Miracle-dz/UniFAR-FTUs huggingface dataset:
Vine-Winery Byproducts as Precious Resource of Natural Antimicrobials: In Vitro Antibacterial and Antibiofilm Activity of Grape Pomace Extracts against Foodborne Pathogens

Grape pomace is the main by-product of vine-winery chains. It requires adequate treatment and disposal but is also an economically underused source of bioactive plant secondary metabolites. This study aimed to investigate the antibacterial effects of polyphenolic extracts from Aglianico (Vitis vinifera L.) grape pomace. In particular, hydroethanolic extracts obtained via an ultrasonic-assisted extraction technique were selected for antimicrobial tests. The extracts were screened for their antibacterial effects against foodborne pathogens that were both Gram-positive, in the case of Staphylococcus aureus and Bacillus cereus, and Gram-negative, in the case of Escherichia coli and Salmonella enterica subsp. enterica serovar Typhimurium, showing variable bacteriostatic and bactericidal effects. In addition, our results demonstrated that the tested grape pomace extracts can reduce the inhibitory concentration of standard antibiotics. Interestingly, selected extracts inhibited biofilm development by S. aureus and B. cereus. Overall, these new insights into the antibacterial properties of grape pomace extracts may represent a relevant step in the design of novel therapeutic tools to tackle foodborne diseases, and in the management of resistant biofilm-related infections.
Overview of ASDEX Upgrade results

The ASDEX Upgrade (AUG) programme is directed towards physics input to critical elements of the ITER design and the preparation of ITER operation, as well as addressing physics issues for a future DEMO design. Since 2015, AUG is equipped with a new pair of 3-strap ICRF antennas, which were designed for a reduction of tungsten release during ICRF operation. As predicted, a factor two reduction on the ICRF-induced W plasma content could be achieved by the reduction of the sheath voltage at the antenna limiters via the compensation of the image currents of the central and side straps in the antenna frame. There are two main operational scenario lines in AUG. Experiments with low collisionality, which comprise current drive, ELM mitigation/suppression and fast ion physics, are mainly done with freshly boronized walls to reduce the tungsten influx at these high edge temperature conditions. Full ELM suppression and non-inductive operation up to a plasma current of = I 0.8 p MA could be obtained at low plasma density. Plasma exhaust is studied under conditions of high neutral divertor pressure and separatrix electron density, where a fresh boronization is not required. Substantial progress could be achieved for the understanding of the confinement degradation by strong D puffing and the improvement with nitrogen or carbon seeding. Inward/outward shifts of the electron density profile relative to the temperature profile effect the edge stability via the pressure profile changes and lead to improved/decreased pedestal performance. Seeding and D gas puffing are found to effect the core fueling via changes in a region of high density on the high field side (HFSHD). The integration of all above mentioned operational scenarios will be feasible and naturally obtained in a large device where the edge is more opaque for neutrals and higher plasma temperatures provide a lower collisionality. The combination of exhaust control with pellet fueling has been successfully demonstrated. High divertor enrichment values of nitrogen ⩾ E 10 N have been obtained during pellet injection, which is a prerequisite for the simultaneous achievement of good core plasma purity and high divertor radiation levels. Impurity accumulation observed in the all-metal AUG device caused by the strong neoclassical inward transport of tungsten in the pedestal is expected to be relieved by the higher neoclassical temperature screening in larger devices.
Streamline Tracing Methods Based on Piecewise Polynomial Pressure Approximations

In this paper, a unified approach for developing streamline tracing method is proposed based on piecewise polynomial pressure approximation functions. It is designed for the numerical schemes that solve the pressure solution at grid blocks while the interior velocity field remains unknown. The suitable velocity approximation functions are derived through analytical differentiation of pressure functions. They better represent the relationship between velocity field and pressure distribution in reality, satisfy the Laplace equation everywhere in a grid block, and ensure local mass conservation and normal flux continuity. Based on different polynomial pressure functions, the Trilinear/Bilinear and Cubic streamline tracing methods are developed. Additionally, a piecewise parabolic velocity reconstruction method is proposed to extend the application of the Cubic method to first‐order numerical schemes. The accuracy and efficiency of the newly proposed methods are evaluated through comparing it with the Pollock and the high‐order method in terms of velocity approximations and computational cost in numerical cases. Comparison results indicate that the Cubic method delivers the most accurate results at the same computational cost.
DeepCBS: shedding light on the impact of mutations occurring at CTCF binding sites

CTCF-mediated chromatin loops create insulated neighborhoods that constrain promoter-enhancer interactions, serving as a unit of gene regulation. Disruption of the CTCF binding sites (CBS) will lead to the destruction of insulated neighborhoods, which in turn can cause dysregulation of the contained genes. In a recent study, it is found that CTCF/cohesin binding sites are a major mutational hotspot in the cancer genome. Mutations can affect CTCF binding, causing the disruption of insulated neighborhoods. And our analysis reveals a significant enrichment of well-known proto-oncogenes in insulated neighborhoods with mutations specifically occurring in anchor regions. It can be assumed that some mutations disrupt CTCF binding, leading to the disruption of insulated neighborhoods and subsequent activation of proto-oncogenes within these insulated neighborhoods. To explore the consequences of such mutations, we develop DeepCBS, a computational tool capable of analyzing mutations at CTCF binding sites, predicting their influence on insulated neighborhoods, and investigating the potential activation of proto-oncogenes. Futhermore, DeepCBS is applied to somatic mutation data of liver cancer. As a result, 87 mutations that disrupt CTCF binding sites are identified, which leads to the identification of 237 disrupted insulated neighborhoods containing a total of 135 genes. Integrative analysis of gene expression differences in liver cancer further highlights three genes: ARHGEF39, UBE2C and DQX1. Among them, ARHGEF39 and UBE2C have been reported in the literature as potential oncogenes involved in the development of liver cancer. The results indicate that DQX1 may be a potential oncogene in liver cancer and may contribute to tumor immune escape. In conclusion, DeepCBS is a promising method to analyze impacts of mutations occurring at CTCF binding sites on the insulator function of CTCF, with potential extensions to shed light on the effects of mutations on other functions of CTCF.
Censorship: A Reaction to Disinformation on the World Wide Web

The Internet and the popularization of smartphones in the 21st century have given instantaneity to information. By these same digital means –imbued with the power to influence politics, the market, culture, and health– disinformation blossoms. Censorship, understood as the suppression of content and/or suspension of users on social media, has been used as one way to combat disinformation on the Web. This drive to sanitize digital networks carries inherent risks. In the context of ‘Infodemics,’ investment in user’s media education should be encouraged. As methodology, the phenomenon of disinformation on the Web and the efforts to curb it were researched in both scientific literature and in Brazilian legislation. To combat disinformation, the users’ education (in a sense of enabling them to filter, understand, and interpret the information that they gather) should be the main goal, as entrusting this task to third parties could bring undesirable side effects. Meanwhile, platforms, traditional media, and governments pose as ‘Guardians of the Truth. ’ The social impacts of disinformation and of the efforts to suppress false content on the Web are discoursed in this paper through usage of MapReduce Text Mining. The paper concludes that disinformation takes on many connotations –from humorous appeal to manipulation–. As long as the users’ informational competence has not been developed, both the platforms as well as governments must act to minimize the undesirable effects of this phenomenon.
Interpretation of Factors Controlling Low Cloud Cover and Low Cloud Feedback Using a Unified Predictive Index

AbstractThis paper reports a new index for low cloud cover (LCC), the Estimated Cloud-Top Entrainment Index (ECTEI), which is a modification of estimated inversion strength (EIS) and takes into account a cloud top entrainment (CTE) criterion. Shipboard cloud observation data confirm that the index is strongly correlated with LCC. We argue here that changes in LCC cannot be fully determined from changes in EIS only, but can be better determined from changes in both EIS and sea surface temperature (SST) based on the ECTEI. Furthermore, we argue that various proposed predictors of LCC change, including the moist static energy vertical gradient, SST, and mid-level clouds, can be better understood from the perspective of the ECTEI.
Compositional variation of chrome spinels in the ore-bearing zones of the Kraka ophiolite and the chromitite origin

The article considers a chemical variation of accessory and ore-forming chrome spinels from the Kraka ultramafic massif at the different scales, from the deposit to the thin section. A correlation analysis of compositional and structural features of ultramafic rocks and ores was performed. The ultramafic rocks and chromitites in the studied massif show the distinct deformation structures and tectonite olivine fabric. A typical chemical gap (i.e. Cr#=Cr/(Cr+Al)) was observed between peridotite, on the one hand, and dunite and chromitite, on the other hand, on the scale of deposits and ore-bearing zones. The location and size of this gap depend on the type of deposit. The gap becomes wider from the disseminated tabular bodies to the typical podiform ones. It has been found that in the thin initial dunite veinlets in peridotite the chrome spinels chemistry changes gradually and there is no Cr# gap between peridotite and dunite. The dunite venlets show a strong olivine fabric, which is an evidence of their high-temperature plastic flow origin. It has been revealed that new chrome spinel grains previously formed as rods or needles and then coarsened. We explained this observation as the result of impurity segregation, coalescence and spheroidization induced by the plastic deformation of olivine. It is inferred that a solid crystal flow is the main requirement for the dunite and chromitite body formation in the Kraka ophiolite massif. In the solid stream, the mineral phase separation takes place. For example, olivine and orthopyroxene grains of parental peridotite separate from one another, and weaker (more mobile) olivine grains form dunite bodies in which chromitite appears as a result of impurity segregation. deposits and structural features of chromitite (c – h): a – cross-sections through Menzhinsky deposit and compositional variations of spinels, 1–5 – chrome ore, 1 – massive (>80% chromite grains), 2–4 – disseminated with different chromite content (2 – 50–80 %, 3 – 30–50 %, 4 – 15–30 %), 5 – chromite-bearing dunite, 6 – dunite, 7 – peridotite; D – dunite, Chrt – chromitite, Hb-Lc – spinel peridotite between lherzolite and harzburgite in mineralogy, Pl-Sp Lc – plagioclase-spinel lherzolite; b – cross-sections through Bolshoj Bashart deposit; c – h – structural features of chromitite and their relationship with dunite; c, d, g, h – boudinage structures on different scale, e, f – «snow-ball» textures showing a mechanism of chromite grains segregation within solid plastic flow
ANALYSIS OF THE NUMBER INFANT AND MATERNAL MORTALITY IN CENTRAL JAVA INDONESIA USING SPATIAL-POISSON REGRESSION

Maternal and infant mortality are one of the most dangerous problems of the community since it can profoundly affect the number and composition of the population. Currently, the government has been taking heed on the attempt of reducing the number of maternal and newborn mortality in Central Java which requires data and information entirely. Poisson regression is a nonlinear regression that is often used to model the relationship between response variables in the form of discrete data with predictor variables in the form of discrete or continuous data. In space analysis, GWPR is one of method in space modeling which can model regional-based regression. It is based on some factors including the number of health facilities, the number of medical personnel, the percentage of deliveries performed with non-medical assistance; the average age of a woman's first marriage; the average education level of married women; average amount of per capita household expenditure; percentage of village status; the average rate of exclusive breastfeeding; percentage of households that have clean water and the percentage of poor people. Based on the analysis, it is revealed that the determinants of maternal and infant mortality in Central Java using Poisson and GWPR models, among others are the number of health facilities, the number of medical personnel, the average number of per capita household expenditure and the percentage of the poor. In the maternal and infant mortality model, the AIC value of GWPR model produces better modeling than Poisson regression. Keywords: Maternal and Infant mortality, Poisson, GWPR
A Two-Stream Stacked Autoencoder With Inter-Class Separability for Bilinear Hyperspectral Unmixing

Deep learning-based hyperspectral unmixing (HU) is getting increasing attention in the field of remote sensing, aiming at endmember extraction and abundance estimation at pixel scale. However, many existing deep learning-based unmixing methods base on linear mixing models, neglecting complex nonlinear light scattering interactions. Furthermore, these methods often treat all spectral bands indiscriminately, ignoring characteristic differences between endmembers, hampering endmember separation. To address these issues, we present BU-Net, a novel approach for HU based on the generalized bilinear mixing model (GBM), which is a two-stream stacked autoencoder architecture designed to enhance inter-class separability. In the encoder, we employ 3D convolutions with multiple receptive field to extract multiscale spatial and spectral features simultaneously. Additionally, we design a novel band selection based on inter-class separability (BSICS), which identifies bands with inter-class separability (BICS) and the obtained bands are taken as an additional stream for improving performance. In the decoder, BU-Net develops a two-stream structure encompassing linear and bilinear elements, aligning with the theoretical components and constraints of GBM. To further enhance separability between endmembers during training, we use the spectral angle distance between BICS and its reconstruction as a loss regularization term. Moreover, we utilize materials' representative pixels obtained in the process of BSICS to initialize endmembers, which offers effective guidance for modeling the spectral properties. Experimental results on synthetic and real hyperspectral datasets show that our method outperforms state-of-the-art methods. This novel approach addresses limitations of linear mixing models while leveraging deep learning to improve accuracy of HU.
Review of Industry 4.0 from the Perspective of Automation and Supervision Systems: Definitions, Architectures and Recent Trends

Industry 4.0 is a new paradigm that is transforming the industrial scenario. It has generated a large amount of scientific studies, commercial equipment and, above all, high expectations. Nevertheless, there is no single definition or general agreement on its implications, specifically in the field of automation and supervision systems. In this paper, a review of the Industry 4.0 concept, with equivalent terms, enabling technologies and reference architectures for its implementation, is presented. It will be shown that this paradigm results from the confluence and integration of both existing and disruptive technologies. Furthermore, the most relevant trends in industrial automation and supervision systems are covered, highlighting the convergence of traditional equipment and those characterized by the Internet of Things (IoT). This paper is intended to serve as a reference document as well as a guide for the design and deployment of automation and supervision systems framed in Industry 4.0.
A Novel Wideband Splitter for a Four-Element Antenna Array

In the paper, a novel design of a wideband power splitter for a four-element antenna array using two RF antiphase segments is proposed. Based on a detailed analysis of the power splitter circuit, an analytical model was set up in the MATLAB environment. The derived analytical model allows the development of a design of the described structure for any operating frequency and estimates the properties of the designed structure. In addition to the RF electrical part, the copper cover is also considered in this study. The copper cover serves as both a support and shielding part of the proposed structure. The electrical part consists of two sections of transmission lines. The first transmission line is symmetrical, while the second transmission line is asymmetrical. The given transmission lines can be realized using any technology (microstrip, coaxial, etc.). A prototype of the proposed wideband splitter operating at 650 MHz with a fractional bandwidth of 84.3% was designed and tested in real-world conditions to prove the concept. The board of the manufactured prototype has dimensions of 25 × 152 mm. A double-sided FR4 material with a substrate height of 1.48 mm, copper thickness of 50μm, and ϵr ≅ 4.3, with a dielectric loss tangent of 0.021 was used to manufacture the prototype. The prototype was tested and its parameters were verified in practical conditions as a part of the current radio communication system for the 5G band. Under these conditions, verification measurements of the proposed splitter with a four-element antenna array were carried out.
Performance Comparison among Different Precise Satellite Ephemeris and Clock Products for PPP/INS/UWB Tightly Coupled Positioning

To meet the requirements of different applications, the International Global Navigation Satellite System (GNSS) Service (IGS) has provided Global Positioning System (GPS) satellite ephemerides and clock products with different accuracy levels (final, rapid and ultra-rapid products). Comparison of Precise Point Positioning/Inertial Navigation System/Ultra Wideband (PPP/INS/UWB) tightly coupled positioning with different precise satellite ephemeris and clock products is made and corresponding data analysis is provided. Final, rapid and ultra-rapid products are applied in a PPP/INS/UWB integrated system. The field trajectories, position and velocity errors of the integrated system with different products are compared. The results indicate that PPP/INS/UWB tightly coupled positioning with final and rapid products achieves an accurate performance. Compared with the position resolution using final products, the position accuracy with the ultra-rapid products (observed half) is slightly reduced and the position accuracy with the ultra-rapid products (predicted half) has a 0·1–0·2 m reduction. The influence of precise satellite ephemeris and clock products is very minor for non-real-time positioning, relative to the accuracy of PPP/INS/UWB tightly coupled positioning.
Statistical Method to Evaluate the Relationship between Climatic Factors during the Development Periods of Flowering and Boll Formation and Cotton Production

This study investigated the relationship between climatic factors and production of flowers and bolls obtained during the development periods of the flowering and boll stage, and to determine the most representative period corresponding to the overall crop pattern. Evaporation, sunshine duration, relative humidity, surface soil temperature at 1800 h, and maximum air temperature, are the important climatic factors that significantly affect flower and boll production. There was a negative correlation between flower and boll production and either evaporation or sunshine duration, while that correlation with minimum relative humidity was positive. Higher minimum relative humidity, short period of sunshine duration, and low temperatures enhanced flower and boll formation. It could be concluded that the fourth quarter period of the production stage is the most appropriate and usable production time to collect data for determining efficient prediction equations for cotton flower and boll production, and making valuable recommendations.
A Bio-Inspired Probabilistic Neural Network Model for Noise-Resistant Collision Perception

Bio-inspired models based on the lobula giant movement detector (LGMD) in the locust’s visual brain have received extensive attention and application for collision perception in various scenarios. These models offer advantages such as low power consumption and high computational efficiency in visual processing. However, current LGMD-based computational models, typically organized as four-layered neural networks, often encounter challenges related to noisy signals, particularly in complex dynamic environments. Biological studies have unveiled the intrinsic stochastic nature of synaptic transmission, which can aid neural computation in mitigating noise. In alignment with these biological findings, this paper introduces a probabilistic LGMD (Prob-LGMD) model that incorporates a probability into the synaptic connections between multiple layers, thereby capturing the uncertainty in signal transmission, interaction, and integration among neurons. Comparative testing of the proposed Prob-LGMD model and two conventional LGMD models was conducted using a range of visual stimuli, including indoor structured scenes and complex outdoor scenes, all subject to artificial noise. Additionally, the model’s performance was compared to standard engineering noise-filtering methods. The results clearly demonstrate that the proposed model outperforms all comparative methods, exhibiting a significant improvement in noise tolerance. This study showcases a straightforward yet effective approach to enhance collision perception in noisy environments.