Miracle-dz/UniFAR-FTUs huggingface dataset:
Development, optimization and evaluation of tinidazole functionalized electrospun poly(ε-caprolactone) nanofiber membranes for the treatment of periodontitis

The aim of this study was to alleviate shortcomings in the treatment of periodontitis by electrospinning of a novel biodegradable poly(e-caprolactone) (PCL) based nanofiber membrane functionalized with tinidazole (TNZ). Box–Behnken design was employed for evaluating influence of formulation and processing variables on entrapment efficiency (EE) and diameter of nanofiber. The optimum batch selected by desirability approach was subjected to physicochemical characterization such as FTIR, DSC and PXRD which revealed entrapment of drug in a molecular dispersion devoid of any chemical interaction with the excipients. Electron microscopy showed smooth structure in nanometre range, without any visible sign of fiber break-up or disruption within the nanofiber membrane. Optimized TNZ-PCL nanofiber membrane exhibited a diameter of 147.6 ± 7.6 nm and EE 84.36 ± 1.5%. In vitro release study and antibacterial study demonstrated sustained drug release for up to 20 days, depending upon the drug to polymer ratio and solvent composition. Further, reduction of contact angle (from 123.6 ± 2.8 to 57.2 ± 1.9) revealed that incorporation of TNZ enhanced the hydrophilicity of the nanofiber membrane which would facilitate its adhesion to the site of action and instigate proliferation of cells. MTT assay and CLSM study suggested that nanofiber membrane showed no cytotoxicity on mouse fibroblasts (L-929 cell lines). Moreover, in vivo study by ligature-induced periodontitis in rats confirmed that TNZ loaded nanofiber membrane can significantly (p < 0.05) improve continuity of epithelium and transseptal fiber of interdental papilla in comparison to tinidazole gel.
Verification of thermal models in the design of personal protective equipment against cold

In the article research results are presented, which aim to verification of thermal models in the design of personal protective equipment against cold. Under the effect of wind, movement, and pressure of the human being himself on the clothing, the design thermal resistance of the clothing decreases and does not provide the stable human comfort. To eliminate such risks, special heated garments are being developed, which are fitted with electric batteries. The basis for such a development is the mathematical model of the system “Human-Clothes-Environment”, which allows to establish the main parameters of the heating intensity of each individual section of the clothing, taking into account the overall functioning of the system. Such a model has been developed. The problems of verification of thermal models for designing if the clothing are associated with the assessment of experimental data on the parameters of the system under study. A structure for the formation of a method for experimental verification of the models of thermal protective clothing has been developed. Design of experimental protective clothing against cold with heating based on a mathematical model has been developed. The ratio of feelings of thermal comfort and temperature under heated clothing of small thickness under wind load, cold and lack of physical activity has been installed.
Kajian Perbandingan Cobit 5 dengan Cobit 2019 sebagai Framework Audit Tata Kelola Teknologi Informasi

Teknologi Informasi atau biasa disebut TI merupakan hal terpenting bagi kemajuan perusahaan, semakin berkembangkan TI maka harus dilakukan tata kelola TI yang baik sesuai dengan standart yang ada dan harus dievaluasi terus menerus. Salah satu cara untuk mengevaluasi tata kelola TI yaitu dengan dilakukanya audit teknologi informasi. Salah satu framework yang sering digunakan untuk melakukan audit tata kelola TI adalah COBIT. Tujuan dari penelitian ini adalah mengetahui perbandingan antara COBIT 5 dengan COBIT 2019 serta mengetahui kekurangan dan kelebihan dari masing-masing COBIT tersebut. Metode yang digunakan yaitu kualitatif dengan studi literatur yang berisi tentang uraian teori, temuan dan bahan penelitian lain yang diperoleh sebagai acuan untuk dijadikan landasan kegiatan penelitian. Hasil yang diperoleh dari penelitian ini yaitu terdapat beberapa perbedaan dari segi gambaran secara umum pada COBIT 2019 memiliki desain faktor, segi domain, pada COBIT 2019 terdapat tiga domain tambahan dan bersifat objective. Selanjutnya dari segi prinsip COBIT 2019 memiliki 9 prinsip yang telah dikembangkan dari COBIT sebelumnya. Selanjutnya dari goal cascade dan dari perhitungan kinerja, COBIT 5 hanya menggunakan capability level tetapi pada COBIT 2019 adanya penambahan menjadi capability level dan maturity level. Dari segi kelebihan COBIT 5 lebih banyak digunakan sedangkan dari kekurangannya tidak bersifat flexible sedangkan COBIT 2019 kelebihannya lebih bersifat flexible dan kekurannya prinsip dan detail domainya lebih banyak sehingga akan lebih sulit dalam implementasi.
Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees

A multichannel random access system is considered in which each user accesses a single channel among multiple orthogonal channels to communicate with an access point (AP). Users arrive to the system at random and be activated for a certain period of time slots and then disappear from the system. Under such dynamic network environment, we propose a distributed multichannel access protocol based on multi-agent reinforcement learning (RL) to improve both throughput and fairness between users. Unlike the previous approaches adjusting channel access probabilities at each time slot, the proposed RL algorithm deterministically selects a set of channel access policies for several consecutive time slots. To effectively reduce the complexity of the proposed RL algorithm, we adopt a branching dueling Q-network architecture and propose a training methodology for producing proper Q-values under time-varying user sets. Numerical results demonstrate that the proposed scheme significantly improve both throughput and fairness.
Comparison of platelet count and mean platelet volume values before and after treatment in rheumatoid arthritis and ankylosing spondylitis patients receiving tumor necrosis factor alpha inhibitor therapy

Aim: In our study, we planned to reveal the change of the mean platelet volume (MPV) with anti-TNF treatment given in patients with Rheumatoid-Arthritis (RA) and Ankylosing-Spondylitis (AS) and to evaluate the clinical importance of MPV in the follow-up of these diseases. Materials and Methods: Patients who applied to our hospital's between 2011 and 2013 were included in the study. 40 patients participated in the study. The average ages of the patients were 43.8±9.9 years (range:25-68 years). All patients were examined retrospectively, Hemoglobin (Hb), Hematocrit (Hct), Platelet count, Mean Red-Blood-Cell Volume (MCV), MPV, ErythrocyteSedimentation-Rate (ESR), C-Reactive Protein (CRP) before anti-TNF treatment were recorded. Then, the same values were checked six months after the treatment. Averages of the Hb, Hct, MCV, PLT, MPV, CRP, ESH parameters of patients before and after treatment were compared with each other. Results: When the values before and after the treatment were compared, there was a significant decrease in ESR (p:0.002). There was also a statistically significant increase in Hb and Hct values after treatment (p:0.002, p:0.039, respectively). A significant decrease was observed in mean platelet count and MPV values after treatment (p:0.001). Although there was an increase in the MCV value, it was not statistically significant. Likewise, there was a decrease in CRP value but it was not statistically significant. Conclusion: In this study, it was concluded that the MPV value and platelet count could be inexpensive biomarkers in the follow-up of RA and AS. However, more controlled prospective studies are needed in this regard.
Bayesian Optimistic Optimisation with Exponentially Decaying Regret

Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bounds ranging from $\mathcal{O}(\frac{logN}{\sqrt{N}})$ to $\mathcal O(e^{-\sqrt{N}})$, where $N$ is the number of evaluations. This paper explores the possibility of improving the regret bound in the noiseless setting by intertwining concepts from BO and tree-based optimistic optimisation which are based on partitioning the search space. We propose the BOO algorithm, a first practical approach which can achieve an exponential regret bound with order $\mathcal O(N^{-\sqrt{N}})$ under the assumption that the objective function is sampled from a Gaussian process with a Mat\'ern kernel with smoothness parameter $\nu>4 +\frac{D}{2}$, where $D$ is the number of dimensions. We perform experiments on optimisation of various synthetic functions and machine learning hyperparameter tuning tasks and show that our algorithm outperforms baselines.
Discovering Community Interests Approach to Topic Model with Time Factor and Clustering Methods

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users’ interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of e-commerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster’s nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and e-commerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.
Networks, SMEs, and the University

Exploring the process of university collaboration from the perspective of small- and medium-sized enterprises (SMEs), this book offers an in-depth examination of the collaboration process, dispelling the myth of the disengagement of these firms. Andrew Johnston and Robert Huggins present a thorough account of how SMEs can â€˜unlock the ivory towerâ€™ and gain access to university knowledge to support their own innovation.
Study on Estimate and Affecting Factors of the Minimum Oxygen Concentration of Flammable Gases (Liquid Vapors)

The minimum oxygen concentration (MOC) is one of the important safety parameters of flammable gases and liquid vapors theoretical calculation method of MOC was studied. At the same time, based on the large quantities of experiments, the affecting factors on MOC was examined,which is influenced by temperature, pressure, inert gases and so on. At last, it is presented that MOC and lower explosion limit are corresponding relationship;it attach certain safety factor when using theoretical calculation value of MOC;by the ways of reducing oxygen concentration in reaction, adding inert gas and so on,to narrow the scope of the explosion limit, increase MOC value, ultimately which control it beyond the explosion scope.1fig. ,4tabs. ,9refs.
Determining diazinon degradation using potassium hydrogen monopersulfate and magnetic copper ferrite nano-particles in contact with ozone in aqueous solutions

In this paper, magnetic copper ferrite nano-particles (CuFe2O4, MNPs) are applied in the ozone/ potassium hydrogen monopersulfate (KMPS) system to degrade diazinon pesticide (DZN). To do so, the features of MNPs, the factors affecting the system, and the kinetic of the reaction are investigated. In this matter, the KMPS/MNPs/ozone system is able to degrade 20 mg/L of DZN completely in 20 min under the conditions of 6 mg/L ozone, 0.6 g/L KMPS, 0.1 g/L MNPs in pH = 7. The obtained results pertaining to reusability and stability verify that MNPs can be utilized in this system under several times without losing its catalytic activity. Moreover, both KMPs and ozone increase copper and iron chelation. Various single, binary, and ternary systems of ozone/KMPS/MNPs are assessed in the DZN degradation. The effectiveness of the process is assessed in the existence of different types of anions toward the conclusion, in which bicarbonate has the highest whereas nitrate has the lowest scavenging. Additionally, some decreases in the degradation can be observed while the process in several different real situations is surveyed. Furthermore, the effective agents in the scavenging experiments prove to play a role in degradation of DZN such as sulfate radical and hydroxyl radical. Mineralization of diazonin in the process is evaluated via the total organic carbon (TOC) parameter in the ozone/MNPs/KMPS system. As expected, the mineralization occur with the elimination of 61%/9% of TOC. Ultimately, the by-products of the process are analyzed using gas chromatography–mass spectrometry.
Application of Landsat ETM+ and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and Machine Learning Method

Foliage fuel load (FFL) is a critical factor affecting crown fire intensity and rate of spread. Satellite observations provide the potential for monitoring FFL dynamics across large areas. Previous studies commonly used empirical methods to estimate FFL, which potentially lacks reproducibility. This study applied Landsat 7 ETM+ and 8 OLI data for FFL retrieval using radiative transfer model (RTM) and machine learning method. To this end, the GeoSail, SAIL, and PROSPECT RTMs were first coupled together to model the near-realistic scenario of a two-layered forest structure. Second, available ecological information was applied to constrain the coupled RTM modeling phases in order to decrease the probability of generating unrealistic simulations. Third, the coupled RTMs were linked to three machine learning models—random forest, support vector machine, and multilayer perceptron—as well as the traditional lookup table. Finally, the performance of each method was validated by FFL measurements from Southwest China and Sweden. The resulting multilayer perceptron (R2 = 0.77, RMSE = 0.13, and rRMSE = 0.43) outperformed the other three methods. The evaluation of the applicability of the FFL estimates was conducted in a southwest China forest where two occurred in 2014 and 2020. The FFL dynamics from 2013 through 2020 showed that the fire was likely to occur when the FFL accumulated to a critical point (around 27 × 106 kg), highlighting the relevance of remote sensing derived FFL estimates for understanding potential fire occurrence.
Edge AI for Industrial IoT Applications

In this paper, we study the edge artiﬁcial intelligence (AI) for industrial internet of things (IIoT) applications. We discuss about the edge AI technology that is considered the combination of AI with edge computing and provide an overview of edge AI applications for IIoT networks, where the following three challenges are important to address: a) personalization, b) responsiveness and c) privacy preserving. To this end, we propose a federated active tranfer learning (FATL) model, which through training and testing is able to address those open challenges. Details about the training and testing of the proposed FATL global model are given including the corresponding simulation setup. This work concludes with a discussion and comparison of the obtained simulation results with existing edge AI training solutions, which provide useful insights about the proposed FATL model. The simulation results highlight how the FATL global model can address efﬁciently the open challenges of edge AI for future IIoT applications.
Rebounding hygroscopic inorganic aerosol particles: Liquids, gels, and hydrates

ABSTRACT Particle rebound was studied for ten atmospherically relevant inorganics. Experiments were conducted with submicron particles in aerosol form to a relative humidity (RH) of <5% followed by progressive exposure to RH up to 95% for 2 s. At low RH, particles of MgCl2, NaCl, NH4Cl, KCl, (NH4)2SO4, and Na2SO4 crystallized. As RH increased, these particles completed the transition from rebounding to adhering close to their deliquescence RH (DRH). The onset of decreased rebound, however, was below the DRH. Rebound curves for particles of MgCl2, NH4NO3, MgSO4, and NaNO3 had different features than explained by water adsorption and deliquescence. Particles of MgCl2 had rebound curves characterized by two domains, corresponding to its two hydrates. At low RH, particles of MgSO4 and NaNO3 did not crystallize but rebound occurred, suggesting a glassy or high-viscosity though noncrystalline state. Gel formation for MgSO4 can increase viscosity, affecting rebound behavior. Particles of NH4NO3 adhered even to <5% RH, suggesting a low-viscosity state even to low RH. Particles of NH4HSO4 were investigated as a special case by exposure to 5 ppm ammonia at 10% and 90% RH. At low RH, these particles still had sufficient molecular diffusivity to maintain active heterogeneous chemistry, although with some kinetic limitations. The different behaviors between nitrates and sulfates suggest different roles of heterogeneous chemistry in regions affected by NOx compared to SO2 emissions. The results of this study could have implications for the use of different wet and dry seed particles in chamber experiments. Copyright © 2017 American Association for Aerosol Research
Statistical inference for continuous-time locally stationary processes using stationary approximations

We establish asymptotic properties of $M$-estimators, defined in terms of a contrast function and observations from a continuous-time locally stationary process. Using the stationary approximation of the sequence, $\theta$-weak dependence, and hereditary properties, we give sufficient conditions on the contrast function that ensure consistency and asymptotic normality of the $M$-estimator. As an example, we obtain consistency and asymptotic normality of a localized least squares estimator for observations from a sequence of time-varying L\'evy-driven Ornstein-Uhlenbeck processes. Furthermore, for a sequence of time-varying L\'evy-driven state space models, we show consistency of a localized Whittle estimator and an $M$-estimator that is based on a quasi maximum likelihood contrast. Simulation studies show the applicability of the estimation procedures.
Design of Ship Repair Yard Ranking Website Based on Customer Reviews

Many ship owners currently choose ship repair yards based on conventional ways; there is no specific platform that can provide information objectively ranking the ship repair yards that can be accessed in public. On the other hand, technological developments have shifted in community business activities from an offline way to the online method. The initial process of designing the application is by determining and weighing the criteria for assessing ship repair shipyards using the AHP method and then obtained nine criteria with weights for each standard. After getting the requirements and value, then we make a website-based application design. This application will display shipyard ranking according to the ratings and reviews provided by customers. The calculation of each shipyard’s rating and scale will be determined based on the per-criteria value, which is then accumulated based on the weight of each predetermined criterion. After testing the application in a questionnaire on several respondents from shipping companies, this application gets a value of 28.5 out of 30, or 94.9%, which means this application necessary to be applied in the ranking of ship repair yard.
Molecular Simulations for Understanding the Stabilization of Fullerenes in Water

Making materials out of buckminsterfullerene is challenging, because it requires first dispersing the molecules in a solvent, and then getting the molecules to assemble in the desired arrangements. In this computational work, we focus on the dispersion challenge: How can we conveniently solubilize buckminsterfullerene? Water is a desirable solvent because of its ubiquity and biocompatibility, but its polarity makes the dispersion of nonpolar fullerenes challenging. We perform molecular dynamics simulations of fullerenes in the presence of fullerene oxides in implicit water to elucidate the role of interactions (van der Waals and Coulombic) on the self-assembly and structure of these aqueous mixtures. Seven coarse-grained fullerene models are characterized over a range of temperatures and interaction strengths using HOOMD-Blue on high performance computing clusters. We find that dispersions of fullerenes stabilized by fullerene oxides are observable in models where the net attraction among fullerenes is about 1.5 times larger than the at-tractions between oxide molecules. We demonstrate that simplified models are sufficient for qualitatively modeling micellization of these fullerenes and provide an efficient starting point for investigating how structural details and phase behavior depend upon the inclusion of more detailed physics.
Hierarchically modelling Kepler dwarfs and subgiants to improve inference of stellar properties with asteroseismology

With recent advances in modelling stars using high-precision asteroseismology, the systematic effects associated with our assumptions of stellar helium abundance (Y) and the mixing-length theory parameter (αMLT) are becoming more important. We apply a new method to improve the inference of stellar parameters for a sample of Kepler dwarfs and subgiants across a narrow mass range ($0.8 \lt M \lt 1.2\, \rm M_\odot$). In this method, we include a statistical treatment of Y and the αMLT. We develop a hierarchical Bayesian model to encode information about the distribution of Y and αMLT in the population, fitting a linear helium enrichment law including an intrinsic spread around this relation and normal distribution in αMLT. We test various levels of pooling parameters, with and without solar data as a calibrator. When including the Sun as a star, we find the gradient for the enrichment law, $\Delta Y / \Delta Z = 1.05\substack{+0.28\\-0.25}$ and the mean αMLT in the population, $\mu _\alpha = 1.90\substack{+0.10\\-0.09}$. While accounting for the uncertainty in Y and αMLT, we are still able to report statistical uncertainties of 2.5 per cent in mass, 1.2 per cent in radius, and 12 per cent in age. Our method can also be applied to larger samples that will lead to improved constraints on both the population level inference and the star-by-star fundamental parameters.