A Critical Review of the Morpheme Order Studies: The State of the Art

The purpose of this paper is to make a critical review of the history of the so-called morpheme order studies. First of all, a brief contextualisation of the morpheme order studies is presented at the time when the linguistic field shifted from behaviourist to innatist theories; put differently, from Structural Linguistics to Generative Linguistics. The morpheme order studies not only contributed as evidence in favour of innatist theories but also had an impact on the formulation of the Natural Order Hypothesis, which was proposed by Krashen in the late 70’s and the early 80’s. Thereafter, the paper sheds some light on the morpheme order studies, which are divided into two sections. On the one hand, the early stages of the morpheme order studies in which the papers of three pioneer researchers in the area of the first language (L1) are commented; Roger Brown, de Villiers and de Villiers. A more detailed examination on second language (L2) acquisition research follows this section in which relevant researchers such as Dulay and Burt proposed a “universal” order among L2 learners of English. On the other hand, as some investigations claimed that not all L2 learners follow the same consistent order, the paper takes into consideration some factors, also known as the multiple-determinant approach, that influence the order of L2 English morphemes. Furthermore, this research discusses the criticisms that the morpheme order studies have been subjected to and the influence they have had in the construction of teaching materials. The paper concludes with a revision of the factors that affect the acquisition order in L2, which show how there are many factors which influence the acquisition of L2 English morpheme order

A Critical Review of the Morpheme Order Studies: The State of the Art

The purpose of this paper is to make a critical review of the history of the so-called morpheme order studies. First of all, a brief contextualisation of the morpheme order studies is presented at the time when the linguistic field shifted from behaviourist to innatist theories; put differently, from Structural Linguistics to Generative Linguistics. The morpheme order studies not only contributed as evidence in favour of innatist theories but also had an impact on the formulation of the Natural Order Hypothesis, which was proposed by Krashen in the late 70’s and the early 80’s. Thereafter, the paper sheds some light on the morpheme order studies, which are divided into two sections. On the one hand, the early stages of the morpheme order studies in which the papers of three pioneer researchers in the area of the first language (L1) are commented; Roger Brown, de Villiers and de Villiers. A more detailed examination on second language (L2) acquisition research follows this section in which relevant researchers such as Dulay and Burt proposed a “universal” order among L2 learners of English. On the other hand, as some investigations claimed that not all L2 learners follow the same consistent order, the paper takes into consideration some factors, also known as the multiple-determinant approach, that influence the order of L2 English morphemes. Furthermore, this research discusses the criticisms that the morpheme order studies have been subjected to and the influence they have had in the construction of teaching materials. The paper concludes with a revision of the factors that affect the acquisition order in L2, which show how there are many factors which influence the acquisition of L2 English morpheme order

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Artificial intelligence, particularly machine learning, carries high potential to automatically detect customers’ pain points, which is a particular concern the customer expresses that the company can address. However, unstructured data scattered across social media make detection a nontrivial task. Thus, to help firms gain deeper insights into customers’ pain points, the authors experiment with and evaluate the performance of various machine learning models to automatically detect pain points and pain point types for enhanced customer insights. The data consist of 4.2 million user-generated tweets targeting 20 global brands from five separate industries. Among the models they train, neural networks show the best performance at overall pain point detection, with an accuracy of 85% (F1 score = .80). The best model for detecting five specific pain points was RoBERTa 100 samples using SYNONYM augmentation. This study adds another foundational building block of machine learning research in marketing academia through the application and comparative evaluation of machine learning models for natural language–based content identification and classification. In addition, the authors suggest that firms use pain point profiling, a technique for applying subclasses to the identified pain point messages to gain a deeper understanding of their customers’ concerns.©2022 SAGE Publications. The article is protected by copyright and reuse is restricted to non-commercial and no derivative uses. Users may also download and save a local copy of an article accessed in an institutional repository for the user's personal reference.fi=vertaisarvioitu|en=peerReviewed

Alternate Fuels for Use in Commercial Aircraft

The engine and aircraft Research and Development (R&D) communities have been investigating alternative fueling in near-term, midterm, and far-term aircraft. A drop in jet fuel replacement, consisting of a kerosene (Jet-A) and synthetic fuel blend, will be possible for use in existing and near-term aircraft. Future midterm aircraft may use a biojet and synthetic fuel blend in ultra-efficient airplane designs. Future far-term engines and aircraft in 50-plus years may be specifically designed to use a low- or zero-carbon fuel. Synthetic jet fuels from coal, natural gas, or other hydrocarbon feedstocks are very similar in performance to conventional jet fuel, yet the additional CO2 produced during the manufacturing needs to be permanently sequestered. Biojet fuels need to be developed specifically for jet aircraft without displacing food production. Envisioned as midterm aircraft fuel, if the performance and cost liabilities can be overcome, biofuel blends with synthetic jet or Jet-A fuels have near-term potential in terms of global climatic concerns. Long-term solutions address dramatic emissions reductions through use of alternate aircraft fuels such as liquid hydrogen or liquid methane. Either of these new aircraft fuels will require an enormous change in infrastructure and thus engine and airplane design. Life-cycle environmental questions need to be addressed

Topic-driven toxicity: Exploring the relationship between online toxicity and news topics

Hateful commenting, also known as 'toxicity', frequently takes place within news stories in social media. Yet, the relationship between toxicity and news topics is poorly understood. To analyze how news topics relate to the toxicity of user comments, we classify topics of 63,886 online news videos of a large news channel using a neural network and topical tags used by journalists to label content. We score 320,246 user comments from those videos for toxicity and compare how the average toxicity of comments varies by topic. Findings show that topics like Racism, Israel-Palestine, and War & Conflict have more toxicity in the comments, and topics such as Science & Technology, Environment & Weather, and Arts & Culture have less toxic commenting. Qualitative analysis reveals five themes: Graphic videos, Humanistic stories, History and historical facts, Media as a manipulator, and Religion. We also observe cases where a typically more toxic topic becomes non-toxic and where a typically less toxic topic becomes "toxicified" when it involves sensitive elements, such as politics and religion. Findings suggest that news comment toxicity can be characterized as topic-driven toxicity that targets topics rather than as vindictive toxicity that targets users or groups. Practical implications suggest that humanistic framing of the news story (i.e., reporting stories through real everyday people) can reduce toxicity in the comments of an otherwise toxic topic

Alternate-Fueled Combustor-Sector Performance

Alternate aviation fuels for military or commercial use are required to satisfy MIL-DTL-83133F(2008) or ASTM D 7566 (2010) standards, respectively, and are classified as "drop-in" fuel replacements. To satisfy legacy issues, blends to 50% alternate fuel with petroleum fuels are certified individually on the basis of processing and assumed to be feedstock agnostic. Adherence to alternate fuels and fuel blends requires "smart fueling systems" or advanced fuel-flexible systems, including combustors and engines, without significant sacrifice in performance or emissions requirements. This paper provides preliminary performance (Part A) and emissions and particulates (Part B) combustor sector data. The data are for nominal inlet conditions at 225 psia and 800 F (1.551 MPa and 700 K), for synthetic-paraffinic-kerosene- (SPK-) type (Fisher-Tropsch (FT)) fuel and blends with JP-8+100 relative to JP-8+100 as baseline fueling. Assessments are made of the change in combustor efficiency, wall temperatures, emissions, and luminosity with SPK of 0%, 50%, and 100% fueling composition at 3% combustor pressure drop. The performance results (Part A) indicate no quantifiable differences in combustor efficiency, a general trend to lower liner and higher core flow temperatures with increased FT fuel blends. In general, emissions data (Part B) show little differences, but with percent increase in FT-SPK-type fueling, particulate emissions and wall temperatures are less than with baseline JP-8. High-speed photography illustrates both luminosity and combustor dynamic flame characteristics

Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type

YesAs complex data becomes the norm, greater understanding of machine learning (ML) applications is needed for content marketers. Unstructured data, scattered across platforms in multiple forms, impedes performance and user experience. Automated classification offers a solution to this. We compare three state-of-the-art ML techniques for multilabel classification - Random Forest, K-Nearest Neighbor, and Neural Network - to automatically tag and classify online news articles. Neural Network performs the best, yielding an F1 Score of 70% and provides satisfactory cross-platform applicability on the same organisation's YouTube content. The developed model can automatically label 99.6% of the unlabelled website and 96.1% of the unlabelled YouTube content. Thus, we contribute to marketing literature via comparative evaluation of ML models for multilabel content classification, and cross-channel validation for a different type of content. Results suggest that organisations may optimise ML to auto-tag content across various platforms, opening avenues for aggregated analyses of content performance

Uso de las Metodologías de Aprendizaje Colaborativo con TIC: Un análisis desde las creencias del profesorado

El objetivo principal de este estudio es analizar las creencias de profesores de enseñanza media o bachillerato de la República Dominicana sobre la metodología basada en el aprendizaje colaborativo mediado por las TIC (CSCL). Se obtuvo una muestra de 542 docentes a los que se aplicó un cuestionario adaptado, compuesto por 33 ítems que miden diferentes aspectos relacionados con el proceso de enseñanza y aprendizaje en entornos de uso de CSCL. Los resultados ponen de manifiesto una alta valoración positiva sobre esta metodología por parte de los docentes, especialmente para mejorar su desarrollo profesional. No perciben, sin embargo, tantos beneficios al emplearla con los estudiantes, ya sea en la mejora del aprendizaje o del proceso de enseñanza en general. También se observan algunas diferencias entre los grupos en función básicamente de la variable años de experiencia docente. La sensibilización del profesorado sobre las ventajas de la metodología CSCL es una asignatura pendiente para la mejora de la calidad educativa; y ello exige el impulso de debates internos en las instituciones escolares por parte de las administraciones educativas, de modo que se promueva el uso de esta metodología en los procesos de enseñanza y aprendizaje y en el desarrollo profesional docente

Alternate-Fueled Combustion-Sector Emissions

In order to meet rapidly growing demand for fuel, as well as address environmental concerns, the aviation industry has been testing alternate fuels for performance and technical usability in commercial and military aircraft. Currently, alternate aviation fuels must satisfy MIL-DTL- 83133F(2008) (military) or ASTM D 7566- Annex(2011) (commercial) standards and are termed drop-in fuel replacements. Fuel blends of up to 50% alternative fuel blended with petroleum (JP-8), which have become a practical alternative, are individually certified on the market. In order to make alternate fuels (and blends) a viable option for aviation, the fuel must be able to perform at a similar or higher level than traditional petroleum fuel. They also attempt to curb harmful emissions, and therefore a truly effective alternate fuel would emit at or under the level of currently used fuel. This paper analyzes data from gaseous and particulate emissions of an aircraft combustor sector. The data were evaluated at various inlet conditions, including variation in pressure and temperature, fuel-to-air ratios, and percent composition of alternate fuel. Traditional JP-8+100 data were taken as a baseline, and blends of JP- 8+100 with synthetic-paraffinic-kerosene (SPK) fuel (Fischer-Tropsch (FT)) were used for comparison. Gaseous and particulate emissions, as well as flame luminosity, were assessed for differences between FT composition of 0%, 50%, and 100%. The data showed that SPK fuel (a FT-derived fuel) had slightly lower harmful gaseous emissions, and smoke number information corroborated the hypothesis that SPK-FT fuels are cleaner burning fuels

Alternate-Fueled Combustor-Sector Emissions

In order to meet rapidly growing demand for fuel, as well as address environmental concerns, the aviation industry has been testing alternate fuels for performance and technical usability in commercial and military aircraft. In order to make alternate fuels (and blends) a viable option for aviation, the fuel must be able to perform at a similar or higher level than traditional petroleum fuel. They also attempt to curb harmful emissions, and therefore a truly effective alternate fuel would emit at or under the level of currently used fuel. This report analyzes data from gaseous and particulate emissions of an aircraft combustor sector. The data were evaluated at various inlet conditions, including variation in pressure and temperature, fuel-to-air ratios, and percent composition of alternate fuel. Traditional JP-8+100 data were taken as a baseline, and blends of JP-8+100 with synthetic-paraffinic-kerosene (SPK) fuel (Fischer-Tropsch (FT)) were used for comparison. Gaseous and particulate emissions, as well as flame luminosity, were assessed for differences between FT composition of 0, 50, and 100 percent. The data show that SPK fuel (an FT-derived fuel) had slightly lower harmful gaseous emissions, and smoke number information corroborated the hypothesis that SPK-FT fuels are cleaner burning fuels