Miracle-dz/UniFAR-FTUs huggingface dataset:
Fratelli d’Italia in the European Parliament: between radicalism and conservatism

This article studies the behaviour of Fratelli d’Italia (FdI) in the European Parliament (EP). Analysing a large sample of roll-call votes in the ninth EP (2019–2022), together with a selected sample of highly significant votes on COVID-19 and post-pandemic recovery, it shows that FdI has the strongest voting agreement with its allies in the European Conservatives and Reformists (ECR) group and votes more often with the centre-right European People’s Party (EPP) than with the more extreme Identity and Democracy (ID) group. FdI is often part of the winning coalition, shaping legislation or seeking to influence the policy agenda of the EU. On COVID-19 and post-pandemic votes, FdI often took a moderate approach on key issues, and did so even when it was the only party in opposition to the Draghi executive in Rome. At the same time, FdI has not renounced its ideological battles on specific policies, such as those having to do with socio-cultural issues and, to some extent, EU institutional issues. Yet, notwithstanding the evidence of more moderate and cooperative behaviour at the EU level, FdI may find it challenging to fully recast itself as a conservative, rather than a radical-right party, at the national level.
Optical skipping rope induced transverse OAM for particle orbital motion parallel to the optical axis

Abstract In structured light tweezers, it is a challenging technical issue to realize the complete circular motion of the trapped particles parallel to the optical axis. Herein, we propose and generate a novel optical skipping rope via combining beam shaping technology, Fourier shift theorem, and beam grafting technology. This optical skipping rope can induce the transverse orbital angular momentum (OAM) (i.e., nominal OAM, whose direction is perpendicular to the optical axis) and transfer it to the particles, so that the particles have a transverse torque, thereby causing the particles to rotate parallel to the optical axis. Experimentally, our optical tweezers validate that the designed optical skipping rope realizes the orbital motion of polystyrene particles parallel to the optical axis. Additionally, the experiments also demonstrate that the optical skipping ropes manipulate particles to move along the oblique coil trajectory and three-dimensional (3D) cycloidal trajectory. Using the laser beam induced OAM, this innovative technology increases the degree of freedom for manipulating particles, which is of great significance for the application of optical tweezers in optical manipulation, micromechanics, and mimicry of celestial orbits.
An Investigation of Fluid Flow Simulation in Bioprinting Inkjet Nozzles Based on Internet of Things

In the realm of 3D bioprinting, the ultimate objective is the creation of functional tissue constructs, achieved through precise control of inkjet performance and the ideal bioink composition. While the experimental standardization of these attributes can be time-consuming, their comprehension is facilitated through simulation analysis. In this paper, we use COMSOL Multiphysics to compare the rheological behaviour of three different bioprinting-friendly fluids: pure silk, silk mixed with PCL, and silk-PCL mixed with eggshell membrane. Our analysis focuses on the fluid flow dynamics in an air medium at various time intervals, specifically assessing the magnitude of velocity in each scenario. By considering both velocity and the uniformity of fluid dispersion throughout the inkjet process, we aim to identify the most advantageous fluid combination and optimal time interval for bioink applications. The transmission of this real-time data to a centralized Internet of Things (IoT) platform might facilitate the ability of researchers to remotely assess and visually represent the efficacy of various fluid combinations during the printing procedure.
Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis

Antibiotic-resistant bacteria have proliferated at an alarming rate as a result of the extensive use of antibiotics and the paucity of new medication research. The possibility that an antibiotic-resistant bacterial infection would progress to sepsis is one of the major collateral problems affecting people with this condition. 31,000 lives were lost due to sepsis in England with costs about two billion pounds annually. This research aims to develop and evaluate several classification approaches to improve predicting sepsis and reduce the tendency of underdiagnosis in computer-aided predictive tools. This research employs medical datasets for patients diagnosed with sepsis, and it analyses the efficacy of ensemble machine learning techniques compared to nonensemble machine learning techniques and the significance of data balancing and conditional tabular generative adversarial nets for data augmentation in producing reliable diagnosis. The average F Score obtained by the nonensemble models trained in this paper is 0.83 compared to the ensemble techniques average of 0.94. Nonensemble techniques, such as Decision Tree, achieved an F score of 0.90, an AUC of 0.90, and an accuracy of 90%. Histogram-basedgradient boosting classification tree achieved an F score of 0.96, an AUC of 0.96, and an accuracy of 95%, surpassing the other models tested. Additionally, when compared to the current state-of-the-art sepsis prediction models, the models developed in this study demonstrated higher average performance in all metrics, indicating reduced bias and improved robustness through data balancing and conditional tabular generative adversarial nets for data augmentation. The study revealed that data balancing and augmentation on the ensemble machine learning algorithms boost the efficacy of clinical predictive models and can help clinics decide which data types are most important when examining patients and diagnosing sepsis early through intelligent human-machine interface.
A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification

Abstract Motivation Differential network (D-Net) analysis has attracted great attention in systems biology for its ability to identify genetic variations in response to different conditions. Current approaches either estimate the condition-specific networks separately followed by post-procedures to determine the differential edges or estimate the D-Net directly. Both types of analysis overlook the probabilistic inference and can only provide deterministic inference of the edges. Results Here, we propose a Bayesian solution and translate the probabilistic estimation in the regression model to an inferential D-Net analysis for genetic association and classification studies. The proposed PRobabilistic Interaction for Differential Edges (PRIDE) focuses on inferring the D-Net with uncertainty so that the existence of the differential edges can be evaluated with probability and even prioritized if comparison among these edges is of interest. The performance of the proposed model is compared with state-of-the-art methods in simulations and is demonstrated in glioblastoma and breast cancer studies. The proposed PRIDE performs comparably to or outperforms most existing tools under deterministic evaluation criteria. Additionally, it offers the unique advantages, including prioritizing the differential edges with probabilities, highlighting the relative importance of hub nodes, and identifying potential sub-networks in a D-Net. Availability and implementation All the data analyzed in this research can be downloaded at https://xenabrowser.net/datapages/. The R code for implementing PRIDE is available at https://github.com/YJGene0806/PRIDE_Code.
NFT Primary Sale Price and Secondary Sale Prediction via Deep Learning

Non Fungible Tokens (NFTs) are blockchain-based unique digital assets defining ownership deeds. They can characterize various different objects such as collectible, art, and in-game items. In general, NFTs are encoded by blockchains smart contracts, and they are traded via cryptocurrencies. Their price and investors attention on them has remarkably increased especially in 2021, making them promising alternative class of investment. Surprisingly, predicting their prices has only recently started to be analyzed systematically. In this paper, we focus on predicting NFT primary sale price and secondary sale via deep learning. We use multimodal data, NFT images and NFT text characteristics when predicting their prices. Here, we show that contrasting the different and similar (DS) hierarchical features of images and text serves as an important identifying marker for their price, with the consequence that we only need to direct our attention to this aspect when designing a multimodal NFT price predictor. When designing NFT price predictor from multimodal data without using any financial attributes, we come up with Fine-Grained Differences-Similarities Enhancement Network (FG-DSEN), which improves detection with a simple and interpretable structure to enhance the DS aspect between images and text. According to detailed assessment on publicly available NFT dataset, our proposed approach outperforms baselines on both price direction prediction and secondary sale participation prediction according to several machine learning classification metrics.
Real-time Terrain Enhancement with Controlled Procedural Patterns

Assisting the authoring of virtual terrains is a perennial challenge in the creation of convincing synthetic landscapes. Particularly, there is a need for augmenting artist‐controlled low‐resolution models with consistent relief details. We present a structured noise that procedurally enhances terrains in real time by adding spatially varying erosion patterns. The patterns can be cascaded, i.e. narrow ones are nested into large ones. Our model builds upon the Phasor noise, which we adapt to the specific characteristics of terrains (water flow, slope orientation). Relief details correspond to the underlying terrain characteristics and align with the slope to preserve the coherence of generated landforms. Moreover, our model allows for artist control, providing a palette of control maps, and can be efficiently implemented in graphics hardware, thus allowing for real‐time synthesis and rendering, therefore permitting effective and intuitive authoring.
ACOUSTIC ESTIMATES OF KRILL DENSITY AT SOUTH GEORGIA, 1981 TO 1998

Acoustic estimates of densities of Antarctic krill, Euphausia superba, at South Georgia during 11 austral summers between 1981 and 1998 are presented. Krill density at the island fluctuated widely from year to year over that time, ranging from ≈2 to ≈150 g m-2(fresh mass). The 1982, 1991 and 1994 austral summer seasons were characterised by particularly low densities of krill (upper 95% confidence limits <15 g m-2). For five of the summers between 1990 and 1998 it was possible to calculate separate density estimates for northeastern and northwestern South Georgia, and in four of these seasons density was higher to the east.
Transport and transformations of chlorinated-solvent contamination in a saprolite and fractured rock aquifer near a former wastewater-treatment plant, Greenville, South Carolina

^ Introduction -. ^ Purpose and Scope 3 Description of Study Area 3 Previous Investigations 5 Methods ~^ Vapor-Diffusion Samplers 10 Ground-Water Sample Collection and Analysis 12 Borehole Geophysics 13 Microcosm Investigation of Vinyl Chloride Degradation 13 Transport of Ground-Water Contamination 14 Discharge of Contaminated Ground Water to Little Rocky Creek 14 Major Pathways of Subsurface-Contaminant Transport 18 Geochemical Tracers of Contaminant Transport 18 Fracture-Trace Analysis 23 Transformations of Ground-Water Contamination 30 Distribution of Terminal Electron-Accepting Processes 30 Field Evidence for Chlorinated Volatile Organic Compound Dechlorination 31 Laboratory Evidence for Vinyl Chloride Depletion 40 Summary ^ References ^ Appendix I. Vertical Flow Measured With a Heat-Pulse Flowmeter in Selected Wells, Greenville, S.C. , May 10 to 13, 1994 49 Appendix II. Plots of Downhole Geophysical Logs Obtained From Selected Wells, Greenville, S.C ^ Appendix III. Reflectors Identified by Single-Hole Borehole-Radar Surveys, Greenville, S.C. , July 26 Through August 2, 1994 71
Leaf Orientation, Incident Sunlight, and Photosynthesis in the Alpine Species Suassurea superba and Gentiana straminea on the Qinghai-Tibet Plateau

Abstract The extremely high level of solar radiation on the Qinghai-Tibet Plateau may induce photoinhibition and thus limit leaf carbon gain. To assess the effect of high light, we examined gas exchange and chlorophyll fluorescence for two species differing in light interception: the prostrate Saussurea superba and the erect-leaved Gentiana straminea. In controlled conditions with favorable water and temperature, neither species showed apparent photoinhibition in gas exchange measurements. In natural environment, however, their photosynthetic rate decreased remarkably at high light. Photosynthesis depression was aggravated under high leaf temperature or soil water stress. Relative stomatal limitation was much higher in S. superba than in G. straminea and it remarkably increased in the later species at midday when soil was dry. Fv/Fm as an indicator for photoinhibition was generally higher in S. superba than in the other species. Fv/Fm decreased significantly under high light at midday in both species, even when soil moisture was high. F0 linearly elevated with the increment of leaf temperature in G. straminea, but remained almost constant in S. superba. Electron transport rate (ETR) increased with photosynthetically active photon flux density (PPFD) in S. superba, but declined when PPFD was high than about 1000 μmol m−2 s−1 in G. straminea. Compared to favorable environment, the estimated daily leaf carbon gain at PPFD above 800 μmol m−2 s−1 was reduced by 32% in S. superba and by 17% in G. straminea when soil was moist, and by 43% and 53%, respectively, when soil was dry. Our results suggest that the high radiation induces photoinhibition and significantly limits photosynthetic carbon gain, and the limitation may further increase at higher temperature and in dry soil.
Mbt-gym: Reinforcement learning for model-based limit order book trading

Within the mathematical finance literature there is a rich catalogue of mathematical models for studying algorithmic trading problems such as market making and optimal execution. This paper introduces mbt_gym, a Python module that provides a suite of gym environments for training reinforcement learning (RL) agents to solve such model-based trading problems in limit order books. The module is set up in an extensible way to allow the combination of different aspects of different models. It supports highly efficient implementations of vectorised environments to allow faster training of RL agents. In this paper, we motivate the use of RL to solve such model-based limit order book problems, we explain the design of our gym environment, and then demonstrate its use and resulting insights from solving standard and novel problems. Finally, we lay out a roadmap for further development and use of our module for research into limit-order-book trading.
Critical Issues in Tourism: A Geographical Perspective

List of Tables. List of Boxes. List of Figures. Preface to the Second Edition. Part I: An Approach to Tourism: 1. Introduction. 2. The International Dimension. Part II: Access to Tourism Consumption: 3. Social Access to Tourism and Leisure. 4. Tourist Motivation and Behaviour. Part III: The Production of Tourism Services: 5. The Tourism and Leisure Industries. 6. Tourism and Entrepreneurship. 7. Tourism, Employment and Labour Markets. Part IV: Tourism Environments: 8. Tourism and Leisure Environments. 9. Mass Tourism. 10. Urban Tourism. 11. Rural Tourism. 12. Tourism and the Environment: the Challenge of Sustainable Tourism. Bibliography. Recommended Reading. Index.
Live4D: A Real-time Capture System for Streamable Volumetric Video

Volumetric video holds promise for virtual and augmented reality (VR/AR) applications but faces challenges in interactive scenarios due to high hardware costs, complex processing and substantial data streams. In this paper, we introduce Live4D, a cost-effective, real-time volumetric video generation and streaming system using an RGB-only camera setup. We propose a novel deep implicit surface reconstruction algorithm, that combined neural signed distance field with observed truncated signed distance field to generate the watertight meshes with low latency. Moreover, we achieve a robust non-rigid tracking method that provides temporal stability to the meshes while resisting tracking failure cases. Experimental results show that Live4D achieves a performance of 24fps using mid-range graphic cards and exhibits an end-to-end latency of 95ms. The system enables live streaming of volumetric video within a 20Mbps bandwidth requirement, positioning Live4D as a promising solution for real-time 3D vision content creation in the growing VR/AR industry.
Lactogenic activity of ethanolic extract of Pluchea indica Less leaf in lactating rats

Introduction: The leaves of Pluchea indica (L.) Less is empirically used to enhance milk production. This study aimed to evaluate the lactogenic effect of ethanolic extract of P. indica leaves (EPI) on milk production, prolactin, cortisol, oxytocin levels, and histological changes of mammary tissue in lactating rats and weight gain of their pups. Methods: Twenty-five lactating rats with six pups were randomized into five groups. The groups were control (reverse osmosis water), standard (domperidone 2.5 mg/kg BW), and EPI (250, 500, and 750 mg/kg BW). The daily treatments were administered by oral gavage, starting from the second day until day 15 of parturition. Milk production and the weight of the pups were measured daily. Serum prolactin, cortisol, and oxytocin levels in the lactating rats were determined by enzyme immunoassay. Histomorphological alterations of mammary tissues were investigated by hematoxylin and eosin-stained slides. Data were analyzed by one-way ANOVA tests and P<0.05 was considered significant. Results: The daily milk production in groups treated with domperidone and EPI at doses of 500 and 750 was significantly increased compared to the control group (P<0.05). Weight gain of the pups of dams that received domperidone and EPI at doses 500 and 750 mg/kg BW was significantly higher than controls (P<0.05). EPI 500 insignificantly increased both prolactin and oxytocin, insignificantly decreased cortisol levels, and insignificantly enhanced both numbers of alveoli and branching alveoli with milk secretion (P>0.05). Conclusion: Pluchea indica leaves revealed lactogenic activity in lactating rats.
Rapua te kurahuna: an occupational perspective of internalised oppression

Internalised oppression can be tricky to recognise and hard to talk about. Described as the most devastating kind of racism, it remains poorly researched and understood. Nuanced and expansive ways of understanding internalised oppression are necessary for the work of being recognised and seeing each other as fully human. For many complex reasons, internalised oppression can be performed by targets of oppression in service of white supremacy, turning Indigenous spaces into new foci for racism via everyday occupations. This article outlines our critical examinations and steps to grapple with internalised oppression as Indigenous occupational therapists who observe how racism is transmitted in daily tasks of life. Steps include growing critical consciousness, developing a relational ethic of manaaki (to support, take care of, and give hospitality) and building community to support recognition and healing from internalised oppression. We call for the development of a critical ally workforce in solidarity with the racially targeted.
An advection-aridity approach to estimate actual regional evapotranspiration.

Actual regional evapotranspiration is calculated by means of a procedure requiring only meteorological data, which are those commonly used in the various versions of the combination approach for potential evaporation. The approach is based on a conceptual model involving, first, the effect of regional advection on potential evaporation, and second, an assumed symmetry between potential and actual evaporation with respect to the evaporative power of the air in the absence of advection. Thus the degree of nonavailability of water for evapotranspiration, that is the aridity of the region, is deduced from the regional advection of drying power of the air, as implied by the atmospheric conditions. The approach was found to give good agreement with daily data of evapotranspiration obtained by means of an energy budget method for a period of severe drought in a rural watershed in a sandy region. One of the advantages is that no soil moisture data, no stomatal resistance properties of the vegetation, nor any other additional aridity parameters are required to determine actual evapotranspiration.
Bibliometric Analysis of Customers’ Purchase Intention in e-commerce

This study aims to conduct a bibliometric analysis of purchase intentions in e-commerce published in 2001 to 2022. This study shows publication trends by utilizing scientific mapping and analyzing publication performance. This study also identified the contributions of authors, journals, countries, and articles using bibliometric analysis. Several countries have contributed to publications of purchase intention in e-commerce. China and the United States have the largest contribution in scientific publications around the world. In addition, "The Journal of retailing and customer services" published the most articles in this field. According to the citation analysis of the author, Gefen D. , Karahanna. E, Straub D.W. has a total of 5,315 citations. Another result found that some keywords have large nodes related to their frequency of occurrence and represent information about the relationship between keywords. A visualization of publication trends is presented to serve as a reference and guidance for researchers in future studies.
Late Quaternary stratigraphy of the northern Rockall trough and Faeroe‐Shetland channel, northeast Atlantic Ocean

A late Quaternary deep-water stratigraphic framework has been established for the deep-water areas (>450m) of the northern Rockall Trough and Faeroe-Shetland Channel. Four stratigraphic units (1–4) are identified; these are primarily biostratigraphic units based on dinoflagellate cyst evidence. Unit 1 represents the late Weichselian glacial (pre-13 000 yr BP); unit 2 the Late Glacial Interstadial (11 000-13 000 yr BP); unit 3 is of Younger Dryas age (10 000-11 000 yr BP); and unit 4 represents the Holocene interglacial (post-10 000 yr BP). This stratigraphy is supported by the discovery of the mixed Vedde Ash (10 600 yr BP) and North Atlantic Ash zone 1, and the Saksunarvatn Ash (9000–9100 yr BP), concentrated in units 3 and 4 respectively. The sedimentology indicates that the oceanographic regime underwent a major change between the glacial and interglacial stages. This is marked by the onset of strong bottom current activity, allied to the restoration of overflow of the Norwegian Sea Deep Water into the North Atlantic, towards the end of the Younger Dryas Stadial. Despite intense bioturbation and bottom-current reworking the basic stratigraphic framework is maintained. Recognition of two volcanic ash markers enables correlation with established onshore and offshore sequences of marine and non-marine environments.