Miracle-dz/UniFAR-FTUs huggingface dataset:
Placement of edge server based on task overhead in mobile edge computing environment

Mobile edge computing (MEC) deployed cloud computing resources (such as storage and computing power) to the edge of wireless access networks to better meet the development of 5G communication and computing‐intensive applications. As the first step of MEC architecture deployment, the placement of the edge server (ES) is the foundation and key, and its location affects the user experience and system performance. In this article, we study the placement of ESs in the heterogeneous networks and express it as an optimization problem. Weighing the response delay and energy consumption as the task overhead, and place the ESs on the optimal access point (AP). An adaptive clustering algorithm MTO based on AP suitability evaluation is proposed to solve the optimal solution, which minimizes the task overhead of computing tasks. Extensive experimental simulations evaluate the performance of the algorithm, and the results show that the MTO algorithm is superior to other representative methods.
Exploring the Direction of Charge Transfer in Porphyrin – PbSe Quantum Dot Hybrids

The binding interaction of amine, thiol and carboxylic acid functionalized porphyrins to PbSe semiconductor quantum dots (QDs) has been studied by focusing on absorption and fluorescence of porphyrins in presence of PbSe QDs and absorption and fluorescence of PbSe QDs in presence of porphyrins. Only tetra-carboxylic acid functionalized porphyrins appear to have significant binding to QDs. Fluorescence life time measurements show that the binding is dynamic rather than static, opposed to the case of metallic gold nanoparticles previously studied. QDs appear to reduce in size by dissolution, when they are exposed for longer times to porphyrins containing carboxylic acid functional groups. Transient absorption spectroscopy shows that charge transfer occurs uni-directional from porphyrins to QDs as expected and not the other way round.
Semantic Field ‘Fairy Tales Heroes’ Relative to English

The article is presenting the results of the Tatar realia research. The authors have calculated the semantic distance coefficient (SDC) for one of the semantic fields of the Tatar realia “Fairy tales’ heroes”. It makes up 11.8, which shows that on average to explain the meanings of Tatar realia, we need approximately 12 words. This figure states the difference in perceiving world picture between Tatar and English peoples. The method of research is based on the statement proved by the linguists of Voronezh University (Russia). They believe the more words we need to explain the word meaning the less common concepts the pair languages have. We also compared the SDC for the other fields of the Tatar realia. For the field “Family” it makes up 8.57, for the field “Animals” it is 6.5, for the field “Religion” it is 12.44, for the field “Measures” t is 11.7, for the field “Food” it is 9.93, for the field “Household objects” it is 8.2. Thus, the numbers show in which fields we have fewer common concepts to avoid misunderstanding.
Reserves Estimation for Coalbed Methane Reservoirs: A Review

A continuous growth in the global economy and population requires a sustainable energy supply. Maximizing recovery factor out of the naturally occurring hydrocarbons resources has been an active area of continuous development to meet the globally increasing demand for energy. Coalbed methane (CBM), which is one of the primary resources of natural gas, associates complex storage mechanisms and requires some advanced recovery techniques, rendering conventional reserve assessment methods insufficient. This work presents a literature review on CBM in different aspects. This includes rock characteristics such as porosity, permeability, adsorption capacity, adsorption isotherm, and coal classification. In addition, CBM reservoirs are compared to conventional reservoirs in terms of reservoir quality, reservoir properties, accumulation, and water/gas saturation and production. Different topics that contribute to the production of CBM reservoirs are also discussed. This includes production mechanisms, well spacing, well completion, and petrophysical interpretations. The main part of this work sheds a light on the available techniques to determine initial-gas-in-place in CBM reservoirs such as volumetric, decline curve, and material balance. It also presents the pros and cons of each technique. Lastly, common development and economic challenges in CBM fields are listed in addition to environmental concerns.
DESIGN OF CHRONIC KIDNEY DISEASE PREDICTION MODEL ON IMBALANCED DATA USING MACHINE LEARNING TECHNIQUES

- The objective of this paper is to build a CKD prediction model using machine learning techniques that can predict the risk of chronic kidney disease (CKD) in patients with Cardiovascular Disease (CVD) or at high risk of CVD. CVD is associated with worsening of renal functions. But patients with CVD remains often underdiagnosed and undertreated for CKD because mostly the clinical diagnosis and treatment are single organ centered in earlier stages. Machine learning algorithms have been widely used to predict and classify diseases in healthcare. Healthcare data is often imbalanced. In this analysis, the CKD prediction model is built using CVD data with imbalanced distribution of positive and negative cases. The analysis involves three stages: Stage I involves selecting the best model based on performance metrics that support imbalanced class distribution without applying any resampling techniques. Stage II involves oversampling the training data of the minority class using Synthetic Minority Oversampling Technique (SMOTE) and stage III involves randomly under-sampling the training data of the majority class to solve the class imbalance. The experimental results show that the MLP (Multi-Layer Perceptron)-SMOTE model performs better in predicting CKD with a better F-score, recall, precision, G-mean, balanced accuracy and RUC-AUC when compared to other models.
Hydro-morphodynamic model with immersed boundary method for local scour at bridge piers

ABSTRACT Bridge pier scour was simulated by a coupled fully 3D hydro-morphodynamic model and validated against 24 experimental datasets for a combination of different pier shapes, approach velocities and sediment bed material. The proposed numerical model adopts an Eulerian multi-fluid model to account for rolling and saltating particles and computes bed shear stresses from implicit wall functions. ANSYS Fluent solved the continuity and Navier-Stokes equations, closed by the RSM turbulence model, which were coupled with sediment transport submodels by source terms. Numerical instabilities were addressed in the sediment transport submodels which were ascribed to the fine mesh resolution required to resolve the crucial horseshoe vortex and the diffusion resulting from the discretization of the Immersed Boundary method.
Phytochemical composition and antioxidant potential of different varieties viz. Flor Branca, Costa Rica and Junco of green unripe acerola (Malphigia emarginata D.C.) fruits

Brazil is known as one of the three main countries in the world where a large production of fruits are cultivated. The region of the São Francisco Valley (SFV), situated in the Northeast Brazil is one of the main production areas of the country (Batista et al., 2018). Scientific evidences suggest that consumption of fruits is associated with the prevention of oxidative stress related disorders since fruits are the source of important bioactive compounds (Amarowicz & Shahidi, 2017). This justifies the search and elucidation of novel plant fruits with bioactive properties, which can be used as an alternative agent for synthetic drugs, which contain health risk factors and high toxicity.
"I had no idea what online lessons were" : experiencing the lockdown as a student of a rural school

Although providing students with access to equal education is one of the priorities and challenges of schools in general, students in rural schools are more likely to have limited access to equal education in comparison with their peers in urban schools. When it comes to strategies to provide equal access to education, these strategies may not always be applied successfully and accessed equally. For instance, in cases of global crises such as the Covid-19 pandemic the need for new strategies may emerge. However, in this case again, it is more difficult for rural schools to catch up with ‘new reality’ and ensure all students have access to education. This study investigates how the nationwide lockdown due to the global pandemic influences access to education from the perspectives of students of a rural school. Using qualitative data generated through individual interviews and photographs, the study provides valuable insights into how students of a rural school experience new realities of their life such as remote teaching and learning. The results of the research reveal the diverse reactions to ‘new normality’ among students, the challenges they face with access to online education and the promoting and discouraging conditions influencing them to pursue their education online.
Comparative Transcriptome Analysis Revealed Candidate Genes Potentially Related to Desiccation Sensitivity of Recalcitrant Quercus variabilis Seeds

Chinese cork oak (Quercus variabilis) is a widely distributed and highly valuable deciduous broadleaf tree from both ecological and economic perspectives. Seeds of this species are recalcitrant, i.e., sensitive to desiccation, which affects their storage and long-term preservation of germplasm. However, little is known about the underlying molecular mechanism of desiccation sensitivity of Q. variabilis seeds. In this study, the seeds were desiccated with silica gel for certain days as different treatments from 0 (Control) to 15 days (T15) with a gradient of 1 day. According to the seed germination percentage, four key stages (Control, T2, T4, and T11) were found. Then the transcriptomic profiles of these four stages were compared. A total of 4,405, 4,441, and 5,907 differentially expressed genes (DEGs) were identified in T2 vs. Control, T4 vs. Control, and T11 vs. Control, respectively. Among them, 2,219 DEGs were overlapped in the three comparison groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these DEGs were enriched into 124 pathways, such as “Plant hormone signal transduction” and “Glycerophospholipid metabolism”. DEGs related to hormone biosynthesis and signal transduction (ZEP, YUC, PYR, ABI5, ERF1B, etc.), stress response proteins (LEA D-29, HSP70, etc.), and phospholipase D (PLD1) were detected during desiccation. These genes and their interactions may determine the desiccation sensitivity of seeds. In addition, group specific DEGs were also identified in T2 vs. Control (PP2C62, UNE12, etc.), T4 vs. Control (WRKY1-like, WAK10, etc.), and T11 vs. Control (IBH1, bZIP44, etc.), respectively. Finally, a possible work model was proposed to show the molecular regulation mechanism of desiccation sensitivity in Q. variabilis seeds. This is the first report on the molecular regulation mechanism of desiccation sensitivity of Q. variabilis seeds using RNA-Seq. The findings could make a great contribution to seed storage and long-term conservation of recalcitrant seeds in the future.
Video tutorials as academic writing and research support for students of International Business

Many studies have made claims for the positive effects of multimedia in education; however, there is a lack of systematic and comparable research, especially when it comes to video tutorials. This study evaluates the use and benefits of short screencast video tutorials, produced with Camtasia and published on YouTube, in preparing students for research-based writing assignments. The study employs a multi-method research design, comprising an analysis of video-tutorial viewership data from YouTube and a student questionnaire on the perceived benefits of these video tutorials. The data on how the tutorials are used, as well as the questionnaire responses, enable us to highlight which aspects of these tutorials positively affect the learning process. Findings indicate that the use of such tutorials is more dependent on the type of information included (e.g., theory, instructions or examples), than their length (within the range of 3-6 min). Additionally, novice, introductory-level students appear to have received greater benefit from the tutorials than students with some previous academic writing experience.
Nonlinear Digital Simulation Models of Switched Reluctance Motor Drive

The paper deals with nonlinear simulation models of a drive consisting of the four-phase 8/6 doubly salient switched reluctance motor (SRM), the four-phase dissymmetric bridge power converter and the closed-cycle rotor speed control strategy carried out by the pulse width modulation (PWM) with variable angle and combined control scheme with the PI algorithm. All presented considerations are based on a MATLAB-SIMULINK platform. The nonlinear mathematical model of the analyzed SRM drive was obtained as a combination of the two dimensional (2D) finite element model (FEM) of the motor and the nonlinear model of the electrical network of the power supply circuit. The main model and its seven sub-modules, such as the controller module, one phase simulation module, rotor position angle transformation module, power system module, phase current operation module, “subsystem” module, and electromagnetic torque of one phase operation module, are described. MATLAB functions store the magnetization curves data of the motor obtained by the 2D FEM electromagnetic field calculations, as well as the data of magnetic co-energy curves of the motor calculated from the magnetization curves. The 2D specimen insert method is adopted in MATLAB functions for operating the flux linkage and the magnetic co-energy at the given phase current and rotor position. The phase current waveforms obtained during simulations match with the tested experimentally phases current waveforms at the same rotor speed and the same load basically. The simulated rotor speed curves also agree with the experimental rotor speed curves. This means that the method of suggested nonlinear simulation models of the analyzed SRM drive is correct, and the model is accurate.
Numerical Study on the Evolution Mechanism of the Crater under a Millisecond Laser

A two-dimensional numerical model considering recoil pressure and Hertz-Knudsen ablation rate was established on the foundation of the laser remelting model to investigate the influence of laser processing parameters on crater feature and melted zone, and it was verified through experiments. The temperature and flow velocity distribution of the molten pool during the formation of the crater were analyzed. The results showed that the ablation velocity could be considered under a higher laser peak power density or higher pulse width due to the metal evaporation caused by heat accumulation. The depth and diameter of the crater were significantly affected by laser peak power density and laser pulse duration. Simultaneously, the height of the edge bulge decreased with the increase in pulse duration after 1.5 ms, and the growth rate of central depth was more rapid than that of edge bulge height with the increase of laser peak power density. In the texture with the same depth, a larger melted zone could be obtained with a longer laser duration than the higher peak power density.
Numerical model for analysis of steel frame structures with included geometric imperfections

: Numerical model based on finite element method for analysis of steel frame structures is presented. Model is implemented in computer program [9] and implies application of 1D (beam) finite element. Large displacement theory with incremental-iterative solution of equilibrium equations is applied. Application of numerical model is presented on an example of steel frame structure where initial geometric imperfections are also included. It is shown that presented numerical model and computer program [9] can be used for practical design of steel frame structures according to EN 1993-1-1 [10]. Main conclusions are given at the end.
Properties of Tilapia Collagen as a Biomaterial for Tissue Engineering: A Review

Collagen is one of the common biopolymers used as a biomaterial in tissue engineering applications due to its biocompatibility and biodegradability properties. Collagen derived from various sources and mostly found in porcine and bovine skin. However, due to religious concerns and the dangers of animal-borne diseases possesses by these mammalian derived collagens, marine collagens are extensively investigated as an alternative to substitute mammalian collagens in tissue engineering applications. Among those marine collagens, tilapia (Oreochromis niloticus) collagens are known to have a great potential to be used as biomaterials for tissue engineering application due to its higher thermal stability compared to other marine sources. Therefore, this study aims to review chemical and biological properties of tilapia collagen as a biomaterial for tissue engineering applications. The contents are mainly focused on the extraction yield, amino acid composition, thermal stability, cross-linking, biocompatibility, biodegradability, immunogenicity and hemostasis of tilapia collagen.
Efficient split likelihood‐based method for community detection of large‐scale networks

The stochastic block model (SBM) is widely employed as a canonical model for network community detection. Recovering community labels under SBM is not a trivial task, since its theoretical optimization problem is NP‐hard. To solve this problem, numerous statistical methods have been developed in the literature, most of which are, however, not applicable to large‐scale networks. To overcome this limitation, in this paper, we propose a split likelihood (SL) framework, under which we provide a fast converging algorithm, named the SL algorithm, with significant advantages in terms of both community detection accuracy and computational efficiency. Moreover, to deal with networks with hub nodes or those with substantial degrees of variation within communities, we develop a variant of the SL algorithm, named the conditional SL (CSL) algorithm. We present the computational and statistical properties of the proposed algorithm. Then, we demonstrate the superiority of the proposed methods through a large amount of numerical experiments as well as two empirical analyses on real‐world networks.
Pairwise networks for feature ranking of a geomagnetic storm model

Feedforward neural networks provide the basis for complex regression models that produce accurate predictions in a variety of applications. However, they generally do not explicitly provide any information about the utility of each of the input parameters in terms of their contribution to model accuracy. With this in mind, we develop the pairwise network, an adaptation to the fully connected feedforward network that allows the ranking of input parameters according to their contribution to model output. The application is demonstrated in the context of a space physics problem. Geomagnetic storms are multi-day events characterised by significant perturbations to the magnetic field of the Earth, driven by solar activity. Previous storm forecasting efforts typically use solar wind measurements as input parameters to a regression problem tasked with predicting a perturbation index such as the 1-minute cadence symmetric-H (Sym-H) index. We re-visit the task of predicting Sym-H from solar wind parameters, with two ‘twists’: (i) Geomagnetic storm phase information is incorporated as model inputs and shown to increase prediction performance. (ii) We describe the pairwise network structure and training process – first validating ranking ability on synthetic data, before using the network to analyse the Sym-H problem.
Zero bus load flow method for the integration of renewable DGs by mixed‐discrete particle swarm optimisation‐based fuzzy max–min approach

This study proposes an approach to determine the optimal placement and sizing of renewable distributed generations (DGs) and shunt capacitor in the distribution network (DN) with a pre-defined energy contract strategy with the main grid by using fuzzy integrated mixed-discrete particle swarm optimisation (MDPSO) based method. The proposed pre-defined contract strategies are implemented in this study using the modified Newton–Raphson (NR) load flow method with the concept of the P Q V δ − zero bus. The modifications in the Jacobian matrix in the NR load flow method is developed. MDPSO method is adopted to take care of both the continuous and the discrete variables, to determine the proper locations and the sizes of renewable DGs and shunt capacitor by reducing the active power loss, improving the voltage profile, and reducing the annual costs of the DGs. The multi-objective optimisation is solved by using the fuzzy max–min-based technique. The proposed method of DG placement is tested on 69 bus DN for three different scenarios. The results obtained from the proposed multi-objective method are also compared with the weighted sum approach for different combinations.
Flame-resistant polymer nanocomposites based on layered silicates

Current literature on the synthesis, investigation, and properties, as well as on possible application areas of polymer nanocomposites based on layered silicates, is surveyed. One of the most important properties of thesenanocomposites is their reduced flammability. It was assumed that this effect is due to their ability to initiate the formation of a char barrier on the surface of a burning polymer nanocomposite, which strongly retards heat and mass transfer processes in the combustion zone.
Multikernel Clustering via Non-Negative Matrix Factorization Tailored Graph Tensor Over Distributed Networks

Next-generation wireless networks are witnessing an increasing number of clustering applications, and produce a large amount of non-linear and unlabeled data. In some degree, single kernel methods face the challenging problem of kernel choice. To overcome this problem for non-linear data clustering, multiple kernel graph-based clustering (MKGC) has attracted intense attention in recent years. However, existing MKGC methods suffer from two common problems: (1) they mainly aim to learn a consensus kernel from multiple candidate kernels, slight affinity graph learning, such that cannot fully exploit the underlying graph structure of non-linear data; (2) they disregard the high-order correlations between all base kernels, which cannot fully capture the consistent and complementary information of all kernels. In this paper, we propose a novel non-negative matrix factorization (NMF) tailored graph tensor MKGC method for non-linear data clustering, namely TMKGC. Specifically, TMKGC integrates NMF and graph learning together in kernel space so as to learn multiple candidate affinity graphs. Afterwards, the high-order structure information of all candidate graphs is captured in a 3-order tensor kernel space by introducing tensor singular value decomposition based tensor nuclear norm, such that an optimal affinity graph can be obtained subsequently. Based on the alternating direction method of multipliers, the effective local and distributed solvers are elaborated to solve the proposed objective function. Extensive experiments have demonstrated the superiority of TMKGC compared to the state-of-the-art MKGC methods.