Large magnetoresistance at room-temperature in semiconducting polymer sandwich devices

We report on the discovery of a large, room temperature magnetoresistance (MR) effect in polyfluorene sandwich devices in weak magnetic fields. We characterize this effect and discuss its dependence on voltage, temperature, film thickness, electrode materials, and (unintentional) impurity concentration. We usually observed negative MR, but positive MR can also be achieved under high applied electric fields. The MR effect reaches up to 10% at fields of 10mT at room temperature. The effect shows only a weak temperature dependence and is independent of the sign and direction of the magnetic field. We find that the effect is related to the hole current in the devices.Comment: 3 pages, 4 figure

Large magnetoresistance at room-temperature in small molecular weight organic semiconductor sandwich devices

We present an extensive study of a large, room temperature negative magnetoresistance (MR) effect in tris-(8-hydroxyquinoline) aluminum sandwich devices in weak magnetic fields. The effect is similar to that previously discovered in polymer devices. We characterize this effect and discuss its dependence on field direction, voltage, temperature, film thickness, and electrode materials. The MR effect reaches almost 10% at fields of approximately 10 mT at room temperature. The effect shows only a weak temperature dependence and is independent of the sign and direction of the magnetic field. Measuring the devices' current-voltage characteristics, we find that the current depends on the voltage through a power-law. We find that the magnetic field changes the prefactor of the power-law, whereas the exponent remains unaffected. We also studied the effect of the magnetic field on the electroluminescence (MEL) of the devices and analyze the relationship between MR and MEL. We find that the largest part of MEL is simply a consequence of a change in device current caused by the MR effect.Comment: 8 figure

Hyperfine interaction and magnetoresistance in organic semiconductors

We explore the possibility that hyperfine interaction causes the recently discovered organic magnetoresistance (OMAR) effect. Our study employs both experiment and theoretical modelling. An excitonic pair mechanism model based on hyperfine interaction, previously suggested by others to explain magnetic field effects in organics, is examined. Whereas this model can explain a few key aspects of the experimental data, we, however, uncover several fundamental contradictions as well. By varying the injection efficiency for minority carriers in the devices, we show experimentally that OMAR is only weakly dependent on the ratio between excitons formed and carriers injected, likely excluding any excitonic effect as the origin of OMAR.Comment: 10 pages, 7 figures, 1 tabl

Discussions About COVID-19 Vaccination on Twitter in Turkey: Sentiment Analysis

Objectives: The present study aims to examine COVID-19 vaccination discussions on Twitter in Turkey and conduct sentiment analysis. Methods: The current study performed sentiment analysis of Twitter data with artificial intelligence (AI)'s Natural Language Processing (NLP) method. The tweets were retrieved retrospectively from March 10, 2020, when the first Covid-19 case was seen in Turkey, to April 18, 2022. 10308 tweets accessed. The data were filtered before analysis due to excessive noise. First, the text is tokenized. Many steps were applied in normalizing texts. Tweets about the COVID-19 vaccines were classified according to basic emotion categories using sentiment analysis. The resulting dataset was used for training and testing machine learning classifiers. Results: It was determined that 7.50% of the tweeters had positive, 0.59% negative, and 91.91% neutral opinions about the COVID-19 vaccination. When the accuracy values of the ML algorithms used in this study were examined, it was seen that the XGB algorithm had higher scores. Conclusions: Three out of four tweets consist of negative and neutral emotions. The responsibility of professional chambers and the public is essential in transforming these neutral and negative feelings into positive ones. © 2022 Cambridge University Press. All rights reserved

Using Artificial Intelligence in the COVID-19 Pandemic: A Systematic Review

Artificial intelligence applications are known to facilitate the diagnosis and treatment of COVID-19 infection. This research was conducted to investigate and systematically review the studies published on the use of artificial intelligence in the COVID-19 pandemic. The study was conducted between April 25 and May 6, 2020 by scanning national and international studies accessed in "Web of Science, Google Scholar, Pubmed, and Scopus" databases with the keywords ("Coronavirus" or "COVID-19") and ("artificial intelligence" or "deep learning" or "machine learning"). As a result of the scanning process, 1495 (Google Scholar: 1400, Pubmed: 58, Scopus: 30, WOS: 7) studies were accessed. The studies were first examined according to their titles, and 1385 studies, which were not related to the research topic, were not included in the scope of the research. 50 articles, which did not meet the inclusion criteria, were excluded. The abstract and complete texts of the remaining 60 studies were scanned for the study's inclusion and exclusion criteria. A total of 10 studies, consisting of reviews, letters to the editor, meta-analysis studies, animal studies, conference presentations, studies not related to COVID-19, and incomplete studying protocols, were excluded. There were 50 studies left. 9 articles with duplication were identified and excluded. The remaining 41 studies were examined in detail. A total of 26 studies were found to meet the criteria for the systematic review study. In this systematic review, AI applications were found to be effective in COVID-19 diagnosis, classification, epidemiological estimates, mode of transmission, distribution, the density of lesions, case increase estimation, mortality/mortality risk, and early scans. © 2022 Tehran University of Medical Sciences. All rights reserved

Numerical simulation of water table fluctuation in sloping aquifers

40th IAHR World Congress, 2023 -- 21 August 2023 through 25 August 2023 -- 309059In this study, the simulation of the water table change in sloping aquifers was carried out using the finite difference method (FDM-MOL) based on method of lines. The nonlinear partial differential equation representing this problem has no analytical solution. However, approximate analytical solutions obtained by linearizing the related equation are available in the literature. Since the numerical solutions given in the literature are quite different from the approximate analytical results and Chauhan's experimental results, in this study, almost exact solutions are obtained by choosing a very small grid size both in time and space. Two sloping aquifer examples were examined, and the solutions obtained with FDM-MOL and the methods given in the literature were compared. These obtained solutions can be used as benchmark solutions in future studies. © 2023 IAHR – International Association for Hydro-Environment Engineering and Research

Social perceptions of breast cancer by women still undergoing or having completed therapy: A qualitative study

PubMed ID: 26925635Background: Diagnosis and treatment of breast cancer is a crisis situation which effects women's lives physically, socially and spiritually. Investigating women's perceptions of this disease is crucially important for treatment decisions. We therefore determined social perceptions and interpretations of women diagnosed with breast cancer during therapy and in the post-treatment period. Materials and Methods: In the study, focus group and in-depth interviews were made with women still undergoing or having completed breast cancer treatment. Some 25 women were included in the research. Content analysis was used in the analysis of the qualitative data obtained after the focus group and in-depth interviews. Results: Some of the women demonstrated positive perceptions towards accepting the disease, whereas others had emotions such as rebellion and anger. The loss of a breast is important with different interpretations. Conclusions: Women's acceptance or rebellion against the disease varies within their social interpretations after the treatment, as at the stage of diagnosis/treatment. All stages of breast cancer negatively affect the social life of the affected individual as much as her body. Nurses assume crucial roles in coping with these negative effects. Thus, it is necessary to know, and sociologically interpret, what is indicated by the information on what the negative effects concerning the disease are and how they are interpreted