Miracle-dz/UniFAR-FTUs huggingface dataset:
Chemical coatings relying on the self-polymerization of catechol for retrievable vena cava filters

The heparin–dopamine conjugate was synthesized by an amidation reaction between heparin's carboxyl and dopamine's amine, whereas the paclitaxel (PTX)-catechol conjugate was prepared via an esterification reaction between the carboxyl in 3-catechol-1-acetic acid and PTX's hydroxyl. The former can be chemically coated on a Ti–Ni alloy substrate via pH sensitive self-polymerization of the catechol moieties in aqueous solution, and the latter can also be chemically attached to a Ti–Ni alloy substrate in acetone by the thermo-sensitive self-polymerization of catechol. Successful chemical coating of heparin and PTX can be proven by static contact angle and XPS measurements, respectively. In vitro evaluation showed that the heparin coating exhibited anti-thrombosis properties, whereas the PTX coating also illustrated anti-intimal hyperplasia properties. Consequently, the promising potential to develop chemical coatings relying on the self-polymerization of catechol moieties to effectively extend the in vivo retention time of retrievable vena cava filters in the clinic was shown.
Simultaneous determination of some steroid hormones in milk and dairy products by liquid chromatography tandem mass spectrometry (LC-MS/MS)

A sensitive method has been developed and validated for simultaneous determination of some steroid hormones (trenbolone, 17β-estradiol, progesterone, testosterone, estrone, melengestrol acetate and zeranol). Samples were hydrolyzed to release analytes, and analytes were then extracted from sample matrix using QuEChERS technique, detected and quantified by using reversed phase liquid chromatography coupled with tandem mass spectrometry. The tandem mass spectrometric detection was done by using ESI ion source working in positive mode and multiple reaction monitoring (MRM) quantitation. Validation results show that the method is sensitive and selective for analyzing seven steroid hormones on milk and dairy products. The quantitation limit of the method for these analytes ranged from 0.1 to 2.0 µg/kg. Method recovery was from 70.4 to 110.0 %. Relative coefficient of variation varied from 2.7 to 12%. The method was applied to determine steroid hormones in 40 milk and dairy product samples collected from different markets and commercial centers in Hanoi.
Holistic Model for Guiding Good Design Quality in Learning Multimedia Design Development

Development of electronic learning (E-learnings), learning resources are teaching materials and a variety of information developed and packaged in various forms based on information and communication technology and used in the learning process. For this reason, special techniques for subject design, techniques are needed. specific learning, special methodologies through various media, and special organizational and organizational arrangements. In multimedia learning, the design process requires design methods, development tools, inspection models and skills in Information Technology systems. It is necessary to formulate a Holistic Method which considers all related aspects in order to guide the quality of good design in designing Multimedia Learning. Implementing the findings of the Holistic Method formulation to build multimedia learning with good design quality. This study formulates the stages of multimedia development which include: Creative Brief, Design, Material Preparation, Scripting, Testing, Distribution. The multimedia software development process will be guided by 15 guiding elements which are grouped into 5 elements. CONTENT first guiding element include : Perception of meaning, Information Message, Storytelling. APPEARANCE second guiding elements include : Aesthetic expression, Symbolic value, Material Medium. UTILITY ASPECT third guiding element include : Engineering Quality, Usability, Navigation Structure. USER CARACTER fourth guiding element include : Biophysic, Psycho, Sosio Culture. DIDACTIC fifth guiding element include : Learning situation, Pedagogical strategy, Personalization. These guiding elements are used to produce good quality learning multimedia designs. The methodology is called Holistic Multimedia Development Model (HMDM).
Effect of various nitrogen flow ratios on the optical properties of (Hf:N)-DLC films prepared by reactive magnetron sputtering

Hf and N co-doped diamond-like carbon [(Hf:N)-DLC] films were deposited on 316L stainless steel and glass substrates through reactive magnetron sputtering of hafnium and carbon targets at various nitrogen flow ratios (R=N2/[N2+CH4+Ar]). The effects of chemical composition and crystal structure on the optical properties of the (Hf:N)-DLC films were studied. The obtained films consist of uniform HfN nanocrystallines embedded into the DLC matrix. The size of the graphite clusters with sp2 bonds (La) and the ID/IG ratio increase to 2.47 nm and 3.37, respectively, with increasing R. The optical band gap of the films decreases from 2.01 eV to 1.84 eV with increasing R. This finding is consistent with the trends of structural transformations and could be related to the increase in the density of π-bonds due to nitrogen incorporation. This paper reports the influence of nitrogen flow ratio on the correlation among the chemical composition, crystal structure, and optical properties of (Hf:N)-DLC films.
A Self-Supervised Method Based on Noise2Noise Model for SAR Image Despeckling

Synthetic aperture radar (SAR) imaging plays an important role in recent earth observation research, which is a form of radar imaging technology that collects information with a high spatial resolution even in adverse weather and lighting conditions. Different from optical images, SAR images are contaminated by speckle noises and the presence of speckle noises in SAR images makes it difficult to understand and be widely applied. A self-supervised speckle denoising model is proposed in this paper based on the Noise2Noise model. The experimental results show that the proposed method can preserve details and reduce smoothing not only in speckle noise multiplicative domain but also additive domain. The tentative study demonstrates the potential of SAR images despeckling without clean data.
The PRAKTOR Metamodel: Mobile Agents as Executable Parametric Object Petri Nets

: Mobile agents have emerged as an attractive paradigm for the conceptualization and realisation of massively distributed and autonomous systems. We describe an original meta-model that can be used to design scalable, highly concurrent multiagent system architectures potentially consisting of very large numbers of fully mobile, persistent agents of arbitrary granularity and complexity. The model consists of an agent modelling language based on High Level Petri Nets and a functional reference implementation that enables the direct reification of design artefacts into executable mobile agent automata.
University of Birmingham Oxidative stress and the pathophysiology and symptom profile of schizophrenia spectrum disorders

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Comparison of variability in results of acoustic tomographs in pedunculate oak (Quercus robur L.)

Tree acoustic tomography is a widely used device-supported method for tree stability assessment. In this work, the results of the three most commonly used devices for acoustic tomography of standing trees (ARBOTOM, ArborSonic, and PiCUS) were compared on selected individuals of sessile oak in Brno, Czech Republic. According to the statistical analysis, there was a significant difference between values measured by the PiCUS acoustic tomograph and those measured by both the ARBOTOM and ArborSonic acoustic tomographs. Based on the measured data, velocities measured by PiCUS were considerably lower than those recorded by the other acoustic tomographs (ARBOTOM and ARBORSONIC). Measured radial and tangential velocities differed from each other. In data obtained from the defective cross-sections, this difference was attenuated. Image reconstructions (tomograms) from the acoustic tomographs differed from each other. Complex shapes of defects in standing trees can significantly influence acoustic tomography results. According to the statistical analysis, there was no significant relationship between sound velocity and density, while there was a relatively strong positive correlation between sound velocity and moisture content.
Sustainable Human Resource Management through Leadership Styles and School Performance：A Study within An Eritrean Context

Context/literature: Principals employ a range of leadership styles that directly or indirectly impact school performance. However, there is always a debate among scholars about which leadership style is the most effective for school performance. Purposes: The purpose of this study was to investigate and analyze the relationship between different leadership styles of principals and school performance in Eritrean secondary schools. Research method: The study followed a descriptive research design, and it employed both qualitative and quantitative methods to establish a relationship between leadership styles and school performance. Using non-probability sampling, a sample (N = 375) comprising 30 schools/principals, 250 teachers, 50 students, and 45 parents participated in the study. Findings: The study explored and analyzed the relationship between the two variables, namely leadership styles of principals and school performance. The study found that principals’ leadership styles do influence school performance either positively or negatively, and it has become clear that no single leadership style is appropriate at all times. The study found that democratic and situational leadership styles have a positive impact on school performance; while autocratic and laissez-faire leadership styles were found to have negative impacts on school performance. Conclusion & recommendations: It can be concluded that no single leadership style is appropriate at all times, because no leadership style is right at all times. Based on empirical findings, contrary to established norms in the Eritrean school system, the study recommends that school principals should opt to implement principles of democratic and situational leadership for better school performance. Among other things, the study recommends that school principals should receive professional development to upgrade their leadership capacity in order to improve overall school performance and student attainment.
SPDL-Stock Price Prediction with Deep Learning

Stock market variation data is collected in the form of breaking news from various business websites. The stock market trend changes with key financial reforms, weather conditions, and political events. The dataset is created using financial news text data. The dataset features consist of TF-IDF of various companies of Bombay Stock Exchange (BSE) along with open, high, low price parameters for prediction. Research considers the next day's stock price prediction using a deep learning framework (SPDL). The data set is transformed by various deep learning techniques to get more accurate results. The proposed model produces approximately 85 % of accurate predictions with a deep learning framework. The market trends in terms of high and low stock values are matching exactly. The research work is converted to Shiny Application (SPruH). Google analytics is used to analyze the usage of an application. The results can be improved with the use of high-frequency trading algorithms in the future.
Tunable Terahertz Plasmon-Induced Transparency in Resonator-Coupled Dirac Semimetal Waveguides

The bulk Dirac semimetal (BDS) is an interesting material, similar to graphene, which can dynamically adjust its optical properties via a variation in its Fermi energy or electrical voltage. In this work, a BDS-based plasmonic device, which enables tunable terahertz plasmon-induced transparency, was proposed and designed. By using the finite element method, the surface plasmon polariton and plasmon-induced transparency of this device were systematically investigated. The results demonstrate that the plasmon-induced transparency of such device can be dynamically tuned by varying its Fermi energy. When the Fermi energy changes from 55 meV to 95 meV, the maximum group delay time of the device increases from 13.2 ps to 21 ps. In the case of a cascading device, the maximum group delay time can be further pushed up to 44.57 ps. The influence of the ambient refractive index on the optical properties of the proposed device was also considered and investigated.
Orientation‐based edge‐colorings and linear arboricity of multigraphs

The Goldberg–Seymour Conjecture for f $f$ ‐colorings states that the f $f$ ‐chromatic index of a loopless multigraph is essentially determined by either a weighted maximum degree or a weighted maximum density parameter. We introduce an oriented version of f $f$ ‐colorings, where now each color class of the edge‐coloring is required to be orientable in such a way that every vertex v $v$ has indegree and outdegree at most some specified values g(v) $g(v)$ and h(v) $h(v)$ . We prove that the associated (g,h) $(g,h)$ ‐oriented chromatic index satisfies a Goldberg–Seymour formula. We then present simple applications of this result to variations of f $f$ ‐colorings. In particular, we show that the Linear Arboricity Conjecture holds for k $k$ ‐degenerate loopless multigraphs when the maximum degree is at least 4k−2 $4k-2$ , improving a recent bound by Chen, Hao, and Yu for simple graphs. Finally, we demonstrate that the (g,h) $(g,h)$ ‐oriented chromatic index is always equal to its list coloring analogue.
On the use of test smells for prediction of flaky tests

Regression testing is an important phase to deliver software with quality. However, flaky tests hamper the evaluation of test results and can increase costs. This is because a flaky test may pass or fail non-deterministically and to identify properly the flakiness of a test requires rerunning the test suite multiple times. To cope with this challenge, approaches have been proposed based on prediction models and machine learning. Existing approaches based on the use of the test case vocabulary may be context-sensitive and prone to overfitting, presenting low performance when executed in a cross-project scenario. To overcome these limitations, we investigate the use of test smells as predictors of flaky tests. We conducted an empirical study to understand if test smells have good performance as a classifier to predict the flakiness in the cross-project context, and analysed the information gain of each test smell. We also compared the test smell-based approach with the vocabulary-based one. As a result, we obtained a classifier that had a reasonable performance (Random Forest, 0.83%) to predict the flakiness in the testing phase. This classifier presented better performance than vocabulary-based model for cross-project prediction. The Assertion Roulette and Sleepy Test test smell types are the ones associated with the best information gain values.
Efficient On-Chip Communication for Parallel Graph-Analytics on Spatial Architectures

Large-scale graph processing has drawn great attention in recent years. Most of the modern-day datacenter workloads can be represented in the form of Graph Processing such as MapReduce etc. Consequently, a lot of designs for Domain-Specific Accelerators have been proposed for Graph Processing. Spatial Architectures have been promising in the execution of Graph Processing, where the graph is partitioned on several nodes and each node works in parallel. We conduct experiments to analyze the on-chip movement of data in graph processing on a Spatial Architecture. Based on the obser-vations, we identify a data movement bottleneck, in the execution of such highly-parallel processing accelerators. To mitigate the bottleneck we propose a novel power-law aware Graph Partitioning and Data Mapping scheme to reduce the communication latency by minimizing the hop counts on a scalable network-on-chip. The experimental results on popular graph algorithms show that our implementation makes the execution 2 − 5 × faster and 2 . 7 − 4 × energy-efficient by reducing the data movement time in comparison to a baseline implementation.
Model Predictive Control approach to adaptive messaging intervention for physical activity

In this work, we have developed a framework for synthesizing data driven controllers for a class of uncertain switched systems arising in an application to physical activity interventions. In particular, we present an application of probabilistic model predictive control to design an efficient, tractable, and adaptive intervention using behavioral data sets i.e. physical activity behavior. The models of physical activity for each individual are provided for the design of controllers that maximize the probability of achieving a desired physical activity goal subject to intervention specifications. We have tailored the mixed-integer programming-based approach for evaluating the Model Predictive Control decision at each time step.
Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of Federated Learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance of RL while preserving data-privacy. According to the distribution characteristics of the agents in the framework, FRL algorithms can be divided into two categories, i.e. Horizontal Federated Reinforcement Learning (HFRL) and Vertical Federated Reinforcement Learning (VFRL). We provide the detailed definitions of each category by formulas, investigate the evolution of FRL from a technical perspective, and highlight its advantages over previous RL algorithms. In addition, the existing works on FRL are summarized by application fields, including edge computing, communication, control optimization, and attack detection. Finally, we describe and discuss several key research directions that are crucial to solving the open problems within FRL.
Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges

Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses. Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially.
A Unified Approach for Constructing Confidence Intervals and Hypothesis Tests Using h-function

We introduce a general method, named the h-function method, to unify the constructions of level-alpha exact test and 1-alpha exact confidence interval. Using this method, any confidence interval is improved as follows: i) an approximate interval, including a point estimator, is modified to an exact interval; ii) an exact interval is refined to be an interval that is a subset of the previous one. Two real datasets are used to illustrate the method.
Distributed Soft Bayesian Additive Regression Trees

Bayesian Additive Regression Trees(BART) is a Bayesian nonparametric approach which has been shown to be competitive with the best modern predictive methods such as random forest and Gradient Boosting Decision Tree.The sum of trees structure combined with a Bayesian inferential framework provide a accurate and robust statistic method.BART variant named SBART using randomized decision trees has been developed and show practical benefits compared to BART. The primary bottleneck of SBART is the speed to compute the sufficient statistics and the publicly avaiable implementation of the SBART algorithm in the R package is very slow.In this paper we show how the SBART algorithm can be modified and computed using single program,multiple data(SPMD) distributed computation with the Message Passing Interface(MPI) library.This approach scales nearly linearly in the number of processor cores, enabling the practitioner to perform statistical inference on massive datasets. Our approach can also handle datasets too massive to fit on any single data repository.We have made modification to this algorithm to make it capable to handle classfication problem which can not be done with the original R package.With data experiments we show the advantage of distributed SBART for classfication problem compared to BART.
Byzantine Fault-Tolerance in Federated Local SGD Under $2f$-Redundancy

In this article, we study the problem of Byzantine fault-tolerance in a federated optimization setting, where there is a group of agents communicating with a centralized coordinator. We allow up to <inline-formula><tex-math notation="LaTeX">$f$</tex-math></inline-formula> Byzantine-faulty agents, which may not follow a prescribed algorithm correctly, and may share arbitrarily incorrect information with the coordinator. Associated with each nonfaulty agent is a local cost function. The goal of the nonfaulty agents is to compute a minimizer of their aggregate cost function. For solving this problem, we propose a local gradient-descent algorithm that incorporates a novel <italic>comparative elimination</italic> filter (aka. aggregation scheme) to provably mitigate the detrimental impact of Byzantine faults. In the deterministic setting, when the agents can compute their local gradients accurately, our algorithm guarantees <italic>exact</italic> fault-tolerance against a bounded fraction of Byzantine agents, provided the nonfaulty agents satisfy the known necessary condition of <italic><inline-formula><tex-math notation="LaTeX">$2f$</tex-math></inline-formula>-redundancy</italic>. In the stochastic setting, when the agents can only compute stochastic estimates of their gradients, our algorithm guarantees <italic>approximate</italic> fault-tolerance where the approximation error is proportional to the variance of stochastic gradients and the fraction of Byzantine agents.