Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward an EEG-Based System for Monitoring Cognitive Load in Neurosurgeons

In this study, a method combining statistical and machine learning approaches is proposed to select the most informative EEG features for the detection of the cognitive load linked to fine motor activities, during a Purdue Pegboard Test (PPT). The proposed method is validated by an experimental case study on neurosurgeons monitored by means of a wearable electroencephalographic (EEG) acquisition system during PPT execution at four increasing levels of complexity. EEG features of cognitive workload related to fine motor activity are identified by a Speraman rank correlation analysis and Friedman test. The EEG features selected by the statistical approach are provided as input to the machine learning algorithms. Classification accuracy is the metric adopted to validate the results of feature selection. Among the tested classifiers, the k-Nearest Neighbor (k-NN) reaches 53.3 ± 4.5% average accuracy in detecting the four levels of cognitive load.
Análisis de dependencias no lineales utilizando redes neuronales artificiales

En este articulo, se desarrolla una nueva tecnica para detectar dependencias no lineales en series temporales, basadas en el uso de una red neuronal autorregresiva y el concepto de coeficiente de correlacion. Teniendo en cuenta que el modelo de redes neuronales utilizado es capaz de aproximar cualquier funcion en un dominio compacto, las medidas propuestas son capaces de detectar no-linealidades en los datos. Nuestra tecnica es probada para varios conjuntos de datos tanto simulados como reales, y comparada con las funciones clasicas de autocorrelacion simple y parcial; los resultados muestran que en los casos lineales, las medidas propuestas tienen un comportamiento similar a las autocorrelaciones simple y parcial, pero en los casos no-lineales ellas son capaces de detectar otras relaciones no-lineales.
The role of anti-arrhythmic drugs and ablation in arrhythmogenic right ventricular cardiomyopathy

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disease characterized by fibrofatty replacement of the ventricular myocardium, leading to ventricular arrhythmias and sudden cardiac death. Treating this condition can be challenging due to progressive fibrosis, phenotypic variations and small patient cohorts limiting the feasibility of conducting meaningful clinical trials. Although widely used, the evidence base for anti-arrhythmic drugs is limited. Beta-blockers are theoretically sound, yet their efficacy in reducing arrhythmic risk is not robust . Additionally, the impact of sotalol and amiodarone is inconsistent with studies reporting contradictory results. Emerging evidence suggests that combining flecainide and bisoprolol may be efficacious. Radiofrequency ablation has shown some potential in disrupting ventricular tachycardia circuits, with endo/epicardial ablation yielding better results when done jointly, possibly as an index procedure. In addition, stereotactic radiotherapy may be a future option that can decrease arrhythmias beyond simple scar formation by altering levels of Nav 1.5, Cx 43, and Wnt signaling, potentially modifying myocardial fibrosis. Future therapies, such as adenoviruses and GSk3b modulation, are still in early-stage research. While implantable cardioverter-defibrillator (ICD) implantation is the key mechanism for reducing arrhythmic death, the risks of inappropriate shocks and device complications must be carefully considered.
Learning From Alarms: A Novel Robust Learning Approach to Learn an Accurate Photoplethysmography-Based Atrial Fibrillation Detector using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms

Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health implications. Passive monitoring using photoplethysmography (PPG) is desirable for long-term detection of AF. Deep neural networks (DNNs) show promise, but it requires massive training data with clean label, which is hard to obtain. To address the challenge, in this study, a large-scale dataset is created using PPG signals from hospital monitors, and each PPG signal is automatically annotated by concurrent alarms. However, the labels of collected PPG can be noisy because of the existence of false alarms. Then a novel loss function, cluster membership consistency (CMC) loss, is introduced to handle label noise caused by inaccurate PPG labels. The proposed approach shows superior performance in handling label noise and poor-quality signals. This novel approach is shown to be effective in further improving the model performance. It achieves superior or comparable results when evaluated against five different state of the art robust learning algorithms for noisy labels, while at the same time maintains computational efficiency advantage.
PPG Signal-Based Classification of Blood Pressure Stages Using Wavelet Transformation and Pre-Trained Deep Learning Models

This article proposes a new method for identifying photoplethysmography (PPG) data, a non-invasive blood pressure (BP) measurement. Individual characteristic inconsistencies hinder PPG signal feature extraction. An innovative method for extracting PPG characteristics using continuous wavelet transform (CWT) and transfer learning is presented in this research. Pre-trained deep learning models like InceptionV3, VGG-16, and ResNet101 use robust characteristics to assess BP severity. The major purpose of this approach is to classify blood pressure values into average, pre-hypertension, and hypertension stages 1 and 2. The proposed method was trained on 219 individuals' PPG datasets. At the same time, InceptionV3, VGG-16, and ResNet101 had 99.5%, 22.5%, and 92.5% accuracy. Despite that, the study's findings reveal classifier behavior and how to improve the model's assessment metrics by utilizing category variances and modifying deep-learning network parameters. The proposed method for noninvasive blood pressure assessment utilizing PPG signals appears promising. Based on these findings, non-invasive blood pressure monitoring systems can be improved.
DIGITAL ECOSYSTEM: TREND IN STRATEGIC DEVELOPMENT OF RUSSIAN COMPANIES

In the 21 st century, with the widespread development of the digital economy, there is a tendency for companies to create digital business ecosystems. This intention of big companies is explained by the desire to get more profit in a competitive environment by attracting more consumers and some other factors. The development of the digital business ecosystem market is also stimulated by consumer demand for such services, since in most cases the use of digital ecosystem services is convenient and beneficial for the user. Nevertheless, the authors had considered possible risks associated with the prospects for the development of business ecosystems in the consumer market, such as troubled market’s entry to small businesses and decrease the consumer’s freedom of choice due to market’s oligopolization. The authors conducted comparative analysis of different domestic approaches to study the issue of regulating the activities of digital business ecosystems. Paper examines the evolution of digital business ecosystems in Russia using the examples of Yandex and Sberbank, two Russian largest companies developing their own digital ecosystems and creating competing services. The study explores current companies’ positions on Russian market through the analysis of financial activities, as well as a comparative analysis of companies' policies in the field of strategic development. The forecast for Yandex and Sberbank based on the analysis in the field of digital business ecosystems was considered, as well as an overview of further directions for the development of digital business ecosystems for Russian companies, paying special attention to the peculiarities of Russian economy and legislation.
TRACE: Tennessee Research and Creative TRACE: Tennessee Research and Creative Exchange Exchange

Abiotic and biotic variation has been shown to be important in regulating nutrient cycling and belowground communities in natural systems. However, genetic variation in dominant plants as a driver of rates of nutrient cycling is still poorly understood and few studies have looked at genotype interactions across multiple environments. Using Populus angustifolia and a common garden approach, we hypothesized that all three factors: tree genetic variation, environmental conditions and genetic by environment (G x E) interactions would affect soil carbon (C) storage and nitrogen (N) cycling. Replicated copies of five different reciprocally planted Populus genotypes were studied in three separate 18-21 year old common gardens at different elevations (1300m, 1384m and 1587m) in northern Utah, to measure the genotype and environmental effects on pools of soil C and N as well as rates of soil net N nitrification and net mineralization. Our results indicate that genotypes influence pools of soil C, total N and C:N, but genotype did not influence net rates of nitrogen mineralization. Environmental variation significantly influenced pools of soil C, total N, soil C:N and rates of net nitrification and net N mineralization. As predicted, G x E interactions significantly influenced both pools and processes of soil C and N cycling. Overall, we found that genetic variation in plant traits (tree diameter and leaf/root chemistry) as well as soil texture across gardens were significant predictors of soil C and N pools and fluxes across seasons. These data help us understand the relative role of genotypic variation on above-and belowground interactions in different environments and the consequences of these interactions on ecosystem processes. The results from this study show that across an environmental gradient Populus angustifolia genotypes can influence nitrogen mineralization through feedbacks between environmental variation, tree phenotype and soils.
Benchmarking the Reliability of Sentinel-2 Satellite Data for Estimating Vineyard NDVI and LAI Parameters through UAV LiDAR and Multispectral Imagery

: The use of satellite data in precision agriculture, especially for woody crops, has gained prominence. Such data aids in forecasting yield, predicting crop quality, and irrigation management by providing insights into crop conditions. However, the accuracy of parameters such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) derived from satellites is questioned. This research compares Sentinel-2 satellite imagery with LiDAR data and multispectral imagery gathered over a vineyard in northern Spain using Unmanned Aerial Vehicles (UAVs) in July, August, and September 2022, focusing on veraison, a key stage in precision viticulture. The findings reveal a moderate correlation between satellite and UAV NDVI values (up to R 2 = 0.6) but a discrepancy in leaf area estimations from satellite imagery, suggesting its limited use for such applications in hedgerow systems in agriculture. Nonetheless, satellite data ’ s ability to detect crop spatial variability remains useful for field management. The study emphasizes the potential benefits and drawbacks of diversifying remote sensing techniques for effective agricultural management.
INVESTIGATION Preschoolers’ dental caries: a cross-sectional-population-based study in Montevideo, Uruguay

Prevalence/extent/severity of caries, registered according to ICDAS, in 5-year-old children from Montevideo-Uruguay. Population-based cross-sectional study of 614 children from public/private schools. The association between predictor variables, prevalence/extent of caries was evaluated using Poisson regression. Prevalence 70.9%(95%CI=66.40-75.50) and extension 10.0(95%CI=8.70-11.30) surfaces. The prevalence showed significant differences between categories of schools, SEC, educational level of the mother (NEM) and diet
Decoupling Classiﬁer for Boosting Few-shot Object Detection and Instance Segmentation

This paper focus on few-shot object detection (FSOD) and instance segmentation (FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances. The existing methods severely suffer from bias classiﬁcation because of the missing label issue which naturally exists in an instance-level few-shot scenario and is ﬁrst formally proposed by us. Our analysis suggests that the standard classiﬁcation head of most FSOD or FSIS models needs to be decoupled to mitigate the bias classiﬁcation. Therefore, we propose an embarrassingly simple but effective method that decouples the standard classiﬁer into two heads. Then, these two individual heads are capable of independently addressing clear positive samples and noisy negative samples which are caused by the missing label. In this way, the model can effectively learn novel classes while mitigating the effects of noisy negative samples. Without bells and whistles, our model without any additional computation cost and parameters consistently outperforms its baseline and state-of-the-art by a large margin on PASCAL VOC and MS-COCO benchmarks for FSOD and FSIS tasks. 1
INFLUENCE OF FERTILIZING AND TILLAGE SYSTEMS ON HUMUS CONTENT OF TYPICAL CHERNOZEM

The study aimed to measure the effect of tillage and fertilizing systems in short crop rotation on the content, and сomposition of humuse’s substances in soil’s type as typical chernozem of the zone Forest-Steppe of Ukraine. The highest humus content was determined in the variant with the application of 11.5 t ha –1 of organic fertilizers + N 27 P 38 K 45 kg ha –1 . The humus content in the 0–10 cm layer against the background of shallow tillage without a moldboard was 4.25%, with moaldboard-subsurface tillage – 4.23%. These tillage systems have the most favourable effect on the formation and humus matter (t ha –1 ). The humus storage in the variant of shallow subsurface tillage was 41.4 t ha –1 , i.e. 1.3 t less than in differentiated tillage. The use of soil without fertilizing led to significant humus losses and deterioration of the compound of humus substances due to mineralization processes that prevail over immobilization processes. When applying 11.5 t of organic fertilizers and (N 27 P 38 K 45 ) mineral fertilizers per hectare of crop rotation area, the number of humic substances increased significantly and the amount of fulvic acids in the top layer decreased. The ratio of HA (Humic acids) to FA (Fulvic acids) is 2.48. The type of humus formation is humus-fulvatic.
CLIMATE CHANGE DEBATES

Join the discussion of this article online at http://tiny.cc/clichng In one of the earliest and most eloquent pleas for open discussion and debate, John Milton wrote: And though all the winds of doctrine were let loose to play upon the earth, so Truth be in the field, we do injuriously, by licensing and prohibiting, to misdoubt her strength. Let her and Falsehood grapple; who ever knew Truth put to the worse, in a free and open encounter. (1) Two centuries after Milton, in the same year in which Charles Darwin published the Origin, John Stuart Mill's essay On Liberty (2) added further arguments for the free exchange of ideas, suggesting that such exchange is vital for intellectual and social health. Although both Milton and Mill stand behind our current acquiescence in the value of extensive free discussion, both of them knew that they were opposing ancient suspicions about the viability of democracy. The political theorists and philosophers of the Greco-Roman world viewed ordinary folk as vulnerable to deception and exploitation. Allowed to determine the direction of the state, the folk would be easily seduced into believing falsehoods aligned with the interests of charismatic leaders, so that the popular voice would enthusiastically clamor for disastrous policies. Better, then, to entrust the ship of state to wise navigators, whose wisdom embraced both depth of understanding and moral integrity. The contrast between these two perspectives on public discussion and policy bears on our own times, although the risks may affect our species as a whole and the stakes may be far higher. For three decades, prominent climate scientists have been warning of the dangerous effects of the continual emission of greenhouse gases into Earth's atmosphere. They have been attempting to identify and to explain just what those effects The Climate Change Debates
Evaluation of Optimal Insertion Sites and Angles for Orthodontic Mini-Implants at the Anterior Nasal Spine Region Based on Cone-Beam Computed Tomography

Background: To infer the optimal angulation and height level of mini-implant insertion in the anterior nasal spine (ANS) region from alveolar bone depth (BD) and labial cortical bone thickness (CBT) through cone-beam computed tomography (CBCT), the influences of sex, age, and growth pattern on BD, CBT, and the starting point of the ANS were examined. Material and methods: BD and CBT were measured on CBCT median sagittal images at specific angles (0, 15, 30, 45, and 60 degrees) to simulate the angulation of insertion. The height level of the first axial image with obvious bone ridge was recorded as the start of ANS. Results: The average height of ANS start was 9.42 mm from the alveolar bone crest between the central incisors. The variations in height level and insertion angle combined, or either of them individually, significantly influenced BD and CBT, demonstrating overall decreasing trends (p < 0.001). BD was not influenced by sex, age, or growth pattern (p > 0.05). However, CBT was found to be correlated with sex and growth pattern, while the height level of ANS start was associated only with growth pattern, particularly in hypodivergent patients (p < 0.05). Conclusion: The recommended insertion height level is 14–16 mm from the alveolar bone crest and the recommended angulation is 30–45°. An innovative inequity in the relationship between a certain height level and the insertion angulation is proposed.
Response of Tree-Ring Width of Pinus tabulae for mis to Climate Factors in Luoshan Mountains of Ningxia

Tree-ring cores are sampled at lower and upper forest border of pure forests of Pinus tabulaeformis in Luoshan Mountains of Ningxia.The authors develop the tree-ring width index standard chronologies and analyze the statistical characters of two sites' chronologies.After anglicizing the response relationship between tree-ring width and precipitation,temperature and moisture index(P/T),it is found out that the standard chronologies of two sites are all significantly related to March,May,spring and the annual precipitation and July temperature.There is a significant relation between the tree-ring width and the prior September precipitation,autumn precipitation and summer temperature at lower forest border.Based on the size of the correlation coefficient and the number of significant factors,it is pointed out that the tree-ring width index standard chronology at lower forest border is relatively sensitive to the precipitation and temperature than that at upper forest border.Furthermore the tree-ring width trends are all positively correlative with the humid index,and the correlation coefficient between the tree-ring width standard chronology at upper forest border and March to August moisture index reaches 99% level.The standard chronology at upper forest border is relatively sensitive to the combination of water and heat.
DECLOUDING TIME SERIES OF LANDSAT DATA

Novel schemes based on multiresolution transforms were introduced to pre-process long time series of Landsat data. Particularly, removal of clouds and their shadows was tackled. We applied the product of wavelet scales to generate binary masks of corrupted observations, the robust smoother-cleaner wavelets to remove outliers in the data, and the wavelet shrinkage to estimate new values. Cloud contamination was simulated and the missing values were estimated using five methods: 1) mean value, 2) minimum value, 3) maximum value, 4) linear regression, and 5) the wavelet-based procedure. The product of wavelet scales not only identified clouded and shadowed pixels but also other anomalies like misregistration effects and changes of short duration (e.g., burn scars). The wavelet-based approach was more accurate for interpolating the missing values in clouded areas, whereas linear regression performed better in shadowed areas. The robust non-linear wavelet regression holds promise for effective time series analysis and has the potential to produce noise-reduced images at any point in the time series.
PETROTECTONIC CHARACTERIZATION OF THE KOKCHETAV MASSIF AND THE DABIE-SULU TERRANE- ULTRAHIGH-P METAMORPHISM IN THE SO-CALLED P-T FORBIDDEN ZONE

The Kokchetav Massif of northern Kazakhstan (metamorphosed at 540 - 530 Ma) and the Dabie-Sulu terrane of east central China (metamorphosed at 240-210 Ma) are characterized by their large geographical extent, widespread trace occurrences of UHP index minerals such as microdiamond and coesite, and P-T estimates within a field of extremely low geothermal gradient. The exhumed UHP/HP units occur as thin subhorizontal slabs, bounded by normal faults on the top, and reverse faults on the bottom. For the Kokchetav Massif, microdiamond and coesite are best preserved in supracrustal rocks (pelitic-psammitic gneiss, dolomitic marble and whiteschist); minor inclusions of coesite and its pseudomorphs occur in mafic eclogites. Rare Ti-clinohumite-bearing garnet peridotite appears to represent crustal-derived Fe-Ti ultramafics or slab/mantle metasomatic products. P-T estimates for diamondgrade eclogite and garnet peridotite based on the K2O-in-Cpx geobarometer yield maximum P-T conditions of 7 GPa, 1000 o C. On the other hand, UHP records for the Dabie-Sulu belt are mainly from mafic eclogite and ultramafic garnet peridotite within felsic gneissic units. Only traces of diamond were reported in eclogite and garnet peridotite, whereas gneissic rocks contain minor coesite inclusions in zircon. Hydrous phases and magnesite are common in various Dabie-Sulu UHP rocks. Both mantle-derived and crustal-hosted garnet peridotites yield very high-P estimates (4.0-6.5 ± 0.2 GPa at 750-950 o C). The occurrence of majoritic garnet and high-P clinoenstatite in garnet peridotite is consistent with their deep upper mantle origin.
Waypoint Transfer Module between Autonomous Driving Maps Based on LiDAR Directional Sub-Images

Lane graphs are very important for describing road semantics and enabling safe autonomous maneuvers using the localization and path-planning modules. These graphs are considered long-life details because of the rare changes occurring in road structures. On the other hand, the global position of the corresponding topological maps might be changed due to the necessity of updating or extending the maps using different positioning systems such as GNSS/INS-RTK (GIR), Dead-Reckoning (DR), or SLAM technologies. Therefore, the lane graphs should be transferred between maps accurately to describe the same semantics of lanes and landmarks. This paper proposes a unique transfer framework in the image domain based on the LiDAR intensity road surfaces, considering the challenging requirements of its implementation in critical road structures. The road surfaces in a target map are decomposed into directional sub-images with X, Y, and Yaw IDs in the global coordinate system. The XY IDs are used to detect the common areas with a reference map, whereas the Yaw IDs are utilized to reconstruct the vehicle trajectory in the reference map and determine the associated lane graphs. The directional sub-images are then matched to the reference sub-images, and the graphs are safely transferred accordingly. The experimental results have verified the robustness and reliability of the proposed framework to transfer lane graphs safely and accurately between maps, regardless of the complexity of road structures, driving scenarios, map generation methods, and map global accuracies.