ZnO and ZnO1−x_{1-x} based thin film memristors: The effects of oxygen deficiency and thickness in resistive switching behavior

In this study, direct-current reactive sputtered ZnO and ZnO1-x based thin film (30 nm and 300 nm in thickness) memristor devices were produced and the effects of oxygen vacancies and thickness on the memristive characteristics were investigated. The oxygen deficiency of the ZnO1-x structure was confirmed by SIMS analyses. The memristive characteristics of both the ZnO and ZnO1-x devices were determined by time dependent current-voltage (I-V-t) measurements. The distinctive pinched hysteresis I-V loops of memristors were observed in all the fabricated devices. The typical homogeneous interface and filamentary types of memristive behaviors were compared. In addition, conduction mechanisms, on/off ratios and the compliance current were analyzed. The 30 nm ZnO based devices with native oxygen vacancies showed the best on/off ratio. All of the devices exhibited dominant Schottky emissions and weaker Poole-Frenkel conduction mechanisms. Results suggested that the oxygen deficiency was responsible for the Schottky emission mechanism. Moreover, the compliance currents of the devices were related to the decreasing power consumption as the oxygen vacancies increased.Comment: Accepted manuscript. Journal: Ceramics Internationa

A study on wear rates of 100Cr6 steel running against sintered steel surfaces under dry and starved lubrication

This paper investigates the tribological behavior of 100Cr6 steel pin running against sintered steel bearing material used in hermetic compressors. Tests were conducted under dry and starved lubrication sliding conditions in air at room temperature. Although porous structure acts as crack initiation sites thus limiting the wear resistance of sintered iron in dry sliding conditions under high contact stresses, it is believed to be beneficial in lubricated sliding conditions as it absorbs a large amount of lubricant. Wear tests without lubrication show that these pores are completely filled by abrasive particles in the initial stages of the test and no longer maintain their oil absorption capability. Initial results show that oxidation of frictional surfaces by flash temperature in dry conditions reduces weight loss volume by decreasing the coefficient of friction

GraphMatcher: A Graph Representation Learning Approach for Ontology Matching

Ontology matching is defined as finding a relationship or correspondence between two or more entities in two or more ontologies. To solve the interoperability problem of the domain ontologies, semantically similar entities in these ontologies must be found and aligned before merging them. GraphMatcher, developed in this study, is an ontology matching system using a graph attention approach to compute higher-level representation of a class together with its surrounding terms. The GraphMatcher has obtained remarkable results in in the Ontology Alignment Evaluation Initiative (OAEI) 2022 conference track. Its codes are available at ~\url{https://github.com/sefeoglu/gat_ontology_matching}.Comment: The 17th International Workshop on Ontology Matching, The 21st International Semantic Web Conference (ISWC) 2022, 23 October 2022, Hangzhou, Chin

Strategy Training in Pre-writing Phases of EFL Classes

The aim of the current study is to find out whether strategy training with the use of visual aids in pre-writing section of EFL writing classes has any facilitative effect on L2 writing development. Even though a number of studies confirmed that strategy training has a positive impact on L2 writing skills (Kobayashi and Rinnert, 2008; Ong and Zahan, 2010; Shi; 1998; among many others) the role of visual aids as a supplementary tool has not been obscured yet. Hence, the current study targets to highlight whether a strategy training by employing four distinct visual aids in pre-writing phase influences L2 writing in a positive way. For the purposes of the current study, twenty participants (ten as experimental and ten as control group members) were recruited on a voluntary basis. Strategy training consisted of four one-hour sessions in which a different aspect of L2 writing such as generating ideas, outlining, extending the text, and reflection was practiced via visuals. Data collection tools were pre and post English writing exams and a questionnaire. Data were analyzed both qualitatively and quantitatively. The results revealed a significant difference between the control and experimental groups. This finding suggests that strategy trainings lead to observable progress in L2 writing scores. Moreover, visual that was related to text organization was also found to be the most effective tool while concept maps that were employed to generate ideas were favored less by the participants. Keywords: EFL Writing, English Language Teaching, Strategy Training, Visual Aids, L2 Writing Proficienc

Advancing Vibrational Spectroscopy for Cellular and Sub Cellular Analysis: Raman Spectroscopy as a Novel In Vitro Nanotoxicological Assessment Protocol

This work is designed to establish a ‘High content Nanotoxicological Screening method’ using in vitro Raman microspectroscopy. The undeniable increase of nanotechnology based products has brought challenges in terms of determining their toxicological properties. Considering the total time and cost of screening nanomaterials by conventional methods, the need for a rapid, label-free technique which will provide a wide range of information on multiple parameters is unquestionable. This study investigated the applicability of Raman microspectroscopy as a High Content Screening technique to clarify cell-nanoparticle interaction by determining the localisation of the nanoparticles and consequent effects in these localised areas in terms of cyto- and geno- toxicity. For this purpose, in the first part of the study, the potential of Raman spectroscopy has been explored to monitor sequential trafficking of nanoparticles in cellular organelles and to determine the differing spectral signatures of the organelles. Human lung carcinoma cells (A549) were exposed to non-toxic carboxylate-modified and fluorescently-labelled polystyrene nanoparticles for 4, 12 and 24 hours and Raman spectral maps were acquired from the subcellular regions to determine their localisation. With the aid of multivariate analysis techniques, the study demonstrated the applicability of Raman microspectroscopy to provide information regarding localisation and to determine the local environment based on differing signatures of intracellular compartments such as endosomes, lysosomes and endoplasmic reticulum, in a completely label free manner. Aminated polystyrene nanoparticles (PS-NH2) and polyamidoamine (PAMAM) dendrimers are known to show acute toxicity and in order to observe this toxicity and corresponding responses, time and dose dependant Raman spectroscopic markers of cellular toxic events were systematically monitored upon nanoparticle exposure to A549 cells. Alamar Blue (AB) and 3-(4,5-Dimethylthiazol-2-yl)-2,5diphenyltetrazoliumbromid (MTT) assays were employed to determine the mean effective concentration, EC50 and Raman spectroscopy was used to acquire multiple point spectra from the cytoplasm, nucleus and nucleolus. The most significant changes were observed in the cytoplasm for both time and dose dependent cases. The Raman spectral markers for lipidosis and oxidative stress were determined as a function of dose and time, and the responses were correlated with conventional cytotoxicity assays. With the aid of multivariate analysis techniques, the study showed the ability of Raman spectroscopy to provide information about cellular responses at different particle concentrations and exposure times. Following this, the potential of Raman microspectroscopy was analysed by comparing spectral marker evolution in non-cancerous cells (immortalized human bronchial epithelium) with cancerous cells (A549 and human lung epidermoid). Spectral markers of oxidative stress, cytoplasmic RNA aberrations and liposomal rupture were identified and cell-line dependent variations in these spectral markers were observed, and were correlated with cellular assays and imaging techniques. The findings from the comparison of spectral markers, especially in the low wavenumber region, have shown the applicability of Raman spectroscopy to identify different cell death pathways in cancerous and non-cancerous cell lines. Different cell death mechanisms were also identified based on common and/or differing spectral markers of cyto- and geno- toxicity upon PS-NH2 and PAMAM exposure. The results were correlated with flow cytometry and cytotoxicity assays. The study further demonstrated the potential of Raman microspectroscopy to iii differentiate apoptotic and necrotic cell death mechanisms, as a function of time (from 4 to 72 h) and applied dose (sub-lethal/lethal). Finally, in order to establish a toxicological assessment protocol based on Raman microspectroscopy, 3D graphs of biomarker intensities are plotted as a function of time and dose and also intensities are correlated with % viability values. The established 3D models can be used to predict nanotoxicity, which can also be applied to nanomedicine

Effect of R600a on tribological behaviour of sintered steel under starved lubrication

This study aims to develop and characterize wear resistant and low friction tribopairs that are compatible with new ozone-friendly Isobutane refrigerant to run at hermetic compressor bearings. The tribological behavior of 100Cr6 steel pin is investigated under starved lubrication condition in air and R600a environments when running against sintered steel with and without steam treatment. EDS and SEM are carried out on pin and plate samples after wear tests. The results indicate that durability distance is lower for the tests with R600a than those with air. The adverse effect of R600a on wear rate is linked to the change in the viscosity and foaming characteristics of the oil in the presence of R600a as well as the lack of oxides. Kemal Sariibrahimoglu1, Huseyin Kizil1*, Mahmut F. Aksit2, Ihsan Efeoglu3, and Husnu Kerpicci

Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks

Information Extraction (IE) is crucial for converting unstructured data into structured formats like Knowledge Graphs (KGs). A key task within IE is Relation Extraction (RE), which identifies relationships between entities in text. Various RE methods exist, including supervised, unsupervised, weakly supervised, and rule-based approaches. Recent studies leveraging pre-trained language models (PLMs) have shown significant success in this area. In the current era dominated by Large Language Models (LLMs), fine-tuning these models can overcome limitations associated with zero-shot LLM prompting-based RE methods, especially regarding domain adaptation challenges and identifying implicit relations between entities in sentences. These implicit relations, which cannot be easily extracted from a sentence's dependency tree, require logical inference for accurate identification. This work explores the performance of fine-tuned LLMs and their integration into the Retrieval Augmented-based (RAG) RE approach to address the challenges of identifying implicit relations at the sentence level, particularly when LLMs act as generators within the RAG framework. Empirical evaluations on the TACRED, TACRED-Revisited (TACREV), Re-TACRED, and SemEVAL datasets show significant performance improvements with fine-tuned LLMs, including Llama2-7B, Mistral-7B, and T5 (Large). Notably, our approach achieves substantial gains on SemEVAL, where implicit relations are common, surpassing previous results on this dataset. Additionally, our method outperforms previous works on TACRED, TACREV, and Re-TACRED, demonstrating exceptional performance across diverse evaluation scenarios.Comment: preprin

Retrieval-Augmented Generation-based Relation Extraction

Information Extraction (IE) is a transformative process that converts unstructured text data into a structured format by employing entity and relation extraction (RE) methodologies. The identification of the relation between a pair of entities plays a crucial role within this framework. Despite the existence of various techniques for relation extraction, their efficacy heavily relies on access to labeled data and substantial computational resources. In addressing these challenges, Large Language Models (LLMs) emerge as promising solutions; however, they might return hallucinating responses due to their own training data. To overcome these limitations, Retrieved-Augmented Generation-based Relation Extraction (RAG4RE) in this work is proposed, offering a pathway to enhance the performance of relation extraction tasks. This work evaluated the effectiveness of our RAG4RE approach utilizing different LLMs. Through the utilization of established benchmarks, such as TACRED, TACREV, Re-TACRED, and SemEval RE datasets, our aim is to comprehensively evaluate the efficacy of our RAG4RE approach. In particularly, we leverage prominent LLMs including Flan T5, Llama2, and Mistral in our investigation. The results of our study demonstrate that our RAG4RE approach surpasses performance of traditional RE approaches based solely on LLMs, particularly evident in the TACRED dataset and its variations. Furthermore, our approach exhibits remarkable performance compared to previous RE methodologies across both TACRED and TACREV datasets, underscoring its efficacy and potential for advancing RE tasks in natural language processing.Comment: Submitted to Semantic Web Journal. Under Revie

Monitoring Stem Cell Differentiation Using Raman Microspectroscopy: Chondrogenic Differentiation, Towards Cartilage Formation

Mesenchymal Stem Cells (MSCs) have the ability to differentiate into chondrocytes, the only cellular components of cartilage and are therefore ideal candidates for cartilage and tissue repair technologies. Chondrocytes are surrounded by cartilage-like extracellular matrix (ECM), a complex network rich in glycosaminoglycans, proteoglycans, and collagen, which, together with a multitude of intracellular signalling molecules, trigger the chondrogenesis and allow the chondroprogenitor to acquire the spherical morphology of the chondrocytes. However, although the mechanisms of the differentiation of MSCs have been extensively explored, it has been difficult to provide a holistic picture of the process, in situ. Raman Micro Spectroscopy (RMS) has been demonstrated to be a powerful analytical tool, which provides detailed label free biochemical fingerprint information in a non-invasive way, for analysis of cells, tissues and body fluids. In this work, RMS is explored to monitor the process of Mesenchymal Stem Cell (MSC) differentiation into chondrocytes in vitro, providing a holistic molecular picture of cellular events governing the differentiation. Spectral signatures of the subcellular compartments, nucleolus, nucleus and cytoplasm were initially probed and characteristic molecular changes between differentiated and undifferentiated were identified. Moreover, high density cell micromasses were cultured over a period of three weeks, and a systematic monitoring of cellular molecular components and the progress of the ECM formation, associated with the chondrogenic differentiation, was performed. This study shows the potential applicability of RMS as a powerful tool to monitor and better understand the differentiation pathways and process

In Vitro Monitoring of Time and Dose Dependent Cytotoxicity of Aminated Nanoparticles using Raman Spectroscopy

Monitoring of time and dose dependent molecular changes by using Raman spectroscopy with the aid of multivariate analysis techniques and determination of Raman spectral markers of cellular toxicity.</p