Miracle-dz/UniFAR-FTUs huggingface dataset:
CARACTERIZAÇÃO FÍSICA E ESTABILIDADE DE MICROPARTÍCULAS DE ALGINATO ATRAVÉS DA TÉCNICA DE GELIFICAÇÃO IÔNICA POR ATOMIZAÇÃO

RESUMO – Classificado como um polissacarídeo, o alginato de sódio é extraído de algas marrons e bactérias. São biocompatíveis, biodegradáveis, atóxicos e com alta capacidade gelificante, sendo comum o seu uso como hidrogéis para a entrega de compostos bioativos. Nesse estudo, micropartículas de alginato foram preparadas através do método de gelificação iônica por atomização, variando concentração do banho de CaCl2 (3 e 6% m/m), pressão do ar (0,25 e 0,50 bar) e distância de pulverização (20 e 25 cm). As micropartículas obtidas foram avaliadas quanto à distribuição de tamanho, morfologia da superfície e estabilidade frente a diferentes pHs (pH 2,5, 3,5 e 4,5) por 4 dias. O processo de atomização produziu partículas com tamanhos entre 83 e 146 μm com morfologias diversas (irregulares, esféricas ou elípticas). Observou-se que as partículas produzidas com 0,25 bar sofreram alteração de tamanho e morfologia após 4 dias de estabilidade. As amostras produzidas em concentração de 6% CaCl2 apresentaram maiores tamanhos quando comparadas às formadas com 3%, possivelmente devido à maior força iônica da solução. Quanto à altura de pulverização, as amostras produzidas a 25cm apresentaram morfologia mais esféricas que as demais.
Scheduling a Continuous Casting Batch Machine with Flexible Jobs to Minimize Setup Costs

The continuous casting batch machine scheduling with flexible jobs is proposed based on its character. The original problem is firstly preprocessed based on rule, and then the optimization model with minimizing setup costs is proposed. To improve the efficiency of local search, the Particle Swarm Optimization (PSO) is introduced, and then the PSO and heuristic strategy are embedded into the Iterated Local Search (ILS) to solve the problem. The solution space is divided into many subspaces based on the charge information, and then the subspaces are integrated after local search. The maximum number of iteration is introduced as stopping condition. Then, the proposed algorithm and ILS are compared, and the changing of learning factors, number of generation, and the maximum number of iteration are also tested respectively. Finally, the simulation results show that the proposed algorithm can solve the problem efficiently.
Weak convergence of bounded sequences

Proof. Assume that ||x„|| g M, n = 1, 2, • • • . It suffices to show that for each g in U*, lim g(xn) =g(x). Now, U* is convex and compact (we only consider U* in its weak* topology) and the function Q defined by (Qx)(h) =h(x) for x in B, h in U*, is a linear isometry of B into C(U*), the space of continuous functions on U* with the supremum norm. Suppose that g is an element of U*. By the Bishopde Leeuw generalization [l, Theorem 5.6] of a well-known result of Choquet, there exists a <r-ring of subsets of U* (generated by E and the Baire sets) and a non-negative finite measure p on this <r-ring such that p(U*)=p(E) and g(y) = f^Qydp for each y in B. Since m(í/*'~£)=0, it follows that the functions Qxn converge to Qx almost everywhere with respect to p. Since | Qxn\ g||<2x„|| =||x„|| ÚM, we conclude from Lebesgue's bounded convergence theorem that g(x) — fQxdp = lim fQxndp = Yim g(x„), which completes the proof.
A Vision Transformer Approach for Traffic Congestion Prediction in Urban Areas

Traffic problems continue to deteriorate because of increasing population in urban areas that rely on many modes of transportation, the transportation infrastructure has achieved considerable strides in the last several decades. This has led to an increase in congestion control difficulties, which directly affect citizens through air pollution, fuel consumption, traffic law breaches, noise pollution, accidents, and loss of time. Traffic prediction is an essential aspect of an intelligent transportation system in smart cities because it helps reduce overall traffic congestion. This article aims to design and enforce a traffic prediction scheme that is efficient and accurate in forecasting traffic flow. Available traffic flow prediction methods are still unsuitable for real-world applications. This fact motivated us to work on a traffic flow forecasting issue using Vision Transformers (VTs). In this work, VTs were used in conjunction with Convolutional neural networks (CNN) to predict traffic congestion in urban spaces on a city-wide scale. In our proposed architecture, a traffic image is fed to a CNN, which generates feature maps. These feature maps are then fed to the VT, which employs the dual techniques of tokenization and projection. Tokenization is used to convert features into tokens containing Vision information, which are then sent to projection, where they are transformed into feature maps and ultimately delivered to LSTM. The experimental results demonstrate that the vision transformer prediction method based on Spatio-temporal characteristics is an excellent way of predicting traffic flow, particularly during anomalous traffic situations. The proposed technology surpasses traditional methods in terms of precision, accuracy and recall and aids in energy conservation. Through rerouting, the proposed work will benefit travellers and reduce fuel use.
Virtual Wireless Device-Constrained Robust Extended Kalman Filters for Smartphone Positioning in Indoor Corridor Environment

Indoor positioning technology is the key to achieving seamless indoor/outdoor location-based services (LBSs). As typical nonlinear information, pedestrian accessibility improves the performance of the indoor positioning system. Based on a framework and related positioning algorithms of tightly coupled heterogeneous observables from Wi-Fi/Bluetooth low energy (BLE)/pedestrian dead reckoning (PDR), this work presents a real-time approach for pedestrian accessibility and floor map-constrained hybrid indoor positioning method using virtual wireless devices (VWDs) on smartphones. The nonlinear characteristic of accessibility is transformed into VWDs to constrain the position estimation at the observation level. The evaluation of the proposed technique with three different criteria shows that the accuracy, reliability, and user experience of the estimated locations are comparable to particle filter (PF) results. In a typical corridor environment, the proposed method achieves 0.93 m average positioning accuracy while balancing the computational time consumption.
Microstructure evolution of Al-doped zinc oxide and Sn-doped indium oxide deposited by radio-frequency magnetron sputtering: A comparison

The microstructure and morphology evolution of Al-doped zinc oxide (AZO) and Sn-doped indium oxide (ITO) thin films on borosilicate glass substrates deposited by radio-frequency magnetron sputtering at room temperature (RT) and 300 °C were investigated by X-ray diffraction and atomic force microscopy (AFM). One-dimensional power spectral density (1DPSD) functions derived from the AFM profiles, which can be used to distinguish different growth mechanisms, were used to compare the microstructure scaling behavior of the thin films. The rms roughness Rq evolves with film thickness as a power law, Rq ∼ dfβ, and different growth exponents β were found for AZO and ITO films. For AZO films, β of 1.47 and 0.56 are obtained for RT and 300 °C depositions, respectively, which are caused by the high compressive stress in the film at RT and relaxation of the stress at 300 °C. While for ITO films, β1 = 0.14 and β2 = 0.64 for RT, and β1 = 0.89 and β2 = 0.3 for 300 °C deposition are obtained, respectively, which is relate.
Radiative lifetime measurements of the C2(A 1Pi/u/) state

Direct lifetime measurements of the C2(A 1Pi/u/) state are reported for vibrational levels from v-prime = 0 up to 8. These levels were populated by 193 nm excimer laser photolysis of C2HBr and up to v-prime = 5 by the photolysis of C2H2. The experimental lifetime data are used to evaluate the transition moment for internuclear distances ranging from 1.1 to 1.3 A in order to recalculate the related absorption oscillator strength. 33 references.
Takaful Operators and Socio-economic Objectives during the Pandemic of Covid-19: Issues and Challenges

Objective: Takaful operations in Malaysia have proven their credibility through the remarkable achievement in market structure and net contribution income. This research paper discusses on the issues and challenges faced by Malaysian Takaful Operators in contributing for the socio-economic objectives during the pandemic of Covid-19. Methodology: In complementing this study, ten respondents from takaful industry will be interviewed in gathering their insight over the subject matter. This study is in line with the VBIT framework which have been introduced to achieve inclusive protection for the underserved or unserved segments of the society and to address relevant socio-economic issues. Findings: Based on the findings, Malaysian Takaful Operators are optimistic about their performance always seek for the best opportunity in contributing to the society. Takaful Operators also believe that all the challenges that they are currently faced would not become the barriers for them to consistently contribute to the socio-economic objectives even it is not in the pandemic era anymore. Implications: The proposed solution for the identified problems is part of the contribution in this study and can be very useful for the industry player. Some of the listed strategies are different from prior studies, and in fact, there is no study previously which have been discussing the challenges in detail.
The Paradoxical Impact of Information Privacy on Privacy Preserving Technology: The Case of Self-Sovereign Identities

Advance of digital technologies brings great bene¯ts but takes users at risk of the dark sides of the internet. Preventive mechanisms and privacy-preserving solutions could overcome this challenge. As such, self-sovereign identities (SSIs) provide users with increased control over personal information. However, users neglect their privacy in favor of the most convenient solution. In this paper, we empirically examine how information privacy in°uences adoption of SSIs. Our results contradict the existing theory that privacy is critical to the success of identity management (IdM) systems. Analogous to the privacy paradox, the study does not lend empirical support that perceived privacy has an impact on the adoption of an SSI. On the contrary, these ¯ndings contradict the prevailing view of privacy as a key factor for IdM systems and contribute to knowledge on privacy and adoption behavior.
Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study

In addition to the standard observational assessment for autism spectrum disorder (ASD), recent advancements in neuroimaging and machine learning (ML) suggest a rapid and objective alternative using brain imaging. This work presents a pipelined framework, using functional magnetic resonance imaging (fMRI) that allows not only an accurate ASD diagnosis but also the identification of the brain regions contributing to the diagnosis decision. The proposed framework includes several processing stages: preprocessing, brain parcellation, feature representation, feature selection, and ML classification. For feature representation, the proposed framework uses both a conventional feature representation and a novel dynamic connectivity representation to assist in the accurate classification of an autistic individual. Based on a large publicly available dataset, this extensive research highlights different decisions along the proposed pipeline and their impact on diagnostic accuracy. A large publicly available dataset of 884 subjects from the Autism Brain Imaging Data Exchange I (ABIDE-I) initiative is used to validate our proposed framework, achieving a global balanced accuracy of 98.8% with five-fold cross-validation and proving the potential of the proposed feature representation. As a result of this comprehensive study, we achieve state-of-the-art accuracy, confirming the benefits of the proposed feature representation and feature engineering in extracting useful information as well as the potential benefits of utilizing ML and neuroimaging in the diagnosis and understanding of autism.
Tolerance of edge cascades with coupled map lattices methods

This paper studies the cascading failure on random networks and scale-free networks by introducing the tolerance parameter of edge based on the coupled map lattices methods. The whole work focuses on investigating some indices including the number of failed edges, dynamic edge tolerance capacity and the perturbation of edge. In general, it assumes that the perturbation is attributed to the normal distribution in adopted simulations. By investigating the effectiveness of edge tolerance in scale-free and random networks, it finds that the larger tolerance parameter λ can more efficiently delay the cascading failure process for scale-free networks than random networks. These results indicate that the cascading failure process can be effectively controlled by increasing the tolerance parameter λ. Moreover, the simulations also show that, larger variance of perturbation can easily trigger the cascading failures than the smaller one. This study may be useful for evaluating efficiency of whole traffic systems, and for alleviating cascading failure in such systems.
Research on Gas Concentration Prediction Based on the ARIMA-LSTM Combination Model

The current single gas prediction model is not sufficient for identifying and processing all the characteristics of mine gas concentration time series data. This paper proposes an ARIMA-LSTM combined forecasting model based on the autoregressive integrated moving average (ARIMA) model and the long short-term memory (LSTM) recurrent neural network. In the ARIMA-LSTM model, the ARIMA model is used to process the historical data of gas time series and obtain the corresponding linear prediction results and residual series. The LSTM model is used in further analysis of the residual series, predicting the nonlinear factors in the residual series. The prediction results of the combined model are compared separately with those of the two single models. Finally, RMSE, MAPE and R2 are used to evaluate the prediction accuracy of the three models. The results of the study show that the metrics of the combined ARIMA-LSTM model are R2 = 0.9825, MAPE = 0.0124 and RMSE = 0.083. The combined model has the highest prediction accuracy and the lowest error and is more suitable for the predictive analysis of gas data. By comparing the prediction results of a single model and the combined model on gas time series data, the applicability, validity and scientificity of the combined model proposed in this paper are verified, which is of great importance to accurate prediction and early warning of underground gas danger in coal mines.
A Multiwavelength Study of 30 Doradus: The Interstellar Medium in a Low-Metallicity Galaxy

We report maps of the 158 micron (C II) line, the 63 micron and 146 micron (C I) lines, the 2.2 micron Br gamma line, and the 2.6 mm CO (1-0) line toward the 30 Doradus complex in the Large Magellanic Cloud. The maps of all tracers emphasize the shell-like structure of the 30 Doradus region which is seen edge-on. The fact that the molecular gas as traced by CO (1-0) and the photo dissociated gas as traced by (C II) are co-extensive over tens of parsecs can only be explained by a highly fragmented structure of the interstellar medium which allows UV radiation to penetrate deep into the molecular cloud. Clumpiness is also the key to understanding the extremely high (C II)/CO line intensity ratios.
A continuous-wave mode elliptic-region-based DOT methodology based on BEM-diffusion modeling

Acquisition of the optical structures within a biological body is critical to all the diffuse light imaging modalities, such as diffuse optical tomography (DOT) and fluorescence molecular tomography (FMT). On an assumption of the optical homogeneity within the organs, it can be cast as a shape-based DOT issue, which aims at simultaneously reconstructing the boundary-describing parameters and optical properties of the disjoint domains of distinct tissue types. As the first step to the solution of this issue, we propose here a continuous-wave mode, elliptic-region-based DOT scheme. The methodology employs the boundary-element-method (BEM) solution to the diffusion equation as the forward model, and solves a nonlinear inverse issue that seeks an optimal boundary configuration as well as the optical properties to minimize the residual norm between measured and predicted data. The proposed scheme is validated using simulated data for a cylindrical geometry embedding two absorption- and scattering-contrasting ellipses at different noise levels.
Stereo Matching As Constrained Optimization Using Scale Continuation Methods

A new stereo matching algorithm is developed and applied to natural scenes. The algorithm is based on a recent approach to matching multiple, multidimensional signals that have been deformed with respect to one another. ' The goal is to optimally recover the deformation map, which in this case represents the stereoscopic disparity between the left and right images. The problem is formulated as the minimization of an energy measure that combines a similarity functional with a controlled-continuity constraint. Applying the continuation method, this nonlinear, nonconvex minimization problem is solved by a deterministic dynamic system governed by a set of coupled, first-order differential equations. The system finds an optimal approximation at a coarse scale, then tracks it continuously to a fine scale, thus avoiding bad local minima. The stereo algorithm succinctly unifies the notions of matching as constrained optimization, of coarse-to-fine search, and of variational surface reconstruction.
Systematic studies of N IV transitions of astrophysical importance

E1, M2, M1 and E2 rates of transitions between n = 2 levels of N IV have been calculated using the tw independent codes CIV3 and MCHF. Convergence of each of the approaches has been studied and comparisons made as the complexity of the calculations increases to include valence, core-valence and core-core correlation. The agreement between the two methods is sufficiently good to allow us to set quite narrow uncertainty bars. For the S-1-P-1 degrees resonance line, our recommended f-value is 0.609 with an estimated uncertainty of 0.002, while our recommended A-value for the S-1(0)-P-3(1) degrees intercombination line is 580 s(-1) with an estimated uncertainty of 10 s(-1).
Hybrid Multiple Access for Network Slicing Aware Mobile Edge Computing

Meeting huge traffic demand with resource constraints imposes a significant challenge for future generation wireless networks. In this work, we propose to utilize limited resources in a dense mobile edge computing (MEC) network to compute user equipment (UE) tasks through a novel hybrid multiple access (HYMA) scheme that employs both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). The main purpose of using HYMA is to reduce the co-channel interference incurred in NOMA by selectively deploying OMA while maintaining the required signal-to-interference ratio. We adopt partial offloading of computing tasks in the MEC network. We also employ network slicing to efficiently utilize the resources of the MEC network to meet different types of application requirements. We prioritize NOMA to increase spectral efficiency as well as energy efficiency. We first formulate a mixed integer nonlinear programming (MINLP) optimization problem to minimize the total energy consumption for both local computing and wireless transmission of the MEC network and propose an algorithm consisting of three parts (UE association, computing resource allocation, and wireless resource and uplink transmission power allocation) to solve the MINLP problem with less computational complexity. We demonstrate the viability of our solution via extensive simulations.
Research Object Rotational Motion Estimation Based on Digital Video Analysis Technique

This paper is about the object rotational motion estimation based on optical flow equation. Being a non-touch measuring technique, it is of important value in some special occasions. It is to set up rigid motion equations by optical flow character, and then using two-step iterative method to estimate motion equation and calculate rotation speed for each coordinate axis. We present a simplified calculation method for some object which specific structure parameters are known. The experiment results show the calculation is accurate.
The correlation between speckle-tracking echocardiography and coronary angiography in suspected coronary artery disease with normal left ventricular function

Introduction: Our study objects to determine the diagnostic accuracy of two-dimensional speckle tracking echocardiography (2DSTE) in predicting presence and severity of coronary artery disease (CAD). Methods: Patients with stable angina pectoris with normal left ventricular function (>50%) undergoing coronary angiography were enrolled and subjected to speckle tracking echocardiography. Global longitudinal peak systolic strain was measured and correlated to the results of coronary angiography for each patient. Results: Number of male (P=0.001), diabetes (P=0.01) and smoking (P=0.01) patients were significantly higher in the CAD group compared to non-CAD patients. Global longitudinal peak systolic strain (GLPSS) was significantly (P=0.0001) lower in CAD patients in comparison to non- CAD patients. GLPSS showed significantly lower in patients with Syntax score (SS)≥22 in comparison to SS<22. Cut-off value -19 for GLPSS could be used to predict the presence of significant CAD with 80.6% sensitivity and 76.5% specificity (area under curve (AUC) -0.83, P=0.0001). The mean GLPSS value decreased as the number of diseased coronary vessels increased (P=0.0001). The optimal cut-off value of -16 GLPSS with a sensitivity of 76.7% and specificity of 83.3% [AUC 0.84, P<0.0001] was found significant to predict CAD severity. Multivariate regression of GLPSS and another risk factor for predicting significant CAD, GLPSS showed OR=1.55 (CI-1.36-1.76) P=0.0001 for predicting the presence of CAD. Conclusion: 2DSTE can be used as a non-invasive screening test in predicting presence, extent and severity of significant CAD patients with suspected stable angina pectoris.
Autumnal funnel clouds over west Hampshire: A precursor to wintertime tornadoes

recently received data. I am grateful to the Centre for Climate Prediction and Research at the UK Meteorological Office and to the Climatic Research Unit at the University of East Angha for helping to compute and provide the data. The latest values of the monthly mean Central England Temperature (CET), England and Wales Rainfall (m) and Comprehensive Areal Rainfall Program (CARP) series, along with the monthly global and hemispheric temperature anomalies, are
ATTRACTOR FOR THE MEAN-FIELD EQUATIONS OF THE HYSTERETIC DYNAMICS OF A QUANTUM SPIN MODEL : ANALYTICAL SOLUTION

Recently Banerjee, Dattagupta, and Sen derived the mean-field equations for the three components of the magnetization in the context of a quantum spin model in a rotating external magnetic field [Phys. Rev. E 52, 1436 (1995)]. We provide here the analytical solution for the attractor of this dynamics and prove that there is no hysteresis loss on the attractor.