. Conclusions: Implementing NEWS2 can facilitate detection of critically ill patients despite decreased staffing. Identifying common barriers and facilitators in five domains described and applying this to the “eight steps for leading change” enabled us to provide a tutorial for implementation of an EWS. This could help master future challenges in in-hospital emergency medicine. Full article

The electrostatic potential in certain types of boundary value problems can be found by solving Laplace’s Equation (LE). It is important for students to develop the ability to recognize the utility of LE and apply the method to solve physics problems. To develop students’ problem-solving skills for solving problems that can be solved effectively using Laplace’s equation in an upper-level electricity and magnetism course, we developed and validated a tutorial focused on finding electrostatic potential in a Cartesian coordinate system. The tutorial was implemented across three instructors’ classes, accompanied by scaffolded pretest (after traditional lecture) and posttest (after the tutorial). We also conducted think-aloud interviews with advanced students using both unscaffolded and scaffolded versions of the pretest and posttest. Findings reveal common student difficulties that were included in the tutorial as a guide to help address them. The difference in the performance of students from the pretest after lecture to the posttest after the tutorial was similar on the scaffolded version of the tests (in which the problems posed were broken into sub-problems) for all three instructors’ classes and interviewed students. Equally importantly, interviewed students demonstrated greater differences in scores from the pretest and posttest on the unscaffolded versions in which the problems were not broken into sub-problems, suggesting that the scaffolded version of the tests may have obscured evidence of actual learning from the tutorial. While a scaffolded test is typically intended to guide students through complex reasoning by breaking a problem into sub-problems and offering structured support, it can limit opportunities to demonstrate independent problem-solving and evidence of learning from the tutorial. Additionally, one instructor’s class underperformed relative to others even on the pretest. This instructor had mentioned that the tests and tutorial were not relevant to their current course syllabus and offered a small amount of extra credit for attempting to help education researchers, highlighting how this type of instructor framing of instructional tasks can negatively impact student engagement and performance. Overall, in addition to identifying student difficulties and demonstrating how the tutorial addresses them, this study reveals two unanticipated but critical insights: first, breaking problems into sub-parts can obscure evidence of students’ ability to independently solve problems, and second, instructor framing can significantly influence student engagement and performance. Full article

As machine learning and artificial intelligence are being integrated into cyber-physical systems, it is becoming important for engineers to know and understand these topics. In particular, sensor data is on the rise in these systems and therefore engineers need to understand which models are appropriate to time-series sensor data and how signal processing can be used with them. The Center for Cyber-Physical Systems (CCPS) at the University of Georgia (UGA) is addressing these issues. Student researchers in the CCPS require skills in these areas. This paper demonstrates a machine learning framework for time-series sensor data that can be used to quickly build, train, and test multiple models on CCPS testbed data. The framework is also a tool that can be used as a tutorial to help student researchers understand the concepts required to be successful in the CCPS. Full article

We investigated the impact of incorporating quantitative reasoning for deeper sense-making in a Quantum Interactive Learning Tutorial (QuILT) on students’ conceptual performance using a framework emphasizing integration of conceptual and quantitative aspects of quantum optics. In this investigation, we compared two versions of the QuILT that were developed and validated to help students learn various aspects of quantum optics using a Mach Zehnder Interferometer with single photons and polarizers. One version of the QuILT is entirely conceptual while the other version integrates quantitative and conceptual reasoning (hybrid version). Performance on conceptual questions of upper-level undergraduate and graduate students who engaged with the hybrid QuILT was compared with that of those who utilized the conceptual QuILT emphasizing the same concepts. Both versions of the QuILT focus on the same concepts, use a scaffolded approach to learning, and take advantage of research on students’ difficulties in learning these challenging concepts as well as a cognitive task analysis from an expert perspective as a guide. The hybrid and conceptual QuILTs were used in courses for upper-level undergraduates or first-year physics graduate students in several consecutive years at the same university. The same conceptual pre-test and post-test were administered after traditional lecture-based instruction in relevant concepts and after student engaged with the QuILT, respectively. We find that the post-test performance of physics graduate students who utilized the hybrid QuILT on conceptual questions, on average, was better than those who utilized the conceptual QuILT. For undergraduates, the results showed differences for different classes. One possible interpretation of these findings that is consistent with our framework is that integrating conceptual and quantitative aspects of physics in research-based tools and pedagogies should be commensurate with students’ prior knowledge of physics and mathematics involved so that students do not experience cognitive overload while engaging with such learning tools and have appropriate opportunities for metacognition, deeper sense-making, and knowledge organization. In the undergraduate course in which many students did not derive added benefit from the integration of conceptual and quantitative aspects, their pre-test performance suggests that the traditional lecture-based instruction may not have sufficiently provided a “first coat” to help students avoid cognitive overload when engaging with the hybrid QuILT. These findings suggest that different groups of students can benefit from a research-based learning tool that integrates conceptual and quantitative aspects if cognitive overload while learning is prevented either due to students’ high mathematical facility or due to their reasonable conceptual facility before engaging with the learning tool. Full article

This study investigates the adoption of PyChatAI, a bilingual AI-powered chatbot for Python programming education, among female computer science students at Jouf University. Guided by the Technology Acceptance Model 3 (TAM3), it examines the determinants of user acceptance and usage behaviour. A Solomon Four-Group experimental design (N = 300) was used to control pre-test effects and isolate the impact of the intervention. PyChatAI provides interactive problem-solving, code explanations, and topic-based tutorials in English and Arabic. Measurement and structural models were validated via Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM), achieving excellent fit (CFI = 0.980, RMSEA = 0.039). Results show that perceived usefulness (β = 0.446, p < 0.001) and perceived ease of use (β = 0.243, p = 0.005) significantly influence intention to use, which in turn predicts actual usage (β = 0.406, p < 0.001). Trust, facilitating conditions, and hedonic motivation emerged as strong antecedents of ease of use, while social influence and cognitive factors had limited impact. These findings demonstrate that AI-driven bilingual tools can effectively enhance programming engagement in gender-specific, culturally sensitive contexts, offering practical guidance for integrating intelligent tutoring systems into computer science curricula. Full article

Background: Multiple sclerosis is a multifactorial neurodegenerative disease characterized by autoimmune and inflammatory processes. Despite advancements in disease-modifying therapies, multiple sclerosis remains challenging due to its complex pathophysiology and variable clinical presentation. Current therapies focus on managing inflammation and promoting immunosuppression but do not achieve complete symptom regression or enhance remyelination. Emerging therapies, such as Peroxisome Proliferator-Activated Receptor gamma (PPARγ) agonists and Bruton tyrosine kinase (BTK) inhibitors, show promise in modulating inflammation and targeting immune cells. Innovative approaches like human fetal neural precursor cells (hfPNCs) and mesenchymal stem cell transplantation are being explored to reduce neural inflammation and improve neuroprotection. Early diagnosis and intervention are crucial for managing multiple sclerosis effectively and preventing progression to severe forms and permanent disability. Therapeutic education for individuals with multiple sclerosis and their caregivers is essential, emphasizing the need for clear, reliable information to support disease management and improve quality of life. Objectives: This review provides an up-to-date overview of multiple sclerosis pathophysiology, current treatments, and emerging therapies, aiming to enhance the knowledge base of healthcare professionals and researchers, facilitating informed decision-making and contributing to ongoing research efforts. Full article

Background: Inflammatory bowel disease (IBD) represents significant health challenges on a global scale, primarily encompassing Crohn’s disease and ulcerative colitis. These conditions are characterized by cycles of relapse and remission. Current treatment options, including conventional chemical therapies and biologics such as anti-Tumor Necrosis Factor α (anti-TNFα), anti-integrin, anti-interleukins 12 (IL-12) or 23 (IL-23) agents, Janus Kinase (JAK) inhibitors, and sphingosine-1-phosphate (S1p) receptor modulators, provide symptomatic relief but do not offer a cure. These therapies are associated with both localized and systemic adverse effects, necessitating careful patient monitoring. Probiotics and prebiotics have been investigated for their potential to enhance gut microbiota diversity, which may assist in managing IBD. However, their efficacy in preventing disease flares remains limited. Recent advances in drug delivery systems, including pressure-based and pH-sensitive formulations, aim at enhancing localized treatment efficacy while minimizing adverse effects. Additionally, a pharmacogenomic approach could improve treatment personalization, optimize therapeutic outcomes, and enhance patients’ quality of life by addressing mental health needs and ensuring comprehensive follow-up care. Despite increased awareness and education among healthcare providers regarding IBD, there is still a need for clearer guidance on available treatment options. Objective: This review aims at providing deeper understanding of IBD management strategies, ultimately striving to improve the quality of care for individuals affected by this disease. Full article