Miracle-dz/UniFAR-FTUs huggingface dataset:
A Commentary on 'A Survey of Time Series'

Summary This commentary discusses a recent survey paper (concerned with time-series methodology) aiming to amend and to complement it. The present writer's admitted bias towards the time domain in general, and Box-Jenkins in particular, will be apparent - but that does reflect a strong current trend. Preface Spyros Makridakis (1976) presented an interesting and ambitious survey of time-series analysis, together with an impressive listing of 139 key references - which, perhaps with some modesty, included only three to work carrying his own name. The intention of that important 42-page paper was to look at the 'methods developed and used in the fields of statistics, engineering, operational research and economics'. For this purpose, it described 'each of the major methods now in existence' and synthesized 'them into an integrated whole by pointing out similarities and differences existing amongst them'. The applicability of the different methods to various real-life requirements was discussed and further research needs were highlighted. Evidently this was an extremely formidable task for any individual to undertake,2 and it is not surprising that another time-series analyst finds some fault with it. This article will comment on what appear to be certain errors and misprints; omissions and inadequacies of Makridakis' paper. However, these efforts must not be construed merely as a censorious contribution: had the present writer attempted such a wide-ranging review, he feels sure that Makridakis would have found much more to censure. This commentary is intended for those who have already read Makridakis (1976)- and were perhaps puzzled by parts of it - or at least have that paper to hand. Our subsequent sections and subsections will correspond exactly to those of his and we will endeavour to distinguish in this account between matters of fact, faith and fancy. (Fantasy, we hope, will be excluded! ) With less modesty than Makridakis, I will cite more of my own works than their intrinsic worth warrants. This is because one naturally knows one's own material, and it more readily
Effects of aberrations in vortex-beams generated with amplitude diffraction gratings

We present a mathematical model for the generation of vortex-beams by using a square profile amplitude fork diffraction grating with arbitrary topological charge. The mathematical framework of aberrations in the forked-shape diffraction grating is analysed, and the resulting diffracted pattern is simulated. Three cases of desired distortions (aberrations) in the diffraction grating are considered, obtaining phase modulation from the amplitude grating. Experimental optical vortices are generated by using a transmission spatial light modulator, which is used as a dynamic diffraction grating, allowing us to aberrate it. We show the effect of aberrations in the experimental diffracted vortex-beams and compare it with the numerical simulation.
Combining Text Semantics and Image Geometry to Identify Relations

Automatically identifying actions and relations between objects in images can be useful for many applications, for example image and video labeling. In this thesis, the goal is to extract a predefined set of objects from the images and identify the relations linking these objects. Wikipedia is the source of images and texts that are analyzed. I created a program that takes both geometrical information from images, and semantic information from texts and outputs classification of relations for each object in the images. The baseline of only using geometrical features is improved on by using bag-of-words features based on the articles, and further enhanced by utilizing predicate information.
Android Malware Detection using Ensemble Learning

Tons of Android apps are being developed every day. Therefore, there is a growing need to determine the security of an effective and accurate tool to detect hidden threats in some of these applications. The objective of this study is to develop a machine learning model capable of detecting malware in Android devices. The volume and sophistication of Android malware continues to grow, posing a significant threat to the security of mobile devices and the services that support them. This has led to a growing focus on using machine learning to improve Android malware detection. Ensemble methods use multiple learning algorithms to achieve better predictive performance, higher accuracy, and lower error rates. Stack Classifier is an ensemble learning classifier that uses multiple heterogeneous classifiers and trains a meta-model, improving overall accuracy and performance.
Fast noniterative algorithm for designing high-accuracy lowpass FIR filters with the evaluation of errors

We present an analytical approach to the design of zero-phase FIR digital passband filters. We describe an easy-to-implement, finite (noniterative) algorithm for determining low-pass filters, whose cost of calculations is O(nlog/sub 2/n). We give the evaluation of errors, taking into account the transition band, from which there follows the main result of this paper, wherein our filters are nearly optimal. We compare them with optimal filters optimal Kaiser's (1974) filters. It results that the errors of our filters are slightly greater than Kaiser's filters. However, a small modification of the presented algorithm may lead to filters that, in numerical tests, have errors like Kaiser's filters or even smaller if the cut-off frequency is not very close to 0 or /spl pi/. Our-algorithms are especially efficient for the design of very accurate (i.e., very long) filters. In numerical calculations, we constructed, using 15 digit floating-point arithmetic filters with errors up to 10/sup -13/, i.e., -260 dB. The main limitation of the filters in question is the negligence of weighting functions.
Analysing younger online viewers’ motivation to watch video game live streaming through a positive perspective

The paper focuses on younger online viewers’ motivation to watch video game live streaming on live streaming platforms. Unlike existing scholars, it analyses younger online viewers’ watching motivation through a positive perspective and draws on the Play-Others-Downtime theory (P-O-D theory) and the motivation theory to establish the research model. By analysing 397 samples based on the variance-based structural equation modelling and partial least squares path modelling (SEM-PLS), the results present that younger viewers’ entertainment, social and leisure purposes positively affect their watching motivation. Control variables (i.e., gender, education background, and income level) demonstrate insignificant effects across the model. Considering the influence of the COVID-19 pandemic, watching video game live streaming is essential entertainment and social activities for younger adults. Future studies should identify the positive impact of video game live streaming and guide younger viewers to participate appropriately in this activity.
A novel deployable structure devised from a Kresling-Scissor interface

Recent developments have shown that spatial structures devised from origami or low-dimensional rigid linkage mechanisms can be used to construct deployable arrays for antennas or satellites. Yet, some of these structures are limited to deploying in fixed planes or directions. This research introduces a reconfigurable single-degree-of-freedom spatial structure devised from a Kresling-inspired mechanism and scissor arms. Analytical models are constructed to demonstrate compaction, deployment, and acoustic wave guiding capabilities of the proposed structure. A case study using the linear scissor arm configuration is presented to illustrate the modular scissor arm behavior and large deployment-to-compaction volume ratio of the system. A second case study is also presented to demonstrate the acoustic wave guiding capabilities of the Kresling-Scissor structure by utilizing spiral scissor arms, thereby proposing a novel concept for constructing deployable spiral wave guiding arrays. The results encourage broader exploration of the interfaces between origami structures and rigid linkage mechanisms.
MicroRNA-146b-5p Suppresses Pro-Inflammatory Mediator Synthesis via Targeting TRAF6, IRAK1, and RELA in Lipopolysaccharide-Stimulated Human Dental Pulp Cells

MicroRNA-146b-5p (miR-146b-5p) is up-regulated during and to suppress the inflammation process, although mechanisms involved in the action of miR-146b-5p have not been fully elucidated. This study examined the anti-inflammation effects of miR-146b-5p in lipopolysaccharide (LPS)-stimulated human dental pulp cells (hDPCs). An increase in human miR-146b-5p (hsa-miR-146b-5p) expression following the mRNA expression of pro-inflammatory cytokines was observed in LPS-stimulated hDPCs. The expression of hsa-miR-146b-5p and pro-inflammatory cytokines was down-regulated by a nuclear factor-kappa B (NF-κB) inhibitor, and the expression of hsa-miR-146b-5p was also decreased by a JAK1/2 inhibitor. Enforced expression of hsa-miR-146b-5p abolished phosphorylation of NF-κB p65 and down-regulated the expression of pro-inflammatory cytokines and NF-κB signaling components, such as interleukin-1 receptor-associated kinase 1 (IRAK1), tumor necrosis factor receptor-associated factor 6 (TRAF6), and REL-associated protein involved in NF-κB (RELA). Expression of rat miR-146b-5p (rno-miR-146b-5p) and pro-inflammatory cytokine mRNA was also up-regulated in experimentally-induced rat pulpal inflammation in vivo, and rno-miR-146b-5p blocked the mRNA expression of pro-inflammatory mediators and NF-κB signaling components in LPS-stimulated ex vivo cultured rat incisor pulp tissues. These findings suggest that the synthesis of miR-146b-5p is controlled via an NF-κB/IL6/STAT3 signaling cascade, and in turn, miR-146b-5p down-regulates the expression of pro-inflammatory mediators by targeting TRAF6, IRAK1, and RELA in LPS-stimulated hDPCs.
The Influence of Communication Messages on Adoption of Contagious Bovine Pleuropneumonia (CBPP) Vaccine among Arid and Semi-Arid Lands (ASAL) Pastoralists in Kenya

Purpose: The purpose was to establish influence of communication messages on adoption of Contagious Bovine Pleuropneumonia (CBPP) Vaccine among Arid and Semi-Arid Lands (ASAL) pastoralists in Kenya. Messages were studied under parameters of inoculation site, benefits, required frequency of vaccination and side effects. The focus on the vaccine messaging was informed by the slow pace of adoption of live T1 vaccines currently being used to eradicate CBPP in Kenya. Diffusion of innovation and social learning theories were used to support the study. Methodology: Study population were pastoralists in Narok South Sub County. 468 respondents inclusive of qualitative and quantitative samples where 440 responded to questionnaire, 24 in focus group discussions, and 4 in key informant interviews participated. Multi stage, purposive, simple random, systematic and stratified sampling techniques were then employed to come up with respondents. Statistical Package for Social Scientists (SPSS) version 20.0 was used to analyze data, which was presented using regression coefficients and ANOVA. Findings: CBPP messaging influenced respondents to vaccinate although some had more influence than others. Messages on inoculation site, benefits, required frequency of vaccination side effects and communal vaccinations were important for the survival of their cattle and significantly influenced the decisions of respondents to vaccinate against the disease. Moreover, messages helped them to know important information details such as vaccination venues, and costs of vaccination and availability of the veterinary officers. CBPP vaccine messages attributes were key in the success of influencing respondents. However, the messaging ran into already held misinformation by some pastoralists confirming earlier study that vaccination rate was at 20-60% because some skipped the exercise. Unique Contribution to Theory, Practice and Policy: CBPP vaccine messages and attributes significantly influenced CBPP vaccinations decisions among pastoralists. This study validated diffusion of innovation and social learning theories that innovation-decision process is essentially an information seeking and processing activity in which an individual is motivated to reduce uncertainty about the advantages and disadvantages of the innovation. For policy and practice, this study recommends development of communication plans, and packaging of CBPP vaccine messages for dissemination in the ASALs where disease is prevalent. Considering that CBPP is a trans-boundary disease, these plans and messages could be harmonized across ASAL counties to enable consistency and coherence.
Machine-learning-based error detection modeling and feature scoring for error cause analysis of CD-SEMs

The measurement process is important in managing semiconductor device yield, which is affected by the availability of measurement equipment such as critical dimension scanning electron microscopes (CD-SEMs). Here, decreasing CD-SEM availability is caused by measurement errors when inappropriate measurement recipes are used. To improve CD-SEM availability, we developed a machine-learning-based error analysis method to identify error causes and x measurement conditions by using accumulated CD-SEM data. However, a single error analysis model can be applied to only a single semiconductor product because different semiconductor products have different data distribution even if they use the same recipe. Additionally, manufacturers often modify recipes every few months. As a result, an error-cause analysis method needs to be able to easily adapt to new recipes. Therefore, we developed a three-stage method that consists of error detection modeling, feature scoring, and error cause estimation on the basis of high-scoring features. Because we found the top scoring features do NOT change as the feature distribution changes when the error causes are the same, the error cause estimation on the basis of high-scoring features enable to be applied to different semiconductor products and new recipes. We evaluated our method with actual operational data, and estimated error causes that often correspond with the results of manual analysis by skilled engineers.
Simulation of NOx and COx Gas Sensor based on Pristine Armchair Stanene Nanoribbon

In this study, the adsorption of the most common hazardous gases (CO, NO, NO2, and CO2) on the stanene armchair nanoribbon of width 6 (H‐ASnNR(6)) is investigated using density functional theory. The most stable adsorption sites, adsorption energies, charge transfer, electronic features, transmission spectra, and current–voltage analysis are explored. All molecules are physisorbed on H‐ASnNR(6) through van der Waals interactions, indicating a potential use for this material in recoverable gas sensors, and all gas molecules act as acceptors. Additionally, the highest and the lowest charge transfer is related to the NO and CO2 molecules, respectively. It is revealed that the adsorption of NO and NO2 affects the electronic properties of the H‐ASnNR(6), and the system displays conducting behavior, while the influence of CO and CO2 gas molecules on the electrical properties of H‐ASnNR(6) is relatively small. The results demonstrate that compared to pristine H‐ASnNR(6), the transmission function of the gas plus H‐ASnNR(6) system undergoes a significant change, particularly after the adsorption of NO and NO2 molecules. The presence and absence of the molecules are shown by current–voltage (I–V) characteristic results. The results suggest that H‐ASnNR(6) has the potential to be used in gas molecule detection applications.
Object classification robot hand system using thermal conductivity

In this paper, an object classification system by thermal conductivity is introduced. This system comprises a module of a flexible thermoelectric device (TED) and a resistance temperature detector sensor (RTD). The module is integrated with a silicone finger cot and is equipped onto a robot hand. Before grasping an object, the TED on the robot hand is heated to a specific temperature in degrees Celsius higher than room temperature. While the robot hand grasps an object, heat generated by the TED is transferred to the object. Sequentially, the RTD sensor detects heat variation of the object. And then, data from the sensor is computer-processed to classify the object. This object classification system successfully manipulates a real-time object identification by utilizing an intrinsic material property: thermal conductivity.
UAV‐Lidar reveals that canopy structure mediates the influence of edge effects on forest diversity, function and microclimate

Widespread forest loss and fragmentation dramatically increases the proportion of forest areas located close to edges. Although detrimental, the precise extent and mechanisms by which edge proximity impacts remnant forests remain to be ascertained. By combining unmanned aerial vehicle laser scanning (UAV‐LS) with field data from 46 plots distributed at varying distances from the edge to the forest interior in a fragmented forest of New‐Caledonia, we investigated edge influence on forest structure, composition, function, above‐ground biomass (AGB) and microclimate. Using simple linear regressions, structural equation modelling and variance partitioning, we analysed the direct and indirect relationships between distance to edge, UAV‐LS‐derived canopy structural metrics, understorey microclimate, AGB, taxonomic and functional composition while accounting for the potential influence of fine‐scale variation in topography. We found that the distance to the closest forest edge was strongly correlated with canopy structure and that canopy structure was better correlated to forest composition, function, biomass and microclimate than distance to the closest forest edge. This suggests that the influence of edge is mediated by changes in canopy structure. Plots located near the edge exhibited a lower canopy with more gaps, higher microclimate extremes, lower biomass, lower taxonomic and functional diversity as well as denser wood and lower specific leaf area. UAV‐LS‐derived canopy structural metrics were relevant predictors of understorey microclimate, biomass and taxonomic and functional composition. Overall, the influence of topography was marginal compared to edge effects. Synthesis. Accounting for fine‐scale variation in canopy structure captured by UAV‐LS provides insights on the multiple edge impacts on key forest properties related to structure, diversity, function, biomass and microenvironmental conditions. Integrating UAV‐LS‐derived data can foster our understanding of cascading and interacting impacts of anthropogenic influence on tropical forest ecosystems and should help to improve conservation strategies and landscape management policies.
Drought and Oxidative Stress in Flax (Linum usitatissimum L.) Entails Harnessing Non-Canonical Reference Gene for Precise Quantification of qRT-PCR Gene Expression

Flax (Linum usitatissimum L.) is a self-pollinating, annual, diploid crop grown for multi-utility purposes for its quality oil, shining bast fiber, and industrial solvent. Being a cool (Rabi) season crop, it is affected by unprecedented climatic changes such as high temperature, drought, and associated oxidative stress that, globally, impede its growth, production, and productivity. To precisely assess the imperative changes that are inflicted by drought and associated oxidative stress, gene expression profiling of predominant drought-responsive genes (AREB, DREB/CBF, and ARR) was carried out by qRT-PCR. Nevertheless, for normalization/quantification of data obtained from qRT-PCR results, a stable reference gene is mandatory. Here, we evaluated a panel of four reference genes (Actin, EF1a, ETIF5A, and UBQ) and assessed their suitability as stable reference genes for the normalization of gene expression data obtained during drought-induced oxidative stress in flax. Taking together, from the canonical expression of the proposed reference genes in three different genotypes, we report that EF1a as a stand-alone and EF1a and ETIF5A in tandem are suitable reference genes to be used for the real-time visualization of cellular impact of drought and oxidative stress on flax.
Vascular Endothelial Function Is Associated with eGFR Slope in Female and Non-Smoking Male Individuals with Cardiovascular Risk Factors: A Pilot Study on the Predictive Value of FMD for Renal Prognosis

Aims: It is known that there are sex differences in vascular endothelial function and the development of chronic kidney diseases; however, it remains unclear whether sex differences influence the association between vascular endothelial function and renal prognosis. Methods: To clarify the relationship between vascular endothelial function and longitudinal eGFR changes in male and female patients with cardiovascular risk factors, we retrospectively evaluated 341 patients (176 males and 165 females) with cardiovascular risk factors in whom vascular function was assessed by flow-mediated dilation (FMD) and brachial-ankle pulse wave velocity (baPWV) and in whom 24-month longitudinal eGFR values were recorded after the vascular function examinations. Associations of values of FMD and baPWV with values of eGFR slope were statistically analyzed. Results: Simple regression analysis showed that the value of FMD was positively associated with eGFR slope in females (p=0.001) and non-smoking males (p=0.033) but not in smoking males. Multiple regression analysis showed that the value of FMD remains a positive contributor for eGFR slope in females (p=0.001) and non-smoking males (p=0.045) but not in smoking males. In contrast, values of baPWV had no significant association with eGFR slope regardless of sex and cigarette smoking. Conclusions: In individuals with cardiovascular risk factors, evaluation of vascular endothelial function enables prediction of renal prognosis in females and non-smoking males.
Polishing material removal correlation on PMMA – FEM simulation

The complexity of polishing is very high and experience in this field is required to achieve reproducible deterministic results concerning shape accuracy. The goal of this work is to predict the material removal of the polishing process on PMMA (Polymethylmethacrylate) using an industrial robot polisher. In order to predict the material removal, a FEM Model was created representing the polishing process. This model will help to predict the material removal when polishing parameters are changed. Experiments were carried out and compared to the results obtained from the different parameters tested in the simulation.
Comparison of a {del}B drift effect model with measured H-mode power thresholds

The H-mode power threshold has a weak but positive B{sub T} dependence when the ion {del}B drift is away from the X-point, in contrast to the nearly linear B{sub T} dependence when the ion {del}B drift is toward the X-point. This indicates that geometry plays an important role in the H-mode power threshold scaling. A simple model of the {del}B drift effect failed to predict this behavior, but successfully predicted the sign change of gas puffing and low X-point height on the power threshold. The difference between the threshold power required for sawtooth and nonsawtooth triggered transitions can be substantial. This effect may contribute to the observed B scaling of the H-mode power threshold.