Database of influencers' tweets in cryptocurrency (2021-2022).

Authors, through Twitter API, collected this database over eight months. These data are tweets of over 50 experts regarding market analysis of 40 cryptocurrencies. These experts are known as influencers on social networks such as Twitter. The theory of Behavioral economics shows that the opinions of people, especially experts, can impact the stock market trend (here, cryptocurrencies). Existing databases often cover tweets related to one or more cryptocurrencies. Also, in these databases, no attention is paid to the user's expertise, and most of the data is extracted using hashtags. Failure to pay attention to the user's expertise causes the irrelevant volume to increase and the neutral polarity to increase considerably. This database has a main table named "Tweets1" with 11 columns and 40 tables to separate comments related to each cryptocurrency. The columns of the main table and the cryptocurrency tables are explained in the attached document. Researchers can use this dataset in various machine learning tasks, such as sentiment analysis and deep transfer learning with sentiment analysis. Also, this data can be used to check the impact of influencers' opinions on the cryptocurrency market trend. The use of this database is allowed by mentioning the source.Also, in this version, we have added the excel version of the database and Python code to extract the names of influencers and tweets

Numerical investigation of CuO nanoparticles effect on forced convective heat transfer inside a mini-channel: Comparison of different approaches

This paper proceeds numerical investigation on forced convective heat transfer of nanofluids in laminar flow inside a mini-channel with circular cross-section under constant heat flux boundary condition at walls. Nanofluid contains CuO nanoparticles with diameter of 50 nanometer in water base fluid. At the entrance of channel, profiles of uniform velocity & temperature prevail. In order to obtain fully developed profiles, geometry of problem considers as L/D = 100. Problem is solved by means of 4 different models, including Homogeneous and Dispersion models in both of constant and variable thermophysical properties through the finite-volume method. The temperature-dependent properties was used for the first time in nanofluids dispersion model. It was regarded in the presence of nanoparticles the heat transfer coefficient will be increased to some considerable extent and the heat transfer enhancement strongly depends on the volume concentration of nanoparticles and Peclet number. Also, comparison with experimental data and literatures' correlations is carried out which indicates the Dispersion model in both cases is more precise and Homogeneous model (single phase) underestimates the Nusselt number in constant thermo physical properties

Database of influencers' tweets in cryptocurrency (2021-2022).

Authors, through Twitter API, collected this database over eight months. These data are tweets of over 50 experts regarding market analysis of 40 cryptocurrencies. These experts are known as influencers on social networks such as Twitter. The theory of Behavioral economics shows that the opinions of people, especially experts, can impact the stock market trend (here, cryptocurrencies). Existing databases often cover tweets related to one or more cryptocurrencies. Also, in these databases, no attention is paid to the user's expertise, and most of the data is extracted using hashtags. Failure to pay attention to the user's expertise causes the irrelevant volume to increase and the neutral polarity considerably. This database has a main table named "Tweets1" with 11 columns and 40 tables to separate comments related to each cryptocurrency. The columns of the main table and the cryptocurrency tables are explained in the attached document. Researchers can use this dataset in various machine learning tasks, such as sentiment analysis and deep transfer learning with sentiment analysis. Also, this data can be used to check the impact of influencers' opinions on the cryptocurrency market trend. The use of this database is allowed by mentioning the source.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

The Effect of Education on Health-Promoting Behaviors at the First Six Weeks Post-delivery on the Quality of Life of Primiparous Women

Background & Aim: Postpartum period is one of the vital stages in the women’s life which influences their quality of life. Instruction can influence their quality of life in this period. This study aimed to determine the effect of education on health-promoting behaviors at the postpartum period on the quality of life of primiparous women. Methods: The present controlled randomized trail study was carried out on 52 primiparous women who were randomly allocated into two groups (experimental and control). Instruction was presented for the experimental group for six weeks after childbirth. Quality of life of women between the two groups was compared by using Specific Postnatal Quality of life questionnaire at the first and sixth weeks. Data were analyzed by Chi-square and Independent T-test. Results: Results showed a significant difference between the mean score of quality of life pre-and post-intervention between the two groups (P<0.001), but this difference in the experimental group was more compared to the control group. Also, this difference in the 5 dimensions of quality of life (feeling about herself, baby, spouse, sexual activity, and health) in the experimental group was more compared to the control group (P<0.001). Conclusion: The results indicated that health promoting can be effective in improving quality of life of primiparous women. Therefore, health care providers should focus on this important issue

Anwendung von Twitter und Web News Mining in Überwachungssystemen für Infektionskrankheiten und Perspektiven der öffentlichen Gesundheit

Aims: With the advancements of communication technology and growing access to social networks, these networks now play an important role in the dissemination of information and news without going through the time-consuming channels of official news networks. Analysis of social networking data is a new, interesting branch of text mining science. This study aimed to develop a text mining technique for extracting information about infectious diseases from tweets and news on social media.Methods: A method called "Fuzzy Algorithm for Extraction, Monitoring, and Classification of Infectious Diseases" (FAEMC-ID) was developed by the use of fuzzy modeling of the Takagi-Sugeno-Kang type. In addition to the real-time classification, the method is able to update its vocabulary for new keywords and visualize the classified data on the world map to mark the high risk areas.Results: As an example, the monitoring was performed for measles-related news items over a 183-hour period from 01/03/2019 (01:00 am) to 08/03/2019 (12:00 pm), which were related to 2,870 tweets from 2,556 users. This monitoring showed that the number of tweets posted from each region ranged from 1 to 47, with the highest number, 47 tweets, belonging to Canada. The origins of most measles-related news were in the Americas and Europe, and they were mostly from the United States and Canada.Conclusion: The performance analysis of the developed method in comparison with other algorithms in the literature demonstrated the excellent precision of the method with a recall ratio of 88.41% and the high inter-correlation of data in each class. The proposed algorithm can also be used in the development of more effective monitoring and tracking systems for other human and even animal health hazards.Zielsetzung: Mit der Weiterentwicklung der Kommunikationstechnologie und dem wachsenden Zugang zu sozialen Netzwerken spielen diese Netzwerke eine wichtige Rolle zur Verbreitung von Informationen und Nachrichten, ohne dass die zeitaufwendigen Kanäle offizieller Nachrichtennetzwerke durchlaufen werden müssen. Die Analyse sozialer Netzwerkdaten ist ein neuer, interessanter Zweig der Text-Mining-Wissenschaft. Diese Studie zielt darauf ab, eine Text-Mining-Technik zu entwickeln, um Informationen über Infektionskrankheiten aus Tweets und Nachrichten in sozialen Medien zu extrahieren.Methode: Als Analysemethode wurde der sog. "Fuzzy-Algorithmus zur Extraktion, Überwachung und Klassifizierung von Infektionskrankheiten" (FAEMC-ID) unter Verwendung des Fuzzy-Modells des Takagi-Sugeno-Kang-Typs entwickelt. Zusätzlich zur Echtzeitklassifizierung kann die Methode neue Schlüsselwörter aktualisieren und die klassifizierten Daten auf der Weltkarte visualisieren, um Hochrisikobereiche zu markieren.Ergebnisse: Als Beispiel wurde das Monitoring für Nachrichten mit Bezug zu Masern über einen Zeitraum von 183 Stunden vom 01.03.2014 (01:00 Uhr) bis 08.03.2014 (12:00 Uhr) durchgeführt, das 2.870 Tweets von 2.556 Benutzern umfasste. Das Monitoring ergab als Anzahl der von jeder Region geposteten Tweets 1 und 47 mit der höchsten Anzahl von 47 Tweets aus Kanada. Der Ursprung der meisten Nachrichten über Masern war in Amerika und Europa; die Tweets stammten größtenteils aus den Vereinigten Staaten und Kanada.Schlussfolgerung: Die Analyse der entwickelten Methode liefert im Vergleich zu anderen Algorithmen in der Literatur eine ausgezeichnete Präzision mit einer Rückrufquote von 88,41% und einer hohen Interkorrelation der Daten in jeder Klasse. Der vorgeschlagene Algorithmus kann auch zur Entwicklung wirksamer Überwachungs- und Nachverfolgungssysteme für andere Gesundheitsgefahren für Mensch und Tier verwendet werden

Isolation, purification and characterization of a new gum from Acanthophyllum bracteatum roots

A new gum was isolated from the roots of Acanthophyllum bracteatum (ABG) by warm-water extraction. Purification was carried out by barium complexing to give a yield of 12.4% of pure air-dried or 5.8% of freeze-dried gum. The ABG contained 13.2% moisture, 84.3% carbohydrate, 0.9% protein and 1.5% ash. Its mineral content was comparable to commercial hydrocolloids. Monosaccharide analysis by HPLC showed the presence of galactose, glucose, arabinose, rhamnose and uronic acids in the ratio 16.0:7.2:3.0:1.0:3.1 respectively. The viscosity and pH value of 1% ABG solution at 25C were 51.5 mPa s and 6.85 respectively. ABG solutions (5e30 wt%) showed shear-thinning flow behavior at shear rates < 10 s 1. The viscosity decreased as temperature increased, and was highest at the neutral state. ABG had low surface and emulsification properties but moderate foaming capacity and relatively high foaming stability, which suggests that ABG could potentially be used in food systems to improve foaming propertie

Isolation, structural characterization and antioxidant activity of a new water-soluble polysaccharide from Acanthophyllum bracteatum roots

ABPS-1, a new water-soluble polysaccharide with molecular weight of 26 kDa and a specific optical rotation of +170◦ (c 1.0, H2O), was extracted from the roots of Acanthophyllum bracteatum by warm water and further successively purified through DEAE-cellulose A52 and Sephadex G-100 columns. Monosaccharide analysis revealed that the ABPS-1 was composed of Glc, Gal and Ara with a relative molar ratio of 1.4:5.2:1.0. Its structural features were elucidated by a combination of FT-IR, methylation and GC–MS analysis, periodate oxidation and Smith degradation, partial acid hydrolysis and 13C and 1H NMR spectroscopy. The data obtained indicate that ABPS-1 possessed a backbone of -(1 → 6)-linked Gal with branches attached to O-2 by -1 → linked Glc and at O-3 by -1 → linked Gal and by -(1 → 3)-linked Ara. The in vitro antioxidant activity showed that ABPS-1 possesses DPPH radical-scavenging activity in a concentration-dependent manner with an EC50 value of 2.6 mg/ml