Pattern Language Research Articles

‘These results are inconsistent’

Abstract Shell nouns (SNs; e.g., fact and problem) are an open group of abstract nouns defined functionally through use as emergent ‘shells’ referencing and labeling ideas in surrounding discourse. This paper analyzes the ‘this/these + [SN]’ pattern in second language (L2) English Master’s theses and published English research articles (RAs) across three Engineering disciplines (Electrical, Mechanical, Chemical), with a secondary focus on unattended ‘this/these’ use and disciplinary variation. The corpus includes 60 RAs per discipline (840,683 words) and 25 Master’s theses per discipline (899,182 words). Corpus methods were used to support manual identification of ‘this/these + [SN]’. Results show that L2 English Master’s thesis writers used this pattern significantly less than writers of RAs. Normalized frequencies, frequent SNs, and functional patterns are also presented across genres and disciplines. L2 writers and experts use a similar range of SN types, and expert writers adopt a more rhetorically sophisticated means of organizing information.

The Embodiment of National Image in Ceramic Products with China and European Countries Exchange and Dissemination Case Study: Turkish Coffee Cups

Culture is a broad concept encompassing material and spiritual aspects acquired through socialization and experience. It includes language, beliefs, ethical standards, and consumption patterns, uniting people within a community and significantly influencing their behavior. Culture plays an important role in design and influences the design process according to ever-changing behavior patterns. Product design, a subset of the design field, provides a tangible platform for expressing cultural knowledge. Consequently, product design is a significant medium for illustrating the impact of cultural influences. The interaction between different cultures further shapes design fields, leading to the development of products that may acquire new characteristics or undergo adaptations based on the specific geographical context in which they are situated. Especially when the field of gastronomy, and food and beverage utensils is examined, it is known that the ceramic tools used are in new design relationships in this context. Traditional Turkish coffee culture encompasses the convergence of Chinese, Turkish, and European influences in product design today, exemplified by the use of Turkish ceramic coffee cups. The purpose of this study is to illustrate how national images and a country’s national identity are constructed and expressed in the designs of its ceramic products from the perspective of material culture studies, from past to present, starting from the Chinese, Ottoman Empire and Europe interaction, how shaped today’s ceramic Turkish coffee cups of today and also new possibilities that can process around this area.

Parallel Pattern Compiler for Automatic Global Optimizations

High-performance computing (HPC) systems enable scientific advances through simulation and data processing. The heterogeneity in HPC hardware and software increases the application complexity and reduces its maintainability and productivity. This work proposes a prototype implementation for a parallel pattern-based source-to-source compiler to address these challenges. The prototype limits the complexity of parallelism and heterogeneous architectures to parallel patterns that are optimized towards a given target architecture. By applying high-level optimizations and a mapping between parallel patterns and execution units during compile time, portability between systems is achieved. The compiler can address architectures with shared memory, distributed memory, and accelerator offloading.The approach shows speedups for seven of the nine supported Rodinia benchmarks, reaching speedups of up to twelve times. Porting LULESH to the Parallel Pattern Language (PPL) shows a compression of code size by 65% (3.4 thousand lines of code) through a more concise expression and a higher level of abstraction. The tool’s limitations include dynamic algorithms that are challenging to analyze statically and overheads during the compile time optimization. This paper is an extended version of a previous PMAM publication (Schmitz et al., 2024).

Using natural language processing approaches to characterize professional experiences of child welfare workers

High turnover rates among child welfare workers, including caseworkers and supervisors, are associated with negative attitudes and perceptions about work (NAPW). Our study aimed to (a) explore how child welfare workers’ demographic characteristics, professional attributes, and their work narratives are related to NAPW and (b) extract themes from these narratives to understand their professional needs and concerns. In 2022, a statewide online survey was administered to active child welfare caseworkers and supervisors in a large Midwestern state. This survey, combining standardized scales and an open-ended question about work experiences, yielded numeric (N = 551 survey respondents) and text data (N = 2,193 words). We analyzed the numeric data using general linear modeling and the text data using natural language processing (NLP) techniques. Results showed that workers associated with higher NAPW were female, younger, from racial and ethnic minority backgrounds, less tenured, and more likely to be employed in public agencies and to work longer hours. They are predominantly caseworkers rather than supervisors. Worker role and agency affiliation had the largest effect sizes. NLP analysis showed that workers with more NAPW frequently used third-person pronouns and emotionally negative language, suggesting a disconnection from colleagues and job dissatisfaction. Caseworkers often used first-person singular pronouns, indicating feelings of isolation. Two key themes emerged: a) the need for better agency support during home visits and b) concerns over traumatic experiences and ineffective trauma-informed practices. The findings suggest that future interventions should prioritize the well-being of caseworkers in public agencies and consider language patterns in workers’ narratives to understand their vulnerability to NAPW.

Engagement, Acceptability, and Effectiveness of the Self-Care and Coach-Supported Versions of the Vira Digital Behavior Change Platform Among Young Adults at Risk for Depression and Obesity: Pilot Randomized Controlled Trial.

Adolescence and early adulthood are pivotal stages for the onset of mental health disorders and the development of health behaviors. Digital behavioral activation interventions, with or without coaching support, hold promise for addressing risk factors for both mental and physical health problems by offering scalable approaches to expand access to evidence-based mental health support. This 2-arm pilot randomized controlled trial evaluated 2 versions of a digital behavioral health product, Vira (Ksana Health Inc), for their feasibility, acceptability, and preliminary effectiveness in improving mental health in young adults with depressive symptoms and obesity risk factors. A total of 73 participants recruited throughout the United States were randomly assigned to use Vira either as a self-guided product (Vira Self-Care) or with support from a health coach (Vira+Coaching) for 12 weeks. The Vira smartphone app used passive sensing of behavioral data related to mental health and obesity risk factors (ie, activity, sleep, mobility, and language patterns) and offered users personalized insights into patterns of behavior associated with their daily mood. Participants completed self-reported outcome measures at baseline and follow-up (12 weeks). All study procedures were completed via digital communications. Both versions of Vira showed strong user engagement, acceptability, and evidence of effectiveness in improving mental health and stress. However, users receiving coaching exhibited more sustained engagement with the platform and reported greater reductions in depression (Cohen d=0.45, 95% CI 0.10-0.82) and anxiety (Cohen d=0.50, 95% CI 0.13-0.86) compared to self-care users. Both interventions also resulted in reduced stress (Vira+Coaching: Cohen d=-1.05, 95% CI -1.57 to --0.50; Vira Self-Care: Cohen d=-0.78, 95% CI -1.33 to -0.23) and were perceived as useful and easy to use. Coached users also reported reductions in sleep-related impairment (Cohen d=-0.51, 95% CI -1.00 to -0.01). Moreover, participants increased their motivation for and confidence in making behavioral changes, with greater improvements in confidence among coached users. An app-based intervention using passive mobile sensing to track behavior and deliver personalized insights into behavior-mood associations demonstrated feasibility, acceptability, and preliminary effectiveness for reducing depressive symptoms and other mental health problems in young adults. Future directions include (1) optimizing the interventions, (2) conducting a fully powered trial that includes an active control condition, and (3) testing mediators and moderators of outcome effects. ClinicalTrials.gov NCT05638516; https://clinicaltrials.gov/study/NCT05638516.

The Analysis of the Architectural Languages of Herzog & de Meuron via Regionalism, Materials, and Patterns

In contemporary architecture, architects have been pursuing architectural languages to express various experiences and impressions visually, psychologically, and physically. The analysis of the architectural language of Herzog and de Meuron aims to divide their language into elements that can show architectural characteristics such as materials, patterns, and forms. The objective of this paper is to understand the flow and trend of contemporary architecture through the analysis of examples of works applying their experimental architectural language. As a methodology, there is a synchronous analysis of the architectural characteristics of German-speaking architects in Switzerland and their works. Diachronically, the era of Herzog and de Meuron's architecture spanned the 1980s, 1990s, and 2000s. The result showed that first, in terms of materials, ordinary materials that we use daily, such as concrete, glass, and wood, were used to express the building more richly and energetically. However, they did not experiment with new things but sought to find hidden inner qualities in these familiar materials. Second, they used a pattern language with formative meaning by applying various studies and experiments. Natural images, not sensuous and stimulating, were planned to fit the regional and spatial purpose. Third, in terms of form, it can be seen that they thoroughly considered the external environment and influence, functionality, harmonious arrangement, and variability.

EXPRESS: Who Shares Fake News? Uncovering Insights from Social Media Users' Post Histories

We propose that social-media users’ own post histories are an underused yet valuable resource for studying fake-news sharing. By extracting textual cues from their prior posts, and contrasting their prevalence against random social-media users and others (e.g., those with similar socio-demographics, political news-sharers, and fact-check sharers), researchers can identify cues that distinguish fake-news sharers, predict those most likely to share fake news, and identify promising constructs to build interventions. Our research includes studies along these lines. In Study 1, we explore the distinctive language patterns of fake-news sharers, highlighting elements such as their higher use of anger and power-related words. In Study 2, we show that adding textual cues into predictive models enhances their accuracy in predicting fake-news sharers. In Study 3, we explore the contrasting role of trait and situational anger, and show trait anger is associated with a greater propensity to share both true and fake news. In Study 4, we introduce a way to authenticate Twitter accounts in surveys, before using it to explore how crafting an ad copy that resonates with users’ sense of power encourages the adoption of fact-checking tools. We hope to encourage the use of novel research methods for marketers and misinformation researchers.

A Space of Choice: Exploring New Patterns of Common Student Spaces

The paper describes a project conducted by a group of architecture students at the Cracow University of Technology. The research problem focused on common spaces in the learning environment, employing the theoretical framework of the sociology of architecture and the participatory design methodology. The project aimed to propose current design model solutions for inclusive and universally-accessible student places, reflecting on the initial studies and the design workshop experiences. The socio-spatial patterns proposed in this study were inspired by the concept of a pattern language developed by the team led by Christopher Alexander in the 1970s. In contrast to the original patterns, the new proposals for patterns of common student spaces are not statements, but questions. The answers to these questions involve different relationships between individuals (users) and the built environment, including those connecting users to the architectural work, the natural environment, other individuals, or user groups.

Urban Language Practices in a UNESCO World Heritage Site: A Case Study of George Town, Penang

The case of multilingual and multicultural George Town World Heritage Site (GTWHS) illustrates that language practices among its urban community are varied across different settings and interlocutors. The study aims to identify current language practices in GTWHS, particularly those influenced by urban space and repertoires. Using a mixed-method research design, the study included a sample size of 156 respondents and covered areas in both the Core and Buffer Zones of George Town, Penang. Data analysis then comprises discussions on language repertoires of the interlocutors within the domains of the respective social interactions. Findings of the study show that the use of English, Malay, Mandarin and code-switching are several of the patterns of language use that can be observed in the city. The ability to style shift among the respondents is also indicative of language patterns that interact with both the physical and social settings that they belong to. As a result, these language practices can be fluid in order to facilitate the needs of the communicative activities as the interlocutors relocate themselves in different spaces or domains within the George Town city. More importantly, the study highlights heterogenous populace, linguistic repertoire and cultural practices as determinants that further characterise the overall cityscape of urban social environment and its speech community.

TV방송과 유튜브의 상대 높임 사용 양상 비교 - 토론 매체에서의 ‘합쇼체/해요체/해체’를 중심으로

This study aims to compare and analyze the usage patterns of relative honorifics in discussion programs on TV broadcasts and individual media platforms like YouTube. The goal is to identify the characteristics of honorific usage in both media and to present various strategic approaches to relative honorifics observed in these contexts. To achieve this, the study will examine actual utterance data from discussion programs on TV broadcasts and YouTube to explore the patterns of honorific usage. Previous research on the usage of honorifics in news media has mostly focused on TV broadcasts, often based on the formal and ritualistic nature of broadcast language. However, with the advent of smartphones and the easy accessibility of YouTube, informal language patterns have become more prevalent. YouTube allows participants to speak freely without the constraints of formal broadcast language, creating new social relationships based on familiarity with the audience. Therefore, this study seeks to provide foundational data on changes in the use of honorifics by comparing the usage patterns observed on YouTube with those in TV broadcast discussion programs. This comparison aims to offer insights into the evolving patterns of honorific usage in broadcast media.

The Role of L1 and Age Effect in the Acquisition of EnglishArticles among Croatian Young EFL Learners

The category of definiteness/indefiniteness is often seen as a linguistic universal (Chomsky 2000; Silić 2000) that exists in every human language but is expressed with different linguistic means and patterns in different languages. In English, this category is grammaticalized primarily via articles (Zergollern-Miletić 2008). The Croatian language does not contain an article system, and acquiring English articles represents a major problem for most learners of English as a foreign language (Balenović and Medved Krajnović 2013; Zergollern-Miletić 2015). The present paper investigates difficulties in acquiring English articles among young learners in Croatia in written tasks. Pupils involved in the research started learning English in the first grade of primary school. A total of 76 primary school learners from six different classes (schools) and different age groups were included in this research. The initial hypothesis is that the correct use of articles increases as learners’ overall L2 proficiency increases due to exposure to instructed EFL learning. A qualitative analysis was carried out using error analysis, i.e., proper/wrong use, or omission of the article. The findings partially confirmed the initial hypothesis and showed that our participants were becoming aware of the existence of English articles (they comprehended that articles should precede nouns), but we found very frequent article omission, which, according to our research results, indicates that the process of developing and understanding the semantic and pragmatic role of English articles is long and fluctuating specifically with learners whose L1 lacks the article system.

Specialized Knowledge, Text-Based Discussion, and Preparing Elementary Teachers for Disciplinary Literacy in English Language Arts Classrooms

ABSTRACT Text-based discussion is a form of disciplinary literacy that engages students in collaboratively building understanding of texts. It is a form of instruction that benefits students’ reading comprehension, but is rarely used in classrooms, despite its potential. This rarity may be because facilitating discussion requires teachers to have specialized knowledge of text. Specifically, teachers must understand how authors use discipline-specific text features and language patterns to convey meaning and how readers notice and evaluate these aspects of texts as they build textual understandings. This mixed methods study adopts a convergent-parallel design to investigate the effects of a 14-week intervention on pre-service teachers’ (PSTs) acquisition of specialized knowledge and its application to text-based discussions conducted with students in field placements. Findings suggest that the intervention improved PSTs’ specialized knowledge and that teachers felt more prepared for their discussions after applying their knowledge when planning discussions. However, even at the end of the intervention, PSTs continued to demonstrate gaps in their specialized knowledge, which influenced their ability to lead text-based discussions. The findings have implications for teacher educators attempting to prepare novice teachers for text-based discussions.

Extraction of Substance Use Information From Clinical Notes: Generative Pretrained Transformer-Based Investigation.

Understanding the multifaceted nature of health outcomes requires a comprehensive examination of the social, economic, and environmental determinants that shape individual well-being. Among these determinants, behavioral factors play a crucial role, particularly the consumption patterns of psychoactive substances, which have important implications on public health. The Global Burden of Disease Study shows a growing impact in disability-adjusted life years due to substance use. The successful identification of patients' substance use information equips clinical care teams to address substance-related issues more effectively, enabling targeted support and ultimately improving patient outcomes. Traditional natural language processing methods face limitations in accurately parsing diverse clinical language associated with substance use. Large language models offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of the generative pretrained transformer (GPT) model in specific GPT-3.5 for extracting tobacco, alcohol, and substance use information from patient discharge summaries in zero-shot and few-shot learning settings. This study contributes to the evolving landscape of health care informatics by showcasing the potential of advanced language models in extracting nuanced information critical for enhancing patient care. The main data source for analysis in this paper is Medical Information Mart for Intensive Care III data set. Among all notes in this data set, we focused on discharge summaries. Prompt engineering was undertaken, involving an iterative exploration of diverse prompts. Leveraging carefully curated examples and refined prompts, we investigate the model's proficiency through zero-shot as well as few-shot prompting strategies. Results show GPT's varying effectiveness in identifying mentions of tobacco, alcohol, and substance use across learning scenarios. Zero-shot learning showed high accuracy in identifying substance use, whereas few-shot learning reduced accuracy but improved in identifying substance use status, enhancing recall and F1-score at the expense of lower precision. Excellence of zero-shot learning in precisely extracting text span mentioning substance use demonstrates its effectiveness in situations in which comprehensive recall is important. Conversely, few-shot learning offers advantages when accurately determining the status of substance use is the primary focus, even if it involves a trade-off in precision. The results contribute to enhancement of early detection and intervention strategies, tailor treatment plans with greater precision, and ultimately, contribute to a holistic understanding of patient health profiles. By integrating these artificial intelligence-driven methods into electronic health record systems, clinicians can gain immediate, comprehensive insights into substance use that results in shaping interventions that are not only timely but also more personalized and effective.

Guardians of the Circassian Heritage Language: Exploring a Teacher’s Agency in the Endeavour of Endangered Language Maintenance

The Circassian language is the heritage language (HL) of a small minority group in Israel. Since its classification as an endangered language around the globe, the focus has been put on effective maintenance programs. Changes in education methods within Circassian communities were inevitable. In Israel, the Circassian Maintenance Program is crucial in primary schools, where formal literacy instruction in the Circassian language begins in fifth grade. This study examined the agentic role of the Circassian literacy teacher in engaging students in learning their heritage language and its maintenance, as expressed in her attitudes, beliefs and knowledge and her classroom strategies. This three-month-long ethnographic data collection was conducted at the teacher’s study centre “Nal” (i.e., a treasure in Circassian). Data was gathered through classroom observations and semi-structured interviews with a Circassian HL teacher to understand her attitudes, beliefs and knowledge and to identify strategies to encourage student engagement in HL learning. In general, the teacher showed expertise in the HL and authentic use of it, as well as cultural and religious competence, adaptability, and flexibility. In addition, she encouraged children’s agency and autonomy and provided individualised support, integration of technology and reflective practice. The results indicated that a sense of identity, unity and belonging to the community are all held together by the pillar of HL knowledge. By engaging her students in translanguaging practices, the teacher promoted their awareness of language patterns, vocabulary and grammar in both Circassian and other languages of their wide linguistic repertoire (Hebrew, Arabic and English).

Patterns of language

ABSTRACT Neuroimaging studies have increasingly leveraged the information in multivariate patterns of brain activity, transitioning from voxel-by-voxel activation comparisons to multi-voxel pattern analysis (MVPA) and decoding approaches. Representational Similarity Analysis (RSA) has further advanced this field, enabling comparisons of representational structures across neuroimaging modalities and computational models, in particular in fMRI and EEG/MEG research. Recent applications have extended to brain connectivity estimation, enhancing our understanding of neural representations in language and cognition. This special issue, “Patterns Of Language”, showcases recent applications and methodological developments of multivariate analysis approaches in the neuroscience of language.

Am I Hurt?: Evaluating Psychological Pain Detection in Hindi Text Using Transformer-based Models

The automated evaluation of pain is critical for developing effective pain management approaches that seek to alleviate pain while preserving patients’ functioning. Transformer-based models can aid in detecting pain from Hindi text data gathered from social media by leveraging their ability to capture complex language patterns and contextual information. By understanding the nuances and context of Hindi text, transformer models can effectively identify linguistic cues and sentiments and expressions associated with pain, enabling the detection and analysis of pain-related content present in social media posts. The purpose of this research is to analyze the feasibility of utilizing NLP techniques to automatically identify pain within Hindi textual data, providing a valuable tool for pain assessment in Hindi-speaking populations. The research showcases the HindiPainNet model, a deep neural network that employs the IndicBERT model, classifying the dataset into two class labels {pain, no_pain} for detecting pain in Hindi textual data. The model is trained and tested using a novel dataset, दर्द-ए-शायरी (pronounced as Dard-e-Shayari ), curated using posts from social media platforms. The results demonstrate the model's effectiveness, achieving an accuracy of 70.5%. This pioneer research highlights the potential of utilizing textual data from diverse sources to identify and understand pain experiences based on psychosocial factors. This research could pave the path for the development of automated pain assessment tools that help medical professionals comprehend and treat pain in Hindi-speaking populations. Additionally, it opens avenues to conduct further NLP-based multilingual pain detection research, addressing the needs of diverse language communities.

Natural Language Processing Pretraining Language Model for Computer Intelligent Recognition Technology

Computer intelligent recognition technology refers to the use of computer vision, Natural Language Processing (NLP), machine learning and other technologies to enable computers to recognize, analyze, understand and answer human language and behavior. The common applications of computer intelligent recognition technology include image recognition, NLP, face recognition, target tracking, and other fields. NLP is a field of computer science, which involves the interaction between computers and natural languages. NLP technology can be used to process, analyze and generate natural language data, such as text, voice and image. Common NLP technology applications include language translation, emotion analysis, text classification, speech recognition and question answering system. Language model is a machine learning model, which uses a large number of text data for training to learn language patterns and relationships in text data. Although the language model has made great progress in the past few years, it still faces some challenges, including: poor semantic understanding, confusion in multilingual processing, slow language processing and other shortcomings. Therefore, in order to optimize these shortcomings, this article would study the pre-training language model based on NLP technology, which aimed at using NLP technology to optimize and improve the performance of the language model, thus optimizing the computer intelligent recognition technology. The model had a higher language understanding ability and more accurate prediction ability. In addition, the model could learn language rules and structures by using a large number of corpus, so as to better understand natural language. Through experiments, it could be known that the data size and total computing time of the traditional Generative Pretrained Transformer-2 (GPT-2) language model were 10 GB and 97 hours respectively. The data size and total computing time of BERT (Bidirectional Encoder Representations from Transformer) were 12 GB and 86 hours respectively. The data size and total computing time of the pre-training language model based on NLP were 18 GB and 71 hours respectively. Obviously, the pre-training language model based on NLP had a larger data size and shorter computing time. The experimental results showed that the NLP technology could better optimize the language model and effectively improve its various capabilities. This article opened up a new development direction for computer intelligent recognition technology and provided excellent technical support for the development of language models.

MosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI.

Generative pretrained models represent a significant advancement in natural language processing and computer vision, which can generate coherent and contextually relevant content based on the pre-training on large general datasets and fine-tune for specific tasks. Building foundation models using large scale omic data is promising to decode and understand the complex signaling language patterns within cells. Different from existing foundation models of omic data, we build a foundation model, mosGraphGPT, for multi-omic signaling (mos) graphs, in which the multi-omic data was integrated and interpreted using a multi-level signaling graph. The model was pretrained using multi-omic data of cancers in The Cancer Genome Atlas (TCGA), and fine-turned for multi-omic data of Alzheimer's Disease (AD). The experimental evaluation results showed that the model can not only improve the disease classification accuracy, but also is interpretable by uncovering disease targets and signaling interactions. And the model code are uploaded via GitHub with link: https://github.com/mosGraph/mosGraphGPT.

The attitudes of Arabic language learners towards learning Arabic for specific purposes, Kanar 1 course, Language Institute, International University of Africa (IUA), Sudan

Many educational institutions have introduced Arabic language for specific purpose courses in order to help learners master the terminologies and language patterns used in specific fields. This study aims to identify the attitudes and needs of Arabic language learners who will enroll in Arabic language course for the first stage organized by Language Institute, International University of Africa (IUA), Sudan. This study relied on the descriptive and inductive approaches to find out the attitudes and needs of Arabic language the participants in this course 2023/2024. It is known that many learners have an attachment to the Arabic language that goes beyond religious purposes to professional ones such as economics and culture. For all this, it is necessary to design curriculum that pay attention to these directions and the objectives that will be demonstrated by the participants of this course. On the other hand the study aims to find out how to improve the effectiveness of Arabic for specific purpose to help learners gain academic and career advantages in the long run.

Scoping review on natural language processing applications in counselling and psychotherapy.

Recent years have witnessed some rapid and tremendous progress in natural language processing (NLP) techniques that are used to analyse text data. This study endeavours to offer an up-to-date review of NLP applications by examining their use in counselling and psychotherapy from 1990 to 2021. The purpose of this scoping review is to identify trends, advancements, challenges and limitations of these applications. Among the 41 papers included in this review, 4 primary study purposes were identified: (1) developing automated coding; (2) predicting outcomes; (3) monitoring counselling sessions; and (4) investigating language patterns. Our findings showed a growing trend in the number of papers utilizing advanced machine learning methods, particularly neural networks. Unfortunately, only a third of the articles addressed the issues of bias and generalizability. Our findings provided a timely systematic update, shedding light on concerns related to bias, generalizability and validity in the context of NLP applications in counselling and psychotherapy.