Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic groundwater quality management: Role of spatial variability and conditioning

In this paper a methodology for the stochastic management of groundwater quality problems is presented. A stochastic algorithm to solve the coupled flow and mass transport inverse problem is combined with a stochastic management approach to design a reliable pump‐and‐treat scheme. Three main differences exist between this research and previously published research in the field of groundwater management. The first is the method of parameterization, where we have explicitly considered spatial variability of flow and mass transport parameters in our method. Second, we have used adjoint and/or sensitivity equations to calculate first and second derivatives of state variables with respect to hydraulic parameters. Finally, the third point is the nonlinear multiobjective formulation we adopt which enables us to explore the whole trade‐off curve between reliability and cost of remediation. A number of key results are listed here. The stochastic inverse algorithm used here, the representers method, makes it possible to estimate heterogeneity as reflected by the measurements of parameters and state variables. Meanwhile, using the adjoint operator to accurately estimate sensitivity of state variables with respect to the parameters has facilitated the use of the head and concentration measurements in the inverse algorithm. Moreover, stochastic management, which is conditioned on head and concentration measurements simultaneously are shown to provide more realistic and reliable remediation schemes. Finally, with the aid of the multiobjective formulation of the management problem and analysis of its results, it is possible to identify a significant remediation scheme. Such a scheme has the advantage of producing maximum possible reliability with the least possible increase in the total cost of remediation.
Mobile Bluetooth Control Car

Abstract: Our project aim is to design Bluetooth control car which are going to interface with the android app. To developed an android application android studio application is used. In that application we have given the movement to move a Bluetooth car like left, right, turn left, turn right etc. Next step is creating that car which is connected to android app via Bluetooth module HC-05. Our main part to control car using Arduino software. In that software we havewritten our code and upload it on the Arduino uno whichis connected to car. Power supply is given by 12V lithium-ion battery. Operational range of Bluetooth module that is used is around 10 meters approx. Next main part is motor driver circuit to connect the tt gear motors to it. In this project we are actually trying to analyse the working of car how that is controlling via Bluetooth. How all part is working with co-ordination with each other. At last, we have tested the movement of Bluetooth car using android application.
Measurement of the drift of a droplet due to the presence of a plane

The drift of a deformable droplet of low viscosity (viscosity ratio λ=0.08) in a Couette device is examined. The drift is measured both in the plane of shear (due to the rigid outer bounding walls of the Couette device) and also normal to the plane of shear (due to the upper bounding stress‐free surface). A general relationship between normal stresses induced by the deformation of a droplet in an arbitrary shear flow and the leading‐order drift normal to rigid and stress‐free plane surfaces is described theoretically. This relationship is consistent with previous theoretical predictions for droplet migration in shear flows, and is used to compare results from the drift measurement experiments with first‐order deformation theories. The measured drift velocities are in reasonable agreement with the theory of Schowalter et al. [J. Colloid Interface Sci. 26, 152 (1968)].
Minimizing cell size dependence in micromagnetics simulations with thermal noise

Langevin dynamics treats finite temperature effects in a micromagnetics framework by adding a thermal fluctuation field to the effective field. Several works have addressed the dependence of numerical results on the cell size used to split the ferromagnetic samples on the nanoscale regime. In this paper, some former problems dealing with the dependence on the spatial discretization at finite temperature have been revised. We have focused our attention on the stability of the numerical schemes used to integrate the Langevin equation. In particular, a detailed analysis of results was carried out as a function of the time step. It was confirmed that the mentioned dependence can be minimized if an unconditional stable integration method is used to numerically solve the Langevin equation.
A Finite Volume Method for the Approximation of Diffusion Operators on Distorted Meshes

A new finite volume method is presented for discretizing general linear or nonlinear elliptic second-order partial-differential equations with mixed boundary conditions. The advantage of this method is that arbitrary distorted meshes can be used without the numerical results being altered. The resulting algorithm has more unknowns than standard methods like finite difference or finite element methods. However, the matrices that need to be inverted are positive definite, so the most powerful linear solvers can be applied. The method has been tested on a few elliptic and parabolic equations, either linear, as in the case of the standard heat diffusion equation, or nonlinear, as in the case of the radiation diffusion equation and the resistive diffusion equation with Hall term.
Involving machine learning techniques in heart disease diagnosis: a performance analysis

Artificial intelligence is a science that is growing at a tremendous speed every day and has become an essential part of many domains, including the medical domain. Therefore, countless artificial intelligence applications can be seen in the medical domain at various levels, which are employed to enhance early diagnosis and prediction and reduce the risks associated with many diseases, including heart diseases. In this article, machine learning techniques (logistic regression, random forest, artificial neural network, support vector machines, and k-nearest neighbors) are utilized to diagnose heart disease from the Cleveland Clinic dataset got from the University of California Irvine machine learning (UCL) repository and Kaggle platform then create a comparison between the performance of these techniques. In addition, some literature related to machine learning and deep learning techniques that aim to provide reasonable solutions in monitoring, detecting, diagnosing, and predicting heart disease and how these technologies assist in making health decisions are reviewed. Ten studies are selected and summarized by the authors published between 2017 and 2022 are illustrated. After executing a series of tests, it is seen that the most profitable performance in diagnosing heart disease is the support vector machines, with a diagnostic accuracy of 96%. This article has concluded that these techniques play a significant and influential role in assisting physicians and health care workers in analyzing heart patients' data, making health decisions, and saving patients' lives.
Supervised and unsupervised data mining approaches in loan default prediction

Given the paramount importance of data mining in organizations and the possible contribution of a data-driven customer classification recommender systems for loan-extending financial institutions, the study applied supervised and supervised data mining approaches to derive the best classifier of loan default. A total of 900 instances with determined attributes and class labels were used for the training and cross-validation processes while prediction used 100 new instances without class labels. In the training phase, J48 with confidence factor of 50% attained the highest classification accuracy (76.85%), k-nearest neighbors (k-NN) 3 the highest (78.38%) in IBk variants, naïve Bayes has a classification accuracy of 76.65%, and logistic has 77.31% classification accuracy. k-NN 3 and logistic have the highest classification accuracy, F-measures, and kappa statistics. Implementation of these algorithms to the test set yielded 48 non-defaulters and 52 defaulters for k -NN 3 while 44 non-defaulters and 56 defaulters under logistic. Implications were discussed in the paper.
Evaluation of endogenous species involved in brain tumors using multiphoton photoacoustic spectroscopy

It has been shown that using non-resonant multiphoton photoacoustic spectroscopy (NMPPAS), excised brain tumor (grade III astrocytoma) and healthy tissue can be differentiated from each other, even in neighboring biopsy samples[1, 2]. Because of this, this powerful technique offers a great deal of potential for use as a surgical guidance technique for tumor margining with up to cellular level spatial resolution[3]. NMPPAS spectra are obtained by monitoring the non-radiative relaxation pathways via ultrasonic detection, following two-photon excitation with light in the optical diagnostic window (740nm-1100nm). Based upon significant differences in the ratiometric absorption of the tissues following 970nm and 1100nm excitation, a clear classification of the tissue can be made. These differences are the result of variations in composition and oxidation state of certain endogenous biochemical species between healthy and malignant tissues. In this work, NADH, NAD+ and ATP were measured using NMPPAS in model gelatin tissue phantoms to begin to understand which species might be responsible for the observed spectral differences in the tissue. Each species was placed in specific pH environments to provide control over the ratio of oxidized to reduced forms of the species. Ratiometric analyses were then conducted to account for variability caused due to instrumental parameters. This paper will discuss the potential roles of each of the species for tumor determination and their contribution to the spectral signature.
Propagation of Large Amplitude Waves in Annealed Aluminum

An experimental study of large amplitude compression waves in annealed aluminum for the case of constant velocity impact has given a close agreement with the strain rate independent theory of one‐dimensional plastic wave propagation. Radial effects in the first diameter are observed and calculations of Poisson's ratio indicate the presence of relatively large dilatation. Propagation velocities, maximum strain, and energy considerations are in agreement with predictions from the slopes of the static stress‐strain curve. These data were obtained using the author's diffraction grating technique for measuring strain and surface angle.
Supreme court dialogue classification using machine learning models

Legal classification models help lawyers identify the relevant documents required for a study. In this study, the focus is on sentence level classification. To be more precise, the work undertaken focuses on a conversation in the supreme court between the justice and other correspondents. In the study, both the naïve Bayes classifier and logistic regression are used to classify conversations at the sentence level. The performance is measured with the help of the area under the curve score. The study found that the model that was trained on a specific case yielded better results than a model that was trained on a larger number of conversations. Case specificity is found to be more crucial in gaining better results from the classifier.
Selection pressure transforms the nature of social dilemmas in adaptive networks

We have studied the evolution of cooperation in structured populations whose topology coevolves with the game strategies of the individuals. Strategy evolution proceeds according to an update rule with a free parameter, which measures the selection pressure. We explore how this parameter affects the interplay between network dynamics and strategy dynamics. A dynamical network topology can influence the strategy dynamics in two ways: (i) by modifying the expected payoff associated with each strategy and (ii) by reshaping the imitation network that underlies the evolutionary process. We show here that the selection pressure tunes the relative contribution of each of these two forces to the final outcome of strategy evolution. The dynamics of the imitation network plays only a minor role under strong selection, but becomes the dominant force under weak selection. We demonstrate how these findings constitute a mechanism supporting cooperative behavior.
Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning

Public sector decision-making typically involves complex problems that are riddled with competing performance objecttives and possess design requirements which are difficult to capture at the time that supporting decision models are constructed. Environmental policy formulation can prove additionally complicated because the various system components often contain considerable stochastic uncertainty and frequently numerous stakeholders exist that hold completely incompatible perspectives. Consequently, there are invariably unmodelled performance design issues, not apparent at the time of the problem formulation, which can greatly impact the acceptability of any proposed solutions. While a mathematically optimal solution might provide the best solution to a modelled problem, normally this will not be the best solution to the underlying real problem. Therefore, in public environmental policy formulation, it is generally preferable to be able to create several quantifiably good alternatives that provide very different approaches and perspectives to the problem. This study shows how a computationally efficient simulation-driven optimization approach that com- bines evolutionary optimization with simulation can be used to generate multiple policy alternatives that satisfy required system criteria and are maximally different in decision space. The efficacy of this modelling-to-generate-alternatives method is specifically demonstrated on a municipal solid waste management facility expansion case.
A computational study of some numerical schemes for a test case with steep boundary layers

In this paper, three numerical methods have been used to solve a 1-D Convection-Diffusion equation with specified initial and boundary conditions. The methods used are the third order upwind scheme [1], fourth order upwind scheme [1] and a Non-Standard Finite Difference (NSFD) scheme [4]. The problem we considered has steep boundary layers near x=1 [3] and this is a challenging test case as many schemes are plagued by nonphysical oscillation near steep boundaries. We compute the L2 and L∞ errors, dissipation and dispersion errors when the three numerical schemes are used and observe that the NSFD is much better than the other two schemes for both coarse and fine grids and also at low and high Reynolds numbers.
Community Structure Detection Algorithm Based on the Node Belonging Degree

In this paper, we propose a novel algorithm to identify communities in complex networks based on the node belonging degree. First, we give the concept of the node belonging degree, and then determine whether a node belongs to a community or not according to the belonging degree of the node with respect to the community. The experiment results of three real-world networks: a network with three communities with 19 nodes, Zachary Karate Club and network of American college football teams show that the proposed algorithm has satisfactory community structure detection. DOI: http://dx.doi. org/10.11591/telkomnika.v11i12. 3694 Full Text: PDF
Prognostic Equation for Radar Radial Velocity Derived by Considering Atmospheric Refraction and Earth Curvature

AbstractThe prognostic equation for the radial velocity field observed with a Doppler radar is derived to include the effects of atmospheric refraction and earth curvature on radar-beam height and slope angle. The derived equation, called the radial velocity equation, contains a high-order small term that can be truncated. The truncated radial velocity equation is shown to be much more accurate than its counterpart radial velocity equation derived without considering the effects of atmospheric refraction and earth curvature. The truncated equation has the same concise form as its counterpart radial velocity equation but remains to be sufficiently accurate as a useful dynamic constraint for radar wind analysis and assimilation (in normal situations) even up to the farthest 300-km radial range of operational Weather Surveillance Radar-1988 Doppler (WSR-88D) scans where its counterpart radial velocity equation becomes erroneous.
The Actual Benefits of Zinc for Cardiovascular Diseases: Mini Review

The leading causes of death, particularly among the elderly, are cardiovascular illnesses, which are frequently linked to altered lifestyle patterns. Numerous academic research on health and disease have stressed the role of micronutrients like zinc. Furthermore, both healthy and unwell patients are increasingly consuming dietary supplements that contain micronutrients for wellbeing. Cardiovascular disease can be brought on by trace element deficiencies, particularly zinc insufficiency. This study's goal is to analyze current theories regarding the benefits of zinc supplementation for people with cardiovascular problems. We used reliable websites like Google Scholar, PubMed
Estimation de l'incertitude et prévision d'ensemble avec un modèle de chimie transport - Application à la simulation numérique de la qualité de l'air

La these s'attache a evaluer la qualite d'un modele de chimie-transport, non pas par une comparaison classique aux observations, mais en estimant ses incertitudes a priori dues aux donnees d'entrees, a la formulation du modele et aux approximations numeriques. L'etude de ces trois sources d'incertitude est menee respectivement grâce a des simulations Monte Carlo, des simulations multi-modeles et des comparaisons entre schemas numeriques. Une incertitude elevee est mise en evidence, pour les concentrations d'ozone. Pour depasser les limitations dues a l'incertitude, une strategie reside dans la prevision d'ensemble. En combinant plusieurs modeles (jusqu'a quarante-huit modeles) sur la bases des observations passees, les previsions peuvent etre significativement ameliorees. Ce travail a aussi ete l'occasion de developper un systeme de modelisation innovant, Polyphemus.
Effect of Void Network on Cosmic Microwave Background Anisotropy

We study the effect of a void network on the cosmic microwave background (CMB) anisotropy in the Einstein-de Sitter background using Thompson & Vishniac's model. We consider comprehensively the Sachs-Wolfe effect, the Rees-Sciama effect, and the gravitational lensing effect. Our analysis includes the model of primordial voids existing at recombination, which is realized in some inflationary models associated with a first-order phase transition. If there exist primordial voids whose comoving radius is larger than ~10 h-1 Mpc at recombination, not only the Sachs-Wolfe effect but also the Rees-Sciama effect is appreciable even for multipoles l≲1000 of the anisotropy spectrum. The gravitational lensing effect, on the other hand, slightly smooths the primary anisotropy; quantitatively, our results for the void model are similar to the previous results for a cold dark matter model. All the effects together would give some constraints on the configuration or origin of voids with high-resolution data of the CMB anisotropy.
Intermediate state in pressurized silica glass: Reversibility window analogue

We propose that silica glass exists in a special intermediate state for pressures between ; 3 and 5 GPa, which can be identified as an analogue of the ‘‘reversibility window’’ observed in chalcogenide glasses. In this state there is a gradual reduction of the degree of low-energy flexibility of the structure, which matches a gradual change in the average coordination number. At lower pressures ( P,;3 GPa) the structure can accommodate buckling of the structure network without any deformations of the SiO4 polyhedra, and at higher pressures ( P.;5 GPa) the structure is rigid and deforms through rearrangements of the bonding on a local scale. For pressures within the 3-5-GPa window, there is balance between the two mechanisms, with the flexibility assisting in the global rebonding processes. The most dramatic manifestation of the window is seen at high temperature, where there is a much greater relative volume decrease on heating at pressures within the window than for other pressures, with the window widening at higher temperatures.
The development of a turbulent wake in a distorting duct

Characteristics of the turbulent motion in a cylinder wake have been measured during passage through a distorting section of a wind tunnel, the overall effect of the distortion being considerable lateral extension and compression without a considerable change of flow velocity. However, the sectional area is not constant and rates of longitudinal extension are comparable with the rates of lateral straining. Hot-wire anemometers, mostly in X-configurations, are used to measure mean velocities, turbulent intensities, Reynolds stresses, intermittency factors and spectra, and velocity correlations have been calculated from digital recordings of the outputs from arrays of eight single-wire anemometers. In contrast to previous investigations, the direction of compression is parallel to the axis of the cylinder, and the original entrainment eddies of the plane wake are suppressed rather than amplified. The results show that substantial changes in stress-intensity ratios, entrainment rates, dissipation rates and turbulence length-scales occur in response to the three-dimensional distortion. In particular, the ratio of Reynolds stress to total intensity increases during the initial acceleration of the flow before decreasing as the flow is strained laterally, and correlations show that length-scales do not change in proportion to the lateral extension and become relatively small compared to the flow width.
Some developments in the theory of turbulence

This is in no way intended as a review of turbulence-the subject is far too big for adequate treatment within a reasonably finite number of pages; the monumental treatise of Monin & Yaglom (1971, 1975) bears witness to this statement. It is rather a discourse on those aspects of the problem of turbulence with which I have myself had contact over the last twenty years or so. My choice of topics therefore has a very personal bias - but that is perhaps consistent with the style and objectives of this rather unusual issue of JFM. I have approached the dynamical problem of turbulence via two simpler (but nevertheless far from trivial) problems – viz the convection and diffusion of a passive scalar field and of a passive vector field by turbulence of known statistical properties. Particular emphasis is given to the method of successive averaging (a simplified version of the renormalization-group technique) which seems to me to have considerable potential. The difficulty of extending this method to the dynamical problem is discussed. In a final section (§ 6) I have allowed myself the luxury of discussing a somewhat esoteric topic - the problem of inviscid invariants and their relationship with the topological structure of a complex vorticity field. The helicity invariant is the prototype; it is identifiable with the Hopf invariant (1931) and it may be obtained from appropriate manipulation of Noether's theorem (Moreau 1977). A suggestion is made concerning possible measurement of this fundamental measure of ‘lack of reflexional symmetry’ in a turbulent flow.