Low-level Analysis Research Articles

Highly Selective and Portable Fluorescence Turn-On Detection of Sc3+ in Ore and Water Based on Strong Lewis Acid-Base Coordination.

Detecting scandium (Sc) with high selectivity and sensitivity is a challenging task due to its chemical similarity to other rare earth ions. Our findings show that the fluorescence of the complex fluorescent indicator calcein (CL) is quenched under acidic conditions (pH = 2), and Sc3+ strongly inhibits this process. The results demonstrate that CL forms multimers and precipitates out of the solution under acidic conditions, while Sc3+ causes a significant decrease in the scattering intensity of the solution. Additional experiments revealed that the strong Lewis acid nature of Sc3+ complexes with the carboxyl groups of CL leads to increased dispersion of CL even under acidic conditions, thus enhancing its absorption and fluorescence. The complexation ratio of Sc3+ and CL was investigated through spectral titrations and theoretical calculations. The interaction between Sc3+ and CL is the strongest among rare earth and common metal ions due to the smallest ionic radius, resulting in high selectivity. The fluorescence turn-on strategy had a linear range of 0.04 to 2.25 μM under optimal conditions, with a detection limit of 20 nM for Sc3+. The combination of 3D printing and a smartphone program allows for portable on-site analysis of Sc3+. Mineral and water samples were used to demonstrate the potential of this strategy for the rapid, selective, and sensitive analysis of low levels of Sc3+.

Artificial intelligence applications in aviation accident classification: A preliminary exploratory study

Artificial intelligence (AI) applications are already integrated and expected to expand within the aviation sociotechnical ecosystem. Machine Learning (ML) models are capable and could excel on accident report classification tasks within the aviation context, assuming adequate and proper training. Human subject matter experts (SMEs) and the open access AI ChatGPT reanalyzed the TransAsia Airways flight GE235, Air France AF447, and Helios Airways Flight HCY552 accidents using the Human Factors Analysis and Classification System (HFACS) to investigate whether the AI agent is capable of conducting aviation accident analysis and comparing raters’ results, of verifying AI’s learning abilities, and, finally, investigating whether AI analysis could expand in other systems-based models. The results suggested that: (a) AI/ML applications could be an efficient tool for learning organizations’ continuous learning, growth, and improvement by creating new knowledge from previous failures, (b) the proper prompts and well-established frameworks could optimize the agreement between human SMEs and AI in accident analysis, (c) the ChatGPT performed better on linear models (i.e., Bowtie) compared to complex systemic accident causation models (e.g., HFACS), (d) the ChatGPT could not conduct high- and low-level analysis simultaneously, and, (e) a multidisciplinary team including human factors, IT, and accident investigators experts is essential in developing and overseeing the W-shaped learning assurance process.

MUSE: Visual Analysis of Musical Semantic Sequence.

Visualization has the capacity of converting auditory perceptions of music into visual perceptions, which consequently opens the door to music visualization (e.g., exploring group style transitions and analyzing performance details). Current research either focuses on low-level analysis without constructing and comparing music group characteristics, or concentrates on high-level group analysis without analyzing and exploring detailed information. To fill this gap, integrating the high-level group analysis and low-level details exploration of music, we design a musical semantic sequence visualization analytics prototype system (MUSE) that mainly combines a distribution view and a semantic detail view, assisting analysts in obtaining the group characteristics and detailed interpretation. In the MUSE, we decompose the music into note sequences for modeling and abstracting music into three progressively fine-grained pieces of information (i.e., genres, instruments and notes). The distribution view integrates a new density contour, which considers sequence distance and semantic similarity, and helps analysts quickly identify the distribution features of the music group. The semantic detail view displays the music note sequences and combines the window moving to avoid visual clutter while ensuring the presentation of complete semantic details. To prove the usefulness and effectiveness of MUSE, we perform two case studies based on real-world music MIDI data. In addition, we conduct a quantitative user study and an expert evaluation.

Configurable calorimeter simulation for AI applications

A configurable calorimeter simulation for AI (CoCoA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project is aimed to support the development of machine learning algorithms in high energy physics that rely on realistic particle shower descriptions, such as reconstruction, fast simulation, and low-level analysis. Specifications such as the granularity and material of its nearly hermetic geometry are user-configurable. The tool is supplemented with simple event processing including topological clustering, jet algorithms, and a nearest-neighbors graph construction. Formatting is also provided to visualise events using the Phoenix event display software.

From BEYONDPLANCK to COSMOGLOBE: Preliminary WMAP Q-band analysis

We present the first application of the COSMOGLOBE analysis framework by analyzing nine-year WMAP time-ordered observations that uses similar machinery to that of BEYONDPLANCK for the Planck Low Frequency Instrument (LFI). We analyzed only the Q-band (41 GHz) data and report on the low-level analysis process based on uncalibrated time-ordered data to calibrated maps. Most of the existing BEYONDPLANCK pipeline may be reused for WMAP analysis with minimal changes to the existing codebase. The main modification is the implementation of the same preconditioned biconjugate gradient mapmaker used by the WMAP team. Producing a single WMAP Q1-band sample requires 22 CPU-hrs, which is slightly more than the cost of a Planck 44 GHz sample of 17 CPU-hrs; this demonstrates that a full end-to-end Bayesian processing of the WMAP data is computationally feasible. In general, our recovered maps are very similar to the maps released by the WMAP team, although with two notable differences. In terms of temperature, we find a ∼2 μK quadrupole difference that most likely is caused by different gain modeling, while in polarization we find a distinct 2.5 μK signal that has been previously referred to as poorly measured modes by the WMAP team. In the COSMOGLOBE processing, this pattern arises from temperature-to-polarization leakage from the coupling between the CMB Solar dipole, transmission imbalance, and sidelobes. No traces of this pattern are found in either the frequency map or TOD residual map, suggesting that the current processing has succeeded in modeling these poorly measured modes within the assumed parametric model by using Planck information to break the sky-synchronous degeneracies inherent in the WMAP scanning strategy.

Development of a certified reference material for the accurate analysis of the acrylamide content in infant formula.

A certified reference material (CRM), KRISS CRM 108-02-006, was developed for the accurate analysis of low levels of acrylamide in infant formula matrices. The CRM is an infant formula fortified with acrylamide at a similar level as that stipulated by the European Union regulation for baby food. Commercially available infant formulas were processed by freeze-drying, and the subsequent homogenization of the fortified material to produce 961 bottles of the CRM in one batch. The CRM bottles containing approximately 15g of the material in each unit were stored in a storage room at - 70 ℃. High-purity acrylamide was used as the primary reference material, and its purity was assessed using an in-house mass-balance method to obtain results metrologically traceable to the International System of Units. The acrylamide content of the infant formula CRM was evaluated using isotope dilution-liquid chromatography/mass spectrometry as a reference method, which was established by our research group. An acrylamide content of 55.7 ± 2.1μg/kg was assigned as the certified value of the CRM with the expanded uncertainty at a 95% confidence level. The homogeneity study showed good uniformity of the acrylamide content among units, providing a relative standard deviation of 1.2% of the mean value. A stability study was also performed by monitoring the CRM under different temperature conditions and periods. The stability results indicated that the acrylamide content in the CRM under the storage conditions of - 70 ℃ remained stable for up to 10months.

Perspectives on Recovery-Oriented Care in Mental Health Practices: Health Professionals Experiences

In several Western countries, mental health professionals work in accordance with the principles of recovery-oriented practices, but there is little research into what opportunities there are for fostering recovery-oriented practices in mental health settings. To investigate how central elements of recovery-oriented practices are reflected in health professionals experiences of care and treatment in mental health. Four focus group interviews with nurses and other health professionals are conducted and analysed using manifest content analysis to carry out a low-level analysis of the participants’ experiences in mental healthcare. The study was designed in accordance with the ethical principles of the Helsinki Declaration (1) and Danish law (2). The participants gave informed consent after verbal and written information. The main theme, ‘recovery-oriented practices framed within institutional structural conditions’, was based on three subthemes: 1) users need help to find meaning during hospitalisation and find hope, 2) health professionals experience it as an obligation that users achieve personal recovery, and 3) user perspectives versus the mental health practices’ structural logic. This study sheds light on health professionals experiences with a recovery-oriented practice. The health professionals believe in this as a positive approach and see it as an important obligation to help the user find their own aims and hopes. On the other hand, it can be difficult to work in recovery-oriented practices. It requires an active commitment from users; for many, this can be difficult to live up to.

High Latitude Ionospheric Gradient Observation Results from a Multi-Scale Network.

In this article, a cluster comprised of eight Continuously Operating Reference Station (CORS) receivers surrounding five supplemental test stations located on much shorter baselines is used to form a composite multi-scale network for the purpose of isolating, extracting, and analyzing ionospheric spatial gradient phenomena. The purpose of this investigation is to characterize the levels of spatial decorrelation between the stations in the cluster during the periods with increased ionospheric activity. The location of the selected receiver cluster is at the auroral zone at night-time (cluster centered at about 69.5° N, 19° E) known to frequently have increased ionospheric activity and observe smaller size of high-density irregularities. As typical CORS networks are relatively sparse, there is a possibility that spatially small-scale ionospheric delay gradients might not be observed by the network/closest receiver cluster but might affect the user, resulting in residual errors affecting system accuracy and integrity. The article presents high level statistical observations based on several hundred manually validated ionospheric spatial gradient events along with low level analysis of specific events with notable temporal/spatial characteristics.

Taming Molecular Field-Coupling for Nanocomputing Design

Molecular Field-Coupling Nanocomputing (FCN) is one of the most promising technologies for overcoming Complementary Metal Oxide Semiconductor (CMOS) scaling issues. It encodes the information in the charge distribution of nanometric molecules and propagates it through local electrostatic intermolecular interaction. This technology promises very high speed at ambient temperatures with minimal power dissipation. The main research focus on molecular FCN is currently either on single-molecule low-level analysis or circuit design based on naïve assumptions. We aim to fill this gap, assessing the potential and feasibility of FCN. We present a bottom-up analysis and design framework that starts from the physical characterization of molecular and technological parameters and enables physical-aware FCN designs. The framework explicitly considers molecular physics, allowing the designer to tame the molecular interaction to ensure the computational capabilities of the final device. The framework permits studying possible physical effects that create cross-implications and correlations among physical and system-level layers considering possible behavior variability. We characterize and verify molecular propagation in increasingly structured layouts to design complex arithmetic circuits. The results highlight molecular FCN advantages, especially in area occupation, and provide valuable quantitative feedback to designers and technologists to support the assessment of molecular FCN and the realization of an eventual prototype.

Mapping a hierarchical dimensional structure of high experiential permeability: A bass-ackward approach to linking positive schizotypy and openness to experience.

Research on dimensional models of personality pathology has achieved a good deal of consensus on the 5 broad constructs that span adaptive and maladaptive personality traits. Nonetheless, connections between the 5th domain, typically called openness to experience within general personality measures and psychoticism or schizotypy on maladaptive measures, have proven more challenging to integrate. Using experiential permeability as a guiding framework, the current study seeks to develop a hierarchical, empirically derived lower order structure with these concepts. Using a top-down "Bass-Ackward" approach, we examined the item-level structure of adaptive and maladaptive components of high experiential permeability. Results showed support for a hierarchical model with a 6-component solution including mystical beliefs/experiences, oddity, intellect, openness to aesthetics, openness to ideas, and introspection. These components from items drawn from general and maladaptive measures related to one another within and across levels, specifically oddity and openness to aesthetics. Findings also highlight the importance of using lower level analyses when examining larger, heterogeneous constructs such as schizotypal thinking and perception and openness. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

How Low Is Too Low? A Retrospective Analysis of Very Low LDL-C Levels in Veterans.

Low-density lipoprotein cholesterol (LDL-C) can build up on the walls of blood vessels, leading to coronary heart disease. Medications used to lower LDL-C levels have demonstrated decreased risks of atherosclerotic cardiovascular disease, but currently, there is no consensus on how to define very low LDL-C levels. It is necessary for the body to have LDL-C to maintain proper brain function; however, the safety and effects of prolonged very low LDL-C levels are unknown. The current study sought to gather information to determine the risks of very low LDL-C levels in a veteran population. A retrospective chart review was conducted at a US Department of Veterans Affairs medical center. Patients with hyperlipidemia/dyslipidemia treated with HMG-CoA reductase inhibitors or proprotein convertase subtilisin/kexin type 9 (PCSK9) therapy and LDL-C levels < 40 mg/dL between January 1, 2010, and September 1, 2020, were included. The primary outcome was the rate of intracranial hemorrhage that could be caused by an LDL-C level < 40 mg/dL. The secondary outcomes included actions taken by clinicians, adverse drug reactions (ADRs), duration of therapy, and medication adherence. This study included 3027 patients. Of the included patients, 8 had an intracranial hemorrhage within 1 year from a documented LDL-C level < 40 mg/dL (0.26%). Thirty-two patients with an LDL-C level < 40 mg/dL did not have a documented ADR with the studied medications. Of the 32 charts, 26 had a clinician address the LDL-C level < 40 mg/dL with either documentation and/or modification of the medication prescribed. The most common ADRs among the studied medications were muscle and joint pain, rash, and cramps. Adherence to the medications was consistently similar for all studied medications. Of the patient population included in this study, 0.26% of patients had an intracranial hemorrhage within 1 year of having an LDL-C level < 40 mg/dL. The rate of ADRs related to the medications analyzed in this study shows no statistical significance (P > .05). When compared with low- and moderate-intensity statin medications, high-intensity statin medications were statistically significant in resulting in an LDL-C level < 40 mg/dL (P < .001). LDL-C levels < 40 mg/mL were not routinely documented as being addressed in the chart by the clinician.

Improving Analysis and Prediction of Tropical Cyclones by Assimilating Radar and GNSS-R Wind Observations: Ensemble Data Assimilation and Observing System Simulation Experiments Using a Coupled Atmosphere–Ocean Model

Abstract This study investigates the impact of assimilating ground-based radar reflectivity and wind data on tropical cyclone (TC) intensity prediction. The effect on a high-impact TC in the western North Pacific region that penetrated the Bashi Channel is examined. A multiscale correction based on the successive covariance localization (SCL) method is adopted to improve the analysis and forecast performance. In addition, GNSS-R wind speed is assimilated jointly in the rapid update assimilation framework to complement the TC boundary layer where radar data are limited. Model experiments are conducted and evaluated using the observing system simulation experiments (OSSEs) framework with a coupled atmosphere–ocean model nature run. Taking the experiment without data assimilation as the baseline, assimilating the radar data with the standard localization and SCL methods reduces the wind speed analysis error by 12% and 44%, respectively. The SCL method dominates the improvement in TC intensity prediction with a lead time longer than 2 days and the TC’s peak intensity forecast is improved by 18 hPa. The additional assimilation of the GNSS-R wind speed observation further reduces the wind speed error in the low-level analysis by 12% and 5% under the standard and SCL radar assimilation framework, respectively. GNSS-R wind assimilation leads to a further 6-hPa improvement in TC’s peak intensity. However, the sampling error introduced by the SCL method restrains the effect of GNSS-R assimilation. Sensitivity experiments with different GNSS-R data arrangements show that better GNSS-R wind coverage and additional wind direction information can further improve the TC analysis. Significance Statement Tropical cyclone (TC) intensity prediction over the western North Pacific (WNP) region remains a significant challenge due to limited observations. This study aims to improve the TC intensity prediction in WNP by assimilating the ground-based radar data using a multiscale correction framework and incorporating with the satellite ocean surface wind speed observation. We particularly focus on a high-impact TC like Typhoon Hato (2017), which penetrated the Bashi Channel and later made landfall in China, causing great damage. Our results showed that the assimilation strategy improved the TC intensity prediction for a lead time longer than 2 days. These results demonstrate the great potential of these observations and can provide guidance for future applications in operation centers.

Methodology of Low-Level Analysis of Glycol Content in Industrial Wastewater Using Gas Chromatography

The paper considers a low-level analysis of the glycol content in industrial wastewater using a split injection. The investigation method of mass concentrations of ethylene glycol in wastewater samples HDPE F 14.1:2.250-08 'Method of measuring mass concentrations of ethylene glycol and diethylene glycol in natural and wastewater samples by gas chromatography', based on chromatographic separation of sample components. The analysis of ethylene glycol content in airport wastewater is very important due to its high toxicity (waste of anti-icing fluid based on ethylene glycol of hazard class 3 waste). Achievement consistent results for low-level analysis of glycols in water samples is difficult with splitless injection primarily due to backfire problems, poor peak shape, and varying retention times. The split injection method discussed in this article avoids these problems and gives good and reliable chromatographic results. Most antifreezes used today are based on ethylene glycol, but propylene glycol-based products are becoming more common. Both have high boiling points, low vapor pressure, excellent heat transfer capabilities, and the ability to lower the freezing point of water, but ethylene glycol is a slightly more effective freezing point depressant. It is necessary to check the contamination of the waste stream in areas where antifreeze is stored or used refrigerant is disposed of, since ethylene glycol is classified as a hazard class 3 substance. Increased glycol levels in rivers can cause an increase in biological oxygen demand (BOD). In severe cases of infection, aquatic organisms can die as a result of oxygen starvation, rather than direct poisoning. Many environmental problems, such as negative effects on aquatic life, destruction of aquatic ecosystems, soil pollution, decreased oxygen levels in water absorption through skin contact, brain damage, damage to the central nervous system and joints, eye damage, toxicity, have been associated with the content of ethyleneglycolin waste water.

Smart grid substation equipment maintenance management functionality based on control center SCADA data

Classic approach to preventive maintenance of equipment in power system substations was based on periodical maintenance according to regulations and best practices recommended by vendors. SCADA system in HOPS is used primary for real-time monitoring and control of the equipment in substations. Real time data available in SCADA databases contain useful information for substation maintenance optimization based on operational conditions. Malfunctions and failures in the substations are monitored via SCADA system. Analyses of these data can be used to evaluate maintenance strategy and plan the reconstructions and replacement of the equipment. This article presents current maintenance management approach in HOPS, which is the only transmission system operator in Croatia, and possible improvements in efficiency of equipment maintenance using SCADA data. Prerequisite for the analyses of these kinds of data is standardization and unification of information names and structures. HOPS put a lot of effort in standardization of SCADA data during last refurbishment of control centers. Use of SCADA data for analysis drives the need for standardization and vice versa. Examples of statistical analyses of the SCADA data are done on multiple levels. High level analyses is used to provide the global overview of the real time data. It can point to suspicious information that require detailed analyses. The signals that appear often in the list can indicate that there is a problem in the primary equipment, monitoring system or alarm configuration. Low level analyses can be done on target information important for equipment maintenance. In this paper some low level analyses are presented for switching equipment, transformer lifetime, equipment in operation, and tap changer operation. An analysis is made for number of switching operations for breakers and disconnectors in different operational conditions. Preventive maintenance should be done based on number of switching in normal operations and even more often for breakers that are often interrupting fault current. SCADA data can be used for availability of the equipment and duration of failures thus providing valuable information to managers. An analysis on RTU communication availability is shown.

A three-level analysis of values related to socially responsible retirement investments

ABSTRACT The aim is to investigate the value basis of Socially Responsible Retirement Investments (SRRI) in a study of Swedish pension investors in the age range 18 to 65 years (N=1005). Logistic regression analyses were performed with self-reported SRRI choice as dependent variable and different levels of values as independent variables. On a higher level of analyses, self-transcendent values, especially universalism (e.g., equality, protecting the environment, and social justice), have the most important influences on SRRI choice. In contrast, on a lower-level analysis, SRRI choice is influenced by self-enhancement values with high priority for authoritarian power and low priority for wealth. The three-level analysis of values (self-transcendence vs self-enhancement value orientation, motivational domain, and value) questions the contradiction between dimension poles of values and the structuring of values in interrelated motivational domains. The results thereby clarify some previous findings and increase the understanding of the value basis of SRRI.

Thin-film microextraction combined with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry screening for presence of multiclass organic pollutants in drinking water samples.

Organic compounds in drinking water can be potentially hazardous. In this study, we demonstrate for the first time that combining in-bottle thin-film microextraction (TFME), comprehensive two-dimensional gas chromatography, and time-of-flight mass spectrometry (GC×GC-TOFMS) can produce a powerful, green tool for determining multiple organic pollutants in drinking water and monitoring changes in water samples after treatment with a water purification system. In the developed approach, water is added to an amber sampling bottle containing TFME membranes and left to sit until equilibration is achieved. Once equilibration has been achieved, the membranes are withdrawn, and the extracted compounds are quantified via thermal desorption in the GC×GC-TOFMS system. This approach enabled a large number of organic compounds with a wide range of physicochemical properties to be identified based on their mass spectra. A series of drinking water samples (raw water, water after softener filter, and drinking water) were collected from a standard house faucet in order to analyze changes following treatment with a water purification system. The developed strategy was also applied to identify the concentrations of the 9 selected organic compounds in the water samples. Ultimately, the in-bottle TFME-GC×GC-TOFMS method is straightforward and provides comparable performance to other methods for low-level analyses of organic pollutants in drinking water samples.

Impact of assimilating Formosat-7/COSMIC-II GNSS radio occultation data on heavy rainfall prediction in Taiwan

This study investigates the impact of assimilating Formosat-7/COSMIC-II (FS7/C2) radio occultation (RO) refractivity data on predicting the heavy rainfall event that occurred in Taiwan on August 13, 2019. This event was characterized by heavy rainfall over the coastal region of central and southwestern Taiwan. Our investigation is performed using the Weather Research and Forecasting-Local Ensemble Transform Kalman Filter. Generally, assimilating the RO data increases the amount of moisture over the northern South China Sea (SCS) and the Pearl River area in southern China. It was expected that assimilating the RO data would improve low-level moisture analysis, given that more RO data are available for the lower atmosphere compared to those from Formosat-3/COSMIC-I. However, our results show that the experiment that does not include the RO data below 3 km facilitates better rainfall prediction over Taiwan in terms of the intensity and location of heavy rainfall. This heavy rainfall event can be attributed to moisture transport from the Pearl River area, where the RO data at the altitude of 3–5 km provide effective moisture enhancement to deepen the high-moisture layer. The experiment using the local spectral width (LSW) to conduct the quality control (QC) also helps improve rainfall prediction. However, such an LSW-based QC procedure tends to reject significant amounts of RO data 3 km above the land. Based on this case study, our results show that the QC procedure brings a larger impact to rainfall prediction than counterparts that adjust the observation error variance. A sophisticated QC procedure should be developed to optimize the impact of low-level RO data.

CONFIRMS: A Toolkit for Scalable, Black Box Connectome Assessment and Investigation.

The nanoscale connectomics community has recently generated automated and semi-automated “wiring diagrams” of brain subregions from terabytes and petabytes of dense 3D neuroimagery. This process involves many challenging and imperfect technical steps, including dense 3D image segmentation, anisotropic nonrigid image alignment and coregistration, and pixel classification of each neuron and their individual synaptic connections. As data volumes continue to grow in size, and connectome generation becomes increasingly commonplace, it is important that the scientific community is able to rapidly assess the quality and accuracy of a connectome product to promote dataset analysis and reuse. In this work, we share our scalable toolkit for assessing the quality of a connectome reconstruction via targeted inquiry and large-scale graph analysis, and to provide insights into how such connectome proofreading processes may be improved and optimized in the future. We illustrate the applications and ecosystem on a recent reference dataset.

Heterogeneity of SARS-CoV-2 virus produced in cell culture revealed by shotgun proteomics and supported by genome sequencing

COVID-19 is the most disturbing pandemic of the past hundred years. Its causative agent, the SARS-CoV-2 virus, has been the subject of an unprecedented investigation to characterize its molecular structure and intimate functioning. While markers for its detection have been proposed and several diagnostic methodologies developed, its propensity to evolve and evade diagnostic tools and the immune response is of great concern. The recent spread of new variants with increased infectivity requires even more attention. Here, we document how shotgun proteomics can be useful for rapidly monitoring the evolution of the SARS-CoV-2 virus. We evaluated the heterogeneity of purified SARS-CoV-2 virus obtained after culturing in the Vero E6 cell line. We found that cell culture induces significant changes that are translated at the protein level, such changes being detectable by tandem mass spectrometry. Production of viral particles requires careful quality control which can be easily performed by shotgun proteomics. Although considered relatively stable so far, the SARS-CoV-2 genome turns out to be prone to frequent variations. Therefore, the sequencing of SARS-CoV-2 variants from patients reporting only the consensus genome after its amplification would deserve more attention and could benefit from more in-depth analysis of low level but crystal-clear signals, as well as complementary and rapid analysis by shotgun proteomics.Graphical abstract

Low-level Argumentative Text Analysis and Teaching Methods Based on Rhetorical Structure Theory

Low-level Argumentative Text Analysis and Teaching Methods Based on Rhetorical Structure Theory