Miracle-dz/UniFAR-FTUs huggingface dataset:
Triggered massive-star formation on the borders of Galactic H II regions - III. Star formation at the periphery of Sh2-219

Context. Massive-star formation triggered by the expansion of HII regions. Aims. To understand if sequential star formation is taking place at the periphery of the HII region Sh2-219. Methods. We present 12CO(2-1) line observations of this region, obtained at the IRAM 30-m telescope (Pico Veleta, Spain). Results. In the optical, Sh2-219 is spherically symmetric around its exciting star; furthermore it is surrounded along three quarters of its periphery by a ring of atomic hydrogen. This spherical symmetry breaks down at infrared and millimetre wavelengths. A molecular cloud of about 2000\msol lies at the southwestern border of Sh2-219, in the HI gap. Two molecular condensations, elongated along the ionization front, probably result from the interaction between the expanding HII region and the molecular cloud. In this region of interaction there lies a cluster containing many highly reddened stars, as well as a massive star exciting an ultracompact HII region. More surprisingly, the brightest parts of the molecular cloud form a `chimney', perpendicular to the ionization front. This chimney is closed at its south-west extremity by H-alpha walls, thus forming a cavity. The whole structure is 7.5 pc long. A luminous H-alpha emission-line star, lying at one end of the chimney near the ionization front, may be responsible for this structure. Confrontation of the observations with models of HII region evolution shows that Sh2-219 is probably 10^5 yr old. The age and origin of the near-IR cluster observed on the border of Sh2-219 remain unknown.
FACTORS THAT INFLUENCE THE SELECTION OF A MOBILE STOCK TRADING APPLICATION

The public may select from a plethora of competing mobile stock trading application. Everyone's perspective on a certain application is further differentiated by the benefits and disadvantages that each one has to offer. Therefore, this study aims to examine the variables that impact an individual's choice when selecting a mobile stock trading application that they opt to use or currently utilizing. This study offers an integrated theoretical framework employing a modified Technology Acceptance Model and survey data from 109 Indonesian users of mobile stock trading applications using the structural equation modeling method. Six constructs—ease of use, user interface experience, perceived advantages, security and privacy risk, social impact, and trust—are included in the recommended model. According to the findings, social influence and trust significantly impact how mobile stock trading applications are chosen in Indonesia. Companies should concentrate on factors that encourage app adoption, such as social impact strategies, transparency, and top-notch customer service, in order to improve app utilization. The study is restricted to users who reside in Indonesia, however future studies may cover a larger audience and additional samples.
THE INFLUENCE OF UTILITARIAN VALUE AND HEDONIC VALUE ON OMNI-CHANNEL SHOPPING INTENTION THROUGH TAM PERCEIVED USEFULNESS AND PERCEIVED EASE OF USE

This study aims to determine the effect of Utilitarian Value and Hedonic Value on Omni-Channel Shopping Intention through TAM Perceived usefulness and Perceived ease of use in Indonesia. This technological advancement gave rise to many new phenomena that have been very pronounced lately. The invasion of e-commerce became the starting point for the use of internet facilities in retail business activities. The sample for this study was 250 respondents consisting of retail users who had implemented an omni-channel strategy in selling their products. The analytical method of this research uses quantitative methods using SEMPLS. The results of this study found that Hedonic Value had no statistically significant effect on the Perceived usefulness construct, Hedonic Value had a positive and statistically significant effect on the Omni-Channel Shopping Intention construct, Utilitarian Value construct had a positive and statistically significant effect on the Perceived ease of use construct, Utilitarian The construct value has a positive and statistically significant effect on the Omni-Channel Shopping Intention construct, the perceived usefulness construct has a statistically significant effect on the Omni-Channel Shopping Intention construct and the Perceived ease of use construct has a statistically significant effect on the Omni-Channel Shopping Intention construct. The recommendation from this study is the use of TAM is a source of success for Omni-Channel business.
ELLIOTT WAVE PRINCIPLE WITH RECURRENT NEURAL NETWORK FOR STOCK MARKET PREDICTION

Nowadays, academics and finance industries are being discussed highly in the domain of stock market trading due to which the improvement in economic globalization. Connections of stock markets are seen among various countries that develop risk factors associated with the market. Everyday rise and fall in the stock market make it a challenging area yet an important one. The increasing dynamic features and complexity of stock markets show difficulty in the industry of finance. The existing methods of inflexible trading were developed by researchers who had utilized larger features of the stock market and failed to establish effective outcomes during different scenarios of markets. Further, the existing data mining methods were inefficient and incomplete in predicting the stock market. To overcome such an issue, a stock market recommendation method based on the Elliott Wave Principle (EWP) with Recurrent Neural Network (RNN) was proposed. The proposed EWP-RNN identifies the impulse waves that sets the pattern and opposes the larger trend using EWP. The RNN assists the traders regarding the future stock trends to enhance the investment profit of a lesser period of time. The proposed EWP-RNN method utilizes the Fibonacci Series (FS) with EWP and RNN to analyze the future trends in the finance market. The proposed method achieved accuracy of 98.67% for the stock market prediction, whereas the existing BPNN showed accuracy of 92.55%.
AFTER and beyond: cancer recurrence fears and a test of an intervention in oral and oropharyngeal patients

Aim: Fear of recurrence has been identified as a major factor for cancer patients. The aim was to assess effects of psychological intervention to reduce recurrence fears and anxiety in outpatients previously treated for oral and oral pharyngeal cancer. Methods: Trial of 90 new patients randomly assigned to psychological intervention or control (usual-care) condition. Patients completed questionnaires to assess psychological distress and quality of life at 3, 7, 11 and 15 months following treatment for oropharyngeal cancer. Simple randomisation occurred immediately after 7 month assessment. Assessor was blind to group assignment. Intervention was delivered in the interval between 7 and 11 month assessments, and consisted of 6 weekly sessions of individual therapy delivered by trained specialist nurse following standardised manual (named AFTER). Results: Three patients were excluded prior to randomisation. The remaining 87 were randomised; 53 in the intervention arm and 34 in the control arm. Overall 10 patients were lost. Change score differences of groups between 7 and 11 month assessments showed reduction in fears of recurrence, anxious preoccupation with cancer (p<.05) but not general anxiety and depression. Some evidence of indirect effects on longer term fears of recurrence was found. Conclusion: Psychological intervention for fear of recurrence in the first year following treatment conferred a small benefit.
MgrB mutations causing colistin resistance in ST11 and ST15 Klebsiella pneumoniaes in a tertiary teaching hospital in China

Introduction:Colistin-resistant carbapenem-resistant Klebsiella pneumoniae (ColR-CRKP) is a threat to public health safety. The main cause of polymyxin resistance is a change in the negative charge and L-Ara-N and PEtN molecules on the cell membrane, which leads to a decrease in the a�nity of polymyxin for the strain. Methodology: In this study, (ColR-CRKP) isolates and clinical data were collected from a tertiary teaching hospital in China . Testing of antimicrobial susceptibility explained on the European Committee on Antimicrobial Susceptibility Testing criteria (EUCAST), Mutations and expression of drug-resistant genes were detected by PCR and RT-PCR. Genotyping of isolates analysised by MLST and PFGE. Results:Fourteen ColR-CRKP strains were collected and all isolates were resistant to colistin and most other clinical antibiotics (except tigecycline and cotrimoxazole), and were divided into 6 different PFGE clusters, and most strains clustered in B,C,D groups. Base mutations in the mgrB gene that can lead to changes in amino acid sequence were found in all 14 strains. Missense mutations a55 → t translated into S32C were found in 85.7% isolates (n=12; KP1-9, KP11-12 and KP14). The expression level of mgrB gene was decreased in all ColR-CRKP strains. Conclusions:Third-generation cephalosporins and enzyme inhibitors, as well as carbapenems, may be causative for the mutation. The new challenge for the treatment of ColR-CRKP requires broader attention.Our study showed that mutations in the mgrB gene can lead to the development of polymyxin resistance in in ST11 and ST15-KPC-2-producing K. pneumoniae in this region.
Markers of Inﬂammation and Monoamine Metabolism Indicate Accelerated Aging in Bipolar Disorder

Background: A mild pro-inﬂammatory status accompanies bipolar disorder (BD). Inﬂammation can cause a shift in monoamine metabolism, thereby activating more cytotoxic pathways. The extent to which low-grade inﬂammation in BD interacts with monoamine metabolism and how this accords to aging and clinical course
The response of statically unstable orographic clouds to small-scale topographic features [presentation]

Recent observations have shown that coastal mountain ranges tend to trigger quasi-stationary rainbands in potentially unstable cross-barrier flows. Miniscloux et al. (2001) found that rainbands formed parallel to the low-level flow during two convective rain events of the Cévennes experiment in southern France. These “static” bands, embedded within a time-evolving larger-scale precipitation field, produced precipitation enhancements of up to 100 mm day. In the western Kyushu region of Japan, long-lived rainbands were observed by Yoshizaki et al. (2000) to dominate the precipitation signature in the lee of small hills. Remarkably coherent and stationary rainbands were also observed over the Coastal Range in western Oregon (Kirshbaum and Durran, 2005b). These bands produced cumulative rainfall of up to 69 mm on the ridge upslope over a six-hour period.
Differences in the lower troposphere in two‐ and three‐dimensional cloud‐resolving model simulations of deep convection

This short note discusses key deficiencies in two‐dimensional (2D) cloud‐resolving model (CRM) simulations. Results differ significantly from three‐dimensional (3D) simulations in the low‐level humidity structure and associated fields. These differences are consistent across two different CRMs that differ substantially in their thermodynamic and microphysical formulations. Our analysis suggests than the near‐surface humidity structure depends on moisture transport in clouds, and we suggest that differences in entrainment between 2D and 3D simulations lead to substantial differences in both cloud amount and moisture transport by the clouds at low levels. When compared with 3D, less entrainment in 2D reduces the likelihood that convective updraughts terminate and moisten the lower troposphere. The differences between the 2D and 3D are significant if the CRM is to be used as a reference for comparison against numerical weather prediction (NWP) or climate models. Copyright © 2008 Royal Meteorological Society, Crown Copyright 2008
Edinburgh Research Explorer Automated detection of uninformative frames in pulmonary optical endomicroscopy (OEM)

— Significance: Optical endomicroscopy (OEM) is a novel real-time imaging technology that provides endoscopic images at a microscopic level. The nature of OEM data, as acquired in clinical use, gives rise to the presence of uninformative frames (i.e. pure-noise and motion-artefacts). Uninformative frames can comprise a considerable proportion (up to >25%) of a dataset, increasing the resources required for analysing the data (both manually and automatically), as well as diluting the results of any automated quantification analysis. Objective: There is therefore a need to automatically detect and remove as many of these uninformative frames as possible while keeping frames with structural information intact. Methods: This paper employs Gray Level Co-occurrence Matrix texture measures and detection theory to identify and remove such frames. The detection of pure-noise frames and motion artefacts is treated as two independent problems. Results: Pulmonary OEM frame sequences of the distal lung are employed for the development and assessment of the approach. The proposed approach identifies and removes uninformative frames with a sensitivity of 93% and a specificity of 92.6%. Conclusion: The detection algorithm is accurate and robust in pulmonary OEM frame sequences. Conditional to appropriate model refinement, the algorithms can become applicable in other organs.
INVESTIGATION OF SPECTRA OF MOBILE COMMUNICATION SYSTEMS USING RTL-SDR RADIO RECEIVER

Mobile phones are becoming an integral part of our daily lives. In fact, the mobile Internet has become one of the most important components of our business transactions and social life. As a result, the number of mobile users has increased significantly. Mobile signal users are distributed across different countries with different densities. This is due to the fact that the provision of mobile services depends on mobile operators and the pace of technological development of communications in countries. And the rise in popularity of mobile communications has made the electromagnetic spectrum of mobile signals unevenly distributed across frequencies, resulting in spectral inefficiencies. Accordingly, some spectra are used extensively, and some are left unused. The relevance of the proposed work lies in the fulfillment of the prerequisites for solving the problem of inefficient use of this mobile spectrum. The uneven distribution of these mobile spectra causes spectrum shortages. More recently, this problem began to be solved with the help of a special technique, such as Cognitive Radio. Cognitive radio first studies the spectra of mobile signals and then determines the signal density. This work proposed to perform the first stage of Cognitive Radio with spectrum recognition of mobile communication standards such as GSM, UMTS and LTE for Kazakhstan using RTL-SDR radio receiver. Since this work is aimed at studying the technical capabilities of the RTL-SDR radio receiver for mobile signals, it considered the study of the standard of mobile signals, such as GSM, UMTS and LTE. The result of the study shows that the technical capabilities of the RTL-SDR radio receiver are fully supported for the GSM and UMTS standards and partially for the LTE standard as a research platform for use in the Cognitive Radio method.
ML Use For Forecasting The NIRF Ranking Of Engineering Colleges In India And PCA To Find The Correct Weightage For The Best Result

Evaluation provides factual position of any person or organization in the long queue. Annual evaluation for all participating institutions is carried out with respect to particular scale for global as well as local ranking. National Institutional Ranking Framework (NIRF) is adopted in India to rank institute of eminence in various sectors such as University, Engineering, Management, Pharmacy, College, Medical, Law, Architecture and Dental. TRL, RP, GO, OI and Peer Perception are the criteria used to evaluate ranking with 30%, 30%, 20%, 10% and 10% weightage respectively. All above parameters are further sub-divided into 17 parameters with different sub-weightage. Some parameters have strong points for rejecting the application. In this paper, Engineering ranking factual data are considered for developing regression model using ML under individual heading. Multiple regression model has also been developed to cross-check the accuracy of the model. It is felt that the weightage given to each major 5 components needs verification. Machine Learning model using Python software has been developed to train computer to forecast the rank of any participating institution. Training has been imparted to this ML software with 80% random data from NIRF rank list. Subsequently, testing has been done with 20% test data from same NIRF rank list. A few numbers of test data has been fed to the system and accordingly, accurate prediction has been made. Many findings from this ML plots need further detailed interpretation and discussion for refinement of the weightage. Further, the overall combined evaluation has
Elements of design for mid-air interaction with distant displays

Mid-air interaction is know to have interesting potential for several interaction contexts, however some problems remain unsolved, including clutching. In this paper we present the concept of sub-space gestures. We describe a participative design user-study, that shows that interacting techniques using a dynamic definition of an imaginary interaction plane made by the user thanks to a gesture, have interesting acceptability. We finally provide some interaction design guidelines for this class of interaction. Key-words: gestural interaction, mid-air gestures, contactles interaction h al -0 08 51 93 5, v er si on 1 19 A ug 2 01 3 Gestes et sous-espaces. Eléments de design pour l’interaction dans l’air sur écrans distants Résumé : Ce document présente le concept de gestes réalisés dans des sous-espaces préalablement définis par l’utilisateur. Nous présentons une étude utilisateurs participative montrant l’utilisation et l’acceptabilité d’un tel concept. Finalement, nous donnons quelques recommandations de conception pour ce type d’interaction. Mots-clés : interaction gestuelle, gestes dans l’air, interaction sans contact ha l-0 08 51 93 5, v er si on 1 19 A ug 2 01 3 Sub-space gestures 3
Data-driven subtypes of major depressive disorder

In psychiatry, comorbidity is the rule rather than the exception. Up to 45% of all patients are classified as having more than one psychiatric disorder. These high rates of comorbidity have led to a debate concerning the interpretation of this phenomenon. Some authors emphasize the problematic character of the high rates of comorbidity because they indicate absent zones of rarities. Others consider comorbid conditions to be a validator for a particular reclassification of diseases. In this paper we will show that those at first sight contrastive interpretations of comorbidity are based on similar assumptions about disease models. The underlying ideas are first, that high rates of comorbidity are the result of the absence of causally defined diseases in psychiatry, and second, that causal disease models are preferable to non-causal disease models. We will argue that there are good reasons to seek after causal understanding of psychiatric disorders, but that causal disease models will not rule out high rates of comorbidity – neither in psychiatry, nor in medicine in general. By bringing to the fore these underlying assumptions, we hope to clear the ground for a different understanding of comorbidity, and of models for psychiatric diseases.
UNHEALTHY PLANT REGION DETECTION IN PLANT LEAVES USING ADAPTIVE GENETIC ALGORITHMS

The major issue faced by the farmers is crop diseases, which is causing a significant reduction in both the quality and quantity of the yield and hence, it needs to be addressed. The emergence of new and accurate techniques in the field of leaf-based image classification has shown impressive results. This paper shows the use of Random Forest in identifying between healthy and unhealthy leaves from the data sets created. Also, this paper consists of various phases of implementation namely dataset creation, feature extraction, training the classifier, masked cells, neural networks, k-means clustering, SGDM Matrix Generation. The created datasets of diseased and healthy leaves are collectively trained under Random Forest to classify the diseased and healthy images. Overall, using machine learning to train the large datasets available publicly gives us a clear way to detect the diseases present in plants .
Effect of EM Bokashi application on control of secondary soil salinization

In order to ameliorate saline-alkaline soil, EM Bokashi has been applied to rice production in conjunction with subdrainage in Ningxia Autonomous Region and Zhejiang Province. The preliminary results can be summarized as follows: EM Bokashi can increase soil organic matter content, improve soil porosity and permeability, and raise the soil's levels of available nutrients; and EM Bokashi combined with subdrainage treatment is more effective in controlling secondary soil salinization and raising the grain yield and quality than other treatments. The results suggest that EM Bokashi can reduce the necessary amount of chemical fertilizer application, thereby improving the agricultural environment, and that the introduction of EM Bokashi into systems of secondary soil salinization control systems has resulted in significant benefits.
Distance learning strategies using technology for elementary school students in Yogyakarta during the COVID-19 pandemic

This research aims to explore the experiences of teachers in implementing distance learning strategies for elementary school students. Participants in this research were 6 teachers from public elementary schools in Yogyakarta who ranked first. In-depth interview method as a data collection technique. Data analysis using semantic reduction. The results of the research found that distance learning in Yogyakarta public elementary schools used an inquiry strategy or deepening of the material itself by students. Learning is carried out by providing material and giving assignments. The media used is an application on the internet that supports distance learning according to the conditions and abilities of students in the form of group WhatsApp. Factors affecting distance learning include teacher competence, student ability to operate learning tools, different cognitive abilities of students, unfamiliarity with distance learning, internet stability, adequate internet quota, and less intensive parental assistance. © 2021 Little Lion Scientific.