Miracle-dz/UniFAR-FTUs huggingface dataset:
Near-infrared Monitoring of the Accretion Outburst in the MYSO S255-NIRS3

We followed-up the massive young stellar object (MYSO) S255-NIRS3 (=S255-IRS1b) during its recent accretion outburst event in the Ks band with Kanata/HONIR for four years after its burst and obtained a long-term light curve. This is the most complete NIR light-curve of the S255-NIRS3 burst event that has ever been presented. The light curve showed a steep increase reaching a peak flux that was 3.4 mag brighter than the quiescent phase and then a relatively moderate year-scale fading until the last observation, similar to that of the accretion burst events such as EXors found in lower-mass young stellar objects. The behavior of the Ks band light curve is similar to that observed in 6.7 GHz class II methanol maser emission, with a sudden increase followed by moderate year-scale fading. However, the maser emission peaks appear 30-50 days earlier than that of the Ks band emission. The similarities confirmed that the origins of the maser emission and the Ks band continuum emission is common as previously shown from another infrared and radio observations by Stecklum et al. (2016); Caratti o Garatti et al. (2017a); Moscadelli et al. (2017). However, the differences in energy transfer paths, such as the exciting/emitting/scattering structures, may cause the delay in the flux-peak c © 2014. Astronomical Society of Japan. 2 Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0
Cosmic Geodesy Contribution to Geodynamics Monitoring

The cosmic geodesy provides methods and ways of various data acquisition. The collected data may be used for research, calculations and analysis in different fields of interest. According to the reliability and redundancy of data provided by cosmic geodesy methods, it is possible to contribute to the geodynamics monitoring. The geodynamics monitoring enables the tectonic plates movement tracking and predicts the movements which may result in disasters. Applying data provided by cosmic geodesy methods in the form of permanent observation station positions and their changes in time, in calculations, whose physical nature is based on the continuum mechanics, makes possible to monitor the direction, locality and size of visualised deformation tensors.
Influence of Operating Condition and Geometry on the Oil Film Thickness in Aeroengine Bearing Chambers

Increasing the efficiency of modem jet engines does not only imply to the mainstream but also to the secondary air and oil system. Within the oil system the bearing chamber is one of the most challenging components. Oil films on the chamber walls are generated from oil droplets, ligaments, or film fragments, which emerge from bearings, seal plates and shafts, and enter the bearing chamber with an angular momentum. Furthermore, shear forces at its surface, gravity forces, and the design of scavenge and vent ports strongly impact the behavior of the liquid film. The present paper focuses on the experimental determination of the film thickness in various geometries of bearing chambers for a wide range of engine relevant conditions. Therefore, each configuration was equipped with five capacitive probes positioned at different circumferential locations. Two analytical approaches are used for a comprehensive discussion of the complex film flow.
Study of the characteristics of the Glossinidae family (Muscoidea, Oestroidea)

Oestridae is a family of flies, which includes species whose larvae are internal parasites of several species of mammals. Some species settle in the host's flesh, while others occur in the viscera. This study objective to report the characteristics of the Glossinidae Family. The research was carried out in studies related to quantitative aspects of the Family, Subfamily and Species (taxonomic groups) and conceptual aspects such as: biology, geographical distribution, species, life cycle, damage, laboratory creation, economic importance, medicinal importance, biological aspects, monitoring, control, and reproduction. A literature search was carried out containing articles published from 1950 to 2021. The mini-review was prepared in Goiânia, Goiás, from September to October 2021, through the Online Scientific Library (Scielo), internet, ResearchGate, Academia.edu, Frontiers, Publons, Qeios, Portal of Scientific Journals in Health Sciences, Pubmed, Online Scientific Library (Scielo), internet, ResearchGate, Academia.edu, Frontiers, Biological Abstract, Publons, Qeios, Portal of Scientific Journals in Health Sciences, and Pubmed, Dialnet, World, Wide Science, Springer, RefSeek, Microsoft Academic, Science, ERIC, Science Research.com, SEEK education, Periódicos CAPES, Google Academic, Bioline International and VADLO.
Takeaway Comments Sentiment Analysis Based on BERT

Analyzing the sentiment of the takeaway review information plays an important role in the improvement of the products by the merchants and the selection of the products by the users. However, Chinese takeaway reviews not only have the characteristics of short corpus and divergent characteristics, but also include the problem of polysemy. In response to these problems, this paper proposes sentiment analysis based on the pre-training model BERT. First, perform Chinese word segmentation on the data set to ﬁlter out meaningless data. Then ﬁne-tune the BERT, extract the word vector, and embed the real emotional semantics into the model through the dynamic adjustment of the word vector by BERT according to the context. In order to prevent the model from overﬁtting, we extract part of the data and use maskelm, add dropout into model, try to decay the learning rate during the training process, and stop the training early. Finally, we compare our model with the classical text classiﬁcation models TextCNN and TextRNN. Experiments show that algorithm model based on BERT is more effective than the traditional model in terms of short text sentiment analysis.
The heart of the matter

In their meta-analysis Bolland and colleagues conclude: “Calcium supplements without vitamin D are associated with an increased risk of myocardial infarction. ”1 This conclusion relies heavily on unpublished data from the RECORD trial, the source of over half of the cardiovascular disease events in the meta-analysis. In this trial, investigators posted calcium, vitamin D, their combination, …
ANALYZING MANEUVER OF HADOOP FRAMEWORK AND MAPR ALGORITHM PROFICIENT IN SUPERVISING BIG DATA

Big data refers to the data sets that are too big to be handled using the existing database management tools and are emerging in many important applications, such as Internet search, business informatics, social networks, social media, genomics, and meteorology. In simple words it can be said that any data which challenges the currently existing techniques for handling data can be referred as big data. Big data presents a grand challenge for database and data analytics research. Gone are the days when memory was used to be measured in terms of Gigabytes, Terabytes or Petabytes. Today even larger units are being used to measure memory like Exabyte, Zettabytes and Yottabytes. In this research paper the central theme is to discuss about different technologies that are involved in handling big data efficiently. We have analyzed MPP (Massive Parallel Processing), NoSQL, Hadoop Frame work and Map Reduce technology. We will also discuss the architecture working behind these technologies in this research paper.
A Study of Measuring the Mortality Compression

Copyright 2014 by the Society of Actuaries. All rights reserved by the Society of Actuaries. Permission is granted to make brief excerpts for a published review. Permission is also granted to make limited numbers of copies of items in this monograph for personal, internal, classroom or other instructional use, on condition that the foregoing copyright notice is used so as to give reasonable notice of the Society’s copyright. This consent for free limited copying without prior consent of the Society does not extend to making copies for general distribution, for advertising or promotional purposes, for inclusion in new collective works or for resale.
A novel, low-cost implementation of "democratic" load-current sharing of paralleled converter modules

A simple, low-cost, and robust "democratic" (autonomous) current-sharing circuit is proposed and analyzed. The circuit maintains good current sharing among the modules by properly adjusting the voltage references of the modules based on the differences between the average current of the paralleled modules and the currents of the individual modules. Design guidelines for achieving a desired current-sharing performance are given and verified on a number of DC/DC power converter modules operating in parallel. <<ETX>>
Low-Cost Adsorbents: Growing Approach to Wastewater Treatment—a Review

Industrial, agricultural, and domestic activities of humans have affected the environmental system, resulting in drastic problems such as global warming and the generation of wastewater containing high levels of pollutants. As water of good quality is a precious commodity and available in limited amounts, it has become highly imperative to treat wastewater for removal of pollutants. In addition, the rapid modernization of society has also led to the generation of huge amount of materials of little value that have no fruitful use. Such materials are generally considered as waste, and their disposal is a problem. Also, there are some materials that are available in nature that have little or no use. The utilization of all such materials as low-cost adsorbents for the treatment of wastewater may make them of some value. An effort has been made to give a brief idea of an approach to wastewater treatment, particularly discussing and highlighting in brief the low-cost alternative adsorbents with a view to utilizing these waste/low-cost materials.
Image Caption Generator Using CNN

: In this paper we are presenting an Image caption generator using CNN. Automation is everybody’s dream whether it is to ease their efforts or to make machines learn how to do something. In this paper we are presenting a way to generate captions based on the information in an image. This image caption generator can take input image and return the contents in the image as a caption. A CNN network along with LSTM is used to extract features and make words one by one and make a sensible sentences’ model is trained in a way that the model will be able to accurately extract the contents of a image as captions. The model is trained on a dataset containing all kinds of images and it is observed that the model can detect the context of the image accurately.
Comparison of spectral ratio techniques for estimation of site effects using microtremor data and earthquake motions recorded at the surface and in boreholes

The authors apply soil response estimation techniques employing accelerograms for fifteen earthquakes recorded at the Yokohama Strong Motion Network and its vertical array of nine sites, plus microtremor data recorded at all 150 sites. Assessment of the reliability of surface to reference site spectral ratios, and horizontal to vertical spectral ratios of S‐waves, coda, and microtremors, relied on cross‐validation with both surface to borehole spectral ratios, and theoretical soil response functions for vertically incident SH plane waves calculated from the vertical array recordings and logging data, respectively. The results indicate the following: (1) the reference site method provides more accurate soil response estimations; (2) the H/V of microtremors provides relatively better soil response estimations than its S‐wave and coda counterparts; and (3) the H/V of microtremors provides coincident soil response estimations at two‐thirds of the sites when considering no more than ±30% mismatch in the determination of the predominant periods. The matching of parameters obtained from the analysis of microtremors, combined with their relatively low economic cost and the possibility of recording without strict spatial or time restrictions turns microtremors into a particularly appealing approach. Copyright © 2003 John Wiley & Sons, Ltd.
EVALUATION OF MICROSOFT HOLOLENS AUGMENTED REALITY TECHNOLOGY AS A CONSTRUCTION CHECKING TOOL

: Increasing productivity is one of the most important objectives of the construction industry. Building Information Modelling (BIM) was introduced to facilitate collaboration and coordination in the life-cycle of buildings and infrastructures. More often, BIM models are preferred to traditional 2-dimensional drawings to communicate the design to the project stakeholders such as design team, contractors, and the client. Conversely, it is common to use traditional 2-D drawings on-site in the construction phase. Construction mistakes are often the results from errors in the construction drawings or from their misinterpretations. Over the last years, augmented and virtual reality has been introduced in the architecture, engineering and construction industry (AEC) as visualization and collaboration tools. This paper aims to evaluate Augmented Reality (AR), in particular, Microsoft HoloLens, as a construction checking tool. The paper compares different construction checking tools such as the traditional tape and measure, Trimble SX10 laser scanner and the Microsoft HoloLens using Trimble Connect as BIM software, to the Trimble SX10 Total Station, a current industry leader in surveying technology. Comparisons were based on accuracy, speed of testing, ease of use and efficiency. Testing was undertaken by comparing benchmark virtual models of the testing rooms, obtained from an initial survey, and the actual physical rooms. Results indicated that, overall, the HoloLens was the most efficient construction checking tool within a 15 mm to 50 mm accuracy range. It was not as accurate as a laser scanner or tape measure, which indicates further improvements are required before this technology can be recommended as a general construction checking tool.
A SURVEY ON DIAGNOSIS OF SKIN CANCER BASED ON IMAGE PROCESSING USING MACHINE LEARNING

In a human body, skin is the core part, which helps to cover the muscles, bones and what's more with the entire body. These days numerous people are suffering from skin cancer. Malignant melanoma is the deadliest form of skin cancer. The most serious type of cancer is Melanoma, which is an enormous type of skin malignant growth and the extent of these skin cancers is increasing day by day. Melanoma can be easily treatable if detected in early stages. Clinical as well as automated methods are being used for melanoma diagnosis. Image-based computer aided diagnosis systems have great potential for early malignant melanoma detection. Recognizing the type of skin cancer automatically from the images can assist in the quick diagnosis and enhanced accuracy saving valuable time. This paper presents a review on automated diagnosis of skin cancer by analyzing images using Image Processing techniques with applying intelligence using Machine Learning. The purpose of this bibliographic review is to provide researchers opting to work in implementing machine learning for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements.
PROJECTIVE STRUCTURES AND THE ANTIPODAL MAP ON MEASURED LAMINATIONS

Motivating example. Consider the complex hyperbolic space CH, the simply connected Kähler manifold with constant holomorphic sectional curvature −4. Let ∂∞CH denote the set of equivalence classes of geodesic rays in CH, where two rays are equivalent if each lies within a bounded distance of the other. This boundary at infinity gives a natural compactification CH = CH t ∂∞CH. The product CH × CH can be interpreted as the set of oriented geodesic segments in CH (including degenerate segments consisting of a single point). The midpoint map m : CH × CH → CH assigns to each pair (x, y) the midpoint of the geodesic segment from x to y; let Mz = m−1(z) denote the set of all pairs with midpoint z. Each z ∈ CH defines an involutive holomorphic isometry iz : CH → CH such that iz(z) = z and (diz)z = −1, and iz extends to an involution iz of ∂∞CH n; this is the geodesic involution about z. Then Mz = {(x, y) ∈ CH × CH | iz(x) = y}. It is easy to see that the closure of Mz in CH × CH is the union of Mz and the graph Γ(iz ) = {(α, β) ∈ ∂∞CH × ∂∞CH | iz (α) = β}; in other words, the closure of the set of geodesic segments that are symmetric with respect to z is the set of complete geodesics passing through z. For all z ∈ CH, Mz is disjoint from the diagonal ∆ ⊂ CH × CH.
Antifouling on Artificial Substrates of Bacillus pumilus Bacteria Against Diatom Biofilms Nitzschia ovalis arnott and Navicula incerta

Microalgae and bacteria are the primary colonists of marine surfaces, forming the initial biofilm layer needed for posterior macro organism fixation. This phenomenon, termed biofouling, greatly deteriorates ships, piers, and aquaculture systems. Biofouling is currently treated with toxic and poorly degradable chemicals, resulting in significant environmental concerns. Consequently, increasing research efforts are focused on antifouling microorganisms as a measure to take care of the environment and avoid the use of harmful chemicals for marine flora and fauna. The objective of the present study was to evaluate the ability of the marine bacterium Bacillus pumilus (C32-MESO) in inhibiting to the substrate fixation of the benthic microalgae Nitzschia ovalis arnott and Navicula incerta known for forming biofilms on substrates. A total of 26 bacterial morphotypes were isolated that grow associated with microalgae cultures N. ovalis arnott and N. incerta. The results showed that the bacteria B. pumilus (C32-MESO) inhibited 96.4% of these isolated morphotypes and significantly decreased microalgae density when inoculated with the cultures. Substrate adherence assessments revealed that B. pumilus (C32-MESO) decreased the adherence of both microalgae to shell and PVC substrates. In conclusion, this study lays the foundation for future research into characterizing the active metabolites derived from B. pumilus (C32-MESO) and evaluate the biotechnological applications of this bacterium in naturally inhibiting the early stages of biofilms such as fixing to substrates of microalgae.
Stomata segmentation using deep learning

Stomata are pores in the epidermal tissue of leaf plants formed by specialised cells called guard cells, which regulate the stomatal opening. Stomata facilitate gas exchange, being pivotal in the regulation of processes such as photosynthesis and transpiration. The analysis of the number and behaviour of stomata is a task carried out by studying microscopic images; and, nowadays, this task is mainly conducted manually, or using programs that can count and determine the position of stomata but are not able to determine their morphology. In this paper, we have conducted a study of 10 deep learning algorithms to segment stom-ata from several species. The model that achieves the best Dice score, with a value of 96.06%, is obtained with the DeepLabV3+ algorithm, whereas the model that provides the best trade-off between inference time and Dice score was trained using the ContextNet architecture. This is a ﬁrst step towards improving the measurements provided by stomata analysis tools, that will in turn help plant biologists to advance their understanding of dynamics in plants.
Road Safety Benefits from Bus Priority

The provision of bus priority is often a major consideration in on-street bus rapid transit design. However, the research is limited and suggests mixed outcomes. This paper explores the impact of bus priority treatments on road safety. An empirical analysis of accident data about bus rapid transit routes in Melbourne, Australia, was conducted. The analysis included an aggregate crash frequency analysis that accounted for statistical effects and a disaggregate analysis that used a safety audit and review of accident types. Overall, bus priority treatments reduced accidents, with a statistically significant reduction of 14.0%. The number of fatal and serious incidents dropped considerably (42 to 29 per annum). The disaggregate safety audit showed reductions in intolerable risks, but some concerns such as interaction of buses and traffic at bus lane setbacks (section of a curbside lane where traffic may enter to make a turn as the bus lane reaches an intersection) and greater pedestrian road-crossing distances caused by bus lanes. However, disaggregate analysis of accident type suggested that bus lanes act as a roadside buffer, reducing collisions with roadside objects and other vehicles. The shifting of stopping buses from the traffic lane into a bus lane was shown to reduce accidents. Bus lanes were thought to increase sight distances at unsignalized intersections and consequently reduce accidents with vehicles to the side. Some treatments were thought to increase traffic density, creating safety benefits through slowed traffic. These findings were statistically robust and suggest that a new perspective on planning for and justifying bus priority measures is warranted.
Genetic cellular neural network applications for prediction purposes in industry.

Genetic Cellular Neural Networks (GCNN) are adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. It was argued that due to the learning ability of neural networks, their speed and capacity to handle large amount of data, they have a potential for predicting components requirements. GCNN use less stability parameters than Back Propagation-Artificial Neural Networks (BP-ANN) and hence better suited to fast changing scenarios as in real supply chain applications. The model has shown promising outcomes in learning and predicting material demand in a supply chain, with high degree of accuracy.
Intertemporal Labor Supply and Involuntary Unemployment in a Rationed Labor Market∗

In this paper we develop a model to consistently estimate the intertemporal labor supply behavior on both the extensive margin (participation decision) and the intensive margin (working hours decision). In this framework we distinguish between voluntary non-participation and involuntary unemployment which is caused by labor market rationing and model the dynamics of labor supply by accounting for true state dependence. In contrast to previous studies, this framework allows us to test for true state dependence of voluntary non-participation, involuntary unemployment, full-time work and over-time work. Moreover, we derive consistent estimates of intertemporal labor supply elasticities and asses the bias of elasticities derived in a pure choice model of labor supply.
The Application Research on Discrete Hopfield Neural Network in Water Quality Evaluation

It is difficult to evaluate water quality, because there are lots of influence factors. It presents discrete Hopfield neural network to evaluate water quality in this paper. The criteria of nutrition water quality used as training samples to train discrete Hopfield neural network, which is stored in discrete Hopfield neural network. The data in a monitoring point used as test samples to evaluate water quality. The experimental results show that discrete Hopfield neural network can evaluate water quality effectively. The classification results can be shown directly, and the running speed is faster than BP neural network. This method has important guiding significance and practical application value to other water quality evaluation.