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Detecting Misinformation on Social Media using Community Insights and Contrastive Learning
No full textSocial media users are more likely to be exposed to similar views and tend to avoid contrasting views, especially when they are part of a community of social media users. In this study, we investigate the presence of user communities and leverage them as a tool to detect misinformation on social media, specifically on X (formerly known as Twitter). We propose a misinformation detection framework, namely Similarity-based Misinformation Detection (SiMiD) that employs microblogs and utilizes user-follower interactions within a social network. Our approach extracts important textual features of social media posts using a transformer-based language model. We use contrastive learning and pseudo-labeling to fine-tune the language model. Then, we measure the similarity for each social media post based on its relevance to each user in the communities. Finally, we train a machine learning model to identify the truthfulness of social media posts using these similarity scores. We evaluate our approach on three social media datasets, compare our method with twelve state-of-the-art approaches, and answer five research questions. The experimental results, supported by statistical tests, show that contrastive learning and user communities can enhance the detection of misinformation on social media. Our model can identify misinformation content by achieving a consistently high weighted F1 score of over 90% across all datasets, even employing only a small number of users in communities. We make our implementations publicly available and provide all details that are necessary for the reproducibility of experiments.
Multi-group organic pollutants in urban and suburban atmospheric particulate matter (PM2.5): Temporal variation, meteorological impact, and sources
No full textSeveral adverse health impacts have been attributed to particulate matter-PM2.5, defined as having a diameter of less than 2.5 µm. The World Health Organization has determined that 5 µg m−3 is the 24-h limit threshold. PM2.5 comes from various primary sources and is also created by secondary atmospheric processes. Finding responsible sources can help regulate by focusing on the biological processes that underlie the observed health impacts. Determining the chemical composition of PM2.5 is the first phase in allocating PM2.5 to various sources. This study outlines the procedure for organic speciation of PM2.5—solvent-extractable polycyclic aromatic hydrocarbons (PAHs), n-alkanes, n-alkanoic acids, and levoglucosan. Daily PM2.5 aerosol samples were collected between July 2014 and September 2015 in Ankara, Turkey. Seasonal average concentrations of measured species ranged from 13.51 to 65.04 ng m−3 for PAHs, 36 to 150 ng m−3 for n-alkanes, 24 to 47 ng m−3 for n-alkanoic acids, 0.44 to 3.6 ng m−3 for levoglucosan. n-Alkanes are the most abundant group at both urban and suburban sites. Concentrations of all groups were higher during winter, which is associated with emissions from space heating and lower mixing height in winter months. The diagnostic ratios between specific atmospheric concentrations of tracers depicted that the particulate organic compounds are mainly from anthropogenic sources like vehicular emission, biogenic combustion, and food cooking
Populism Versus Science in Competitive Authoritarian Regimes
No full textThis article explores the linkage between populist, authoritarian tendencies among citizens and people's dispositions toward the scientific community. It particularly focuses on competitive authoritarian (CA) countries. The article underlines the commonalities between populism and competitive authoritarianism and aspires to explore the inclinations of the voters of populist incumbent parties in competitive authoritarian regimes. In light of an empirical analysis that covers more than 10,000 participants in competitive authoritarian regimes from 9 countries throughout the world, the article examines the correlates of people's viewpoints about science in CA countries. The findings strongly suggest that supporters of populist incumbent parties are more likely to hold reservations about science. We also find that the supporters of strongly populist parties in CA regimes are less likely to have optimistic viewpoints about science
Fixated flue gas desulfurization scrubber sludge-ground granulated blast-furnace slag blends as one-part sustainable binders
No full textThis study investigates the performance of one-part sustainable binders composed of fixated flue gas desulfurization scrubber sludge (FSS) and medium-quality ground granulated blast furnace slag (BFS), both of which are underutilized industrial by-products. FSS, an inexpensive and sustainable sulfate source, enhances the reactivity of medium-quality BFS, enabling the development of 100 % waste-based binders without the need for chemical activators or thermal treatment. The inherent alkalinity and rich composition of calcium, silicon, aluminum, and sulfur in FSS effectively compensate for the shortcomings of medium-quality BFS, particularly its low early reactivity. Blending BFS with FSS yielded higher pH levels, shorter setting times, and significantly improved long-term compressive strengths. Blends with over 50 wt% FSS achieved 90-day compressive strengths of 50–55 MPa, compared to only 6 MPa for BFS-only mixes, although higher FSS contents reduced early age strength and water stability. Microstructural analysis using scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and X-ray diffraction (XRD) revealed ettringite and C-S-H/C-A-S-H as the primary hydration products. Thermal exposure tests revelated a decrease in strength due to the decomposition of these phases. The synergistic utilization of these underused waste materials enhanced their potential for reuse and supports a circular economy in the construction industry
Nanoparticle concentration and solvent exchange via organic solvent ultrafiltration
No full textDownstream processing of nanoparticles after synthesis frequently involves purification, concentration and solvent exchange steps. While these are typically done via centrifugation in lab scale, ultrafiltration is a practical and economical alternative in large scale continuous production. Treating suspensions in organic solvents via ultrafiltration requires solvent-stable membranes and in this study we present the performance of cellulose ultrafiltration membranes in the concentration and solvent exchange of hydrophilic and hydrophobized silica nanoparticles using isopropanol, water, dimethyl formamide and ethanol as solvents. Hydrophobized particles formed less permeable cake layers than hydrophilic particles during concentration. All fouling was reversible upon ultrasonication while physical cleaning via stirring could only recover the complete membrane permeance fouled with hydrophilic particles in dimethyl formamide and hydrophobic particles in isopropanol and water. Quality of nanoparticle recovery was assessed by the amount recovered in retentate and cleaning suspensions as well as the particle size after these steps. Highest recovery of hydrophilic particles was obtained in dimethyl formamide and that of hydrophobic particles in isopropanol. No agglomeration of recovered hydrophilic particles was observed, while hydrophobic particles recovered via physical cleaning partly agglomerated in isopropanol and dimethyl formamide. Finally, continuous solvent exchange from isopropanol to ethanol, water and dimethyl formamide was achieved with no performance loss throughout the process
Science Teachers’ Technological Pedagogical Content Knowledge: An Explanatory Sequential Design
No full textSearch for heavy neutral Higgs bosons A and H in the tt¯Z channel in proton-proton collisions at 13 TeV
No full textA direct search for new heavy neutral Higgs bosons [Figure presented] and [Figure presented] in the [Figure presented] channel is presented, targeting the process [Figure presented] with [Figure presented]. For the first time, the channel with decays of the [Figure presented] boson to muons or electrons in association with all-hadronic decays of the [Figure presented] system is targeted. The analysis uses proton-proton collision data collected at the CERN LHC with the CMS experiment at s=13TeV, which correspond to an integrated luminosity of 138fb−1. No signal is observed. Upper limits on the product of the cross section and branching fractions are derived for narrow resonances [Figure presented] and [Figure presented] with masses up to 2100 and 2000 GeV, respectively, assuming [Figure presented] boson production through gluon fusion. The results are also interpreted within two-Higgs-doublet models, where [Figure presented] and [Figure presented] are CP-odd and CP-even states, respectively, complementing and substantially extending the reach of previous searches
Eu3+doped MB4O7 (M= Ca and Sr) nanoparticles for cancer treatment
No full textInorganic nanoparticles have been used for various biomedical applications, including bioimaging and drug delivery. In this work, the synthesis of various concentrations of Eu3+ doped Calcium Tetraborate (CaB4O7) using the solution-combustion method and Strontium Tetraborate (SrB4O7) using the solid-state method was reported. The particle size of the nanoparticles is approximately 400 nm for CBO, and SBO. Eu3+ has deep red emission at 590 nm and the other main emission band at 614 nm. While CaB4O7 has the highest luminescence intensity and the best crystallinity phase at 12 % Eu-doping, SrB4O7 has at 14 % Eu-doping. The nanoparticles were coated with polyethylene glycol (PEG), folic acid (FA), and fluorescein isothiocyanate (FITC) to gain various properties and provide targeted drug delivery. Then, the drug loading and release profile of the nanoparticles were examined using the anticancer drug doxorubicin (DOX). The loading capacities of bare nanoparticles were very high: CBO has 19.42 % f 0.36, and SBO has 19.51 % f 0.18 drug by weight. The amount of drug loaded in coated particles was 16.52 % f 1.74 in CBO-PEG/FA, and SBO-PEG/FA was 17.28 % f 1.03. Drug release experiments of all nanoparticles proved that they have slow, steady, and controlled release. It was observed that coated materials release the drug more slowly than bare particles. While PEG/FA coating did not affect cell viability for human glioblastoma (T98G) cells and human osteosarcoma (MG-63) cells, the cellular internalization of coated nanoparticles was lower compared to non-coated nanoparticles, particularly in MG-63 cells. In addition, the nanoparticles can be used for the secondary treatment method, Boron Neutron Capture Therapy (BNCT)
IOTA-Based Game-Theoretic Energy Trading With Privacy-Preservation for V2G Networks
No full textVehicle-to-grid (V2G) energy trading based on distributed ledger technologies (DLT), such as blockchains, has attracted much attention due to its promising features, including ease of deployment, decentralization, transparency, and security. However, existing DLT-based models do not support microtransactions due to the low value of such transactions relative to the incentives offered to transaction verifiers. To address this issue, we propose an IOTA DLT-based efficient and secure energy trading model for V2G networks, where electric vehicles (EVs) and grids negotiate energy prices in an off-chain manner. The proposed model utilizes a privacy-preserving protocol to prevent real-time tracking of EV locations. We develop a Stackelberg game model to represent the interactions between the EVs and grids, from which we derive a pricing scheme and propose a deposit mechanism to prevent fake energy trading between the EVs and grids. Extensive simulations demonstrate that our proposed scheme outperforms existing V2G energy trading mechanisms regarding transaction efficiency, provides enhanced EV privacy, and improves resilience against fake energy trading. Offering robust computational performance and addressing computational complexity (time, space, and message), our model presents a comprehensive V2G energy trading solution, balancing efficiency, security, and privacy
Language ideologies and stancetaking in refugee identity negotiations: exploring multilingual refugees' narratives of language use in Turkey
No full textThis study investigates the identity negotiations and language ideologies among/around Syrian refugees residing in Turkey by concentrating on the case of two multilingual, Syrian graduate students who have been forcedly displaced from Syria and resettled in Turkey. The study particularly explores participants' metalinguistic narratives of their language choices, multilingual repertoires and identity positionings. We argue that everyday talk about migration and migrants either within the migrant communities or in the majority society needs to be conceptualised with reference to a language ideological framework combined with a stancetaking approach. Our analysis reveals that the participants' linguistic practices and identity positions are heavily invoked by/through their engagements with the Turkish language and community and influenced by the highly nationalist anti-refugee discourse prevailing in Turkey. The participants negotiate their self-identity with respect to and in sharp contrast with widely- circulating negative discourses of refugee representations. Language choice and code-switching appear as major discursive and interactional strategies indexing stancetaking in the construction of narratives within the interview context