Miracle-dz/UniFAR-FTUs huggingface dataset:
Synergistic Effect of Incorporation of BG 45S5 and Silver Nanoparticles on β-TCP Scaffolds: Structural Characterization and Evaluation of Antimicrobial Activity and Biocompatibility

Bacterial infections after implant surgical procedures are a complication observed in many surgeries to treat bone injuries or diseases. Bacteria can attach to the surface of the implant producing biofilms, and if treatment with antibiotics does not work, further surgery is necessary to remove the infected implant. Among the biomaterials for bone implants, bioceramics based on calcium phosphates (CaPs) such as β-TCP stand out, due to their chemical similarity with bone and high bioresorbability. β-TCP has the characteristic of easily accommodating in its crystalline structure reasonable amounts of doping elements, such as monovalent and trivalent ions, which makes it an efficient transporter of drugs, molecules, and therapeutic ions The objective of this work was the incorporation of bioactive glass (BG 45S5) via sol-gel and silver nanoparticles (Ag-NPs) in β-TCP scaffolds, aiming to confer antimicrobial activity to the scaffolds, without prejudice to biocompatibility. XRD and FT-IR analysis indicated structural changes after the incorporation of BG 45S5 and Ag-NPs in β-TCP scaffolds, and these compounds induced the partial transformation of the β-TCP phase into α-TCP phase and the formation of sodium-calcium silicates and silver silicates. The FT-IR spectra showed characteristic bands of α-TCP after incorporation, in addition to the predominant bands of β-TCP. Biocompatibility after incorporation of BG 45S5 was improved, with a significant increase in cell viability. After the incorporation of Ag-NPs, cell viability was maintained at an acceptable level, no cytotoxic behavior was observed, and the scaffolds showed antibacterial and antifungal activity. The results indicate that BG 45S5 and the Ag-NPs incorporated showed a synergistic behavior, conferring antimicrobial activity to the scaffolds without compromising biocompatibility, showing great potential for applicability in tissue engineering.
DCMAI: A Dynamical Cross-Modal Alignment Interaction Framework for Document Key Information Extraction

Document key information extraction (DKIE) is a crucial topic that aims at automatically comprehending documents with complex formats and layouts (invoices, business insurance, etc.). While pre-trained approaches have shown high performance on many DKIE tasks, they suffer from three major challenges. First of all, these approaches ignore the ambiguity resulting from similar text representations before cross-modal interaction. Secondly, they do not consider cross-modal representation alignment before cross-modal interaction. Finally, self-attention layers in cross-modal interaction incur significant computing costs, making it hard to perform joint representation learning from all negative samples. To address these issues, we propose a Dynamical Cross-Modal Alignment Interaction framework (DCMAI). To be more specific, 1) A prior knowledge-guided module is designed to adaptively mine fine-grained visual information to disambiguate similar text representations. 2) A crossover alignment loss is formulated to align cross-modal representations before cross-modal interaction. 3) A hierarchical interaction sampling scheme is introduced to obtain a small but efficient subset of cross-modal negative samples, and a contrastive loss is employed to improve joint representation learning. Comprehensive experiments show that the proposed DCMAI achieves state-of-the-art performance than competitive baselines on several public downstream benchmarks. Code will be open to the public.
Development of the generalized MacCormack scheme and its extension to low Mach number flows

The low Mach number performance of the MacCormack scheme is examined. The inherent dissipation in the scheme is found to suffer from the degradation in accuracy observed with traditional, density‐based methods for compressible flows. Two specific modifications are proposed, leading to the formation of the generalized MacCormack scheme within a dual‐time framework (called GMC‐PC). The first modification involves reformulating the flux by splitting it into particle convection and acoustic parts, with the former terms treated using the traditional MacCormack discretization and the latter terms augmented by the addition of a pressure‐based artificial dissipation. The second modification involves a reformulation of the traditional nonlinear fix introduced by MacCormack in 1971, which is found to be necessary to suppress pressure oscillations at low Mach numbers. The new scheme is demonstrated to have superior performance, independent of Mach number, compared with standard MacCormack implementations using several canonical test problems. Copyright © 2017 John Wiley & Sons, Ltd.
MENTAL DISTRESS AND COPING STRATEGIES OF VIETNAMESE STUDENTS IN THE US DURING THE COVID-19 PANDEMIC

Đại dịch COVID-19 có những ảnh hưởng vô cùng to lớn lên học sinh/ sinh viên trên toàn thế giời. Tuy nhiên, rtất ít nghiên cứu tìm hiểu về ảnh hưởng của đại dịch COVID-19 đối với sinh viên Việt Nam tại Mỹ. Nghiên cứu này đã đánh giá mức độ đau khổ về tinh thần trong nhóm đối tượng này và các chiến lược đối phó của họ trong đại dịch COVID-19. Do đó, một cuộc khảo sát trực tuyến bằng bảng câu hỏi đã được tiến hành giữa các sinh viên đại học Việt Nam đang sinh sống và học tập tại Hoa Kỳ kể từ khi đại dịch COVID-19 bùng phát thông qua Facebook. Nhà nghiên cứu đã áp dụng thang đo mẫu K10 và Brief COPE trong cuộc khảo sát. Với 103 người trả lời hợp lệ, kết quả nghiên cứu chỉ ra rằng phần lớn (79,61%) sinh viên Việt Nam tại Mỹ đã thực sự gặp phải tình trạng đau khổ về tinh thần (bao gồm cả mức độ nhẹ và nặng). Đối tượng nghiên cứu đã phải đối mặt với những tác nhân gây căng thẳng ngày càng tăng do đại dịch, do đó ảnh hưởng đến sức khỏe tinh thần của họ. Dù vậy, họ cũng cho thấy rằng họ đã cố gắng giảm bớt những gánh nặng tâm lý đó bằng cách áp dụng các chiến lược thích ứng như chấp nhận, làm việc khác và chủ động đối phó.
Molecular identification of Ralstonia pseudosolanacearum causing bacterial wilt in tomatoes from Da Nang by using Colony PCR

Ralstonia solanacearum species complex (RSSC) is the main pathogen causing bacterial wilt disease in tomatoes. This study applied the colony polymerase chain reaction (PCR) technique to rapidly screen and select RSSC strains from isolated bacteria of diseased tomatoes. This method directly used bacterial colonies on the Petri plate as templates to amplify with RSSC’s specific and multiplex primers. The results showed that the Vietnamese isolates were identified as R. pseudosolanacearum, phylotype I. Phylogenetic analysis of the 16S-23S rDNA sequencing also confirmed these results. Therefore, this is the first report to recognize R. pseudosolanacearum phylotype I as the cause of bacterial wilt disease in tomatoes from Danang.
A generalized formulation of the dynamic Smagorinsky model

A generalized formulation of the Dynamic Smagorinsky Model (DSM) is proposed as a versatile turbulent momentum diffusion scheme for Large-Eddy Simulations. The difference to previous versions of the DSM is a modiﬁed test ﬁlter range that can be chosen independently from the resolution scale to separate the impact of the test ﬁlter on the simulated ﬂow from the impact of the resolution. The generalized DSM (gDSM) in a two-dimensional version is validated in a veriﬁcation study as a horizontal momentum diffusion scheme with the Kühlungsborn Mechanistic General Circulation Model at high resolution (wavenumber 330) without hyperdiffusion. Three-day averaged results applying three different test ﬁlters in the macro-turbulent inertial range are presented and compared with analogous simulations where the standard DSM is used instead. The comparison of the different ﬁlters results in all cases in similar globally averaged Smagorinsky parameters c S (cid:3) 0 . 35 and horizontal kinetic energy spectra. Hence, the basic assumption of scale invariance underlying the application of the gDSM to parameterize atmospheric turbulence is justiﬁed. In addition, the smallest resolved scales contain less energy when the gDSM is applied, thus increasing the stability of the simulation.
Science diplomacy from the Global South: the case of intergovernmental science organizations

Intergovernmental science organizations (IGSOs) address many challenges of the 21st century. Several countries of the Global South have joined established IGSOs or have created new ones. Yet we know little about their interests in IGSOs. Our study addresses this blind spot by investigating which objectives Southern actors pursue in IGSOs and under which conditions they are likely to achieve their objectives. Using insights from three strands of literature, we compare four IGSOs with Southern participation: the European Organization for Nuclear Research, the International Thermonuclear Experimental Reactor, the Square Kilometer Array, and the African Lightsource. We show that countries of the Global South pursue a multitude of political and scientific objectives in IGSOs, ranging from capacity-building to casting off political isolation. Moreover, we demonstrate that Southern countries have varying chances of attaining these objectives, depending on their scientific community, domestic politics, industrial capacities and in some cases geographic location as well as an IGSO’s maturity.
Buoyancy-driven bubbly flows: ordered and free rise at small and intermediate volume fraction

Various expressions have been proposed previously for the rise velocity of gas bubbles in homogeneous steady bubbly flows, generally a monotonically decreasing function of the bubble volume fraction. For suspensions of freely moving bubbles, some of these are of the form expected for ordered arrays of bubbles, and vice versa, as they do not reduce to the behaviour expected theoretically in the dilute limit. The microstructure of weakly inhomogeneous bubbly flows not being known generally, the effect of microstructure is an important consideration. We revisit this problem here for bubbly flows at small to moderate Reynolds number values for deformable bubbles, using direct numerical simulation and analysis. For ordered suspensions, the rise velocity is demonstrated not to be monotonically decreasing with volume fraction due to cooperative wake interactions. The fore-and-aft asymmetry of an isolated ellipsoidal bubble is reversed upon increasing the volume fraction, and the bubble aspect ratio approaches unity. Recent work on rising bubble pairs is used to explain most of these results; the present work therefore forms a platform of extending the former to suspensions of many bubbles. We adopt this new strategy also to support the existence of the oblique rise of ordered suspensions, the possibility of which is also demonstrated analytically. Finally, we demonstrate that most of the trends observed in ordered systems also appear in freely evolving suspensions. These similarities are supported by prior experimental measurements and attributed to the fact that free bubbles keep the same neighbours for extended periods of time.
Mini-max-risk and mini-mean-risk inferences for a partially piecewise regression

ABSTRACT We consider a partially piecewise regression in which the main regression coefficients are constant in all subdomains, but the extraessential regression function is variable in different pieces and is difficult to be estimated. Under this situation, two new regression methodologies are proposed under the criteria of mini-max-risk and mini-mean-risk. The resulting models can describe the regression relations in maximum-risk and mean-risk environments, respectively. A two-stage estimation procedure, together with a composite method, is introduced. The asymptotic normality of the estimators is established, the standard convergence rate and efficiency are achieved. Some unusual features of the new estimators and predictions, and the related variable selection are discussed for a comprehensive comparison. Simulation studies and a real-financial example are given to illustrate the new methodologies.
High dimensional kNN-graph construction using space filling curves

The k nearest neighbor (kNN) graph has an important role in many computer science fields including machine learning and data mining. Although many fast methods exist for constructing kNN graph for low dimensional data, it is still an open question how to do it efficiently in high dimensional cases. We present a new method to construct approximate kNN graph for medium to high dimensional data. Our method uses space filling curves to construct initial graph and then continues to improve this using neighborhood propagation. It is targeted for Euclidean distance metric but has potential to be applied for general Minkowski distance metrics. Experiments show that the method is faster than compared methods with three different benchmark data sets which dimensionality ranges from 14 to 544.
Universal Adaptive Construction of Verifiable Secret Sharing and Its Application to Verifiable Secure Distributed Data Storage

Secret sharing is a useful method for secure distributed data storage. Such a distributed data storage can avoid the information leakage under an attack to a limited number of distributed servers. While such distributed servers send their shares to an end user according to the request, there is a risk that malicious distributed servers send incorrect shares. To detect or identify such malicious servers, we need verifiable secure distributed data storage, which can be constructed from verifiable secret sharing. However, many of previous protocols for verifiable secret sharing are constructed in a specific form. This paper proposes an adaptive construction of verifiable secret sharing, which uses an existing secret sharing protocol as a subprotocol. Also, our method can be applied to any existing secret sharing protocol. This type construction realizes an economical construction.
Variational Iteration Method for Infrared Radiative Transfer in a Scattering Medium

AbstractA new scheme is proposed for using the variational iteration method (VIM) to solve the problem of infrared radiative transfer in a scattering medium. This scheme allows the zeroth-order solution to be identified as the absorption approximation and the scattering effect is included in the first-order iteration. The upward and downward intensities are calculated separately in VIM, which simplifies the calculation process. By applying VIM to two single-layer scattering media and a full radiation algorithm with gaseous transmission, it is found that VIM is generally more accurate than the discrete-ordinates method (DOM), especially for cirrostratus. Computationally, VIM is slightly faster than DOM in the two-stream case but more than twice as fast in the four-stream case. In view of its high overall accuracy and computational efficiency, VIM is well suited to solving infrared radiative transfer in climate models.
Three-Dimensional Location and Mapping Analysis in Mobile Robotics Based on Visual SLAM Methods

One of the essential tasks required from a mobile robot is the autonomous and safe navigation of its working environment. However, in many cases, a model of the environment or map is not available to execute this task. Indeed, navigation requires a permanent estimation of the location for a map, which is not available for unknown environments. In such a scenario, the robot must have extended capabilities to solve, concurrently, the problems of localization and mapping. The simultaneous solution of these two problems is known as SLAM (simultaneous localization and mapping) and is a complex problem, not yet fully solved by the scientific community. This is due to the fact that localization requires a map that is not yet available since it is still under construction. In turn, the elaboration of a map requires the estimation of the robot’s location. This is the reason why SLAM has been categorized as similar to the chicken and egg problem. In the case of a robot facing an unknown environment, it would be something like what to solve first, localization or mapping? The answer to this question is that the robot will have to solve both problems at the same time. This article presents a study of some of the most representative open source visual SLAM (vSLAM) methods, beginning from an analysis of their characteristics and presenting criteria selection for an experimental design that allows contrasting their advantages and disadvantages. Two of the most representative algorithms for solving vSLAM were considered (RTAB-Map and ORB-SLAM2). The experiments were validated with a robotic system designed for this purpose, which is fully compatible with ROS (robot operating system).
Neural Networks Satisfying Stone-weiestrass Theorem And Approximating

Neural networks are an attempt to build computer networks called artificial neurons, which imitate the activities of the human brain. Its origin dates back to 1943 when neurophysiologist Warren Me Cello and logician Walter Pits produced the first artificial neuron. Since then there has been tremendous development of neural networks and their applications to pattern and optical character recognition, speech processing, time series prediction, image processing and scattered data approximation. Since it has been shown that neural nets can approximate all but pathological functions, Neil Cotter considered neural network architecture based on Stone-Weierstrass Theorem. Using exponential functions, polynomials, rational functions and Boolean functions one can follow the method given by Cotter to obtain neural networks, which can approximate bounded measurable functions. Another problem of current research in computer graphics is to construct curves and surfaces from scattered spatial points by using B-Splines and NURBS or Bezier surfaces. Hoffman and Varady used Kohonen neural networks to construct appropriate grids. This thesis is concerned with two types of neural networks viz. those which satisfy the conditions of the Stone-Weierstrass theorem and Kohonen neural networks. We have used self-organizing maps for scattered data approximation. Neural network Tool Box from MATLAB is used to develop the required grids for approximating scattered data in one and two dimensions.
Time-Optimal Path Tracking: Online Scaling with Guarantees

Given a geometric path, the Time-Optimal Path Tracking problem consists in finding the control strategy to traverse the path time-optimally while regulating tracking errors caused by model inaccuracies and online perturbations. Online Scaling (OS) is a simple yet effective approach to this problem. The overall OS controller is composed of two components: (i) a path controller, which modulates the parameterization of the desired path in an online manner, yielding a reference trajectory; and (ii) a torque controller, which takes the reference trajectory and outputs torque commands for tracking. However, OS has one major drawback: the path controller might not find any feasible reference trajectory that can be tracked by the torque controller because of torque constraints. In turn, this results in degraded tracking performances. Here, we propose a new controller, termed Online Scaling with Guarantees (OSG), which is guaranteed to find a feasible reference trajectory by accounting for all possible future perturbations. Our approach is based on Robust Reachability Analysis. Simulations show that the OSG controller outperforms existing methods.
Analisis keputusan penggunaan aplikasi Blu by BCA digital

Seeing the development of economic transaction patterns that are starting to change due to the pandemic, BCA has taken adaptive steps to focus on digital transactions. BCA launched a digital application called Blu by BCA Digital. Even though it is launched by BCA Group (PT Bank Digital BCA), Blu by BCA Digital also has challenges because there are several competitors in the digital banking sector. This study aims to analyze the acceptance of Blu by BCA Digital application through an extension of Technology Acceptance Model (TAM). The sample taken was 250 users of Blu by BCA Digital. The data collection method used by the researcher was purposive sampling. Online questionnaires were used to collect primary data. This study uses PLS-SEM to analyze research data. The conclusions of this research are: (a) subjective norms has a positive effect on perceived ease of use, (b) subjective norms and perceived ease of use has a positive effect on perceived usefulness, (c) perceived ease of use and perceived risk has a negative effect on attitude towards using, (d) perceived usefulness and user innovativeness has a positive effect on attitude towards using, (e) attitude towards using has a positive effect on behavioral intention. Melihat perkembangan pola transaksi ekonomi yang mulai berubah akibat momentum pandemi, BCA mengambil langkah adaptif untuk fokus pada transaksi digital. BCA meluncurkan aplikasi digital bernama Blu by BCA Digital. Meski merupakan aplikasi yang diluncurkan oleh Grup BCA (PT Bank Digital BCA), Blu by BCA Digital juga memiliki tantangan karena ada beberapa pesaing di sektor perbankan digital. Penelitian ini bertujuan untuk menganalisis keputusan penggunaan aplikasi Blu by BCA Digital melalui pendekatan Technology Acceptance Model (TAM) yang dikembangkan. Sampel yang diambil adalah 250 pengguna Blu by BCA Digital. Metode pengambilan data yang digunakan adalah purposive sampling. Data primer dikumpulkan melalui kuesioner online. Penelitian ini menggunakan PLS-SEM untuk menganalisis data penelitian. Kesimpulan dari penelitian ini adalah : (a) norma subyektif memiliki pengaruh positif terhadap persepsi kemudahan, (b) norma subyektif dan persepsi kemudahan memiliki pengaruh positif terhadap persepsi kegunaan, (c) persepsi kemudahan dan persepsi risiko memiliki pengaruh negatif terhadap sikap penggunaan, (d) persepsi kegunaan dan keinovatifan pengguna memiliki pengaruh positif terhadap sikap penggunaan, (e) sikap penggunaan memiliki pengaruh positif terhadap niat perilaku.
A study of Penon weak n-categories Part 1: Monad interleaving

We give an alternative construction of the monad used by Penon to define weak n-categories. Penon’s monad adds two pieces of extra structure to an n-globular set: a magma structure, giving composition, and a contraction structure, giving coherence. We add these two structures using an interleaving approach, following the method used by Cheng to construct Leinster’s operad for weak omega-categories. We conclude by using our construction to give an explicit description of the n-globular operad for Penon weak n-categories.
Pedestrian Behavior Prediction Using Deep Learning Methods for Urban Scenarios: A Review

The prediction of pedestrian behavior is essential for automated driving in urban traffic and has attracted increasing attention in the vehicle industry. This task is challenging because pedestrian behavior is driven by various factors, including their individual properties, the interactions with other road users, and the interactions with the environment. Deep learning approaches have become increasingly popular because of their superior performance in complex scenarios compared to traditional approaches such as the social force or constant velocity models. In this paper, we provide a comprehensive review of deep learning-based approaches for pedestrian behavior prediction. We review and categorize a large selection of scientific contributions covering both trajectory and intention prediction from the last five years. We categorize existing works by prediction tasks, input data, model features, and network structures. Besides, we provide an overview of existing datasets and the evaluation metrics. We analyze, compare, and discuss the performance of existing work. Finally, we point out the research gaps and outline possible directions for future research.