Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixing and particle dispersion in the wavy vortex regime of Taylor–Couette flow

Fluid flow and particle dispersion were investigated numerically in the wavy vortex regime of Taylor-Couette flow. The flow field for wavy vortex flow is stationary when viewed in a frame rotating with the azimuthal wave velocity. These steady flow fields are used to track fluid particles and to estimate the effective axial difision resulting from chaotic fluid aduection. The effective diffusion coefficient for a fixed wave state is a function of the Reynolds number. Particle dispersion is a strong function of wave state, showing that a universal relationship between dispersion and Reynolds number cannot be found in this regime of cylindrical Taylor-Couette flow. The effectiue Schmidt number of chaotic advection is less than unity for all wavy vortex flows examined, indicating that chaotic advection plays an important role in fluid mixing in these flow regimes. Fluid particle retention in the cores of the wavy vortices is also predicted for some parameter regimes, although not all. Particles trapped in vortex cores are only poorly mixed within the core and play no role in global mixing. A preliminary examination of inertial particle settling suggests that the fluid flow does not prevent settling in the mean, although the magnitude of the settling velocity significantly affects the dispersion of inertial particles.
Dynamical monopoles and dipoles in a condensed molecular system: The case of liquid water

We propose a definition for the dipole of an individual molecule in a molecular solid or fluid. This problem is currently dealt with by partitioning-according to some prescription-the continuous electron density of the condensed phase. Instead, here we focus directly on the coupling of externally applied electric fields to the molecular motion. Our concept of dipole is therefore based on experimentally accessible quantities, such as the infrared spectra and, more generally, the low-frequency dielectric properties. For an isolated molecule, the coupling to an external field is trivially measured by its static dipole: we define the corresponding (less trivial) quantity in the condensed phase. Our theory can be regarded as a generalization of the one currently used for ionic and polar crystals, where the coupling between atomic displacements and electric fields is measured by the dynamical charge, also known as infrared charge. We show that, in a molecular system, the coupling of an electric field to librational modes is measured by a second-rank tensor; similarly, the coupling to translational modes is also measured by a second-rank tensor: we call them dynamical molecular dipole and dynamical molecular monopole, respectively. First-principles calculations provide us with explicit values for these two tensors in the case of liquid water. The monopole tensor is found to carry nonvanishing charge for specific directions, although it is overall traceless to ensure charge neutrality. This feature is at the root of the infrared translational band at similar to200 cm(-1). The dipole tensor is dominated by its antisymmetric part, equivalent to a vector of modulus 2.1 D. This value differs significantly from estimates for the static dipole in the liquid (2.5-3.1 D). We also briefly comment on the relevance of our findings to the construction of molecular force fields.
Computer generated simple random samples

Three computer methods, namely the Random Draw, the Sequential and the Reservoir Methods, to obtain a simple random sample without replacement are reviewed. Several implementations of each method are given and criteria for the selection of an implementation are discussed. The most promising ones are compared for efficiency in their execution times. It is concluded that for small sampling fractions, Ernvall and Nevalainen's (1982) implementation of the Random Draw Method is most efficient when N, the population size, is known. When N is unknown, an Algorithm Z of Vitter (1985), an implementation of the Reservoir Method, is recommended. For larger sampling the algorithm of Bissell (1986) and Vitter (1984) is quite efficient.
Colour and Higgs charges in G/H Kaluza-Klein theory

Geodesics in a multidimensional Universe with G-invariant metric are studied and differential equations describing the space-time projection are derived. It is shown that, when internal spaces are cosets rather than group manifolds, then, in addition to the colour charge through which the particle interacts with the gauge field, a new charge arises which couples to the scalar fields only. This new charge, called the Higgs charge, is shown to be of a nonlinear nature. For certain cosets it may contribute to the colour charge non-conservation.
Evaluating Direct Multistep Forecasts

ABSTRACT This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy and encompassing applied to direct, multistep predictions from nested regression models. We first derive asymptotic distributions; these nonstandard distributions depend on the parameters of the data-generating process. We then use Monte Carlo simulations to examine finite-sample size and power. Our asymptotic approximation yields good size and power properties for some, but not all, of the tests; a bootstrap works reasonably well for all tests. The paper concludes with a reexamination of the predictive content of capacity utilization for inflation.
Ice Microphysics Observations in Hurricane Humberto: Comparison with Non-Hurricane-Generated Ice Cloud Layers.

Abstract Measurements are presented that were acquired from the National Aeronautics and Space Administration (NASA) DC-8 aircraft during an intensive 3-day study of Tropical Storm/Hurricane Humberto on 22, 23, and 24 September 2001. Particle size distributions, particle image information, vertical velocities, and single- and dual-wavelength Doppler radar observations were obtained during repeated sampling of the eyewall and outer eye regions. Eyewall sampling temperatures ranged from −22° to −57°C and peak updraft velocities from 4 to 15 m s−1. High concentrations of small ice particles, in the order 50 cm−3 and above, were observed within and around the updrafts. Aggregates, some larger than 7 mm, dominated the larger sizes. The slope of the fitted exponential size distributions λ was distinctly different close to the eye than outside of that region. Even at low temperatures, λ was characteristic of warm temperature growth (λ < 30 cm−1) close to the eye and characteristic of low temperature growth outsi.
Queuing with Impatient Customers and Ordered Service

Two types of customer behavior are considered: (1) if a customer is acquired for service before he has waited a time τ0, he remains in the queue until served irrespective of whether or not his total waiting time exceeds τ0. Only those customers who wait for a time τ0 without being acquired for service become “lost” customers, and (2) a customer whose total waiting time is τ0 becomes a lost customer irrespective of whether he is acquired for service or not.
Sensor fusion to enable next generation low cost Night Vision systems

The next generation of automotive Night Vision Enhancement systems offers automatic pedestrian recognition with a performance beyond current Night Vision systems at a lower cost. This will allow high market penetration, covering the luxury as well as compact car segments. Improved performance can be achieved by fusing a Far Infrared (FIR) sensor with a Near Infrared (NIR) sensor. However, fusing with today's FIR systems will be too costly to get a high market penetration. The main cost drivers of the FIR system are its resolution and its sensitivity. Sensor cost is largely determined by sensor die size. Fewer and smaller pixels will reduce die size but also resolution and sensitivity. Sensitivity limits are mainly determined by inclement weather performance. Sensitivity requirements should be matched to the possibilities of low cost FIR optics, especially implications of molding of highly complex optical surfaces. As a FIR sensor specified for fusion can have lower resolution as well as lower sensitivity, fusing FIR and NIR can solve performance and cost problems. To allow compensation of FIR-sensor degradation on the pedestrian detection capabilities, a fusion approach called MultiSensorBoosting is presented that produces a classifier holding highly discriminative sub-pixel features from both sensors at once. The algorithm is applied on data with different resolution and on data obtained from cameras with varying optics to incorporate various sensor sensitivities. As it is not feasible to record representative data with all different sensor configurations, transformation routines on existing high resolution data recorded with high sensitivity cameras are investigated in order to determine the effects of lower resolution and lower sensitivity to the overall detection performance. This paper also gives an overview of the first results showing that a reduction of FIR sensor resolution can be compensated using fusion techniques and a reduction of sensitivity can be compensated.
Determination of gender from fingerprints using dynamic horizontal voting ensemble deep learning approach

Article history Received October 1, 2022 Revised October 23, 2022 Accepted November 26, 2022 Available online November 30, 2022 Despite tremendous advancements in gender equality, there are still persistent gender disparities, especially in important human activities. Consequently, gender inequality and related concerns are a serious problem in our global society. Major players in the global economy have identified the gender identity system as a crucial stepping stone for bridging the enormous gap in gender-based problems. Extensive research conducted by forensic scientists has uncovered a unique pattern hidden in the fingerprint and these distinguishing characteristics of fingerprints can be utilized to determine the gender of individuals. Numerous research has revealed various fingerprint-based approaches to gender recognition. The purpose of this research is to present a novel dynamic horizontal voting ensemble model with hybrid Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) deep learning algorithm as the base learner to automatically determine human gender attribute based on fingerprint pattern. More than four thousand Live fingerprint images were acquired and subjected to training, testing and classification using the proposed model. Result of this study indicated over 99% accuracy in predicting person’s gender. The proposed model also performed better than other state-of-the-art model such as ResNet-34, VGG-19, ResNet-50 and EfficientNet-B3 model when implemented on SOCOFing public dataset.
Materialisation approaches for Façade-based data access with SPARQL

The Knowledge Graph concept is gaining momentum as an ideal approach to data integration. Therefore, it is of paramount importance to equip knowledge engineers with tools for accessing data from multiple, heterogeneous resources. The successful W3C standard SPARQL is the reference language for interacting with RDF knowledge graphs. For that reason, approaches extend SPARQL for accessing data in non-RDF formats. Recent research proposes relying on an intermediate RDF model, named Façade-X , whose components can be transparently mapped to various file formats. However, although Façade-X specifies how its components map to many different formats (CSV, JSON, HTML, Markdown, and others), it is still unclear how to implement a SPARQL execution engine that relies on it. In other words, what are the possible strategies for executing Façade-X queries? This article explores materialisation approaches for executing Façade-X queries. Specifically, we study two in-memory strategies for performing Façade-X data access with SPARQL. A complete materialised view strategy fully transforms the data source into RDF. Instead, a sliced materialised view strategy segments the data source and generates an RDF view on each part. Both strategies can be optimised by only materialising the part of the RDF graph that has potential matches with triple patterns in the query (triple-filtering). In addition, we compare these approaches with an on-disk alternative, which relies on a temporary database instance. We analyse the characteristics of these methods and perform extensive experiments, reporting on benefits and limitations of both approaches. Finally, we contribute guidelines and best practices derived from the findings.
SPARQL query execution time prediction using Deep Learning

Nowadays new graph-oriented database engines (GDBMS) are capable of managing large volumes of graph data. These engines, like any other database engine, have an internal query optimizer that proposes a query execution plan (QEP) for each query they process. This step is entirely necessary since without it queries to large amounts of data may need hours to terminate. Selecting a “good enough” query plan is a difﬁcult task as there may be thousands of possible query plans to consider just for a single query. The development of a good query planner is considered the “Holy Grail” in database management systems. Recent results show how neural network models can help in the optimization process in Relational Database Management System (RDBMS), however, performance prediction in the area of graph databases is still in its infancy. There are two challenges in predicting graph query performance using machine learning models, yet to be addressed: the ﬁrst is to ﬁnd the most suitable query representation in a format that can be interpreted by Machine Learning (ML) algorithms; the second (which depends on the ﬁrst challenge), is the learning algorithm used to predict performance. Herein we propose a method that actually ﬁlls in such a gap, by providing ﬁrst a query characterization that (second) allows us to develop a deep learning model to estimate query latency later on. We adapt an existing model using convolutions over trees to generate a model that SPARQL queries and evaluate it against Wikidata, using their query logs and data. Our model correctly predicts the latency for queries in 87% of the validation set and we provide an explanation to those queries that we fail to predict the latency.
A Survey of the State of Art for Multilingual Ontologies Classified Based on Application 1

Ontologies are semantic components that organize knowledge and enable context-aware communication among Web services. When modeling ontologies, different aspects are to be considered such as the natural language which represents ontology concepts. An ontology can be modeled using one language, two languages (bi-lingual), or several languages (multi-lingual). Multilingual ontologies have several applications which include biomedical, translation, and educational systems. This paper surveys and classifies the work in the domain of multilingual ontologies based on their applications. It summarizes and demonstrates the application of multilingual ontologies in each domain in terms of application, purpose of study, methodology, and multilingualism. The paper also indicates future research directions for modeling and using multilingual ontologies.
A Unified Model for Stroke Recovery and Rehabilitation

Abstract The current model of stroke care delivery in the United States and in many parts of the world is fragmented, resulting in lack of continuity of care, inability to track recovery meaningfully across the continuum, and lack of access to the frequency, intensity, and duration of high-quality rehabilitation necessary to optimally harness recovery processes. The process of recovery itself has been overshadowed by a focus on length of stay and the movement of patients across levels of care. Here, we describe the rationale behind the recent efforts at the Johns Hopkins Sheikh Khalifa Stroke Institute to define and coordinate an intensive, strategic effort to develop effective stroke systems of care across the continuum through the development of a unified Sheikh Khalifa Stroke Institute model of recovery and rehabilitation.
Timing of Recovery After Surgery for Patients With Degenerative Cervical Myelopathy: An Observational Study From the Canadian Spine Outcomes and Research Network

BACKGROUND: The time course over which postoperative neurological recovery occurs after surgery for degenerative cervical myelopathy occurs is poorly understood. OBJECTIVE: To determine the time point at which patients experience significant neurological improvement. METHODS: We reviewed data from an ongoing prospective multicenter cohort study. We measured neurological function at 3 months, 1 year, and 2 years after surgery using the modified Japanese Orthopedic Association (mJOA) scale. We implemented minimal clinical important differences (MCIDs) to guide interpretation of mJOA scores, and we used 1-way analysis of variance to compare changes between follow-up intervals. RESULTS: Among 330 patients, the mean overall mJOA improved from 12.9 (SD 2.6) to 14.6 (SD 2.4) at 3 months, 14.7 (SD 2.4) at 1 year, and 14.8 (SD 2.5) at 2 years. The difference in means was statistically significant (P < .01) at the interval from baseline to 3 months postoperatively, but not from 3 months to 1 year or 1 year to 2 years. The MCID was reached by 161 patients at 3 months, 32 more at 1 year, and 15 more at 2 years, with a statistically significant difference only at 3 months. Patients with moderate or severe disease reached the MCID more frequently than those with mild disease. CONCLUSION: Among patients who underwent surgery for degenerative cervical myelopathy, most significant neurological improvement occurred by 3 months after surgery. These findings will facilitate valid discussions about postoperative expectations during shared clinical decision making between patients and their surgeons.
Nitric Oxide Synthase Inhibition Protects Against Rotenone-Induced Neurodegeneration in vivo

: Neuropathologic and motor features of Parkinson’s disease can be induced in rodents by the pesticide rotenone. In this study, we aimed to determine whether nitric oxide synthase inhibition by N G -nitro-L-arginine methyl ester (L-NAME) could prevent neurodegeneration and biochemical changes induced by rotenone in rat brain. For this purpose, rats received subcutaneous injections of rotenone (1.5 mg/kg) every other day for two consecutive weeks and were treated at the same time with L-NAME at doses of 10 or 20 mg/kg, intraperitoneally. Oxidative stress indictors, malondialdehyde, reduced glutathione, and nitric oxide as well as paraoxonase-1 (PON-1) activity in brain, and the concentration of the antiapoptotic protein B cell/lymphoma-2 (Bcl2) in striatum were determined. Motor strength and coordination was assessed using wire hanging and stair tests. Neuronal degeneration was evaluated using hematoxylin & eosin-stained brain sections. Results: L-NAME reduced, in a dose-dependent manner the increase in lipid peroxidation (malondialdehyde) and nitric oxide and restored reduced glutathione levels in brain of rotenone-treated rats. Additionally, L-NAME prevented the inhibition of paraoxonase-1 activity and the decrease in Bcl2 concentration induced by the pesticide. It also prevented motor impairments in a dose-dependent manner. Furthermore, L-NAME treatment ameliorated the neurodegenerative effects of rotenone, as shown by the decrease in degenerated neurons in striatum and cerebral cortex. The results of the present study suggest that rotenone mediates its neurotoxic effects, at least in part by nitric oxide-dependent mechanisms.
Feature preserving mesh network for semantic segmentation of retinal vasculature to support ophthalmic disease analysis

Introduction Ophthalmic diseases are approaching an alarming count across the globe. Typically, ophthalmologists depend on manual methods for the analysis of different ophthalmic diseases such as glaucoma, Sickle cell retinopathy (SCR), diabetic retinopathy, and hypertensive retinopathy. All these manual assessments are not reliable, time-consuming, tedious, and prone to error. Therefore, automatic methods are desirable to replace conventional approaches. The accuracy of this segmentation of these vessels using automated approaches directly depends on the quality of fundus images. Retinal vessels are assumed as a potential biomarker for the diagnosis of many ophthalmic diseases. Mostly newly developed ophthalmic diseases contain minor changes in vasculature which is a critical job for the early detection and analysis of disease. Method Several artificial intelligence-based methods suggested intelligent solutions for automated retinal vessel detection. However, existing methods exhibited significant limitations in segmentation performance, complexity, and computational efficiency. Specifically, most of the existing methods failed in detecting small vessels owing to vanishing gradient problems. To overcome the stated problems, an intelligence-based automated shallow network with high performance and low cost is designed named Feature Preserving Mesh Network (FPM-Net) for the accurate segmentation of retinal vessels. FPM-Net employs a feature-preserving block that preserves the spatial features and helps in maintaining a better segmentation performance. Similarly, FPM-Net architecture uses a series of feature concatenation that also boosts the overall segmentation performance. Finally, preserved features, low-level input image information, and up-sampled spatial features are aggregated at the final concatenation stage for improved pixel prediction accuracy. The technique is reliable since it performs better on the DRIVE database, CHASE-DB1 database, and STARE dataset. Results and discussion Experimental outcomes confirm that FPM-Net outperforms state-of-the-art techniques with superior computational efficiency. In addition, presented results are achieved without using any preprocessing or postprocessing scheme. Our proposed method FPM-Net gives improvement results which can be observed with DRIVE datasets, it gives Se, Sp, and Acc as 0.8285, 0.98270, 0.92920, for CHASE-DB1 dataset 0.8219, 0.9840, 0.9728 and STARE datasets it produces 0.8618, 0.9819 and 0.9727 respectively. Which is a remarkable difference and enhancement as compared to the conventional methods using only 2.45 million trainable parameters.