Miracle-dz/UniFAR-FTUs huggingface dataset:
New theory of space-time and gravitation: Yilmaz's approach

Space-time under the influence of a gravitational field is dealt with in accordance with the theory of Huseyin Yilmaz. The space-time structure is defined with the concept of local operational procedure of measurement as in the special theory of relativity. The local signal velocity of light is equal to the constant c as it is measured. In static cases, this new theory of space-time physics gives an exponential form for the metric. It is an exponential function of the gravitational potential difference. This functional structure shows a transitive group property of the metric, which is preserved in general cases. The gravitational field is quite analogous to other familiar fields in physics except for the curved space-time structure; it obeys a general Poisson equation and has a stress-energy tensor. The existence of the stress-energy tensor ensures the conservation law of energy-momentum and gives rise to the field-theoretic equation of motion. Thus the new theory has both relativistic and field-theoretic aspects.
Exact Signed Distance Function Representation of Polygons

Introduction: Signed distance functions (SDF) are applied from high-quality text rendering [3] to geometric representation for collision detection [5], 3D printing, additive manufacturing [1], or advanced real-time graphics e ects [7]. The SDF is usually stored as a regular grid of samples for high-performance applications, but various spatial subdivision or interpolation schemes have been proposed for storage, such as octrees [2] or hierarchical T-meshes [6]. In complex shapes, applications mainly focus on storing a discrete approximation to the exact SDF in conjunction with various interpolation techniques. We propose a conservative but exact SDF representation for planar polygons. The exact SDF is composed of two classes of regions, separated by parabolic and linear boundaries. We construct conservative polygonal bounds to these regions. Our algorithm performs a series of cuts to determine the bounding polygons that represent the distance function on the region. The exact SDF can be evaluated using these polygons. Such a formulation is closely related to point and segment Voronoi diagrams [4]; however, our goal is to preserve the inside-outside partitioning of the plane as well.
Artificial intelligence processing electronic health records to identify commonalities and comorbidities cluster at Immuno Center Humanitas

Abstract Background Comorbidities are common in chronic inflammatory conditions, requiring multidisciplinary treatment approach. Understanding the link between a single disease and its comorbidities is important for appropriate treatment and management. We evaluate the ability of an NLP‐based process for knowledge discovery to detect information about pathologies, patients' phenotype, doctors' prescriptions and commonalities in electronic medical records, by extracting information from free narrative text written by clinicians during medical visits, resulting in the extraction of valuable information and enriching real world evidence data from a multidisciplinary setting. Methods We collected clinical notes from the Allergy Department of Humanitas Research Hospital written in the last 3 years and used it to look for diseases that cluster together as comorbidities associated to the main pathology of our patients, and for the extent of prescription of systemic corticosteroids, thus evaluating the ability of NLP‐based tools for knowledge discovery to extract structured information from free text. Results We found that the 3 most frequent comorbidities to appear in our clusters were asthma, rhinitis, and urticaria, and that 991 (of 2057) patients suffered from at least one of these comorbidities. The clusters which co‐occur particularly often are oral allergy syndrome and urticaria (131 patients), angioedema and urticaria (105 patients), rhinitis and asthma (227 patients). With regards to systemic corticosteroid prescription volume by our clinicians, we found it was lower when compared to the therapy the patients followed before coming to our attention, with the exception of two diseases: Chronic obstructive pulmonary disease and Angioedema. Conclusions This analysis seems to be valid and is confirmed by the data from the literature. This means that NLP tools could have significant role in many other research fields of medicine, as it may help identify other important, and possibly previously neglected clusters of patients with comorbidities and commonalities. Another potential benefit of this approach lies in its potential ability to foster a multidisciplinary approach, using the same drugs to treat pathologies normally treated by physicians in different branches of medicine, thus saving resources and improving the pharmacological management of patients.
The Graph Theoretical Approach in Brain Functional Networks: Theory and Applications

The present book illustrates the theoretical aspects of several methodologies related to the possibility of i) enhancing the poor spatial information of the electroencephalographic (EEG) activity on the scalp and giving a measure of the electrical activity on the cortical surface. ii) estimating the directional influences between any given pair of channels in a multivariate dataset. iii) modeling the brain networks as graphs. The possible applications are discussed in three different experimental designs regarding i) the study of pathological conditions during a motor task, ii) the study of memory processes during a cognitive task iii) the study of the instantaneous dynamics throughout the evolution of a motor task in physiological conditions. The main outcome from all those studies indicates clearly that the performance of cognitive and motor tasks as well as the presence of neural diseases can affect the brain network topology. This evidence gives the power of reflecting cerebral "states" or "traits" to the mathematical indexes derived from the graph theory. In particular, the observed structural changes could critically depend on patterns of synchronization and desynchronization - i.e. the dynamic binding of neural assemblies - as also suggested by a wide range of previous electrophysiological studies. Moreover, the fact that these patterns occur at multiple frequencies support the evidence that brain functional networks contain multiple frequency channels along which information is transmitted. The graph theoretical approach represents an effective means to evaluate the functional connectivity patterns obtained from scalp EEG signals. The possibility to describe the complex brain networks sub-serving different functions in humans by means of "numbers" is a promising tool toward the generation of a better understanding of the brain functions.
Could artificial intelligence replace fine-needle aspiration in endoscopic ultrasound?

© Gastrointestinal Stromal Tumor. All rights reserved. Gastrointest Stromal Tumor 2022 | https://dx.doi. org/10.21037/gist-22-11 The recent application of artificial intelligence (AI) in the field of gastroenterology has shown promising results in the diagnosis and management of digestive diseases (1-3). Solutions such as AI-powered detection and diagnosis systems are now commercially available for colorectal polyps (4). The backbone of AI systems for image classification is the convolutional neural network (CNN), a deep-learning algorithm that conducts multi-level image analysis through pattern recognition, and improves its own diagnostic ability by training with large datasets (5,6). With the ability to integrate pixel-level data, CNN is able to aid endoscopists in the rapid interpretation of seemingly ambiguous visual data. One such area of diagnostic dilemma is the evaluation of gastric subepithelial lesions (SELs). While endoscopic ultrasound (EUS) is the most accurate imaging modality, there are no definitive EUS features to differentiate gastrointestinal stromal tumors (GISTs) from the commonly encountered gastrointestinal leiomyomas (GILs) (7-9). Misdiagnosis of GISTs and GILs are thought to comprise the majority of incorrect EUS diagnoses (9). Given the malignant potential of GISTs, it is crucial to accurately diagnose these lesions and to differentiate them from GILs, which are benign. The current standard is to differentiate these two by obtaining tissue samples with fine-needle aspiration or biopsy (EUS-FNA/B). However, FNA/B is invasive and is reported to have a lower diagnostic rate for SELs smaller than 20 mm (9,10). In this issue of Endoscopy, Yang et al. report the result of their AI-powered EUS model for differentiation between GISTs and GILs (11). Using a CNN for image recognition, the AI model was trained, validated, and evaluated on a total of 10,439 EUS images from 752 patients with histologically confirmed GISTs and GILs from four endoscopic centers, collected in aggregate from 2013 to 2020. They report a significantly higher diagnostic accuracy with the AI model compared with the expert endo-sonographer (94.0% vs. 70.2%, P value <0.001). More importantly, in the prospective evaluation of 508 consecutive patients with SELs, of whom 132 underwent histologic confirmation, the diagnostic accuracy remained significantly higher with the AI-powered EUS compared with the expert endosonographer (78.8% vs. 69.7%, P value =0.01). When examining only cases of histologically-confirmed GISTS or GILs, AI-joint diagnosis also had significantly higher accuracy, specificity, and PPV at 92.2%, 95.1%, and 94.1%, respectively, compared to individual diagnosis alone at 76.6% (P value =0.01), 65.9% (P value =0.002), and 69.6% (P value <0.01), respectively. The sensitivity and NPV of AI-joint diagnosis were similar to individual diagnosis. These are promising results in applying deep learning to real-time EUS to distinguish between GISTs and GILs. Previous studies have reported an improved diagnostic Editorial Commentary
Ultraviolet Observations of Hot Stars with Circumstellar Dust.

Absolute fluxes over the wavelength range 1200-3200 A are presented for 13 normal early-type stars and 12 early-type stars known to have large IR excess; the data were obtained to study the UV spectral modifications produced by absorbing circumstellar dust particles. It is found that all the Be/shell stars whose spectral types are later than B6 exhibit greatly enhanced line absorption due to circumstellar Fe II. The circumstellar gaseous line absorption in stars cooler than B6 severely complicates the study of circumstellar dust absorption. However, those B and A stars that exhibit the circumstellar 9.7-micron emission feature have a very large increase in circumstellar absorption for 1800 A; such absorption is expected from circumstellar silicate particles.
Newsroom Disruptions and Opportunities in Times of Crisis: Analysing Southern African Media During the COVID-19 Crisis

ABSTRACT The COVID-19 pandemic currently ravaging the world has brought massive disruptions to every facet of life. However, a crisis can present revolutionary change and growth opportunities for both individuals and institutions. This study is a cross-country study that examines the disruptions and opportunities that the pandemic has wrought to mainstream news reporting practices. Utilising data from Botswana, South Africa and Zimbabwe, we seek to answer the question: in what ways did the pandemic disrupt news production, and what opportunities to the mainstream media did the pandemic open? To answer this question, we utilise in-depth interviews with practising journalists and draw on the interaction of Schudson‘s sociological view of news production and Bourdieu‘s field theory. We note that the pandemic disrupted news sourcing routines in ways that compromised the quality of news production. Paradoxically, amidst this disruption, we notice that media–state relations and trust in mainstream media improved dramatically. This makes us argue that the pandemic represented an opportunity for mainstream media to “reset” their existential purpose, considering how they revitalised their relations and how they reclaimed declining trust in them. We conclude that COVID-19 may have breathed a new lease of life into a declining journalism industry.
Cu-kerosene and Al2O3-kerosene boundary layer nanofluid flow past a stretching/shrinking surface

A mathematical discussion of two different classes of nanofluids, such as Copper (Cu)-Kerosene and Aluminum oxide (Al2O3)-Kerosene, over a stretching/shrinking surface, has been discussed in this manuscript. Here Kerosene based nanofluids carry Copper and Aluminum oxide as nanoparticles. The ODEs are obtained from basic equations by introducing the similarity approach. The respective coupled nonlinear ODEs are solved with the help of a suitable numerical technique named as Runge-Kutta fourth-order method. It is found that Al2O3-Kerosene possesses a slightly greater velocity than Cu-Kerosene, but a reverse effect is found in the case of temperature and nanofluids. The presence of volume concentration is important due to the presence of nanoparticles as nanofluids property depends on the physical properties of nanoparticles.
Guidelines for the Design of High-Performance Perovskite Based Solar Cells

In the aim of finding the optimal solar cell structure which allows better efficiency, stability and reduced cost, a general study of a Methyl Ammonium lead Iodide CH3NH3PbI3 based perovskite solar cell is made. Three different electron transport material compounds ETMs; TiO2, ZnO and SnO2 are comparatively studied considering the same hole transport material HTM, Spiro-OMeTAD. The photovoltaic parameters, i.e. the open circuit voltage (Voc), the short circuit current (Jsc) and the power conversion efficiency (PCE) are performed considering the ETM layers thicknesses, and the defect densities in both interfaces ETM/Perovskite and Perovskite/HTM. It is found that solar cell with SnO2 present the highest PCE for almost all configurations. Finally, the optimized cell is simulated with different organic and inorganic HTMs such as PEDOT: PSS, Cul and CuSbS2.
Real-Time Cyber Attack Detection Scheme for Standalone Microgrids

The safe operation, control, and stability of standalone microgrids (MGs) are highly dependent on their coordination with the MG control center (MGCC). Due to the open communication channel between the MG and the MGCC, the measurement signals are vulnerable to cyber attacks that can compromise the stability of the system. In this article, a false data injection (FDI) attack on the frequency measurement of a standalone MG is considered to disrupt the stable operation of the MG. Therefore, an attack detection and identification method are proposed to protect the MG against the impacts of this attack. The proposed method is based on a dynamic state estimation technique that uses an unknown input observer (UIO) to estimate the MG states and generate a residual function that detects the presence of an FDI attack and triggers a detection alarm for attack isolation and mitigation. The robustness and practicability of the proposed method are demonstrated with real-time simulation results of a real-world MG system.
Petrogenesis, Magma Source, and Geodynamics of Paleogene Mafic Rocks, Huimin Sag, Jiyang Depression, Eastern China

A suite of Paleogene mafic rocks was collected from boreholes in the Huimin Sag of the Jiyang Depression with the aim of investigating the petrogenesis and nature of mantle source for these rocks and further providing insights into the characteristic of related mantle plume. Whole-rock geochemical data indicate that the mafic rocks have relatively lower SiO2 (42.93%–48.57%) contents and similar characteristics to alkaline basalt and belong to transitional calc-alkaline series. These samples were clearly enriched in LREEs and depleted in HREEs and were also characterized by the enrichment of LILEs, incompatible elements, and HFSEs, similar to those of the Ocean Island Basalt (OIB). In addition, they exhibited Pb enrichment; Y, Pr, and Yb depletion; absence of Nb-Ta anomalies; high Hf and low Zr; and Rb/Yb ratios exceeding 1.0, indicating characteristics of intraplate rift-type alkaline basalt. The samples exhibited (Th/Ta)PM and (La/Nb)PM ratios less than 1 and plotted within the OIB, EMI, and EMII fields, indicating that crustal components had no role in the generation of the rocks. With the exception of individual samples that have a distinctive range of ε Nd values, the majority of samples have complex ε Nd values of -1.15 to 5.56, indicating a mixture of different sources, which was also apparent in the δ18O-87Sr/86Sr diagram, in which the samples plot close to the downward nonlinear curve. Based on the isotopic and trace elemental analyses, these igneous rocks are intraplate rift-type alkaline basalt and are of mantle plume origin. The variations in 87Sr/86Sr, 143Nd/144Nd, ε Nd values, LREEs, and HFSEs were probably due to the different locations of the mantle plume for different samples. The primary magma of the rocks likely originated from the melting of a mantle plume and the further metasomatism of lithospheric mantle, continental, or oceanic crust.
Does Knowing Other Workers’ Wage Level Promote Employees’ Pay Fairness Perception? Evidence From a Randomized Survey Experiment

Pay fairness perception is considered a significant determinant of employee turnover. This study analyzes whether public-sector employees’ pay fairness perception is promoted when workers are notified of the wage level of similar employees working in other sectors (i.e., relative wage). A randomized survey experiment was conducted to test the hypothesis that relative wages matter for pay fairness perception. The results show that informing employees of other workers’ wage levels enhances pay fairness perception. The subgroup analysis shows that the effect is more pronounced for employees who are relatively early in their career. The results imply that providing information on relative wage can be an effective way of promoting pay fairness perception, thereby reducing worker turnover and contributing potentially to productivity gains in public-sector organizations where turnover rate is salient.
Optical studies and validation of multidimensional hybrid metamaterial embedded light trapping structure for p-Si/n-ZnO based thin c-Si solar cell

ABSTRACT This paper deals with one competent light trapping structure of metamaterials embedded p-Si/n-ZnO-based thin solar cell assisted by different simulation studies. Through this article, the author exposed the credibility of ZnO as multidimensional material with dual utility to serve as anti-reflective coating with active material of this heterojunction solar cell. Additionally, dielectric metamaterial like silica nanoparticles on top of the structure enhanced the photon cultivation efficiency of the device. Further through this work, the author tries to validate the simulated structure in real world by the process of simple fabrication technique, which also offers same optical responses already given by theoretical studies. This investigation also confirms the metamaterial property of the monolayer silica nanoparticles in higher angle of incidence of light, which validates its utility in solar cell where injection of photon is needed throughout the day.
A kernel method for hierarchical and non-hierarchical clustering

In cluster analysis, choosing a distance measure among given data is not straightforward. For defining a similarity matrix in hierarchical clustering and some types of non-hierarchical clustering such as spectral clustering ([1]), several distance measures such as Pearson correlation, Euclidean distance, and nonparametric measures have been widely used. The result of clustering, however, often depends on a specific choice of the measure. If our purpose is to extract a group of variables, for which we cannot presume the type of dependence, a specific choice of measure may not capture the dependence of variables. This paper proposes a novel approach to define a similarity matrix, which can incorporate any nonlinear correlation of variables. Our method uses kernel Hilbert spaces, which contain a sufficiently rich class of nonlinear functions of variables. We define the similarity of two variables based on the theory of kernel Hilbert spaces and covariance operators ([2]). This similarity represents the sum of all the nonlinear correlations in principle, and provides a more reasonable way for defining the similarity or dependence of two random variables than existing measures, which extract only partial information of variables.
A Domain-Specific Language for Modeling IoT System Architectures that Support Monitoring

The Internet of Things (IoT) is a technological paradigm involved in a diversity of domains with favorable impacts on people’s daily lives and the development of industry and cities. Nowadays, one of the most critical challenges is developing software for IoT systems since the traditional Software Engineering methodologies and tools are unproductive in the face of the complex requirements resulting from the highly distributed, heterogeneous, and dynamic scenarios in which these systems operate. Model-Driven Engineering (MDE) emerges as an appropriate approach to abstract the complexity of IoT systems. However, there are no domain-specific languages (DSLs) aligned to standardized reference architectures for IoT. Furthermore, existing DSLs have an incomplete language to represent the IoT entities that may be needed at the edge, fog, and cloud layers to monitor IoT environments. Therefore, this paper proposes a domain-specific language named Monitor-IoT, which supports developers in designing multi-layer monitoring architectures for IoT systems with high abstraction, expressiveness, and flexibility. Monitor-IoT consists of a high-level visual modeling language and a metamodel aligned with the ISO/IEC 30141:2018 reference architecture. In addition, it provides a language capable of modeling architectures with a wide variety of digital entities and dataflows (synchronous and asynchronous) between them across the edge, fog, and cloud layers to support the monitoring of a diversity of IoT scenarios. The empirical evaluation of Monitor-IoT through the application of an experiment, which contemplates the use of the Technology Acceptance Model (TAM), demonstrates the intention of the participants to use this tool in the future since they consider it easy to use and useful.
Los subgéneros Calhyboma, Hybomidium y Telhyboma (Coleoptera: Scarabaeidae: Scarabaeinae: Deltochilum) en Colombia

Se revisaron 897 ejemplares del género Deltochilum de nueve colecciones entomológicas. Se estudió la taxonomía de las especies de los subgéneros Calhyboma, Hybomidium y Telhyboma. Se reconocieron y redescribieron diez especies y una subespecie pertenecientes a los subgéneros Calhyboma (D. (C.) hypponum, D. (C.) luederwaldti, D. (C.) mexicanum, D. (C.) tessellatum, D. (C.) cristinae, D. (C.) carinatum), Hybomidium (D. (H.) amazonicum, D. (H.) sp. , D. (H.) lobipes, D. (H.) gibbosum panamensis) y Telhyboma (D. (T.) orbiculare). Se describen dos especies nuevas del género y la hembra de D. (Calhyboma) luederwaldti. D. (Calhyboma) luederwaldti y D. (Calhyboma) cristinae son nuevos registros para Colombia. Se amplía la distribución regional de siete de las especies de estos tres subgéneros. Se presenta la descripción del órgano genital masculino de todas las especies y se incluye un análisis comparativo de su variación morfológica. La escultura elitral de todas las especies se presenta e ilustra en detalle. Solo el 10% del material examinado estaba identificado a especie correctamente. Este estudio mejora el conocimiento taxonómico del género Deltochilum en Colombia.