Miracle-dz/UniFAR-FTUs huggingface dataset:
Silicon Oxycarbide—Tin Nanocomposite as a High‐Power‐Density Anode for Li‐Ion Batteries

Tin‐based materials are an emerging class of Li‐ion anodes with considerable potential for use in high‐energy‐density Li‐ion batteries. However, the long‐lasting electrochemical performance of Sn remains a formidable challenge due to the large volume changes occurring upon its lithiation. The prevailing approaches toward stabilization of such electrodes involve embedding Sn in the form of nanoparticles (NPs) in an active/inactive matrix. The matrix helps to buffer the volume changes of Sn, impart better electronic connectivity and prevent particle aggregation upon lithiation/delithiation. Herein, facile synthesis of Sn NPs embedded in a SiOC matrix via the pyrolysis of a preceramic polymer as a single‐source precursor is reported. This polymer contains Sn 2‐ethyl‐hexanoate (Sn(Oct)2) and poly(methylhydrosiloxane) as sources of Sn and Si, respectively. Upon functionalization with apolar divinyl benzene sidechains, the polymer is rendered compatible with Sn(Oct)2. This approach yields a homogeneous dispersion of Sn NPs in a SiOC matrix with sizes on the order of 5–30 nm. Anodes of the SiOC/Sn nanocomposite demonstrate high capacities of 644 and 553 mAh g−1 at current densities of 74.4 and 2232 mA g−1 (C/5 and 6C rates for graphite), respectively, and show superior rate capability with only 14% capacity decay at high currents.
Capillary Self-bending of Micro-manipulation Methon for Flexible Microfibers

A capillary force-based micro-manipulation method for flexible microfibers is proposed, which can achieve high-precision self-alignment of flexible microfibers with hydrophobic/superhydrophobic microgrooves. A theoretical model that can be used to calculate the deflection during the bending of flexible microfibers is established, and the effects of microfiber length and microgroove radius on the deflection during the bending of flexible microfibers are investigated. The conditions of critical bending of flexible microfibers were analyzed experimentally, and the effects of microfiber length and microgroove radius on the bending success rate of flexible microfibers were further investigated. The results show that the bending success rate increases with the increase of microgroove radius; when the microgroove length does not exceed 15 mm, the bending success rate increases with the increase of fiber length. This micro-manipulation method has potential applications in flexible circuits, microelectrode arrays, and MEMS.
A parameterization with wave saturation adjustment of subgrid-scale average wave stress over three-dimensional topography

Nappo and Chimonas (1992) demonstrated that internal gravity waves launched by small-scale topography could be as important in the planetary boundary layer as the large-scale mountain waves are in the free atmosphere. Steeneveld et al (2008a ,b) have used a high-resolution version of the MM5 model to show that wave drag over the gentle terrain of the CASES-99 field site (Poulos et al., 2002) can be important in large-scale processes and possibly account for under estimated turbulence in mesoscale models. Tjernström et al. (2008) observed intermittent fine-scale turbulence structures in the nocturnal residual layer during one night of the CASES-99 experiment but not on another night. Using a three-dimensional linear gravity wave model and observed meteorological profiles, they showed that the calculated wave stress divergence was large in the residual layer during the turbulence night, but near zero in he residual layer during the calm night.
An Exemplary Survey Implementation on Text Mining with Rapid Miner

Recently many disciplines such as databases, statistics, and information retrieval have affected the growth of data mining. By this phenomenon, data mining had been allowing to extract information or knowledge from these data. Data mining employs diverse techniques based on this phenomenon, but most existing approach is data analysis and text mining. Text Mining allows various analyses for data representing the new framework known as Information Extraction. The extraction of information from unstructured resources has released new paths for analyzing, organizing and querying data. Adapting and implementing these patterns has been time-consuming in the past but with the use of some discovery tools now this task has been significantly easier. The first part of the study includes the mining of an unstructured text and the second part is visualization, both provided by Rapid Miner. The visualization part has some subparts which are directly related to the conducted survey dedicated to particular individuals. These subparts are; finding correlation between the attributes of people, determination of the most weighted attribute and clustering subpart which classifies a group of people that has more similarity between them. Each of these parts has been examined and shown by definitions, examples, analysis and conclusions.
Extraction of Buddha Elements from Thangka Images Based on Improved DeepLabV3+

Nowadays, Thangka as China's intangible cultural heritage has attracted more and more attention. Semantic segmentation of Buddha elements in Thangka images can let the public understand Thangka culture. Using the results of semantic segmentation, the artistic elements in the image can be extracted and recreated as material. This paper constructs the first semantic segmentation dataset of Buddha elements based on Thangka image, and proposes an improved DeepLabV3+ model based on DeepLabV3+ model. Firstly, the lightweight MobileNetV2 is used for feature extraction, and then the asymmetric convolution module is used to replace the dilated convolution in the ASPP block to obtain richer features, and the segmentation accuracy is guaranteed without increasing the calculation time. Finally, the segmentation results are used to extract the Buddha elements in the Thangka image. Experiments show that the improved DeepLabV3+ model proposed in this paper has higher accuracy and less calculation than the original DeepLabV3+ model on the Thangka dataset.
Exploring the use of Attention-Based Recurrent Neural Networks For Spoken Language Understanding

This study explores the use of a bidirectional recurrent neural network (RNN) encoder/decoder based on a mechanism of attention for a Spoken Language Understanding (SLU) task. First experiments carried on the ATIS corpus conﬁrm the quality of the RNN baseline system used in this paper, by comparing its results on the ATIS corpus to the results recently published in the literature. Additional experiments show that RNN based on a mechanism of attention performs better than RNN architectures recently proposed for a slot ﬁlling task. On the French ME-DIA corpus, a French state-of-the-art corpus for SLU dedicated to hotel reservation and tourist information, experiments show that a bidirectionnal RNN reaches a f-measure value of 79.51 while the use of a mechanism of attention allows us to reach a f-measure value of 80.27.
Thermal Radiation, Chemical Reaction and Viscous Dissipation Effects on Unsteady MHD Flow of Viscoelastic Fluid Embedded in a Porous Medium

In this paper the effect of unsteady, incompressible, magneto hydrodynamics filled with electrically conducting viscoelastic fluid in an infinite vertical Couette porous channel wall embedded in a porous medium is analyzed. A uniform magnetic field is applied perpendicular to the channel wall. The temperature of the moving channel wall varies periodically and the temperature difference between the two infinite vertical channel walls is high due to thermal radiation. The Eckert number is the ratio of the kinetic energy of the flow to the temperature difference of the channel walls. The solution of the governing equations is obtained using regular perturbation techniques. These techniques are used to transform partial differential equations that are difficult to solve in closed form. These equations are reduced to a set of ordinary differential equations in dimensionless form so can be solved analytically. The effects of physical parameters Viz. Hartmann number, Viscoelastic parameter, Eckert number, Permeability of porous medium, Chemical reaction parameter, thermal Grashof number for heat transfer, modified Grashof number for mass transfer, frequency parameter and Schmidt number on flow parameters Viz., velocity, temperature and concentration has been discussed and shown graphically. The theoretical results have been supported by MATLAB code simulation study. The results show that velocity decreases with increasing values of frequency, Hartmann number and viscoelastic parameter but reverse effect is observed with temperature, thermal Grashof number, modified Grashof number and permeability of porous medium. Furthermore, The result shows that an increment in both thermal radiation parameter and Eckert number results in decrement of temperature near the moving porous channel wall while it approaches to a zero in the region close to the boundary layer of the stationary channel wall,. An increment in both chemical reaction and Schmidt number results in decreasing concentration. The velocity of fluid increases as Grashof number and modified Grashof number increases.
A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text

: The purpose of this study is to evaluate the performance of five supervised machine learning methods for the task of automated genre identification of classical Arabic texts using text most frequent words as features. We design an experiment for comparing five machine-learning methods for the genre identification task for classical Arabic text. We set the data and the stylometric features and vary the classification method to evaluate the performance of each method. Of the five machine learning methods tested, we can conclude that Support Vector Machine (SVM) are generally the most effective. The contribution of this work lies in the evaluation of the five machine learning methods for the task of genre identification for classical Arabic text using stylometric features.
A Survey on Effect of Agro Waste Ash as Reinforcement on Aluminium Base Metal Matrix Composites

In the present day availability of agricultural waste products is very huge quantity. Most of the industries prefer Metal matrix composite (MMC) due to their density, high strength to weight ratio, hardness, corrosion resistance, fatigue and creep resistance. Hence they are widely used in structural applications along with aerospace and automobile industry, marine, sports, electronic and automation industries. In the present paper a study is focused on the mechanical, tribological and corrosion behavior of the metal matrix composite using different agro waste ash which is easily available. Agro waste ash like Rice Husk, groundnut shell, bamboo leaf, coconut shell can be used as reinforcement and applicable for various applications like automotive, structural components. From this current study, it’s clearly identified that addition of agro waste ash as reinforcement with Aluminium improves the properties of metal matrix composite. Aluminium metal with such reinforcement materials has shown a high specific strength, yield strength and ultimate tensile strength, also it will increase hardness, satisfactory levels of corrosion resistance.
A Phase-change Material Integrated Power Module for Electric Vehicles with Enhanced Short-term Overcurrent Capability

High power density IGBT inverters are commonly used in electric vehicles to gain high energy efficiency. However, the high power density can lead to a high junction temperature and cause failure, especially when a short-term overcurrent is demanded during the starting or rapid acceleration. The power module capacity needs to be increased for the high performance. This paper proposes a phase change material (PCM) integrated power module for EVs to enhance performance in this aspect. The power loss was calculated based on the electric model of the EV during various driving cycles. The thermal model was analysed by COMSOL to assess the temperature distribution of the module with and without PCM. The results show that the maximum junction temperature can be reduced by up to $20^{\circ}\mathrm{C}$ under 2pu overcurrent, which can provide the needed overcurrent capacity.
2D Full Particle Simulation on the Effects of Magnetic Field Configuration of Ion Thruster

In order to clarify how magnetic field configuration of discharge chamber impacts on ion thruster characteristics like thrust, impulse, ion beam and so on, a two-dimensional axial symmetry full particle numerical model is established based on the PIC/MCC method. The research is carried out on the generation and movement of plasma in a 10cm×5cm cylindrical discharge chamber. The simulation results show that increasing the proportion of non-magnetic region can improve plasma distribution and macro performance of ion thruster to some extent. The referential parameters contain ionization rate, uniformity, thrust, impulse, discharge loss etc. Moreover, increasing the radius of the downstream magnetic ring close to screen grid alone is an optimization method to further improve overall performance of ion thruster. From the simulation, the influences of magnetic field configuration on plasma characteristics and ion thruster performance are analyzed, which can provide a general reference for the actual design of ion thruster.
A Comprehensive Query System for Grid Dispatching And Control Multivariate Data

As the control center of power grid operation, power grid dispatching and control covers power generation, transmission, transformation and other power production links. After years of construction, the power grid dispatching and control system has generated a large amount of data, showing the characteristics of various data types, huge data scale, and complex database table structure. The current multi-data query method is to write different database query languages for different databases to obtain data, which brings high difficulty to the use and analysis of data. This paper proposed a comprehensive query system for power grid dispatching and control multivariate data. Through natural language processing technology and rule computing engine, the input natural language question can be directly returned to the answer, thereby improving the efficiency of data query.
THE EFFECTS OF COMPRESSION GARMENTS ON RECOVERY PROCESS AFTER A MODERATE-INTENSITY RESISTANCE EXERCISE

The purpose of this study was to determine whether compression garments (CG) are effective in facilitating recovery after moderate-intensity resistance exercise. Fifteen male subjects performed experimental trials in CG and control conditions during recovery after a deep squat exercise. Jump height, muscle soreness, and creatine kinase (CK) were measured before, immediately after, 24, 48 and 72 h after exercise. The CK was higher at 24h after exercise than before exercise in both conditions (p = .009, ES = 1.58). The jump height decreased immediately (p = .003, ES = -1.22) and 24H (p = .012, ES = -.98) after exercise in both conditions. However, there were no significant differences in CK, jump height and muscle soreness between the two conditions at any time. The application of CG cannot aid in the recovery after a moderate-intensity resistance training.
Model-Based Design and Implementation of Dynamic Controller for Autonomous Guidance of a Scale Automobile

This paper presents the design and implementation of a controller based on the dynamic model of a 1:10-scale automobile that allows autonomous guidance. The control architecture was developed at two different hierarchy levels. The low level corresponds to the control of the traction and the steering servo motors, while the high level corresponds to the controller based on the dynamic model of the automobile. The controller was first tested with different degrees of realism by numerical simulations and then implemented on a real mobile platform under the Robot Operating System framework. Finally, the performance of the controller is evaluated for different reference trajectories.
Dimensions of marital conflict and children's social problem-solving skills.

Marital conflict has been shown to be negatively associated with child functioning. This study examined the relationships of negative and positive aspects of marital conflict and frequency of conflict with children's social problem-solving skills, as measured by effectiveness of alternative solutions. Mothers, fathers, and children reported on marital conflict. Mothers' higher negative conflict characteristics, in the context of greater frequency of conflict, and less frequent positive conflict characteristics, in the context of lower frequency of conflict, were significantly associated with their children's less effective social problem-solving solutions. For fathers, none of the negative but one of the positive conflict characteristics was significantly associated with their children's more effective social problem-solving solutions, regardless of the frequency of marital conflict. Children's perceptions of their parents' conflict were not significant predictors of their social problem-solving skills. Findings are interpreted in the framework of children's modeling aggressive conflict.
Medicinal Plant Classification using Convolutional Neural Network and Transfer Learning

Medicinal plants are not only an essential source of therapeutic compounds but also an alternative source of medications used by most people around the world. Due to recent advances in computer vision, plant identification from images has become a rapidly developing research field. Various results showed good accuracy, precision, and real-world applications. This paper aimed to investigate an accurate and precise automated identification of medicinal plants. We present a new medicinal plant dataset containing images of ten (10) classes of medicinal plant species and one (1) class containing a mixed variety of weeds, vines, and non-medicinal plants. Then we proposed a model based on MobileNetV3 architecture for a low-cost, reliable, and efficient medicinal plant classification. Using the proposed model and Transfer Learning, results revealed a 97.43% accuracy on the challenging task. Overall, the findings revealed the feasibility of an efficient and reliable medicinal plant classifier for real-world applications.
2D printing of graphene: a review

The exceptional properties of graphene have inspired widespread efforts to integrate this two-dimensional (2D) material in functional applications in recent years. Within the broad spectrum of graphene processing frameworks, low-cost methods such as scalable, liquid-phase patterning are typically considered among the most straightforward routes for device integration. These ink-based printing methods require parallel research in graphene dispersion engineering, print process optimization, and post-processing methods to enhance target functional properties for a wide range of device applications in flexible electronics, energy storage, display technologies, and sensing. This review examines the current state-of-the-art and future prospects for integrating graphene and graphene composites with these versatile printing techniques to accelerate the development of scalable and low-cost graphene-based devices.
A Novel Distributed Latency-Aware Data Processing in Fog Computing-Enabled IoT Networks

In generic Internet of Things (IoT) architecture, all the data generated is sent via gateways (GWs) and is processed in Cloud. This approach limits many real-time applications due to high propagation latency to the Cloud and also under-utilizes the GWs' compute and storage resources. Fog Computing (FC) extends the computability and storage of Cloud computing paradigm to network's edge devices such as GWs. IoT networks with FC enabled GWs, mitigate the latency and underutilization problems by processing data at the GWs. We propose a distributed latency-aware data processing (DLA-DP) model by which FC enabled GWs dynamically exchange processing and storage capability information and probabilistically forward data to its neighboring GWs or to Cloud only when there is a limit in local processing or storage. Modeled as a network of M/M/m/B queuing systems, a DLA-DP enabled IoT network is validated with extensive simulations. DLA-DP model enables improvements such as reduced system response time, increased gateway processing and buffer occupancy efficiencies.
Correction and retraction practices in library and information science journals

Retraction of scholarly publications ensures that unqualified knowledge is purged from the scientific community. However, there appears to be little understanding about how this is practiced among library and information science (LIS) journals. Hence, this study investigated the correction and retraction practices of LIS journals. Journals included in the Web of Science’s information science and library science subject category were selected for the study and the characteristics of the articles corrected or retracted in those journals between 1996 and 2016 were examined. Findings show that there were 517 corrections and five retractions in LIS journals during the period. Most of the corrections made to articles in LIS journals were minor while the reasons for article retraction included plagiarism, duplication, irreproducible results and methodological errors. Our findings also reveal that on average it took about 587 days for an article to be retracted while some of the retracted articles continued to be cited after retraction. The study concluded that the average number of errors per correction was lower than what had been observed in medical journals while some of the retracted articles continued to receive positive post-retraction citations. It also recommended the inclusion of a check on the validity of literature cited by authors at the review stage as part of the quality control mechanism by publishers of LIS journals.
Distribution and environmental correlations of picoeukaryotes in an Arctic fjord (Kongsfjorden, Svalbard) during the summer

Picoeukaryotes are numerous in the summer in the High-Arctic fjord Kongsfjorden, in western Spitsbergen, Svalbard. However, little research has been conducted on the community structure and diversity of picoeukaryotes. We conducted a detailed investigation of the distribution and environmental correlations of picoeukaryotes in Kongsfjorden in July 2012, using 454-pyrosequencing of 18S rDNA and redundant analysis. Eight classes were classified with proportions larger than 1%. These were Mamiellophyceae, Chrysophyceae, Spirotrichea, Telonemea, Cryptophyceae, Bolidophyceae, Picomonadea and Dictyochophyceae. Five genera were classified, with Micromonas (55.6%) and Bathycoccus (7.8%) as the dominant genera. The diversity and composition of the picoeukaryote community were very distinct in different water masses sampled in the water column (i.e., vertically), but were not distinct from station to station (i.e., horizontally). Biodiversity was greater in the Atlantic waters than in glacial waters. Mamiellophyceae, Bolidophyceae, Picomonadea and Dictyochophyceae had significantly different distributions (p < 0.01) in the three water masses (surface water, intermediate water and transformed Atlantic Water). Nitrogen, salinity and temperature were the first three primary environmental factors shaping the community structure of picoeukaryotes.