Miracle-dz/UniFAR-FTUs huggingface dataset:
Nested term recognition driven by word connection strength

Domain corpora are often not very voluminous and even important terms can occur in them not as isolated maximal phrases but only within more complex constructions. Appropriate recognition of nested terms can thus influence the content of the extracted candidate term list and its order. We propose a new method for identifying nested terms based on a combination of two aspects: grammatical correctness and normalised pointwise mutual information (NPMI) counted for all bigrams in a given corpus. NPMI is typically used for recognition of strong word connections, but in our solution we use it to recognise the weakest points to suggest the best place for division of a phrase into two parts. By creating, at most, two nested phrases in each step, we introduce a binary term structure. We test the impact of the proposed method applied, together with the C-value ranking method, to the automatic term recognition task performed on three corpora, two in Polish and one in English.
A Contrast Based Image Fusion Technique Using Digital Shearlet Transform and Root Mean Square Contrast

2 Abstract: The process of combining methodology from more images into a single highly contrasted image is called as image fusion. A highly contrasted image extracts the best of input images then conveying into the single image. Image fusion has many applications to get the proper solutions in Satellite imaging, Digital imaging, Medical imaging, Multi-focus imaging etc. Image registration process performs to transform different coordinate systems into one coordinate system. A contrast based image fusion technique is introduced using digital shearlet Transform (DST). A digital shearlet transform utilized for multi resolution decomposition of input images. Since the image sharpness is measured using contrast. The low band coefficients of digital shearlet transform are selected based on the contrast level. The noise represented high band coefficients of digital shearlet transform is selected based on root mean square (RMS) contrast. Then, fuse both low and high band coefficients. Final fused image gets by inverse of the digital shearlet transform. The results of the proposed method gives better Correlation Coefficients (CC), Root Mean Square Error (RMSE), Relative Average Spectral Error (RASE) and Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) values as compare with Wavelet Transform (WT), Dual Tree Continuous Wavelet Transform (DTCWT), Curvelet Transform (CT), Contourlet Transform (CLT) fusion techniques.
The Effect of Two Types of Silicone Insole on Selected Kinetic Variables on the Skill landing-Jumping-Landing Active Young Women with Flat foot

Introduction: Since the use of medical insole is recommended as one of the strategies to deal with the potential for joint injuries, Flatfoot is one of the most common abnormalities in body structure that Due to the decrease in the height of the medial longitudinal arch of the foot and its functional defect, the increases risk of joint damage due to changes in the transfer of ground reaction forces. One of the treatment options that is suggested is the use of a medical insole. The purpose of this study was to investigate the effect of two different forms of silicone insoles on the peak of the ground reaction force, loading rate and time to stability in the landing-jumping-landing skill of active young women with flat foot. Materials and methods: 28 women with a history of regular activity exercise were divided into two groups of 14 (flat foot and normal foot). The subjects landed two legs on a level with a height of 0.5 m on the forwards of the first force plate, jumped without stopping and landed on second force plate. In this study, two types of silicone insoles were used that support the entire sole of the foot (type I), and only the inner portion of the foot and the medial longitudinal arch (Type II). The flat foot subjects performed without using the insole and using two types of insoles. The Data Analysis of data Evaluation of Variables were done by MATLAB software. Also its statistical analysis was done by using spss ( P ≤0.05). Results: The results of statistical analysis of variance analysis showed a significant difference in the amount of ground reaction forces in all three axes between normal and flat foot subjects, while the use of medical insoles only in outer medial- lateral & anterior- posterior directions has been significant. The rate of loading at the first landing showed a significant difference between the subjects with flat foot and normal foot. But there was no significant effect after using the medical insole. However, there was no significant difference between the time to stability in the two groups of flat foot and normal foot. Conclusion: According to the results of the study, it can be said that flat foot abnormality in high intensity plometric skill has effect on the ground reaction forces in all three directions, and the use of a Type I insole can reduce the amount of force in all three directions. On the other hand, the decrease in the height of the longitudinal arch of the foot is associated with the rate of loading during the landing, which is identified as a risk factor for joint and muscular damage. But the use of silicone insoles cannot effect on the rate of loading. The results of this study indicate that the time to stability in active subject does not correlate with the height of the medial longitudinal arch, or the time to stability is corrected in the active subjects with flat feet with the help of compensatory mechanisms. Keywords : Flat Foot, Kinetic, Insole
Evaluation of outlier detection method performance in symmetric multivariate distributions

Abstract Determining outliers is more complicated in multivariate data sets than it is in univariate cases. The aim of this study is to evaluate the blocked adaptive computationally efficient outlier nominators (BACON) algorithm, the fast minimum covariance determinant (FAST-MCD) method, and the robust Mahalanobis distance (RM) method in multivariate data sets. For this purpose, outlier detection methods were compared for multivariate normal, Laplace, and Cauchy distributions with different sample sizes and numbers of variables. False-negative and false-positive ratios were used to evaluate the methods’ performance. The results of this work indicate that the performance of these methods varies according to the distribution type.
Learning-Assisted Energy Minimization for MEC Systems With Noncompletely Overlapping NOMA

This article investigates the long-term energy minimization of mobile edge computing (MEC) systems with multiuser noncompletely overlapping nonorthogonal multiple access (NOMA) when time varying channel is considered. Different from the existing work with multiuser noncompletely overlapping NOMA that adopted the delay-based successive interference cancellation (SIC) decoding order, we adopt the channel gain-based SIC decoding order, which is widely used and has been proved to be more energy-efficient. We formulate the problem as a joint transmit power, transmission time, and computing resource optimization problem with multiple deeply coupled variables. To ensure the real-time decision-making, the considered problem is characterized as a deep reinforcement learning (DRL) problem by introducing penalty mechanism into the immediate reward function when the constraints are violated, and we propose a soft actor-critic (SAC) algorithm to solve it. Simulations show that the proposed algorithm can significantly improve the system's performance, and, compared with the noncompletely overlapping NOMA mechanism with delay-based SIC decoding order, the completely overlapping NOMA mechanism and time division multiple access (TDMA) mechanism, the noncompletely overlapping NOMA mechanism with channel gain-based SIC decoding order can achieve an average energy consumption reduction of 32.4%, 49.9%, and 74.4%, respectively.
Volatile Network as a Simple Memory Model

: Technical information systems, from PCs to supercomputers, are characterized over time by ever-increasing storage capacities, while biological systems are permanently characterized by their trainable memory abilities. Although both systems are not comparable with each other, because they are based on different phenomena, the existing efficiency of biological systems offers a constant borrowing for the further development of technical systems. For this purpose, it is necessary to develop technical equivalence models. The following considerations aim to reproduce the factually limitless abilities of biological systems to store memory content as a result of the plasticity of neuronal populations. The difference between technical and biological systems becomes particularly clear under this aspect: while the development of technical systems aims to permanently increase the existing storage capacity, biological systems are based on independently separating relevant from irrelevant information and, moreover, permanently reorienting existing memory structures, called plasticity. Accordingly, the transmitter flow between the neurons constantly changes in direction and intensity. A network with a transient topology that is marginally able to model a memory-capable neuronal population characterized by a permanent loss of neuronal contact points is proposed for discussion. Such a loss permanently changes the direction and intensity of the transmitter flow between the neurons. Another focus of the topic is the question of how different stimuli, meaning optical, acoustic, tactile, etc., can become one and the same memory description of a neuron population. Here it is assumed that a pre-processing takes place in the biological system in the form of a functional transformation, the result of which is a neutral basis for representing the information. Although such an assumption seems to be highly speculative, a discussion of it would contribute to answering the question of which physiological mechanisms have to be taken into account to explain memory phenomena, reproduced in a model.
Effect Ayurveda-based Comprehensive Diabetic Care Program on Glycemic Control in Type 2 Diabetic Mellitus Patients: An Observational Study

Background: The prevalence of diabetes mellitus in India is alarmingly high despite availability of several therapeutic agents. Thus, the current study was conducted to understand the role of Ayurveda based Comprehensive Diabetes Care (CDC) program in the management of type 2 diabetes mellitus. Methods: A retrospective, observational study was conducted from January 2014 to September 2022 at Madhavbaug Clinic, Maharashtra. Patients aged 18 years and above with a diagnosis of type 2 diabetes mellitus according to the American Diabetes Association (ADA) with body mass index >25 participated in the CDC program and were included in this study. Follow-up was conducted after 90 days. Baseline data was compared with follow-up data. Results: A total of 63 patients were assessed. Of the male patients, 29.3%, 43.9%, and 26.8% were found to have impaired, negative and positive glucose tolerance. Among the females, 22.7%, 63.6%, and 13.6% were found to be have impaired, negative and positive glucose tolerance. Weight decreased from 70.70 ± 9.82 kg to 65.15 ± 9.83 kg (p<0.001) from day 1 to day 90. Body mass index decreased from 27.00 ± 3.88 to 24.91 ± 3.75 (p<0.001) from day 1 to day 90. Glycated haemoglobin (HbA1c) decreased from 7.87 ± 1.90% to 5.79 ± 0.51% (p<0.001) from day 1 to day 90. Conclusion: Remission of type 2 diabetes mellitus in obese patients is possible with Ayurvedic principles and diet compliance.
DYNAMICS ANALYSIS OF 1D STRUCTURE INCLUDING RANDOM PARAMETER VIA FREQUENCY-DOMAIN STATE-VECTOR EQUATIONS

Abstract. The consideration of uncertainties in numerical models to obtain the probabilistic descriptions of dynamic response is becoming more desirable in the way to quantify the parametric and non-parametric uncertainties associated with the model. In this work, an alternative approach to the spectral element formulation, in which the exact wave solutions are not required, the spectral element matrix including random parameters is derived from the transfer matrix formulated directly from the frequency-domain state-vector equation of motion. The analyses were made to quantify the effect of uncertainty in the dynamic responses at high frequency bands and the Monte Carlo simulation is used to propagate the variation in dimensional properties of the structural parameters. Some interesting results are presented, showing the effects of uncertainty parameters in the dynamic response of the structure. Keywords: Uncertainty quantification, Transfer matrix, State-Vector, Monte Carlo simulation, Spectral element method.
Network structure classification and features of water distribution systems

The network connectivity structure of water distribution systems (WDSs) represents the domain where hydraulic processes occur, driving the emerging behavior of such systems, for example with respect to robustness and vulnerability. In complex network theory (CNT), a common way of classifying the network structure and connectivity is the association of the nodal degree distribution to specific probability distribution models, and during the last decades, researchers classified many real networks using the Poisson or Pareto distributions. In spite of the fact that degree‐based network classification could play a crucial role to assess WDS vulnerability, this task is not easy because the network structure of WDSs is strongly constrained by spatial characteristics of the environment where they are constructed. The consequence of these spatial constraints is that the nodal degree spans very small ranges in WDSs hindering a reliable classification by the standard approach based on the nodal degree distribution. This work investigates the classification of the network structure of 22 real WDSs, built in different environments, demonstrating that the Poisson distribution generally models the degree distributions very well. In order to overcome the problem of the reliable classification based on the standard nodal degree, we define the “neighborhood” degree, equal to the sum of the nodal degrees of the nearest topological neighbors (i.e., the adjacent nodes). This definition of “neighborhood” degree is consistent with the fact that the degree of a single node is not significant for analysis of WDSs.
Master Production Scheduling Requirements and Blood Transfusion Sustainability.Using SEM Approach

The purpose of the study is to find the effect of master production scheduling (MPS) requirements on blood transfusion sustainability (BTS). Data was collected from 213 respondents of regional and transfusing staff of government university teaching hospital blood banks using a self-administered questionnaire. Simple random sampling was used to draw the sample. Finally, a model including MPS requirements and BTS was validated with a path analysis. The results of this study showed a positive significant effect of MPS requirements on BTS with the prediction rate of 27.1% and in the 95% confidence level
Modelos de quarks no-locales a temperatura y potencial químico finitos: Diagrama de fases y observables hadrónicos

In this thesis, we study some aspects of the behavior of strongly interacting matter within relativistic quark models with non local interactions that include coupling to an order parameter associated with confinement. In Chap. 1, a brief introduction of the relevant subjects is made, and motivations and objectives are presented. In Chap. 2 , we go over some basic ideas of Quantum Chromodynamics. Special attention is paid to the symmetries that are related to deconfinement and chiral symmetry restoration phase transitions. In Chap. 3 we describe the Nambu-Jona–Lasinio (NJL) model and the formalism required to include finite temperature and chemical potential. Moreover, we present the coupling between the Polyakov loop and the fermionic fields, resulting in the Polyakov-Nambu-Jona–Lasinio (PNJL) model. Two alternative potentials for the description of the thermodynamics of the Polyakov Loop are introduced. In Chap. 4 the non local NJL (nlNJL) model is described, where interactions among quarks are non local with which a more realistic description of these is achieved. With this model, mesonic properties at zero temperature are described. The inclusion of the Polyakov Loop is described giving rise to the non-local PNJL (nlPNJL) model, which is used in the main part of this work. In Chap. 5 the transitions of chiral symmetry restauration and deconfinement are analized using the nlPNJL model, for the case of zero chemical potential. We study the effect of modifying the scale parameter that appears in the potential of the Polyakov loop and the dependence of critical temperatures with explicit breaking of chiral symmetry In Chap. 6 we study the phase diagrams of strongly interacting matter, considering the cases of real and imaginary chemical potential. In Chap. 7 the behavior of some mesonic properties as functions of temperature is described. In Chap.8 we summarize the work and show our conclusions.
Service Time Maximization for Data Collection in Multi-UAV-Aided Networks

Unmanned aerial vehicles (UAVs) have been enormously gaining attention to offload traffic or collect data in wireless networks due to their key attributes, such as mobility, flexibility, and cost-effective deployment. However, the limited onboard energy inhibits the UAV from serving for a longer duration. Therefore, this article studies a UAV-aided network where multiple UAVs are launched to collect data from the mobile nodes. In particular, we aim to maximize the service time of the UAVs by jointly optimizing the three-dimensional (3D) trajectory of the UAVs and resources allocated to each node by the UAVs such that each mobile node receives a minimum specified data rate. To facilitate a solution, we construct an equivalent problem that considers the UAV's energy consumption. In particular, we minimize the maximum energy consumed by the UAVs in each time slot. To solve the problem, an iterative approach is presented that decouples the problem into two sub-problems. The optimal location of the UAVs is computed in the first sub-problem, while resource allocation is carried out in the second sub-problem. These two sub-problems are solved in an iterative manner using the alternating optimization approach. We show that the proposed approach improves the service time of the network by 20% on average compared to the existing approaches.
Happiness Economics: A Brief on the Validity of Subjective Well-Being Metrics

In the last decade, there has been considerable criticism of the general reliance on GDP as a proxy indicator of progress and development. One strand of criticism focuses on The Easterlin Paradox and the inability of GDP to capture the subjective well-being (the scientific term for Happiness) of a nation. Richard Easterlin argues that his paradox underlies (mis-)judgments in this particular nature of welfare. New opportunities to empirically explore preconditions and covariates of well-being that go beyond the traditional GDP metrics have spurred impressive and stimulating new research and the Economics of Happiness has emerged as one of the most thriving subjects of the discipline. This briefing paper re-assesses the Easterlin Paradox using recent data on an array of countries to draw out lessons for public policymaking. The brief is intended to provide a concise summary of research knowledge on Happiness Economics in an accessible form to a varied audience including academics, policymakers, civil-societies and students.
Energy Update Restricted Chan–Vese Model for Tumor Extraction from MRI of Human Head Scans

This research paper proposed a newer strategic method for the extraction of tumor from magnetic resonance imaging scans by employing a region-based active contour model (ACM). The earlier methods have applied the process of contour initialization randomly and updating the energy of the contour at every iteration. The proposed method used wavelet-based feature set to initiate the contour and restricts the energy update procedure. The efficiency of the presented technique in terms of tumor extraction is measured through qualitative and quantitative measures further compared with its counterparts Vese–Chan multiphase model, ACM and Selective binary. Gaussian filtering regularized level set and non active contour based models.
Towards Better Survival - A Qualitative Insight into the Impact of Covid-19 Pandemic on Single Mother Entrepreneurs’ (SMPs) in Klang Valley

Since 2019, a global pandemic caused by the corona virus has been one of our worst worries. Due to the covid-19 shutdown, single mother entrepreneurs (SMPs) have been one of the most impacted groups, and while it required time to understand, the usage of technology proved useful for them. The objective of this article is to present what are the factors influencing SMPs in terms of sustaining their business, and whether they have taken any new strategy due to the change effected by the pandemic lockdown. This study applies qualitative approach of investigation that was conducted through online meetings for open group discussions. A total of 15 SMPs were interviewed, based in Klang Valley. The findings show that they have very low resilience level during covid-19 pandemic and until now, they are still struggling to live up to the standard of living within their means including bringing up their children.
Image Encoding and Reconstruction for Portable Magnetic Resonance Imaging

Magnetic Resonance Imaging (MRI) is a successful imaging tool, but due to high cost, high weight and complexity of equipment, MRI is not currently as easily accessible clinically as desired. By making MRI cheaper, lighter, less complex and therefore potentially portable, it can become more widely accessible. A portable MRI system can be used in primary health care, operating and emergency rooms, car and air ambulances, sport and war facilities and remote regions (including outer space). The objective of this study was to show the feasibility of reconstructing MRI signals generated by two different portable MRI systems. The first portable MRI, known as radiofrequency (RF) phase encoded MRI, encodes spatial information through the use of a non-linear spatially varying RF transmit (B1) phase. The second portable MRI, known as rotating field MRI, encodes information through non-uniform radially varying main magnet field (B0). The fact that there is no need for gradient coils in both systems, leads to a smaller, lighter and more affordable MRI than most conventional systems. In RF phase encoded MRI, since the B1 phase spatially varies non-linearly, using Fourier transform (FT) to reconstruct images results in distorted images. Therefore, regularized least squares inversion was used in place of the usual FT. The RF phase encoding coil generates an inhomogeneous B1 field that leads to RF pulse imperfection in terms of flip angles produced versus flip angles intended. Composite pulses were therefore used to minimize the effect of RF transmit field inhomogeneity on tip angles. In rotating field MRI, a Halbach magnet was used to generate a non-uniform radially varying B0 field to encode information in the radial direction. For encoding information in the angular direction two separate Saddle RF receiver coils were used. The main magnet and receiver coils are