Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiclass Diabetic Retinopathy Classification of Eye Fundus Images Small Datasets Performance Improvement – A Neuroevolution Approach

Diabetic retinopathy is an eye complication of a widespread disease named diabetes mellitus. The most widely used method for diagnosing diabetic retinopathy is the analysis of retinal fundus images obtained by retinography. Deep Learning-based methods have shown promising results as a diagnostic tool for diabetic retinopathy, achieving, in some cases, performance close to the human inspection of images. However, the performance of these methods relies heavily on fine-tuning the algorithm hyperparameters and big data sets. In this work, we propose training a Deep Learning network with evolutionary algorithms to classify three stages of Diabetic Retinopathy: i) no sign of diabetic retinopathy, ii) Non-proliferative dia-betic retinopathy, and iii) proliferative diabetic retinopathy. We propose a neuroevolution methodology for selecting the most efficient Deep Learning model. The results of the neuroevolution methodology were improved by including Simulated Annealing strategies, Population Reinitialization, and ensembles. With high accuracy, sensitivity, specificity, and kappa index rates of 0.889, 0.889, 0.951, and 0.822, respectively, in the best case found, the experiments show that our neuroevolution methodology for selecting the Deep Learning model hyperparameters is a competitive alternative for training deep neural networks to classify three stages of diabetic retinopathy even with a small data set.
Air Quality Time Series Forecasting Using Machine Learning Algorithms

This study aimed to develop accurate short-term air quality forecasting models for Asunción, Paraguay using machine learning algorithms. The dataset, which consisted of PM concentrations, air quality indices, meteorological variables, and traffic information, spanned a duration of 18 months. Two machine learning algorithms, Support Vector Regression (SVR) and XGBoost, implemented for window-based regression, were optimized using a grid search and Bayesian optimization process. The optimization explored the impact of the look-back window $(w)$, sampling frequency $(f)$, and prediction horizon $(h)$ on model performance. The models were evaluated through a hold-out scheme and trained and evaluated for all stations in the network. Results showed that the XGBoost model outperformed the SVR model, with a mean absolute error of 2.774, 4.643, and 7.819 for AQI 2.5 predictions using 6, 12, and 24-hour horizons, respectively. The optimization process significantly improved the performance of both models compared to using default settings. The study's findings have implications for public health decisions and could be used to issue alerts or advisories when air pollution levels are expected to be high.
Loggerhead incidental caught from Andalusia and Murcia waters during 2004

The incidental catch of sea turtles in surface drifting long-line fisheries has gained recent international attention. This gear is used to fish different species, mainly albacore, bluefin-tuna, and swordfish, and it is considered the most dangerous fishing gear for the loggerhead (Caretta caretta, Linnaeus, 1758) in the Mediterranean Sea. We estimated the incidental capture of loggerhead along the coasts of the Spanish regions of Andalucia and Murcia during 2004. The data used were compiled by scientific projects from the Spanish Oceanographic Institute (I.E.O.). The Andalusian and Murcian surface long-line fleet consists of 78 vessels from 12 to 27 m total length fishing along the year. Even more other 50 shorter boats licensed for artisanal gears to joint the fishery mainly in summer. During the observed period, 36 logger- head were incidentally captured from boats with onboard observers 8 Loggerheads (22%) died as a result of the capture. The Bycatch (or Incidental capture) per Unit Effort (BPUE) values in open Mediterranean waters of Andalusia and Murcia are low in comparison to other Mediterranean areas. However, the BPUE values in the open waters of Atlantic Andalusia are high when compared to others areas of the
Analysis of a Wind Speed Prediction Model Based on Decision Trees

Predicting wind speed is crucial for efficient wind farm management, aiming to optimize en-ergy production and ensure grid stability. This paper addresses wind speed prediction using data from a LIDAR sensor at five measurement points at different heights (40 to 260 meters). A model based on decision trees, specifically the Random Forests (TreeBagger), has been applied to predict wind speed at 10 to 60-minute intervals. The model's performance has been evaluated using the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coeffi-cient of Determination (R2) indices. The point closest to the coast (PO) yielded the best results, with RMSE ranging from 0.15 to 0.22 m/s, MAPE ranging from 0.69% to 1.51%, and R2 of 0.99. As the measurement points moved away from the coast, errors increased slightly due to variations in temperature and surface roughness. Data collection at 10-minute intervals provided a robust model training and evaluation dataset. The evaluation metrics reveal the quality of predictions regarding accuracy and generalization ability. The adopted approach provides valuable information for improving wind en-ergy prediction under specific conditions, as found at the investigated site.
Research on Influence of Rotor Eccentricity on Electromagnetic Vibration in High-speed Permanent Magnet Generators

Rotor eccentricity faults can cause uneven air gaps in the motor, reducing electromagnetic performance of the motor and increasing vibration noise, which eventually leads to the motor not operating properly, especially in high-speed motors, where the damage will be more serious.In this paper, the effect of static and dynamic eccentricity faults on the electromagnetic vibration of high-speed motors is studied using a back-wound high-speed permanent magnet generator (HSPMG) as an example. Firstly, an eccentricity mathematical model is established and the electromagnetic characteristics of static and dynamic eccentricity are analyzed using the numerical calculation method. The spatial and temporal distribution of the air gap magnetic field under the fault is obtained, revealing the change mechanism of the magnetic field asymmetry after the eccentricity fault. Secondly, based on Fourier time-harmonic analysis, the characteristic parameters of the electromagnetic force wave under different eccentricity rates are studied, and the characteristic parameters of the introduced electromagnetic force wave components after the eccentricity fault are obtained. Finally, the vibration acceleration of the generator stator is obtained by multi-physics field coupling and harmonic response analysis, and the vibration characteristics of the stator under static and dynamic eccentricity faults are determined to provide reference for the diagnosis and identification of eccentricity faults in high-speed motors.
Review on Power Factor Improvement of Vernier Machines

Vernier machines (VMs) operate on the principle of magnetic field modulation and exhibit high torque density. However, their low power factor (PF) restricts broader applications. This paper provides a thorough review that focuses on the PF improvement of VMs. This review introduces the mechanism and influencing elements of the PF, as well as the optimal design schemes for PF from three perspectives: the excitation source, the modulator, and the filter. Additionally, this paper analyzes the optimization principles of these schemes based on magnetic field modulation theory. Moreover, advancements in machine control and the utilization of advanced materials for optimizing PF are discussed. This paper observes relevant technologies, provides a roadmap, and inspire future research direction for solving the PF problem of VMs.
Illinois Climate Network: Site descriptions, instrumentation, and data management

The Illinois Climate Network (ICN) consists of 19 automated weather stations operated by the Illinois State Water Survey. Stations are located on the University of Illinois Agricultural Experiment Station Farms, the Southern Illinois University Agronomy Experiment Farms, and on community college campuses around the state. The ICN is part of the Water and Atmospheric Resources Monitoring (WARM) Network which also collects information on soil moisture, ground water, surface water, and suspended stream sediments. ICN data have been used for agricultural purposes, such as for irrigation scheduling and to evaluate the progress of insect and crop development during spring and summer. The Illinois Department of Agriculture frequently requests the wind data when evaluating spray drift complaints. ICN data are also used to assess the potential for using solar radiation and wind as alternative energy sources in Illinois. At each station a 10 meter tower equipped with weather instruments records hourly average air temperature, relative humidity, solar radiation, wind speed and direction, barometric pressure, and soil temperatures at 10 and 20 centimeters. Detailed descriptions of station sites are presented, including areal photographs of a 1,000 meter radius of each station. Hourly wind speed mean and variances were used to estimate the effect of obstructions around the weather towers on wind flow at each tower. Total hourly precipitation and hourly standard deviations of each weather variable are also recorded. The barometric pressure, including hourly maximum and minimum pressures and the time of the maxima and minima, have been recorded since early 1992. Daily data that are saved include maximum and minimum air temperature, maximum and minimum relative humidity, maximum and minimum soil temperatures, maximum wind gusts and direction, and their times of occurrence. The data are automatically retrieved from the stations once each day between midnight and 4:00 a.m. At least three times a week, a database management technician checks the data for quality control, and then the data are made available to the public on the Midwestern Climate Center’s Midwestern Climate Information System. Reference: Hollinger, Steven E. , Reinke, Beth C. , and Peppler, Randy A. Illinois Climate Network: Site Descriptions, Instrumentation, and Data Management. Illinois State Water Survey, Champaign, Circular 178. 1994. Indexing Terms: Climatic data, weather data, networks, instrumentation, monitoring, automation. STATE OF ILLINOIS HON. JIM EDGAR, Governor DEPARTMENT OF ENERGY AND NATURAL RESOURCES John S. Moore, B.S., Director BOARD OF NATURAL RESOURCES AND CONSERVATION John S. Moore, B.S., Chair Robert H. Benton, B.S.C.E. , Engineering Donna M. Jurdy, Ph.D., Geology H.S. Gutowsky, Ph.D., Chemistry Roy L Taylor, Ph.D., Plant Biology Robert L Metcalf, Ph.D., Biology W.R. (Reg) Gomes, Ph.D. University of Illinois John H. Yopp, Ph.D. Southern Illinois University STATE WATER SURVEY DIVISION John T. O’Connor, Chief 2204 GRIFFITH DRIVE CHAMPAIGN, ILLINOIS 61820-7495
Discreditation of “orthobrochantite” (IMA 78–64) as the MDO1 polytype of brochantite

“Orthobrochantite,” IMA 78–64, was originally approved by the Commission on New Minerals, Nomenclature and Classification (IMA) as the orthorhombic polymorph of brochantite. Described by Wilson W. Crook III and Stanley G. Oswald from the Douglas Hill mine, Yerington, Nevada, USA, the mineral description was never formally published; however, the name and some data have been widely available since the late 1970s. Investigation of material from the Douglas Hill mine shows “orthobrochantite” to consist mostly of the MDO 1 polytype of brochantite, but probably also to contain small domains of the MDO 2 polytype. The crystal structure of the MDO 1 polytype [ a = 13.1117(4), b = 9.8654(4), c = 6.0307(9) A, β = 103.255(7)° and V = 759.31(12) A 3 ] has been refined to R 1 = 6.37 % for 1245 unique reflections [ F o > 4σ F ] and 8.83 % for all 1724 reflections. The incorrect unit cell for “orthobrochantite” is either due to the choice of the B -centered pseudo-orthorhombic cell, which is virtually identical to the MDO 1 cell, or indexing based on {100} twinning of the MDO 1 polytype. New optical determinations for “orthobrochantite” suggest that the indices of refraction reported by Crook and Oswald were significantly in error.
Self-Supervised Learning for Point Clouds through Multi-crop Mutual Prediction

As the demand for high-level autonomous driving continues to grow, efficient processing of large amount of point cloud data from LiDAR sensors has become a severe issue. Despite its great success, it is extremely difficult, if not impossible, to apply supervised learning in many real-world applications with natural scenes lacking annotations. In addition, removing label dependency and improving algorithm generalization ability are the current key research challenges in 3D deep learning. We propose a crop-based self-supervised learning framework for point clouds called Multi-Crop Mutual Prediction (MCMP). By cropping the original point clouds into different number of multiscale patches and making patches from the same object as close as possible through dynamic updates of online prototypes, the encoder is well able to maintain consistency between both local and global features. MCMP framework is model-agnostic so it is convenient to integrate it into encoders of any architectures. We pre-train three commonly used point cloud feature extraction networks on a single object-level dataset MondelNet40 and evaluate MCMP on three downstream tasks, namely, object classification, partial segmentation and semantic segmentation. Experimental results show that MCMP outperforms the previous crop-based self-supervised approaches in the object classification and semantic segmentation tasks and produces comparable results with the supervised benchmark methods.
Characteristic Aromas of Quercus crispula (Japanese Oak)

Nature is important in Japanese culture. Many tree species produce diverse fragrances, making Japan's forests a botanical treasure. However, there have been no detailed investigations of the aroma characteristics of trees, a vital factor in forest bathing. Previously, we applied our analytical method to three tree species found mainly in Japan: Chamaecyparis pisifera, Lindera praecox, and Lindera obtusiloba Blume. In this paper, we report investigations of the aroma of Quercus crispula Blume (Japanese oak) using this analysis method. Japanese oak grows mainly in Japan. The odor compounds of Japanese oak were obtained by hexane extraction and monolithic material sorptive extraction and each extracted compound was identified by gas chromatography olfactometry and gas chromatography-mass spectrometry. Comparison of the aromas and compounds in the extracts resulted in the identification of important aroma compounds in Japanese oak: 10 (including eugenol) in the leaves and 15 (including 3-methyl-4-octanolide) in the wood.
Face Verification Explainability Heatmap Generation

Explainable Face Recognition (XFR) is a critical technology to support the large deployment of learning-based face recognition solutions. This paper aims at contributing to the more transparent usage of Vision Transformers (ViTs) for face verification (FV) tasks, by proposing a novel approach for generating FV explainability heatmaps, for both positive and negative decisions. The proposed solution leverages on the attention maps generated by a ViT and employs masking techniques to create masks based on the highlighted regions in the attention maps. These masks are applied to the pair of faces, and the masking technique with most impact on the decision is selected to be used to generate heatmaps for the probe-gallery pair of faces. These heatmaps offer valuable insights into the decision- making process, shedding light on the most important face regions for the verification outcome. The key novelty of this paper lies in the proposed approach for generating explainability heatmaps tailored for verification pairs in the context of ViT models, which combines the ViT attention maps regions of the probe-gallery pair to create masks that allow evaluating those region's impact on the verification decision for both positive and negative decisions.
Iranian Journal of Educational Sociology

of Pearson correlation coefficients and multiple regression with enter model in SPSS software. Findings: The findings showed that all three variables of academic self-efficacy, academic support and academic engagement had a significant negative relationship with school anxiety in first secondary students (P<0.01). Also, the three mentioned variables significantly were able to predict 56.5 percent of school anxiety changes in first secondary students (P<0.001). Conclusion: The results of this study have practical implications for people who deal with students, and they to reduce school anxiety in students can increase their academic self-efficacy, academic support and academic engagement through educational workshops.
Chunk-BERT: Boosted keyword extraction for long scientific literature via BERT with chunking capabilities

Accurately obtaining the domain intellectual in scientific research literature is crucial in light of the academic research literature’s fast growth. Therefore, keyword extraction technology has been placed on high hopes. Keyword extraction (KE), which is a fundamental textual information processing activity, tries to extract from texts words, keywords, or phrases that can sum up the subject matter. Bidirectional Encoder Representation from Transformers (BERT) is widely used for unsupervised keyword extraction tasks. However, when dealing with lengthy scientific literature, the BERT model’s inherent input length limitation inevitably leads to the issue of missing semantic information, and the ability of local feature extraction is insufficient. To solve the two problems, we proposed Chunk-BERT model, which extracts the features of each position of the scientific literature as a block embedding, and graph-based algorithms are added to strengthen local information. We carried out textual information-processing experiments on SemEval2010, NUS, ACM database and F1 values respectively increased by the maximum of 6.87%, 1.03% and 1.39%, which indicating the effectiveness of the proposed Chunk-BERT.
Scale-arbitrary Infrared Super-resolution Network based on Channel Attention Mechanisms

In our engineering application, it is necessary to up-scale low-resolution infrared images to the size of high-resolution visible light images. We have studied the latest single image superresolution technology based on deep neural networks(CNNs) and found that there are two main practices to improve superresolution performance: increasing the depth of the network and applying attention mechanisms. Therefore, we design a channel attention interaction(CAI) module at the head of the network and channel attention feature groups(RCAFGS) based on the progressive feature fusion(PFF) strategy in the network backbone, which can ease the gradient vanishing problem caused by increasing the depth and treat channel features wisely. And we adopt the scale-aware feature adaptation module and scaleaware upsampling module for tackling the problem of the non-integer asymmetric size. Finally, we propose one scale-arbitrary infrared super-resolution network based on channel attention mechanisms.
Effects of environmental shear and buoyancy on simulated supercell interactions

This study examines the role that buoyancy and vertical wind shear play in modulating the relationship between storm interactions and storm severity. Using an idealized numerical model, 240 supercell interactions are simulated under systematically varied amounts of buoyancy and vertical wind shear. Small changes in buoyancy or vertical wind shear have significant impacts on post-interaction storm morphology. A wide amount of variation in low-level rotation is seen across the simulation suite. Two-cell storm simulations are not always stronger than one-cell control simulations. Migration of low-level vertical vorticity centers is ubiquitous through all runs, but orientation of and interaction between two storms’ gust fronts modulates where the vorticity center will end up. Gust fronts in better alignment have more vorticity centers reach an updraft where they are stretched and intensified. With respect to storm mode, higher buoyancy produced less classic supercells while higher shear produced more classic supercells. High precipitation supercells were favored with two-cell simulations where the second cell was directly to the southwest of the control cell. Secondary cells that were close to the control merged quickly and were often stronger than simulations with large cell separation distance. Further questions remain with trajectories and machine learning algorithms are tthe next steps for a more detailed analysis of this large data set.
Identifying Autism Spectrum Disorder Using Brain Networks: Challenges and Insights

Autism Spectrum Disorder (ASD) affects a large portion of the global population both directly and indirectly. The biological etiology of the disorder is not sufficiently understood, and current diagnoses rely on behavioural indicators which do not provide a reliable basis for diagnosis until about 2 years of age. Identifying a biological marker of ASD would aid in understanding the disorder and potentially allow for earlier, more objective diagnoses and treatments to improve the quality of life of individuals possessing ASD. The analysis of functional connectivity in the brain using functional Magnetic Resonance Imaging (fMRI) has been identified as a promising method for discovering such biological markers. This study recreated a prominent state-of-the-art work in explainable classification of brain networks, but found results inconsistent with what was claimed. The methods were modified in various ways to improve accuracy and performance. A new, simpler method named Discriminative Edges (DE) was developed which achieved similar accuracies with improved performance and explainability. DE was also adapted to receive raw correlation matrices as well as thresholded correlation matrices representing brain networks, and it was found that raw correlation matrices provided more useful information for classification. An imple-mentation package was provided to aid future researchers in validating and improving upon these results. Suggestions for future work based on the findings of this study were provided, the most important being to procure more datasets, discover data-driven subcategories of ASD, and maintain reproducibility in studies.
Journal Pre-proof Metabolic regulation of ferroptosis in the tumor microenvironment

: Ferroptosis is an iron-dependent, non-apoptotic form of regulated cell death triggered by impaired redox and antioxidant machinery and propagated by the accumulation of toxic lipid peroxides. A compendium of experimental studies suggest that ferroptosis is tumor-suppressive. Sensitivity or resistance to ferroptosis can be regulated by cell-autonomous and non-cell-autonomous metabolic mechanisms. This includes a role for ferroptosis that extends beyond the tumor cells themselves, mediated by components of the tumor microenvironment, including T cells and other immune cells. Herein, we review the intrinsic and extrinsic factors that promote the sensitivity of cancer cells to ferroptosis and conclude by describing approaches to harness the full utility of ferroptotic agents as therapeutic options for cancer therapy.