Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid model of radio-frequency low-pressure inductively coupled plasma discharge with self-consistent electron energy distribution and 2D electric field distribution

Low-pressure radio-frequency (RF) inductively coupled plasmas (ICPs) are extensively used for materials processing. In this work, we have developed a hybrid model consisting of two-dimensional (2D) Maxwell equations with an open boundary, zero-dimensional Boltzmann equation under linear and quasilinear approximations, and a power balance equation. The hybrid model is capable of achieving a self-consistent description of the electron heating mechanism and electron kinetics for the RF ICPs at low pressures. This work presents an investigation of the influence of operating conditions on 2D distributions of electric field and power density, normalized electron energy probability function (EEPF) (effective electron temperature), and plasma density in a low-pressure RF Ar ICP using the hybrid model. The results show that the RF frequency and absorption power significantly affect the 2D distributions and amplitudes of electric field and power density. The normalized EEPF is almost independent of RF frequency and weakly dependent on absorption power but significantly modulated by pressure at low RF frequency. The plasma density is also almost independent of RF frequency but increases with absorption power and pressure. In addition, we have validated the hybrid model against experimental data obtained in the driver region of a two-chamber RF Ar ICP source, where the RF frequency is 13.56 MHz, the power range is 200–1000 W and the pressure range is 0.1–1.0 Pa. The hybrid model qualitatively (and even quantitatively for some cases) reproduces the experimentally normalized EEPF and plasma density. The discrepancies in these plasma parameters could be attributed to the simplified collision processes taken into account in the hybrid model. The developed hybrid model can help us to better understand the effect of discharge conditions on electron kinetics and electron heating mechanism, and to ultimately optimize the parameters of RF ICP sources.
MULTIPHYSICS MODELING OF PRECURSORS IN MOLTEN SALT FAST REACTORS USING PROTEUS AND Nek5000

The goal of this work was to calculate the impact of the delayed neutron precursor drift in fast spectrum Molten Salt Reactors (MSRs) using coupled solutions from the neutronics code PROTEUS and the computational fluid dynamics code Nek5000. Specifically, using a multiphysics approach to solve the effective delayed neutron fraction (βeff) or delayed neutron precursor distribution for reactors with flowing fuel salts would provide valuable information for transient simulations and safety assessments. Given the multiple options for the flux solution and geometric resolution/fidelity in PROTEUS, two approaches were developed and applied to various test cases: PROTEUS-NODAL/Nek5000 and PROTEUS-SN/Nek5000. For the former, the precursors are tracked in the built-in precursor drift model in PROTEUS-NODAL, whereas in the latter, Nek5000 directly tracks the precursors. Both approaches were used to solve a single test channel problem and showed excellent agreement in the calculated βeff. Separately, a 3D hourglass-shaped core was modeled using the PROTEUS-SN/Nek5000 approach. This problem was designed to demonstrate the capability of the discrete ordinates (SN) solver and Nek5000 to model complex core designs with axially varying geometries and the ability for Nek5000 to track the precursors and calculate the resulting βeff. In addition, the Nek5000 calculations revealed the presence of recirculation zones in the hourglass design, which could lead to significant temperatures in the fuel salt and surrounding materials. These first coupled solutions show why these approaches may be necessary for not only predicting the precursor drift effect in fast MSRs but also for reactor design and performance assessments.
Numerical Analysis of Serrated Chip Formation Mechanism with Johnson-Cook Parameters in Micro-Cutting of Ti6Al4V

Previous studies have reported significant differences in the Johnson-Cook (J-C) parameters of Ti6Al4V alloy. Thus, various serrated chip morphologies, cutting forces, and cutting temperatures are obtained when different constitutive parameters are used for numerical and simulation analyses, which decreases the reliability of the simulation model. Therefore, it is necessary to investigate and analyze simulation errors due to differences in the J-C parameters. In this study, the mechanism of the serrated chip formation of Ti6Al4V is thoroughly analyzed using the uniformly proportional J-C parameters. The serrated chip sensitivity, shear band spacing, serrated segmentation frequency, chip serration intensity, temperature field, strain energy, and cutting force is obtained. This study aims to improve the accuracy and reliability of the micro-cutting simulation models, as well as a reference for the selection of J-C constitutive parameters of simulation with Ti6Al4V manufactured with different heat treatments and additive manufacturing.
Comparison of Factorial and Latin Hypercube Sampling Designs for Meta-Models of Building Heating and Cooling Loads

Interest in research analyzing and predicting energy loads and consumption in the early stages of building design using meta-models has constantly increased in recent years. Generally, it requires many simulated or measured results to build meta-models, which significantly affects their accuracy. In this study, Latin Hypercube Sampling (LHS) is proposed as an alternative to Fractional Factor Design (FFD), since it can improve the accuracy while including the nonlinear effect of design parameters with a smaller size of data. Building energy loads of an office floor with ten design parameters were selected as the meta-models’ objectives, and were developed using the two sampling methods. The accuracy of predicting the heating/cooling loads of the meta-models for alternative floor designs was compared. For the considered ranges of design parameters, window insulation (WDI) and Solar Heat Gain Coefficient (SHGC) were found to have nonlinear characteristics on cooling and heating loads. LHS showed better prediction accuracy compared to FFD, since LHS considers the nonlinear impacts for a given number of treatments. It is always a good idea to use LHS over FFD for a given number of treatments, since the existence of nonlinearity in the relation is not pre-existing information.
Conventional and enantioselective GC with microfabricated planar columns for analysis of real-world samples of plant volatile fraction

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Movie Recommendation Systems using Sentiment
Analysis and Cosine Similarity

As Artificial Intelligence and Machine Learning is growing at a rapid rate over the past few years, so is the amount of data increasing exponentially on the internet. Due to this people find it difficult to choose the exact information they are looking for , learners find it difficult to suggest users exactly what they require. Here comes Recommendation Systems into picture to guide users towards the information according to their preferences. In context of Recommendation of Movies and TV shows on Online Streaming platforms ,this paper is aimed to explain making and implementation of Movie Recommendation Systems Using Machine Learning Algorithms, Sentiment Analysis and Cosine Similarity
Integrating UAV and Ground Panoramic Images for Point Cloud Analysis of Damaged Building

The effectiveness of damaged building investigation relies on rapid data collection, while jointly applying an unmanned aerial vehicle (UAV) and a backpack panoramic imaging system can quickly and comprehensively record the damage status. Meanwhile, integrating them for generating complete 3-D point clouds (3DPCs) is important for further assisting the 3-D measurement of the damaged areas. During the 2016 Meinong earthquake (Taiwan), the system collected multiview aerial images (MVAIs) and ground panoramic images of two collapsed buildings. However, due to the spatial offsets of the spherical camera result in nonideal panoramic images (NIPIs), an appropriate spherical radius has to be chosen to reduce the distance-related stitching errors. In order to evaluate the impact of using NIPIs for 3-D mapping, the geometric accuracy of the 3-D scene reconstruction (3DSR) and usability of the 3DPCs were assessed. This study introduces the stitching errors of panoramic images, uses sky masks for successful 3DSR, and obtains clean point clouds. It then analyzes the usability of point clouds that were obtained from only NIPIs, only MVAIs, and their integration. The analysis shows that NIPIs have more rapid processing efficiency than their unstitched original images and can increase the completeness of point clouds at the building’s lower floor, while MVAIs can reduce the stitching errors of NIPIs to an acceptable range. Therefore, integrating both images is necessary to achieve rapid and complete point cloud generation.
Assessing polycentric urban development in Shanghai, China, with detailed passive mobile phone data

Despite growing studies on the distinction between morphological and functional polycentricity, the present methods for identifying polycentricity often focus on the morphological dimension due to a lack of information about intra-urban functional flows, and are limited by the multifarious nature of people’s spatiotemporal interactions. This study proposes a new approach, examining the degree of polycentricity in Shanghai at the intra-urban level using passive mobile phone data. A series of polycentricity indicators are used and are benchmarked against previous studies. Notably, we found that people’s daily movements within a subcenter indicate that morphological polycentricity is also at play in Shanghai. We conclude that morphological and functional polycentricity may coexist at the intra-urban level, and that a mobile phone data approach can offer an alternative method to elucidate both the morphological and functional features of subcenters.
IMPLEMENTATION OF A MACHINE LEARNING ALGORITHM FOR SENTIMENT ANALYSIS OF INDONESIA’S 2019 PRESIDENTIAL ELECTION

: In 2019, citizens of Indonesia participated in the democratic process of electing a new president, vice president, and various legislative candidates for the country. The 2019 Indonesian presidential election was very tense in terms of the candidates' campaigns in cyberspace, especially on social media sites such as Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, etc. The Indonesian people used social media platforms to express their positive, neutral, and also negative opinions on the respective presidential candidates. The campaigning of respective social media users on their choice of candidates for regents, governors, and legislative positions up to presidential candidates was conducted via the Internet and online media. Therefore, the aim of this paper is to conduct sentiment analysis on the candidates in the 2019 Indonesia presidential election based on Twitter datasets. The study used datasets on the opinions expressed by the Indonesian people available on Twitter with the hashtags (#) containing "Jokowi and Prabowo. " We conducted data pre-processing using a selection of comments, data cleansing, text parsing, sentence normalization and tokenization based on the given text in the Indonesian language, determination of class attributes, and, finally, we classified
Radio & BH Load-Aware Multi-Objective Clustering in Multi-Cell MIMO Cooperative Networks

Coordinated Multipoint (CoMP) is one of the key technologies identified for future wireless networks to mitigate inter-cell interference, especially in a dense deployment scenario. However, CoMP can’t be realized for the whole network due to its computational complexity, synchronization between coordinating base stations (BSs) and high backhaul (BH) capacity requirement. BSs need to be clustered into smaller groups and CoMP can be activated within these smaller clusters. In this paper, we develop a multi-objective, dynamic clustering model for multi-user, joint-transmission CoMP to jointly optimize spectral efficiency (SE), radio access network (RAN) load and BH load. We formulate our load-aware model as two coalitional sub-games for small cell and user equipment clustering, respectively. Merge/split/transfer actions for each sub-game are defined and a complexity and stability analysis is provided. Extensive simulation results show that our model provides as good SE in low load when compared to a greedy model, and significantly better load balancing with a reduced number of unsatisfied users and increased throughput in high load scenario. On average 49% increase in the overall system throughput is observed in our simulations when compared to the greedy model.
Drivers of Human Migration: A Review of Scientific Evidence

While migration research is at the peak of its productivity, a substantial gap persists between scientific evidence and policy action. As societal complexity increases, migration theory loses track on the numerous factors of human migration; the information on the most relevant factors affecting human migration (i.e., migration drivers), essential for policy decision-making, are hidden and dispersed across the ever-growing literature. Introducing a novel approach to conducting a literature review, emphasizing an unbiased selection of literature and the approach to analysing literature by coding, we collect evidence on the most pertinent migration factors. The study establishes a methodology for a quick but rigorous, collaborative gathering of evidence, as well as an initial inventory and an interactive map of nearly 200 factors working at different migration corridors.
Global burden of medication non-adherence in chronic obstructive pulmonary disease (COPD) and asthma: a narrative review of the clinical and economic case for smart inhalers

Medication non-adherence to asthma and chronic obstructive pulmonary disease therapy poses a significant burden for patients and societies. Non-adherence encompasses poor initiation, implementation (including poor inhalation technique) and non-persistence. Globally, non-adherence is associated with poor clinical outcomes, reduced quality of life and high healthcare and societal costs. Costs are mainly caused by excess hospitalizations and impaired work productivity. Multiple intervention programs to increase adherence in patients with asthma and chronic obstructive pulmonary disease have been conducted. However, these intervention programs are generally not as effective as intended. Additionally, adherence outcomes are mostly examined with non-objective or non-granular measures (e.g., self-report, dose count, pharmacy records). Recently developed smart inhalers could be the key to objectively diagnose and manage non-adherence effectively in patients with asthma and chronic obstructive pulmonary disease. Smart inhalers register usage of the inhaler, record time and date, send reminders, give feedback about adherence and some are able to assess inhaler technique and predict exacerbations. Still, some limitations need to be overcome before smart inhalers can be incorporated in usual care. For example, their cost-effectiveness and budget impact need to be examined. It is likely that smart inhalers are particularly cost-effective in specific asthma and chronic obstructive pulmonary disease subgroups, including patients with asthma eligible for additional GINA-5 therapy (oral corticosteroids or biologics), patients with severe asthma in GINA-5, patients with asthma with short-acting beta2 agonists overuse, patients with asthma and chronic obstructive pulmonary disease with frequent exacerbations and patients with asthma and chronic obstructive pulmonary disease of working-age. While there is high potential and evidence is accumulating, a final push seems needed to cost-effectively integrate smart inhalers in the daily management of patients with asthma and chronic obstructive pulmonary disease.
A Corpus-based Variationist Approach to the Use of It is I and It is Me: A Real-time Observation of a Syntactic Change Nearing Completion in COHA

This is a corpus-based variationist linguistics (CVL) study into a syntactic variation and change that is nearing completion: the shift from it is I (nominative) to it is me (accusative) in American English. Despite a number of recent statements in grammar books and dictionaries that the new variant, i.e., the accusative construction, has earned public acceptance, few attempts have been made to provide empirical support for the entire diachronic shift from nominative case to accusative case. Real-time quantitative data from the diachronic corpus COHA characterizes almost the entire process of the syntactic change, and the analysis of the data using the multivariate model demonstrates: (i) that the shift first occurred in the first-person singular I/me, and then in the other personal pronouns; (ii) that the shift fron I to me had been almost completed by the latter half of the 20th century; (iii) that the shift achieved a level of near-completion much earlier in contracted constructions such as it’s I/me than in non-contracted constructions such as it is I/me. The present study makes a cross-disciplinary contribution to the study of language variation and change by combining advantages of variationist sociolinguistics with those of corpus linguistics. It succeeds in complementing findings from previous studies by providing an empirical, comprehensive and detailed account of the process of this well-known syntactic change. *
Vehicle Object Detection Based on Improved RetinaNet

Aiming at the low efficiency of vehicle object detection in real scenes, this paper proposes an improved RetinaNet. An octave convolution structure and a weight pyramid structure are introduced respectively to improve the detection performance of RetinaNet for vehicles. Specifically, we use octave convolution instead of the traditional convolution layer to improve the feature map’s representation of detailed information. In addition, in order to improve the quality of feature fusion, a weighted feature pyramid network (WFPN) structure is proposed to limit the propagation of gradients between different levels. The experimental results on the DETRAC data set show that the method has good detection results for vehicle targets of different scales in different scenarios, and can better meet the needs of practical applications.
Trechispora daweishanensis and T. xantha spp. nov. (Hydnodontaceae, Trechisporales) found in Yunnan Province of China

Two wood-inhabiting fungal species, Trechispora daweishanensis and T. xantha spp. nov. are proposed based on morphological features and molecular evidence. Trechispora daweishanensis is characterized by basidiomata with an annual growth habit, a tuberculate hymenophore, a monomitic hyphal system with clamped generative hyphae and ellipsoid, hyaline, thin-walled, smooth basidiospores measuring 3.8–5 × 2.7–3.5 µm. Trechispora xantha is characterized by resupinate, cracked basidiomata with a buff-coloured hymenial surface, a monomitic hyphal system and ellipsoid, hyaline, thin-walled, smooth basidiospores measuring 4.3–5.7 × 3.2–4 µm. Sequences of ITS and nLSU gene regions of the studied samples were generated. The phylogenetic analysis based on molecular data of ITS+nLSU sequences supported the novelty of these two species. Furthermore, we provide a key to the known species of Trechispora in China.
Free vibration analysis of a spinning porous nanocomposite blade reinforced with graphene nanoplatelets

This paper investigated the modeling and free vibration characteristics of a spinning graphene nanoplatelet (GPL) reinforced porous nanocomposite blade. The blade is made of porous foam metal matrix reinforced with graphene nanoplatelets (GPLs). Several different GPL distributions and porosity distributions in the blade are taken into account. The effective material properties of the blade, determined via the open-cell scheme, the Halpin-Tsai model, and the rule of mixture, are assumed to be varying continuously along the its thickness direction. According to the Kirchhoff’s plate theory, the governing equations of the spinning blade are derived by adopting the Hamilton principle. On the other hand, the non-uniform spinning blade is modeled by the finite element method which is compared with the theoretical method. The theoretical results match very well with the finite element ones obtained from ANSYS. Particular focus is given to the effects of the spinning speed, porosity coefficient, distribution pattern of GPLs and porosities, GPL weight fraction, length-to-thickness ratio and length-to-width ratio of GPLs, blade length, and spinning radius on the free vibration performance of the blade rotor.
Teachers’ Perceptions of Differentiation and the Struggle for Consistent Implementation

A problem at three elementary schools in an Appalachian state was that some or all instructors were struggling to implement differentiated instruction in the classroom. Because differentiation is a research-based best practice, teachers should be consistently using this strategy to meet the varying needs found within the inclusive classroom. The purpose of this qualitative case study was to investigate perceptions of third- and fourth-grade teachers on their knowledge, implementation, and selfassessment of using differentiated instruction in classrooms in three elementary schools in one Appalachian state. The two research questions that guided this study asked how third- and fourth-grade teachers used differentiation to support all students and what perceived opportunities and struggles these teachers believed affected their ability to implement this strategy. Nine out of the 14 third- and fourth-grade inclusive teachers who volunteered were asked to participate in semistructured phone interviews and lesson plan analysis. Data were hand coded and analyzed using a spreadsheet to look for reoccurring categories and themes. Six themes emerged within the collected qualitative data to include ability grouping, technology, planning for differentiated instruction, professional supports, lack of training, and instructional support. With the findings, specific professional development was created to help the teacher more consistently use differentiation in the classroom. This study has positive social change implications because it might lead to a stronger administrator and teacher understanding of the perceived uses of differentiation as well as the perceived opportunities and struggles to fully implement the strategy.