Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Lung Segmentation Through Preprocessing of Medical Data Using Convolutional Neural Networks

Lung cancer is the leading cause of cancer-related death for women and the second leading cause of cancer-related death for men, according to a report by the World Health Organization in 2020. Detecting lung cancer through a three-dimensional X-ray of a patient's lungs, or a chest X-ray, is a common practice, but it can lead to errors caused by the human factor. To address this issue, advanced algorithms, and CAD technologies can be used to detect lung cancer. A network model can be trained using deep structural learning to detect details and deviations in the X-ray image. However, creating an accurate model using this technique is both time-consuming and advanced, and the model must be trained correctly. Despite the extensive research on various network architectures, the preprocessing technique of lung segmentation and its influence on such models remains largely unexplored. Thus, in this study, we aim to investigate the effect of applying lung segmentation to the training and testing data used by the CNN model on accuracy and loss. To achieve this, we developed two CNN models, one with and one without lung segmentation, respectively. The models were evaluated and compared, and our results demonstrate that lung segmentation can mitigate overtraining, resulting in a significant improvement in accuracy. We anticipate that this study will pave the way for further research in similar problem areas.
Performances of a Savonius-Darrieus Hybrid Wind Turbine

In the current energy context, renewable energies, including wind energy, on-or off-grid, have become a necessity. The present research studies a small off grid wind system, which consists of two turbines with a common vertical shaft: Savonius with two helical blades twisted at 180 degrees with a diameter of 0.456 m and a height of 0.8 m. This works in tandem with Darrieus with 3 blades with a diameter of 1.1 m and a height of 1 m, blades profile NACA 0010 and a solidity factor of 0.545. The advantages of this hybrid system from the experimental research are: very easy start, at a wind velocity of 1.1 m/s, good power coefficients, $\boldsymbol{c}_{\boldsymbol{p}}=\boldsymbol{0.26}$ for Savonius, respectively 0.33 for Darrieus, with the optimal operating range around a wind velocity of 6 m/s.
A different approach to lifting efficacy based on a natural active ingredient

Facial contouring, firming of double chins, body firming and anti-wrinkle have become strong claims of the anti-ageing care market. A new solution from Gattefosse research targets the dermal network and its physiological plasticity. Spilanthes acmella plant extract offers key benefits for the next generation of anti-age applications. It exhibits powerful activity to stimulate, reorganize and strengthen the collagen network.
On nonparaxial single-pixel imaging of semitransparent objects using flat diffractive optics

High numerical apertures can result in distortions appearing in a single-shot image, rendering the acquisition of usable images challenging, if not outright impossible. However, in the realm of single-pixel imaging, various strategies can be employed to effectively inspect objects with an excellent resolution, contrast and brightness. Recent advancements in flat photonic components have facilitated the development of compact nonparaxial imaging systems, which show great promise, particularly in the THz range of wavelengths. These innovations hold the potential to advance fields such as communication, material inspection and spectroscopy. In this study, we delve into the imaging of semi-transparent objects with varying levels of detail. Furthermore, we introduce a nonparaxial design for a flat hyperbolical lens and evaluate its performance in these imaging scenarios, comparing it to structured illumination techniques involving Airy, Bessel, and common thin lens configurations. We present findings regarding potential improvements in imaging attributable to the nonparaxial hyperbolical lens.
Solution of advection-diffusion-reaction inverse problems with Physics-Informed Neural Networks

In this work, two inverse problems related to pollutant dispersion in a river considering the advection-dispersion-reaction equation are studied along with a Neural Network approach. The first inverse problem concerns the estimation of the reaction parameter in an homogeneous equation, and the second one concerns the estimation of source pollution location in the non-homogeneous case. Both inverse problems are solved by two multiplayer perceptron networks: the usual Artificial Neural Network (ANN) and the Physics-Informed Neural Network (PINN), which is a special type of neural network that includes the physical laws that describes the phenomena in its formulation . Numerical experiments related to both inverse problems with ANN and with PINN are presented, demonstrating the feasibility of the proposed approach.
Gradient Pattern Analysis of Coupled Map Lattices: Insights into Transient and Long-Term Behaviors

Gradient Pattern Analysis (GPA) is a useful technique for analyzing the dynamics of nonlinear 2D-spatiotemporal systems, which is based on the gradient symmetry-breaking properties of a matrix snapshot sequence. GPA has found numerous applications in dynamic systems, particularly in studying logistic Coupled Map Lattices (CMLs) and Swift-Hohenberg amplitude equations. In this work, we propose a new mathematical operation related to the first gradient moment ( G 1 ) defined by the GPA theory. The performance of this new measure is evaluated by applying it to two chaotic CML models (Logistic and Shobu-Ose-Mori). The GPA using the new parameter ( G 1 ) provides a more accurate analysis, allowing the identification of conditions that partially break the gradient symmetry over time. Based on the GPA measurements ( G 1 , G 2 and G 3 ), including a combined analysis with the chaotic parameters, our results demonstrate the potential to analyze chaotic spatiotemporal systems improving our understanding of their underlying dynamics.
Knowledge Self-Efficacy, Rewards, and Knowledge Sharing Behavior in Indonesia

Objective: The purpose of this study is to investigate the influence of self-efficacy, knowledge, and rewards on knowledge sharing behavior in Indonesia. Theoretical framework: The theory used in this study consists of independent variables of knowledge self-efficacy, and rewards. While the dependent variable is knowledge sharing behavior. Method: This study uses quantitative research methods. The data collection technique was carried out by distributing questionnaires. The data that has been collected is then analyzed using the Partial Least Square approach. Results and conclusions: The results showed that knowledge self-efficacy has a significant positive influence on knowledge sharing behavior. Expected rewards, which are believed by many to be a driver of individuals to share knowledge, are not proven to be a determinant of knowledge sharing behavior, both in terms of extrinsic and intrinsic rewards. Research implications: This study can contribute to the development of theories on knowledge sharing behavior, regarding how psychological factors such as knowledge self-efficacy and rewards contribute to organizational dynamics. Originality/value: Uncovering important aspects of knowledge sharing behaviour in an industry/organization, especially when viewed from the individual's perspective as an important part that contributes to the achievement of organizational goals.
Design and Implementation of River Water Quality Monitoring System with Microsatellite

This paper discusses the design of a remote water quality monitoring system using nanosatellites simulated using universal software radio portable (USRP). This research was conducted to overcome the problem of unreachable signals in rivers with distant and remote locations. To monitor water quality, a prototype ship will be built that can move automatically and is installed with water quality sensors. To connect between USRPs, an antenna operating at a frequency of 2.22 GHz is designed. Water quality data is sent by the USRP to the satellite, then from the satellite forwarded to the ground station. This ground station is assumed to be the Ministry of Environment (KMLH). The results of the monitoring data will be displayed on the website.
Device-measured physical activity and type 2 diabetes mellitus risk

Objectives We investigated how device-measured physical activity (PA) volume (PA energy expenditure [PAEE]) and intensity (fraction of PAEE from moderate-to-vigorous PA [FMVPAEE]) were associated with the incidence of type 2 diabetes mellites (T2DM). Methods This population-based prospective cohort study included 90,044 participants. The primary exposures were PAEE and FMVPAEE. The secondary exposures were energy expenditure exerted during light, moderate, and vigorous PA and their fraction of PAEE. Results Each 1-SD increase in PAEE was associated with a 17% lower risk of T2DM (hazard ratio [HR]: 0.83, 95% confidence interval [CI]: 0.78–0.98). Each 1-SD increase in FMVPAEE was associated with a 21% lower incidence of T2DM (HR: 0.79, 95% CI: 0.74–0.83). Achieving the same PA volume (KJ/kg/day) through vigorous PA (HR: 0.88, 95% CI: 0.85–0.91) was more effective in preventing T2DM than moderate PA (HR: 0.97, 95% CI: 0.96–0.98) and light PA (HR: 0.99, 95% CI: 0.98–1.00). Conclusion A higher PA volume is associated with a lower incidence of T2DM. Achieving the same PA volumes through higher-intensity PA is more effective than low-intensity PA in reducing T2DM incidence.
New τc regression relationship derived from all P wave time windows for rapid magnitude estimation

Two issues related to the average period τc early‐warning parameter are the magnitude saturation effect on large earthquakes and considerable scatter for small earthquakes. To reduce the effect of these two issues on earthquake early‐warning systems, we introduce a new τc regression relationship derived from all P wave time windows (PTWs) in high‐pass filtered (T = 0.075 Hz) strong‐motion data for three damaging moderate‐to‐large earthquakes. Our results show that this relationship provides a better and more stable magnitude prediction than those derived from 3 s PTW without a saturation effect on large earthquakes with M < 7.5. It is expected that fewer false alerts (those outside the magnitude uncertainty tolerance) would be issued. Additionally, a reduction of the initial PTWs to 1–2 s and evolutionary calculation with an expanding window allow more lead time for small‐to‐moderate events.
World Education Website Localization Using A Culture-Based Design Approach

To observe the effect of a culture-based design approach in developing a web-based application for Indonesian citizens, as well as attempt to increase the efficiency and satisfaction of its usage, the World Education website was localized according to a culture-based process model. The website was localized in two iterations, during which six primary features were designed and implemented. Cultural considerations during the development process include local user observation, local site audit, and a cultural analysis based on Hofstede's model. The localized website was tested against the original website during the final iteration, in which an increase in usability and user experience goal scores, as well as the overall satisfaction of the website's interface was observed.
Multimodal Polysomnography-Based Automatic Sleep Stage Classification via Multiview Fusion Network

Sleep staging is a standard diagnostic method for evaluating sleep quality, which would enable early diagnosis of sleep disorders as well as mental diseases. Polysomnography (PSG), a set of physiological signals recorded externally, is a standard media for sleep staging. Developing automatic algorithms to analyze the PSG signals with the purpose of better sleep staging is demanding, as manual assessment is tedious and time-consuming. However, it is challenged by the noisy nature of the PSG signals, i.e., features contributing to sleep staging are embedded in different types and scales of signals in both time and frequency domains. In this article, we propose a hybrid deep learning architecture that uses multimodal PSG signals, specifically electroencephalogram (EEG) and electrooculogram (EOG), and their frequency representations as inputs, to accomplish sleep stage classification tasks. To this end, we design the multiscale local feature extractor (MSLFE) with a multibranch convolutional neural network (CNN) of different convolutional kernel sizes and the global relationship modeling (GRM) module to extract features in both time and frequency domains effectively. A cross-linked fusion (CLF) module is further introduced to enable an effective fusion of multimodal and multiattribute features while avoiding bidirectional representation redundancy for high-quality feature maps. We carried out a set of experiments on the SleepEDF-ST and SleepEDF-SC to validate the effectiveness of the proposed method, where classification performance in terms of precision, recall, and F1-score higher than 84% are obtained on most of the sleep stages. Comparisons with the state-of-the-art methods confirm the effectiveness of the proposed method in improving sleep quality evaluation and diagnosis. Source code is available at: https://github.com/ZJUT-CBS/MMNet.
Exclusión de paternidad mediante un panel de 89 marcadores SNP en una muestra de ovinos Corriedale y Merino de Uruguay

Abstract. The performance of an 89 single nucleotide polymorphism (SNP) marker panel for paternity exclusion was studied in populations of Corriedale and Merino sheep in Uruguay. Minor allele frequency (MAF) values of the markers ranged from 0.05 to 0.5 for Corriedale and from 0.08 to 0.5 for Merino. Using fewer than 50 markers, high probability values (0.9999) were achieved for excluding both parents and also when one of the parents was known. In the most common case of paternity exclusion, when only the genotype of a possible parent is given and the mother is unknown, achieving a probability value of 0.9999 involved almost all the markers for Merino and it was not possible to reach that level for Corriedale (maximum value = 0.9998). These results suggest that the panel could be useful with Corriedale and Merino populations. However, it may be insufficient to establish paternity in highly related populations. For that reason, development of an SNP panel for paternity exclusion under more complex situations is suggested.
A Wideband 90° Phase Shifting Element Applied in Quadrature Phase Filtering Power Divider

A wideband 90° phase shifting element for application in the quadrature phase filtering power divider (QFPD) is presented in this letter. Instead of the complex calculation procedure of phase information between reference and main branch in traditional phase shifter design, this letter initially reveals the inherent property of 90° phase difference between terminal open- and short-circuited transmission lines (TLs) by investigating their respective electric field distributions. Following this concept and conventional filtering design method, a new topology of QFPD is constructed. For demonstration, a design example of wideband QFPD is implemented and measured. Results show that the proposed wideband QFPD exhibits phase bandwidths of 69.24%, impedance bandwidths of 58.68%, and magnitude imbalances of less than 0.7 dB, together with better than 20 dB isolation performances.
Cutaneous/Tactile Haptic Feedback in Robotic Teleoperation: Motivation, Survey, and Perspectives

Cutaneous haptic feedback has recently received great attention from researchers in the robotic teleoperation field, as it has been proven to convey rich information to the human operator while guaranteeing the safety and stability of the control loop. In fact, delivering ungrounded cutaneous cues keeps the teleoperation system stable even in the presence of time-varying destabilizing factors such as hard contacts or communication delays. This aspect is particularly relevant for all the applications and scenarios where the safety of the system is of paramount importance, as in medical robotics. This article presents an overview on cutaneous haptic interaction followed by a review of the literature on cutaneous/tactile feedback systems for robotic teleoperation, categorizing the considered systems according to the type of cutaneous stimuli they can provide to the human operator. This article ends with a discussion on the role of cutaneous haptics in robotics and the perspectives of the field.
Analysis of the Twitter Response to Superstorm Sandy: Public Perceptions, Misconceptions, and Reconceptions of an Extreme Atmospheric Hazard

Superstorm Sandy was the second-costliest hurricane in U.S. history, causing cata‐ strophic flooding and prolonged power outages in New Jersey and New York. The public’s response to this extreme event on the social network Twitter is examined using statistical analysis and manual inspection of 185,000 “tweets” relating to Sandy. Sentiment analysis of tweets from Manhattan Island reveals a statistically significant trend toward negative perceptions, especially on the southern half of the island, as Sandy made landfall. Inspection of all tweets uncovered scientific misconceptions regarding hurricanes, and a surprising and disquieting anthropomorphic reconception of Sandy. This reconception, divorced from factual information about the storm, dominated the “Twittersphere” compared to official scientific information. The implications of such reconceptions for social media communication during future extreme events, and the utility of the methodology employed for analysis of other events, are discussed.
Research the Dimensional Accuracy of C45 Steel Ring Forgings Produced by Radial Rolling

The rolling process of rings is a commonly used method for producing annular forgings. There are two primary types of this process: radial-axial rolling and radial rolling. This article presents the research results regarding the latter, in which obtaining a product with the assumed dimensions constitutes a major problem. In industrial practice, the process parameters are based on the experience of technologists and/or by trial and error. This is why the authors considered it justified to undertake the research aimed at determining the influence of the main process parameters, that is, preform temperature and tool speed, on the shape and dimensions of the cross-section, which determine the internal and external diameters of the rolled ring. The research was based on numerical simulations and experimental studies. The results obtained proved that the higher the feed speed of the main roll, the greater the change in the cross-sectional height during rolling, and the smaller the cross-sectional deformation (the so-called fishtail). Nevertheless, a higher preform temperature reduces the final height of the ring and reduces cross-section deformation. On the basis of the obtained test results, guidelines for the process design were postulated, considering the influence of temperature and speed parameters on the final dimensions of the forging and the dimensions of the preform.
The Identification of Bee Comb Cell Contents Using Semiconductor Gas Sensors

Beekeeping is an extremely difficult field of agriculture. It requires efficient management of the bee nest so that the bee colony can develop efficiently and produce as much honey and other bee products as possible. The beekeeper, therefore, must constantly monitor the contents of the bee comb. At the University of Warmia and Mazury in Olsztyn, research is being carried out to develop methods for efficient management of the apiary. One of our research goals was to test whether a gas detector (MCA-8) based on six semiconductor sensors—TGS823, TGS826, TGS832, TGS2600, TGS2602, and TGS2603 from the company FIGARO—is able to recognize the contents of bee comb cells. For this purpose, polystyrene and wooden test chambers were created, in which fragments of bee comb with different contents were placed. Gas samples were analyzed from an empty comb, a comb with sealed brood, a comb with open brood, a comb with carbohydrate food in the form of sugar syrup, and a comb with bee bread. In addition, a sample of gas from an empty chamber was tested. The results in two variants were analyzed: (1) Variant 1, the value of 270 s of sensor readings from the sample measurement (exposure phase), and (2) Variant 2, the value of 270 s of sensor readings from the sample measurement (measurement phase) with baseline correction by subtracting the last 600 s of surrounding air measurements (flushing phase). A five-time cross-validation 2 (5xCV2) test and the Monte Carlo cross-validation 25 (trained and tested 25 times) were performed. Fourteen classifiers were tested. The naive Bayes classifier (NB) proved to be the most effective method for distinguishing individual classes from others. The MCA-8 device brilliantly differentiates an empty comb from a comb with contents. It differentiates better between an empty comb and a comb with brood, with results of more than 83%. Lower class accuracy was obtained when distinguishing an empty comb from a comb with food and a comb with bee bread, with results of less than 73%. The matrix of six TGS sensors in the device shows promising versatility in distinguishing between various types of brood and food found in bee comb cells. This capability, though still developing, positions the MCA-8 device as a potentially invaluable tool for enhancing the efficiency and effectiveness of beekeepers in the future.
Student Perceptions of ChatGPT Through an Expectancy Value Theory

In higher education, the integration of AI-powered tools, such as ChatGPT, has gained attention as a means to enhance student learning experiences and support academic activities. This study examines the relationship between student perceptions and their intention to use ChatGPT in higher education. The proposed study was based on Expectancy value theory. Seven constructs were selected to build a model that evaluated the behavioral intention and user behavior of ChatGPT. The multivariate analysis method of structural equation modelling was used for data analysis. The model developed was found to be reliable and valid, also the results were on the basis of self-reported data of 200 students. The perceived value has the strongest positive influence on intention to use, while the perceived cost has a weak negative influence on intention to use. The research highlighted the need for further examination of how AI tools could be adopted in learning and teaching.