Search Results (20)

Education 4.0 arises to provide citizens with the technical/digital competencies and cognitive/interpersonal skills demanded by Industry 4.0. New technologies drive this change, though time-independent learning remains a challenge, because students might face a lack of support, advice and surveillance when teachers are unavailable. This study proposes complementing presential lessons with online learning driven by ChatGPT, applied as an educational tool able to mentor K-12 students learning science at home. First, ChatGPT’s performance in the field of K-12 science is evaluated, scoring A (9.3/10 in 2023, and 9.7/10 in 2024) and providing detailed, analytic, meaningful, and human-like answers. Then, an empirical interventional study is performed to assess the impact of using ChatGPT as a virtual mentor on real K-12 students. After the intervention, the grades of students in the experimental group improved by 30%, and 70% of students stated a positive perception of the AI, suggesting a positive impact of the proposed educational approach. After discussion, the study concludes ChatGPT might be a useful educational tool able to provide K-12 students learning science with the functional and social/emotional support they might require, democratizing a higher level of knowledge acquisition and promoting students’ autonomy, security and self-efficacy. The results probe ChatGPT’s remarkable capacity (and immense potential) to assist teachers in their mentoring tasks, laying the foundations of virtual mentoring and paving the way for future research aimed at extending the study to other areas and levels, obtaining a more realistic view of AI’s impact on education. Full article

Light Detection and Ranging (LiDAR) sensors at signalized intersections can accurately track the movement of virtually all objects passing through at high sampling rates. This study presents methodologies to estimate vehicle and pedestrian traffic signal performance measures using LiDAR trajectory data. Over 15,000,000 vehicle and 170,000 pedestrian waypoints detected during a 24 h period at an intersection in Utah are analyzed to describe the proposed techniques. Sampled trajectories are linear referenced to generate Purdue Probe Diagrams (PPDs). Vehicle-based PPDs are used to estimate movement level turning counts, 85th percentile queue lengths (85QL), arrivals on green (AOG), highway capacity manual (HCM) level of service (LOS), split failures (SF), and downstream blockage (DSB) by time of day (TOD). Pedestrian-based PPDs are used to estimate wait times and the proportion of people that traverse multiple crosswalks. Although vehicle signal performance can be estimated from several days of aggregated connected vehicle (CV) data, LiDAR data provides the ability to measure performance in real time. Furthermore, LiDAR can measure pedestrian speeds. At the studied location, the 15th percentile pedestrian walking speed was estimated to be 3.9 ft/s. The ability to directly measure these pedestrian speeds allows agencies to consider alternative crossing times than those suggested by the Manual on Uniform Traffic Control Devices (MUTCD). Full article

The design of chemical sensors and probes is usually based on selective receptors for individual analytes, however, many analytical tasks are dedicated to multi-analyte sensing or recognizing properties of the sample related to more than one analyte. While it is possible to simultaneously use multiple sensors/receptors in such cases, multi-responsive probes could be an attractive alternative. In this work, we use thiomalic acid-capped CdTe quantum dots as a multiple-response receptor for the detection and quantification of six heavy metal cations: Ag(I), Cd(II), Co(II), Cu(II), Ni(II), and Pb(II) at micromolar concentration levels. Multiplexing is realized via multispectral fluorescence (so-called virtual sensor array). For such a sensing strategy, the effective decoding of the excitation–emission spectrum is essential. Herein, we show how various parameters of chemometric analysis by the Partial Least Squares method, such as preprocessing type and data structure, influence the performance of discrimination and quantification of the heavy metals. The established models are characterized by respective performance metrics (accuracy, sensitivity, precision, specificity/RMSE, a, b, R²) determined for both train and test sets in replicates, to obtain reliable and repeatable results. Full article

Telecommunications profoundly impacts all major aspects of our everyday life. As a consequence, student instruction typically includes a series of specialized courses, each addressing a distinct telecommunication area, separating wireless from fixed (optical) communications. This creates the problem of knowledge fragmentation, hindering the student’s perception of the topic since, at the service level, the applications and services offered to the users seem “virtually” independent from the underlying infrastructure. In this paper, to address this problem, we designed, analyzed, and implemented a 6 h course module on the five generations of wireless and fixed networks, which was presented as an integral part of the undergraduate course “Broadband Communications”, which was offered at the Dept. of Electrical and Electronic Engineering, School of Pedagogical and Technological Education (ASPETE), Athens, Greece. The main targets of this module are the following. Firstly, it aims to familiarize students with the fixed generations taxonomy, defined by the ETSI Industry Specification Group (ISG) F5G. This taxonomy serves as a foundation for understanding the evolution of telecommunications technologies. Secondly, the module seeks to integrate the acquired knowledge of the students in their previous telecommunication-related courses. During their curriculum, this knowledge was divided into two separate parts: wireless and fixed (optical). By coupling these two areas, students can develop a deeper understanding of the field. Lastly, the module aims to explore cutting-edge technologies and advancements in the telecommunications industry. In this way, it prepares students to enter the professional world during the fifth-generation era. Additionally, it provides them with valuable insights into the ongoing research and development in the field of 6G. Overall, this module serves as a comprehensive platform for students to enhance their understanding of telecommunications, from the foundational concepts to the latest advancements. To evaluate the impact of this module, the students were asked to fill out a questionnaire that included seven questions upon module completion. This questionnaire was completed successfully by 32 students in the previous academic year and by 16 students in this academic year. Moreover, a 20-question multiple choice quiz was offered to the students, allowing us to probe more into the typical errors and misconceptions about the topic. Full article

The search for selective anticholinergic agents stems from varying cholinesterase levels as Alzheimer’s Disease progresses from the mid to late stage. In this computational study, we probed the selectivity of FDA-approved and metabolite compounds against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) with molecular-docking-based virtual screening. The results were evaluated using locally developed codes for the statistical methods. The docking-predicted selectivity for AChE and BChE was predominantly the consequence of differences in the volume of the active site and the narrower entrance to the bottom of the active site gorge of AChE. Full article

Two fundamental halocarbon ions, CH₂Cl⁺ and CH₃ClH⁺, were studied in the gas phase using the FELion 22-pole ion trap apparatus and the Free Electron Laser for Infrared eXperiments (FELIX) at Radboud University, Nijmegen (the Netherlands). The vibrational bands of a total of four isotopologs, CH₂^35,37Cl⁺ and CH₃^35,37ClH⁺, were observed in selected wavenumber regions between 500 and 2900 cm⁻¹ and then spectroscopically assigned based on the results of anharmonic force field calculations performed at the CCSD(T) level of theory. As the infrared photodissociation spectroscopy scheme employed probes singly Ne-tagged weakly bound complexes, complementary quantum-chemical calculations of selected species were also performed. The impact of tagging on the vibrational spectra of CH₂Cl⁺ and CH₃ClH⁺ is found to be virtually negligible for most bands; for CH₃ClH⁺–Ne, the observations suggest a proton-bound structural arrangement. The experimental band positions as well as the best estimate rotational molecular parameters given in this work provide a solid basis for future spectroscopic studies at high spectral resolutions. Full article

Throughout the COVID-19 pandemic, an unprecedented level of clinical nasal swab data from around the globe has been collected and shared. Positive tests have consistently revealed viral titers spanning six orders of magnitude! An open question is whether such extreme population heterogeneity is unique to SARS-CoV-2 or possibly generic to viral respiratory infections. To probe this question, we turn to the computational modeling of nasal tract infections. Employing a physiologically faithful, spatially resolved, stochastic model of respiratory tract infection, we explore the statistical distribution of human nasal infections in the immediate 48 h of infection. The spread, or heterogeneity, of the distribution derives from variations in factors within the model that are unique to the infected host, infectious variant, and timing of the test. Hypothetical factors include: (1) reported physiological differences between infected individuals (nasal mucus thickness and clearance velocity); (2) differences in the kinetics of infection, replication, and shedding of viral RNA copies arising from the unique interactions between the host and viral variant; and (3) differences in the time between initial cell infection and the clinical test. Since positive clinical tests are often pre-symptomatic and independent of prior infection or vaccination status, in the model we assume immune evasion throughout the immediate 48 h of infection. Model simulations generate the mean statistical outcomes of total shed viral load and infected cells throughout 48 h for each “virtual individual”, which we define as each fixed set of model parameters (1) and (2) above. The “virtual population” and the statistical distribution of outcomes over the population are defined by collecting clinically and experimentally guided ranges for the full set of model parameters (1) and (2). This establishes a model-generated “virtual population database” of nasal viral titers throughout the initial 48 h of infection of every individual, which we then compare with clinical swab test data. Support for model efficacy comes from the sampling of infection dynamics over the virtual population database, which reproduces the six-order-of-magnitude clinical population heterogeneity. However, the goal of this study is to answer a deeper biological and clinical question. What is the impact on the dynamics of early nasal infection due to each individual physiological feature or virus–cell kinetic mechanism? To answer this question, global data analysis methods are applied to the virtual population database that sample across the entire database and de-correlate (i.e., isolate) the dynamic infection outcome sensitivities of each model parameter. These methods predict the dominant, indeed exponential, driver of population heterogeneity in dynamic infection outcomes is the latency time of infected cells (from the moment of infection until onset of viral RNA shedding). The shedding rate of the viral RNA of infected cells in the shedding phase is a strong, but not exponential, driver of infection. Furthermore, the unknown timing of the nasal swab test relative to the onset of infection is an equally dominant contributor to extreme population heterogeneity in clinical test data since infectious viral loads grow from undetectable levels to more than six orders of magnitude within 48 h. Full article

Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level prediction starting from measured meteorological data (i.e., precipitation and temperature) and observed groundwater levels, by exploiting data-driven approaches. Barely a few research combine the meteorological variables and groundwater level data with unsaturated zone monitored variables (i.e., soil water content, soil temperature, and bulk electric conductivity), and—in most of these—the vadose zone is monitored only at a single depth. Our approach exploits a high spatial-temporal resolution hydrogeological monitoring system developed in the Conero Mt. Regional Park (central Italy) to predict groundwater level trends of a shallow aquifer exploited for drinking purposes. The field equipment consists of a thermo-pluviometric station, three volumetric water content, electric conductivity, and soil temperature probes in the vadose zone at 0.6 m, 0.9 m, and 1.7 m, respectively, and a piezometer instrumented with a permanent water-level probe. The monitored period started in January 2022, and the variables were recorded every fifteen minutes for more than one hydrologic year, except the groundwater level which was recorded on a daily scale. The developed model consists of three “virtual boxes” (i.e., atmosphere, unsaturated zone, and saturated zone) for which the hydrological variables characterizing each box were integrated into a time series forecasting model based on Prophet developed in the Python environment. Each measured parameter was tested for its influence on groundwater level prediction. The model was fine-tuned to an acceptable prediction (roughly 20% ahead of the monitored period). The quantitative analysis reveals that optimal results are achieved by expoiting the hydrological variables collected in the vadose zone at a depth of 1.7 m below ground level, with a Mean Absolute Error (MAE) of 0.189, a Mean Absolute Percentage Error (MAPE) of 0.062, a Root Mean Square Error (RMSE) of 0.244, and a Correlation coefficient of 0.923. This study stresses the importance of calibrating groundwater level prediction methods by exploring the hydrologic variables of the vadose zone in conjunction with those of the saturated zone and meteorological data, thus emphasizing the role of hydrologic time series forecasting as a challenging but vital aspect of optimizing groundwater management. Full article

This study probed how baseline cortisol (BC), negative emotionality (NE), and environmental facets—classroom size and window view—interact to affect executive function (EF) in preschoolers using virtual reality (VR). In a cohort of 144 children aged 61–85 months, BC levels were measured by saliva assays and NE by parental surveys. Participants completed computerized EF assessments both pre- and post-exposure to one of four VR conditions, which varied by classroom size (large vs. small) and window view (natural vs. built). Due to missing data and outlier responses, three children were removed from the analyses. Regression analyses, accounting for initial EFs, revealed that higher BC was significantly associated with better Digit-span task scores in the nature view, while lower BC correlated with improved performance in the built view. With regard to classroom size, children with varying levels of NE benefitted from the large classroom environment, as evidenced by marginally significant improvements on the Corsi block task. However, higher NE children outperformed their lower NE peers in the large classroom, while a trend inverted in the small classroom context. The findings illuminate how the physical components of preschool environments may interact with children’s physiological reactivity, potentially influencing the development of working memory. Full article

Monitoring of the water level and river discharge is an important task, necessary both for assessment of water supply in the current season and for forecasting water consumption and possible prevention of catastrophic events. A network of ground hydrometric stations is used to measure the water level and consumption in rivers. Rivers located in sparsely populated areas in developing countries of Central Asia have a very limited hydrometric network. In addition to the sparse network of stations, in some cases remote probing data (virtual hydrometric stations) are used, which can improve the reliability of water level and discharge estimates, especially for large mountain rivers with large volumes of suspended sediment load and significant channel instability. The aim of this study is to develop a machine learning model for remote monitoring of water levels in the large transboundary (Kazakhstan-People’s Republic of China) Ili River. The optical data from the Sentinel-2 satellite are used as input data. The in situ (ground-based) data collected at the Ili-Dobyn gauging station are used as target values. Application of feature engineering and ensemble machine learning techniques has achieved good accuracy of water level estimation (Nash–Sutcliffe model efficiency coefficient (NSE) >0.8). The coefficient of determination of the model results obtained using cross-validation of random permutations is NSE = 0.89. The method demonstrates good stability under different variations of input data and ranges of water levels (NSE > 0.8). The average absolute error of the method ranges from 0.12 to 0.18 meters against the background of the maximum river water level spread of more than 4 meters. The obtained result is the best current result of water level prediction in the Ili River using the remote probing data and can be recommended for practical use for increasing the reliability of water level estimation and reverse engineering of data in the process of river discharge monitoring. Full article

Percutaneous nephrolithotomy is the gold standard for the treatment of renal stones larger than 20 mm in diameter. The treatment outcomes of PCNL are highly dependent on the accuracy of the puncture step, in order to achieve a suitable renal access and reach the stone with a precise and direct path. Thus, performing the puncturing to get the renal access is the most crucial and challenging step of the procedure with the steepest learning curve. Many simulation methods and systems have been developed to help trainees achieve the requested competency level to achieve a suitable renal access. Simulators include human cadavers, animal tissues and virtual reality simulators to simulate human patients. On the other hand, the availability of pre-operative information (e.g., computed tomography or magnetic resonance imaging) and of intra-operative images (e.g., ultrasound images) has allowed the development of solutions involving augmented reality and robotic systems to assist the surgeon during the operation and to help a novel surgeon in strongly reducing the learning curve. In this context, the real-time awareness of the 3D position and orientation of the considered anatomical structures with reference to a common frame is fundamental. Such information must be accurately estimated by means of specific tracking systems that allow the reconstruction of the motion of the probe and of the tool. This review paper presents a survey on the leading literature on augmented reality and robotic assistance for PCNL, with a focus on existing methods for tracking the motion of the ultrasound probe and of the surgical needle. Full article

Changes in the renin–angiotensin system, known for its critical role in the regulation of blood pressure and sodium homeostasis, may contribute to aging and age-related diseases. While the renin–angiotensin system is suppressed during aging, little is known about its regulation and activity within tissues. However, this knowledge is required to successively treat or prevent renal disease in the elderly. Ercc1 is involved in important DNA repair pathways, and when mutated causes accelerated aging phenotypes in humans and mice. In this study, we hypothesized that unrepaired DNA damage contributes to accelerated kidney failure. We tested the use of the renin-activatable near-infrared fluorescent probe ReninSense680™ in progeroid Ercc1^d/− mice and compared renin activity levels in vivo to wild-type mice. First, we validated the specificity of the probe by detecting increased intrarenal activity after losartan treatment and the virtual absence of fluorescence in renin knock-out mice. Second, age-related kidney pathology, tubular anisokaryosis, glomerulosclerosis and increased apoptosis were confirmed in the kidneys of 24-week-old Ercc1^d/− mice, while initial renal development was normal. Next, we examined the in vivo renin activity in these Ercc1^d/− mice. Interestingly, increased intrarenal renin activity was detected by ReninSense in Ercc1^d/− compared to WT mice, while their plasma renin concentrations were lower. Hence, this study demonstrates that intrarenal RAS activity does not necessarily run in parallel with circulating renin in the aging mouse. In addition, our study supports the use of this probe for longitudinal imaging of altered RAS signaling in aging. Full article

This paper presents the use of virtual level (VL) probes as an alternative image-based approach to investigate the interaction of waves with coastal structures in wave flume experiments. These probes are defined as regions of interest located at specific positions along the horizontal domain of the images, in which edge interfaces are detected and, thus, their vertical motions can be obtained. To demonstrate the use of the methodology, a critical condition of breaking waves interacting with a Cubipod homogeneous low-crested structure (HLCS) in a two-dimensional framework was selected. With the video recorded from the experiments, image calibration, processing, and analysis stages were implemented to analyze the performance of the HLCS in reducing wave elevations and to study the stability of the armor units. The present approach can be extended to a wide range of coastal structures applications where the interface detection between components of the scene is useful to observe the behavior of coastal structures, increasing effectiveness and alternatives to acquire precise data in 2D experimental tests. Full article

This work presents an approach to delay-based reservoir computing (RC) at the sensor level without input modulation. It employs a time-multiplexed bias to maintain transience while utilizing either an electrical signal or an environmental signal (such as acceleration) as an unmodulated input signal. The proposed approach enables RC carried out by sufficiently nonlinear sensory elements, as we demonstrate using a single electrostatically actuated microelectromechanical system (MEMS) device. The MEMS sensor can perform colocalized sensing and computing with fewer electronics than traditional RC elements at the RC input (such as analog-to-digital and digital-to-analog converters). The performance of the MEMS RC is evaluated experimentally using a simple classification task, in which the MEMS device differentiates between the profiles of two signal waveforms. The signal waveforms are chosen to be either electrical waveforms or acceleration waveforms. The classification accuracy of the presented MEMS RC scheme is found to be over 99%. Furthermore, the scheme is found to enable flexible virtual node probing rates, allowing for up to 4× slower probing rates, which relaxes the requirements on the system for reservoir signal sampling. Finally, our experiments show a noise-resistance capability for our MEMS RC scheme. Full article

Dam-break wave propagation usually occurs over irregular topography, due for example to natural contraction-expansion of the river bed and to the presence of natural or artificial obstacles. Due to limited available dam-break real-case data, laboratory and numerical modeling studies are significant for understanding this type of complex flow problems. To contribute to the related field, a dam-break flow over a channel with a contracting reach was investigated experimentally and numerically. Laboratory tests were carried out in a smooth rectangular channel with a horizontal dry bed for three different lateral contraction geometries. A non-intrusive digital imaging technique was utilized to analyze the dam-break wave propagation. Free surface profiles and time variation of water levels in selected sections were obtained directly from three synchronized CCD video camera records through a virtual wave probe. The experimental results were compared against the numerical solution of VOF (Volume of Fluid)-based Shallow Water Equations (SWEs) and Reynolds-Averaged Navier-Stokes (RANS) equations with the k-ε turbulence model. Good agreements were obtained between computed and measured results. However, the RANS solution shows a better correspondence with the experimental results compared with the SWEs one. The presented new experimental data can be used to validate numerical models for the simulation of dam-break flows over irregular topography. Full article