Miracle-dz/UniFAR-FTUs huggingface dataset:
LoRa-Based IoT Network Assessment in Rural and Urban Scenarios

The implementation of smart networks has made great progress due to the development of the Internet of Things (IoT). LoRa is one of the most prominent technologies in the Internet of Things industry, primarily due to its ability to achieve long-distance transmission while consuming less power. In this work, we modeled different environments and assessed the performances of networks by observing the effects of various factors and network parameters. The path loss model, the deployment area size, the transmission power, the spreading factor, the number of nodes and gateways, and the antenna gain have a significant effect on the main performance metrics such as the energy consumption and the data extraction rate of a LoRa network. In order to examine these parameters, we performed simulations in OMNeT++ using the open source framework FLoRa. The scenarios which were investigated in this work include the simulation of rural and urban environments and a parking area model. The results indicate that the optimization of the key parameters could have a huge impact on the deployment of smart networks.
THE JONES POLYNOMIAL OF KNOTS WITH SYMMETRIC UNION PRESENTATIONS

A symmetric union is a diagram of a knot, obtained from diagrams of a knot in the 3-space and its mirror image, which are symmetric with respect to an axis in the 2-plane, by connecting them with 2-tangles with twists along the axis and 2-tangles with no twists. This paper presents an invariant of knots with symmetric union presentations, which is called the minimal twisting number, and the minimal twisting number of 1042 is shown to be two. This paper also presents a sufficient condition for non-amphicheirality of a knot with a certain symmetric union presentation.
Consistent estimation and testing in comparing analytical bias models

Regression techniques are commonly applied to compare two or more analytical methods at several concentration levels. The paper considers consistent estimation in measurement error models with known different variances typically used in such situations. The approach is based on the corrected score methodology, which allows the derivation of consistent estimators for the model parameters and also for the asymptotic covariance matrix of the parameter estimators. Thus, Wald type statistics are proposed for testing hypothesis related to the bias of the analytical methods with the asymptotic chi‐square distribution, which guarantees correct asymptotic significance levels. Results of small‐scale simulation studies are reported to illustrate comparisons with other approaches. Applications to real data sets are also considered. Copyright © 2005 John Wiley & Sons, Ltd.
Asymmetric number–space association leads to more efficient processing of congruent information in domestic chicks

COPYRIGHT © 2023 Loconsole, Regolin and Rugani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Asymmetric number–space association leads to more e cient processing of congruent information in domestic chicks
The Use of Multivariate Generated Regressors in the Presence of Heteroskedasticity

Abstract This paper describes the estimation and testing of regression models that include multivariate generated or computed regressors in the presence of heteroskedasticity in the cross-section case. Hetero- skedasticity is often a problem in cross-section data and the usual tests for its presence cannot be applied when the heteroskedasticity is in some measure due to computed regressors. We investigate the case of multiple computed regressors that are generated from the results of a system of seemingly unrelated regressions and we propose a method to test and correct the covariance estimates for unknown heteroskedasticity in the errors of the model of interest. In contrast to most time-series applications, we allow for the observations in the first step regression to be different from those for the second stage regression. We present an application in which a hedonic model of home purchases is estimated with a set of computed regressors defined from a multivariate regression of school quality. We demonstrate that property values do reflect the characteristics of the neighborhood school. We find evidence that property values can reflect the peer effect of a public school in the Dallas Independent School District (DISD).
Analyzing and predicting the risk of death in stroke patients using machine learning

Background Stroke is an acute disorder and dysfunction of the focal neurological system that has long been recognized as one of the leading causes of death and severe disability in most regions globally. This study aimed to supplement and exploit multiple comorbidities, laboratory tests and demographic factors to more accurately predict death related to stroke, and furthermore, to make inferences about the heterogeneity of treatment in stroke patients to guide better treatment planning. Methods We extracted data from the Medical Information Mart from the Intensive Care (MIMIC)-IV database. We compared the distribution of the demographic factors between the control and death groups. Subsequently, we also developed machine learning (ML) models to predict mortality among stroke patients. Furthermore, we used meta-learner to recognize the heterogeneity effects of warfarin and human albumin. We comprehensively evaluated and interpreted these models using Shapley Additive Explanation (SHAP) analysis. Results We included 7,483 patients with MIMIC-IV in this study. Of these, 1,414 (18.9%) patients died during hospitalization or 30 days after discharge. We found that the distributions of age, marital status, insurance type, and BMI differed between the two groups. Our machine learning model achieved the highest level of accuracy to date in predicting mortality in stroke patients. We also observed that patients who were consistent with the model determination had significantly better survival outcomes than the inconsistent population and were better than the overall treatment group. Conclusion We used several highly interpretive machine learning models to predict stroke prognosis with the highest accuracy to date and to identify heterogeneous treatment effects of warfarin and human albumin in stroke patients. Our interpretation of the model yielded a number of findings that are consistent with clinical knowledge and warrant further study and verification.
Dissociation Mode of the O–H Bond in Betanidin, pKa-Clusterization Prediction, and Molecular Interactions via Shape Theory and DFT Methods

Betanidin (Bd) is a nitrogenous metabolite with significant bioactive potential influenced by pH. Its free radical scavenging activity and deprotonation pathway are crucial to studying its physicochemical properties. Motivated by the published discrepancies about the best deprotonation routes in Bd, this work explores all possible pathways for proton extractions on that molecule, by using the direct approach method based on pKa. The complete space of exploration is supported by a linear relation with constant slope, where the pKa is written in terms of the associated deprotonated molecule energy. The deprotonation rounds 1, …, 6 define groups of parallel linear models with constant slope. The intercepts of the models just depend on the protonated energy for each round, and then the pKa can be trivially ordered and explained by the energy. We use the direct approximation method to obtain the value of pKa. We predict all possible outcomes based on a linear model of the energy and some related verified assumptions. We also include a new measure of similarity or dissimilarity between the protonated and deprotonated molecules, via a geometric–chemical descriptor called the Riemann–Mulliken distance (RMD). The RMD considers the cartesian coordinates of the atoms, the atomic mass, and the Mulliken charges. After exploring the complete set of permutations, we show that the successive deprotonation process does not inherit the local energy minimum and that the commutativity of the paths does not hold either. The resulting clusterization of pKa can be explained by the local acid and basic groups of the BD, and the successive deprotonation can be predicted by using the chemical explained linear models, which can avoid unnecessary optimizations. Another part of the research uses our own algorithm based on shape theory to determine the protein’s active site automatically, and molecular dynamics confirmed the results of the molecular docking of Bd in protonated and anionic form with the enzyme aldose reductase (AR). Also, we calculate the descriptors associated with the SET and SPLET mechanisms.
ON THEORETICAL MODELS FOR COMPETITIVE AND PREDATORY BIOLOGICAL SYSTEMS

Differences of opinion are obviously possible on the degree to which admittedly oversimplified theoretical models can explain some of the complex observational phenomena to be found in nature. Criticisms from biologists of the mathematical work of Lotka (1925), Volterra (1926) and subsequent writers on the growth and interaction of biological populations have, however, sometimes been justified and sometimes unjustified, for in spite of inevitable limitations such work constitutes a permanent contribution to the understanding of how populations may behave. A significant constructive survey was made by Gause (1934) when he attempted to bridge the gap between theoretical models and natural biological phenomena by controlled laboratory experiments in animal ecology. While experiments also have limitations as representations of nature, the role of both theory and experiment in the physical sciences might be recalled by any biologists inclined to be sceptical of the value of either. The interrelation of these approaches in biology may be illustrated in the field of epidemiology. Here the vicissitudes of infected populations have been studied in the laboratory as well as in the field; but, as I have emphasized elsewhere (Bartlett, 1956, 1957), the properties of theoretical models indicate, among other things, the extent to which population size may sometimes be crucial in the probable sequence of events, and thus indicate to what extent laboratory observations will have any similarity to larger-scale field observations even if the same model applies to both. An essential point is that recent theoretical formulations explicitly recognize the discrete character of populations and the stochastic or random aspect of changes, as distinct from strictly deterministic formulations. The need for this in ecology, which was already perhaps envisaged by Gause (1934, p. 124), is quite apparent in the experiments by Park with the flour-beetle Tribolium, in which one of two species together in a container survived not every time, but with a definite probability (e.g. 30 % of times), that could be estimated by replication and changed by changing the environment (see, for example, Neyman, Park & Scott, 1956). There is now no mathematical difficulty in the formulation of stochastic models (see, for example, Bartlett, 1955 a, 1956), and such formulations have already been made for typical ecological models by Chin Long Chiang (see Kempthorne et al. 1954). The greater intractability of even the simplest of these is, however, a serious obstacle to progress, especially in animal ecology, where even in the deterministic formulations of Lotka and Volterra many simplifications, such as neglect of age structure or of other heterogeneity, were made. One aim of the ensuing discussion is to indicate the enhanced value of deterministic formulations of population dynamics when properly interpreted within more comprehensive stochastic
A Fast Algorithm for Image Deblurring with Total Variation Regularization

We propose and test a simple algorithmic framework for recovering images from blurry and noisy observations based on total variation (TV) regularization when a blurring point-spread function is given. Using a splitting technique, we construct an iterative procedure of alternately solving a pair of easy subproblems associated with an increasing sequence of penalty parameter values. The main computation at each iteration is three Fast Fourier Transforms (FFTs). We present numerical results showing that a rudimentary implementation of our algorithm already performs favorably in comparison with two of the existing start-of-the-art algorithms. In particular, it runs orders of magnitude faster than a number of existing algorithms for solving TVL2-based de-convolution problems to good accuracies.
Wavelength tunable laser diodes with Si-wire waveguide ring resonator wavelength filters

We fabricated wavelength tunable laser diodes consisting of ring resonator wavelength filters with Si-wire waveguides. More than 45 nm wavelength tuning operation was obtained by about 100 mW heating power. We also measured spectral linewidth of the tunable laser, and observed about 200 kHz of spectral linewidth.
Convolutional Neural Network for Cancer Treatment Response Prediction

Recently, efforts are exerted on cancer treatment prediction based on the biomarkers related to the tumor. Many biomarkers are used to discriminate cancer but there is no accurate evidence which is more accurate. To save resources and due to the enormous computational power, the analysis of huge sequences such as DNA (DesoxyriboNucleic Acid) becomes a valid process. Machine learning and deep learning algorithms have been used to predict drug response. The indicators show that the performance of deep learning models is better than the performance of machine learning based one. In this paper, we introduce the use of Convolutional Neural Network (CNN) to predict the response of 265 different drugs using the gene expression profile and mutation profile of 19702 genes. DeepInsight algorithm used to convert the input data to images to be more suitable as input to the CNN. We proposed eight CNNs with different architectures. At first, the whole data input passes through the DeepInsight model which converts the gene expression data and mutation data to images. Dimension reduction is then applied using the helper technique inside the DeepInsignt. The CNN are trained with different settings such as image size, no of nodes in layers…etc. Comparative analysis shows that the proposed architectures improve the prediction accuracy in a range between 16% and 24% as a reduction in mean squared error (MSE).
Solutions to the Vertical Structure Equation for Simple Models of the Tropical Troposphere

AbstractObservation and modeling studies indicate that the wave flux from tropical heating sources that propagates into the lower stratosphere is sensitive to the buoyancy frequency profile N(z) in the troposphere. This sensitivity is explained by examining analytic solutions to the vertical structure equation for various simplified models of the tropical troposphere. An efficient method for obtaining expressions for these analytic solutions when N(z) is piecewise constant is presented. The solution is expressed in terms of reflection and transmission coefficients. It is found that the response to heating for Hough modes with small equivalent depth is quite sensitive to the shape of the heating profile, the magnitude of N(z) within the heating profile, and the internal wave reflections that result from the sharp change in N(z) at the tropopause. The location of the primary peak in the wave response, which occurs where the wavelength is twice the depth of the heating for a constant N(z) profile, is also se.
Can Export Market Diversification Mitigate Agricultural Export Volatility? A Trade Network Perspective

ABSTRACT Using the social network analysis (SNA) method to calculate the level of agricultural export market diversification in various countries, this study examined the impact of agricultural export market diversification on export volatility from the supply network perspective based on HS6-digit agricultural export data. We identified that the agricultural export market diversification significantly reduces the export volatility. Specifically, the effect of diversifying the export market of processed agricultural products in stabilizing export volatility is greater than that of primary agricultural products. The stabilizing effect of emerging markets and developing countries or regions is more evident than developed countries or regions. Moreover, the export market diversification can stabilize the volatility of agricultural exports through two mechanisms: increasing the international market share and extending the export duration.
Vectorial solitary waves in optical media with a quadratic nonlinearity

We search for self-trapped beams due to the vectorial interaction of two orthogonally polarized components of a fundamental harmonic and a single component of the second harmonic in a quadratically nonlinear medium. The basic set of equations for both the temporal and the spatial case is derived. The resulting two-parameter family of solitary waves is investigated by means of a variational approximation and by direct numerical methods. Several limiting cases and differences to the scalar interaction in quadratic media are discussed. The propagation of stable solitary waves and mutual collisions are simulated and the decay of unstable solitary waves is demonstrated. We predict an internal boundary in the soliton parameter space which separates stable and unstable domains.
Neural Drug Discovery

In the field of drug production, machine learning methods have already established themselves as a reliable tool for performing various tasks, among which are the prediction of the structure of biologically active molecules and the initial selection of drug candidates. Predictive models based on machine learning have gained great importance almost at every stage of drug development process significantly reducing the financial and time costs for the discovery of a new drug. Despite this, to date there are only a few review papers covering their use in the preparation and production of new drugs. The main goal of this work is to explore the usage of machine learning methods in the field of drug discovery and development as well as to structure the knowledge about some of machine learning methods that are already used in the drug development process. Such an analysis of the current state of affairs in the chosen field will give us an idea of the future prospects for the development of chemical informatics, of the limitations that modern scientists face, as well as possible ways to overcome them. The second part of the work is devoted to experimental work with some of the commonly machine learning algorithms in drug discovery and development. We constructed a Neural Network and managed to tune some hyperparameters on the datasets to outperform some benchmarks.
The Additive Value of Radiomics Features Extracted from Baseline MR Images to the Barcelona Clinic Liver Cancer (BCLC) Staging System in Predicting Transplant-Free Survival in Patients with Hepatocellular Carcinoma: A Single-Center Retrospective Analysis

Background: To study the additive value of radiomics features to the BCLC staging system in clustering HCC patients. Methods: A total of 266 patients with HCC were included in this retrospective study. All patients had undergone baseline MR imaging, and 95 radiomics features were extracted from 3D segmentations representative of lesions on the venous phase and apparent diffusion coefficient maps. A random forest algorithm was utilized to extract the most relevant features to transplant-free survival. The selected features were used alongside BCLC staging to construct Kaplan–Meier curves. Results: Out of 95 extracted features, the three most relevant features were incorporated into random forest classifiers. The Integrated Brier score of the prediction error curve was 0.135, 0.072, and 0.048 for the BCLC, radiomics, and combined models, respectively. The mean area under the receiver operating curve (ROC curve) over time for the three models was 81.1%, 77.3%, and 56.2% for the combined radiomics and BCLC models, respectively. Conclusions: Radiomics features outperformed the BCLC staging system in determining prognosis in HCC patients. The addition of a radiomics classifier increased the classification capability of the BCLC model. Texture analysis features could be considered as possible biomarkers in predicting transplant-free survival in HCC patients.
Contact algorithms for the material point method in impact and penetration simulation

The inherent no‐slip contact constraint in the standard material point method (MPM) creates a greater penetration resistance. Therefore, the standard MPM was not able to treat the problems involving impact and penetration very well. To overcome these deficiencies, two contact methods for MPM are presented and implemented in our 3D explicit MPM code, MPM3D. In MPM, the impenetrability condition may not satisfied on the redefined regular grid at the beginning of each time step, even if it has been imposed on the deformed grid at the end of last time step. The impenetrability condition between bodies is only imposed on the deformed grid in the first contact method, while it is imposed both on the deformed grid and redefined regular grid in the second contact method. Furthermore, three methods are proposed for impact and penetration simulation to determine the surface normal vectors that satisfy the collinearity conditions at the contact surface. The contact algorithms are verified by modeling the collision of two elastic rings and sphere rolling problems, and then applied to the simulation of penetration of steel ball and perforation of thick plate with a particle failure model. In the simulation of elastic ring collision, the first contact algorithm introduces significant disturbance into the total energy, but the second contact algorithm can obtain the stable solution by using much larger time step. It seems that both contact algorithms give good results for other problems, such as the sphere rolling and the projectile penetration. Copyright © 2010 John Wiley & Sons, Ltd.
Dust envelopes around RV Tauri stars

En considerant le diagramme IRAS couleur-couleur [12]-[25], [25]-[60], les etoiles RV Tauri peuplent les regions de temperature froide (T<600 K), de maniere differente des regions occupees par les etoiles Mira au carbone et oxygene. Les flux IRAS sont compatibles avec une densite de poussiere dans l'enveloppe circumstellaire variant comme le carre inverse de la distance radiale, impliquant des processus de formation des grains continus et non pas sporadiques. Les sous-groupes spectroscopiques A et B sont bien separes dans le diagramme 2 couleurs IR-lointain avec les objets du groupe B possedant des enveloppes de poussiere plus froides
Sherlock—A Free and Open-Source System for the Computer-Assisted Structure Elucidation of Organic Compounds from NMR Data

The structure elucidation of small organic molecules (<1500 Dalton) through 1D and 2D nuclear magnetic resonance (NMR) data analysis is a potentially challenging, combinatorial problem. This publication presents Sherlock, a free and open-source Computer-Assisted Structure Elucidation (CASE) software where the user controls the chain of elementary operations through a versatile graphical user interface, including spectral peak picking, addition of automatically or user-defined structure constraints, structure generation, ranking and display of the solutions. A set of forty-five compounds was selected in order to illustrate the new possibilities offered to organic chemists by Sherlock for improving the reliability and traceability of structure elucidation results.