Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Model for Coronary Artery Disease Diagnosis: A Comparative Study of Several Machine Learning Algorithms

Background In today's industrialized world, coronary artery disease (CAD) is one of the leading causes of death, and early detection and timely intervention can prevent many of its complications and eliminate or reduce the resulting mortality. Machine learning (ML) methods as one of the cutting-edge technologies can be used as a suitable solution in diagnosing this disease. Methods In this study, different ML algorithms' performances were compared for their effectiveness in developing a model for early CAD diagnosis based on clinical examination features. This applied descriptive study was conducted on 303 records and overall 26 features, of which 26 were selected as the target features with the advice of several clinical experts. In order to provide a diagnostic model for CAD, we ran most of the most critical classification algorithms, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), Logistic Regression (LR), J48, Random Forest (RF), K-Nearest Neighborhood (KNN), and Naive Bayes (NB). Seven different classification algorithms with 26 predictive features were tested to cover all feature space and reduce model error, and the most efficient algorithms were identified by comparison of the results. Results Based on the compared performance metrics, SVM (AUC = 0.88, F-measure = 0.88, ROC = 0.85), and RF (AUC = 0.87, F-measure = 0.87, ROC = 0.91) were the most effective ML algorithms. Among the algorithms, the KNN algorithm had the lowest efficiency (AUC = 0.81, F-measure = 0.81, ROC = 0.77). In the diagnosis of coronary artery disease, machine learning algorithms have played an important role. Proposed ML models can provide practical, cost-effective, and valuable support to doctors in making decisions according to a good prediction. Discussion. It can become the basis for developing clinical decision support systems. SVM and RF algorithms had the highest efficiency and could diagnose CAD based on patient examination data. It is suggested that further studies be performed using these algorithms to diagnose coronary artery disease to obtain more accurate results.
Reasoning with Ontologies for Non-player Character's Decision-Making in Games

In most games, the decision-making of non-player characters (NPCs) is usually constructed using variants of state machines, behaviour trees, utility-based AI or planning. These methods are relatively simple to implement, but have drawbacks in that it can be difficult to create complex non-hard-coded behaviour for many agents and to maintain the algorithms, especially when scaling up. Game designers usually think of their games with rules that closely resemble logic rules. A methodology is introduced to design both general and modular behaviour using a logic reasoner with hierarchical ontologies. This approach is combined with the well-founded semantics (WFS) to solve the problem of representation and reasoning despite the lack of NPC knowledge.
Diversidad de audiencias y medios. El reto actual de la comunicación social

La comunicación social enfrenta importantes retos para lograr la penetración que requiere, y esto se debe a diversos factores que inciden en dicho fenómeno hoy en día, ya que a diferencia de 20 años atrás las condiciones de los medios de comunicación (ahora digitales), y los distintos contextos sociales de las generaciones que actualmente convergemos, son diversas, dispersas y distintas.En esta investigación, se analizará cada uno de los elementos que forman parte del proceso de comunicación social y, por otro lado, las herramientas digitales de análisis con las que se cuenta. Finalmente se plantean como conclusión algunas alternativas para mejorar o lograr que la comunicación social logre sus objetivos.
De las fake news a la polarización digital. Una década de hibridación de desinformación y propaganda

Pocos acontecimientos pueden ser considerados globales e históricos al mismo tiempo, pero cuando ocurren generan un nivel de intensidad informativa que satura la dieta informativa cotidiana y, paralelamente, también modifican estructuras informativas consolidadas. El objetivo de este trabajo es hacer un análisis descriptivo y estructurado de la evolución de la desinformación en la última década, e intentar apuntar a los principales elementos que caracterizan a la transformación de las nuevas estrategias de poder que se desarrollan en la esfera pública digital. Para ello, se analiza de forma retrospectiva los siguientes procesos: la normalización de los procesos de desinformación política y social, el nuevo papel de intermediación de los partidos políticos, los movimientos sociales y los grupos de presión, así como los nuevos canales de mediación y (des)intermediación. Desde esta perspectiva, hay cuatro momentos fundamentales que explican parcialmente la consolidación del nuevo escenario: el papel de los movimientos sociales y el activismo a principios de la segunda década del siglo XXI (Primavera Árabe, 15-M u Occupy Wall Street), el año 2016 entendido como el año de la consolidación de la polarización política como estrategia política (Bréxit, referéndum en Colombia o elecciones en EEUU), la desinformación relacionada con la pandemia y la globalización de determinadas narrativas (desde 2020 hasta la actualidad) y la hibridación de propaganda y desinformación que se empezó a consolidar tras la invasión rusa en Ucrania a partir de febrero de 2022. Estos cuatro acontecimientos históricos y mediáticos han modificado el papel de las redes sociales (de herramienta de conversación a medios de propaganda, de empresas tecnológicas a intermediarios con capacidad de edición y distribución), la redefinición e importancia de la libertad de expresión e información, la resiliencia y transformación de los sistemas de gobierno en contextos globales (tanto de las democracias como de las dictaduras), el papel de los medios de comunicación en la nueva esfera pública digital (de cuarto poder a altavoces (in)voluntarios del discurso político) y la facilidad para romper las barreras de entrada a los sistemas políticos tradicionales a través de estrategias populistas transnacionales.
Comments on "On Bohm trajectories in two-particle interference devices" by L. Marchildon

Let me begin by first pointing out that my conclusions are based on a very well known result in the study of ergodic problems in mechanics, namely that a decomposable dynamical system is non-ergodic, and that for such systems the space mean and time mean are not the same in general. SQT is necessarily always ergodic whereas Bohmian systems can be non-ergodic, as I will show. This is the basic reason for their incompatibility. For the sake of clarity, let me define what is meant by a decomposable system. Let (M,μ, φt) be a dynamical system, where M is a smooth manifold, μ a measure on M defined by a continuous positive density, and φt : M → M a one-parameter group of measure preserving diffeomorphisms. If f is a complex-valued function on M , its time mean is defined by
THE PROTOSTELLAR LUMINOSITY FUNCTION

The protostellar luminosity function (PLF) is the present-day luminosity function of the protostars in a region of star formation. It is determined using the protostellar mass function in combination with a stellar evolutionary model that provides the luminosity as a function of instantaneous and final stellar mass. In 2010, McKee & Offner considered three main accretion models: the isothermal sphere (IS) model, the turbulent core (TC) model, and an approximation of the competitive accretion (CA) model. We also consider the effect of an accretion rate that tapers off linearly in time and an accelerating star formation rate. For each model, we characterize the luminosity distribution using the mean, median, maximum, ratio of the median to the mean, standard deviation of the logarithm of the luminosity, and the fraction of very low luminosity objects. We compare the models with bolometric luminosities observed in local star-forming regions and find that models with an approximately constant accretion time, such as the TC and CA models, appear to agree better with observation than those with a constant accretion rate, such as the IS model. We show that observations of the mean protostellar luminosity in these nearby regions of low-mass star formation suggest a mean star formation time of 0.3 ± 0.1 Myr. Such a timescale, together with some accretion that occurs non-radiatively and some that occurs in high-accretion, episodic bursts, resolves the classical “luminosity problem” in low-mass star formation, in which observed protostellar luminosities are significantly less than predicted. An accelerating star formation rate is one possible way of reconciling the observed star formation time and mean luminosity. Future observations will place tighter constraints on the observed luminosities, star formation time, and episodic accretion, enabling better discrimination between star formation models and clarifying the influence of variable accretion on the PLF.
A review of challenges and solutions in the design and implementation of deep graph neural networks

The study of graph neural networks has revealed that they can unleash new applications in a variety of disciplines using such a basic process that we cannot imagine in the context of other deep learning designs. Many limitations limit their expressiveness, and researchers are working to overcome them to fully exploit the power of graph data. There are a number of publications that explore graph neural networks (GNNs) restrictions and bottlenecks, but the common thread that runs through them all is that they can all be traced back to message passing, which is the key technique we use to train our graph models. We outline the general GNN design pipeline in this study as well as discuss solutions to the over-smoothing problem, categorize the solutions, and identify open challenges for further research. Abbreviations: CGNN: Continuous Graph Neural Networks; CNN: Convolution NeuralNetwork; DeGNN: Decomposition Graph Neural Network; DGN: Directional GraphNetworks; DGN: Differentiable Group Normalization; DL: Deep Learning; EGAI:Enhancing GNNs by a High-quality Aggregation of Beneficial Information; GAT: GraphAttention Network; GCN: Graph Convolutional Network; GDC: Graph Drop Connect; GDR: Group Distance Ratio; GNN: Graph Neural Network; GRAND: GraphRandom Neural Networks; IIG: Instance Information Gain; MAD: Man AverageDistance; PDE-GCN: Partial Differential Equations-GCN; PTDNet: ParameterizedTopological Denoising network; TDGNN: Tree Decomposition Graph NeuralNetwork;
Creating public value in regional policy. Bringing citizens back in

We develop a novel citizen-centred multi-dimensional approach to public value creation in regional policy. Drawing on 47 citizen focus groups in 16 European regions, public values are analysed through an interpretative comparative approach. Goal attainment is a positive and widely held value. However, evaluations of institutional performance and democratic values are more negative. The findings have significant implications for public value management. We propose a five C’s public value creation framework emphasizing coherence across public values and the communication and co-creation of public value sustained through capacity building and continuous feedback. Implications for public value theory, European regional policy and future research are discussed.
Tooth Surface Design for Variable Transmission Ratio Bevel Gearing

A novel CAD method for tooth profile based on the gearing feature has been proposed in this paper, and this method could be applied to the variable transmission ratio and three-dimension situation. The two pitch curves of the two gears can be generated due to the given transmission ratio. The tooth profile curves are formed in each coordinate system of its pitch curve by the gearing process. Finally, the tooth profile could be extracted and translated into a real dimension by CAD method. It is provided a simply thought for tooth profile design to avoid so much complicated mathematical reasoning.
Stearoyl-CoA Desaturase-1 dependent lipid droplets accumulation in cancer-associated fibroblasts facilitates the progression of lung cancer

Rationale: Cancer-associated fibroblasts (CAFs) are the main components in the tumor microenvironment (TME) and facilitate lung cancer progression. Studies have reported that metabolic reprogramming can regulate the function of CAFs, especially abnormal lipid metabolism. Lipid droplets (LDs) are ubiquitous organelles that store neutral lipids and have a crucial role in lipid metabolism. However, little is known about the synthesis and functions of LDs in lung CAFs. Methods: TetO-EGFRL858R; CCSP-rtTA transgenic mouse model was used to establish a spontaneous pulmonary tumor model and investigate the accumulation of LDs in CAFs. The effect of LDs accumulation on the phenotype change of fibroblasts was estimated in vitro using mouse fibroblast cell lines. RNA sequencing, Western blotting, RT-PCR, and DNA-pull down were performed to determine the mechanism of LDs synthesis in fibroblasts. Results: We found that LDs were enriched in lung CAFs and induced the pro-tumoral phenotype of CAFs with increased expression of α-smooth muscle actin (α-SMA) and Collagen alpha-2 (I) chain (COL1A2). As the main regulator, hypoxia-inducible factor-1α (HIF-1α) was highly expressed in activated fibroblasts and increased the content of LDs. RNA-sequencing results showed that Stearoyl-CoA Desaturase1 (SCD1) was a downstream gene of HIF-1α, which upregulated the number of LDs in fibroblasts. Importantly, SCD1 inhibition reduced the growth of lung tumors, which was correlated with LDs decrease in CAFs. Analysis of human lung adenocarcinoma tissue chip revealed that CAFs with a high level of SCD1 were positively correlated with the expression of HIF-1α and poor survival in lung cancer patients. Conclusions: The HIF-1α/SCD1 axis regulates the accumulation of LDs in CAFs, which might represent a novel target for lung cancer therapy.
On the potential fingerprint of the Antarctic ozone hole in ice-core nitrate isotopes: a case study based on a South Pole ice core

Abstract. Column ozone variability has important implications for surface photochemistry and the climate. Ice-core nitrate isotopes are suspected to be influenced by column ozone variability and δ15N(NO3-) has been sought to serve as a proxy of column ozone variability. In this study, we examined the ability of ice-core nitrate isotopes to reflect column ozone variability by measuring δ15N(NO3-) and Δ17O(NO3-) in a shallow ice core drilled at the South Pole. The ice core covers the period 1944–2005, and during this period δ15N(NO3-) showed large annual variability ((59.2 ± 29.3) ‰ ), but with no apparent response to the Antarctic ozone hole. Utilizing a snow photochemical model, we estimated 6.9 ‰ additional enrichments in δ15N(NO3-) could be caused by the development of the ozone hole. Nevertheless, this enrichment is small and masked by the effects of the snow accumulation rate at the South Pole over the same period of the ozone hole. The Δ17O(NO3-) record has displayed a decreasing trend by ∼ 3.4 ‰ since 1976. This magnitude of change cannot be caused by enhanced post-depositional processing related to the ozone hole. Instead, the Δ17O(NO3-) decrease was more likely due to the proposed decreases in the O3 / HOx ratio in the extratropical Southern Hemisphere. Our results suggest ice-core δ15N(NO3-) is more sensitive to snow accumulation rate than to column ozone, but at sites with a relatively constant snow accumulation rate, information of column ozone variability embedded in δ15N(NO3-) should be retrievable.
ASYMPTOTIC CONFIDENCE REGIONS FOR A LINEAR STRUCTURAL RELATIONSHIP

For the problem of finding a confidence region for the linear structural relation, Brown's method, which assumes that the error variances are known, is shown to be applicable to the case when the error variances are unknown but equal. A new method is presented for obtaining first an asymptotic confidence region for the unknown coefficient vector based on the asymptotic distribution of the maximum likelihood estimator and then one for the structural hyperplane. Numerical computation in the bivariate case shows that our confidence region for the structural line is contained in Brown's region (modified in the above sense) with the same confidence level.
Spectroscopic characterization of a sample of metal-poor solar-type stars from the HARPS planet search program

Stellar metallicity strongly correlates with the presence of planets and their properties. To check for new correlations between stars and the existence of an orbiting planet, we determine precise stellar parameters for a sample of metal-poor solar-type stars. This sample was observed with the HARPS spectrograph and is part of a program to search for new extrasolar planets. The stellar parameters were determined using an LTE analysis based on equivalent widths (EW) of iron lines and by imposing excitation and ionization equilibrium. The ARES code was used to allow automatic and systematic derivation of the stellar parameters. Precise stellar parameters and metallicities were obtained for 97 low metal-content stars. We also present the derived masses, luminosities, and new parallaxes estimations based on the derived parameters, and compare our spectroscopic parameters with an infra-red flux method calibration to check the consistency of our method in metal poor stars. Both methods seems to give the same effective temperature scale. Finally we present a new calibration for the temperature as a function of B − V and [Fe/H]. This was obtained by adding these new metal poor stars in order to increase the range in metallicity for the calibration. The standard deviation of this new calibration is ∼50 K.
Quality Improvement of Raw Material of Natural Fibre Preparation using Pectinase Enzyme Case Study: The Harvest of Kenaf Fibre in Laren District, Lamongan Regency, East Java

This research focuses on the attempt to improve the quality of kenaf natural fibers grade-c (low quality). The fibers are harvested from kenaf plantation in Laren District, Lamongan Regency, East Java. This attempt is to enable the fibers to have a good standard of quality, so that it can be made as a potential textile raw material. All this time, quality of grade-c has been considered as a bad quality fiber, therefore, the utilization is merely as gunny sacks, which at the other side, the demand of gunny sacks is reducing each day since there are plastic sacks as the competitor. To improve the quality of kenaf fiber, it is required an experiment process where pectinase enzyme is used in the preparation stage. This process will result the fiber to have a quality improvement from low quality to good quality, so that it can be processed to a more advanced production of textile material to be made as raw material of innovative and high-selling-valued textile product. Keyword s : kenaf fiber, pectinase enzyme, fiber preparation, exploration, textiles
Revisiting faculty members’ goals for the undergraduate chemistry laboratory

Over a decade has passed since faculty members’ goals for the undergraduate chemistry instructional laboratory were first investigated on a large, national scale in the United States. This study revisits these goals, using data from a 2022 national survey of chemistry faculty members in the United States (n = 521) to investigate current objectives, including how those goals vary with course, institutional context, and receipt of funding for improving undergraduate chemistry courses. A modified version of the Faculty Goals for Undergraduate Chemistry Laboratory Instrument was used to measure goals, with psychometric evidence providing support for its use across the studied contexts, with the exception of the general chemistry laboratory. Goals were associated with course and receipt of funding but not institution type, both with regard to institutions’ highest chemistry degree awarded and approval from the American Chemical Society to award certified bachelor's chemistry degrees. Results suggest that faculty members may adopt a distinct set of goals not immediately associated with the practice of chemistry in the general chemistry laboratory. Further, goals increasingly focus on providing research experience and cultivating disciplinary knowledge and skills with progression through the chemistry curriculum; this focus increases more abruptly when moving from large-enrollment lower-level courses to small-enrollment upper-level courses. Findings imply a need for increased efforts focused on (1) evaluating goals for the general chemistry laboratory, including whether those goals contribute to overarching curricular objectives, (2) promoting adoption of evidence-based pedagogies in large-enrollment contexts to better align instruction with the practice of chemistry, (3) supporting faculty members in procuring funding to improve courses, and (4) refining professional societies’ evaluation criteria for undergraduate chemistry programs.
Housing Satisfaction of The Indonesian Adolescents

This study aims to understand the determining factors of housing satisfaction of the Indonesian adolescent, who are potential homebuyers. It investigates the factors of housing satisfaction in multi-stages, such as the socio-demographic attributes and housing attributes. It assists to unveil the distinctive and prominent housing attributes for the residents, according to their determining socio-demographic attributes for housing satisfaction in a quantitative method. From the collected 534 respondents, age and monthly income are the pivotal socio-demographic factors of housing satisfaction. Location and neighborhood are the housing norms with constant factorial attribute to housing satisfaction across the age and monthly income groups. While space and expenditure are varies in both groups. These findings also provide a general understanding of the important physical and social features of each housing norm to meet the housing satisfaction of the residents.