Language Code Research Articles

FEM-based eigenstructure recovery of a space truss with active members

AbstractLarge truss structures have many potential applications in space, such as antennas, telescopes and space solar power plants. In this scenario, a natural concern is the susceptibility of these lightweight structures to be damaged during their operational life, due to impacts, transient thermal states and fatigue phenomena. The inclusion of active elements, equipped with sensor/actuator systems capable of modulating their shape and strength, makes it possible to transform the truss into a smart structure capable of remedying the damage, once it is detected. In this paper, a procedure is described that is capable of restoring at least the basic functionality of a composite truss for space applications, starting with the observation that damage has occurred, regardless of its specific location. The system eigenstructure is used as a benchmark for damage detection, as well as a target characteristic for the subsequent restoration activity. The observer/Kalman filter identification algorithm (OKID), in cascade with the eigensystem realization algorithm (ERA), is adopted to reconstruct, from sensor recordings, the dynamic response of the truss in terms of system state-space representation and eigen-characteristics. Finally, a static output feedback control is developed to recover the low-frequency dynamic behaviour of the truss. The entire procedure is tested using finite element analysis. All activities are coordinated in an innovative procedure that, within a unique Python language code, automatically generates finite element (FE) models, launches finite element analysis (FEA), extracts output data, implements OKID-ERA, processes the control law and applies it to the final FE simulation.

Działania społeczne a proces profilowania świata społecznego. Casus prawicowej i liberalnej interpretacji sytuacji na polsko-białoruskiej granicy

When in the second half of 2021 the situation on the Polish-Belarusian border rapidly inflamed, it became a subject of numerous articles published in major opinion weeklies of different ideological orientations. Their analysis using the profiling concept developed by Jerzy Bartmiński allowed to reveal two alternative profiles of people trying to cross Polish border from Belarus (illegal immigrant versus refugee) and different images of the social world hidden behind them, manifested in ideological (right-wing and liberal) language codes. According to J. Bartmiński’s concept, profiling is reconstructable practically in every text of culture, and the reconstruction of profiling makes it possible to obtain a cognitive definition, i.e. one that allows to present the way the speakers understand the events of the social world, the ways of ordering, categorizing and evaluating them. The reconstructed profiles were used in the analysed texts to generate different action patterns (defense of sovereignty versus helping people in a humanitarian crisis), which, however, have a common area, which is identifying the figure of external enemy and inner traitor.

Some Finite Summation Identities Comprising Binomial Coefficients for Integrals of the Bernstein Polynomials and Their Applications

Certain finite sums, including the Catalan numbers, factorial functions, binomial coefficients, and their computational formulas are of indispensable importance both in probability and statistics applications and in other branches of science. The primary aim of this article is to give the integral representation of the finite sum containing the products of the Bernstein polynomials, given in our article, by applying the Beta function and the Euler gamma functions. Other aims of this paper are to bring to light novel finite sum formulae containing binomial coefficients by analyzing and unifying this integral representation. Finally, some relations among these sums, binomial coefficients, and the Catalan numbers are given. We also give the Wolfram language codes. By applying these codes to the finite sums, we give some numerical values.

Student-teacher communication preferences in Taiwanese older learners

ABSTRACT The role of teacher-student communication in enhancing the learning experience of older learners has received limited attention. This sociolinguistic study addresses this gap by examining the perspectives on this issue held by Taiwanese older learners. Survey data from 231 Taiwanese older learners were analyzed using ANOVA and independent-samples t-tests, with the aim of establishing how their age ranges and occupational backgrounds influenced their concordance teacher communication strategies found in existing literature. While teachers were reported to assume that older learners from elite backgrounds preferred more communication accommodation in class, this study’s results indicated that those with no work experience or who identified as farmers demanded greater respect, flexibility, and diversity of language codes. Age was also significantly correlated with learners’ communication preferences, with those aged 76+ requiring more extensive accommodations than their younger counterparts did.

AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection

Code Clone Detection, which aims to retrieve functionally similar programs from large code bases, has been attracting increasing attention. Modern software often involves a diverse range of programming languages. However, current code clone detection methods are generally limited to only a few popular programming languages due to insufficient annotated data as well as their own model design constraints. To address these issues, we present AdaCCD, a novel cross-lingual adaptation method that can detect cloned codes in a new language without annotations in that language. AdaCCD leverages language-agnostic code representations from pre-trained programming language models and propose an Adaptively Refined Contrastive Learning framework to transfer knowledge from resource-rich languages to resource-poor languages. We evaluate the cross-lingual adaptation results of AdaCCD by constructing a multilingual code clone detection benchmark consisting of 5 programming languages. AdaCCD achieves significant improvements over other baselines, and achieve comparable performance to supervised fine-tuning.

Narrative-Derived Indices of Metacognition among People with Schizophrenia: Associations with Self-Reported and Performance-Based Social Functioning.

Metacognitive functioning-which broadly encompasses the mental processes involved in thinking about the thinking of one's self and the thinking of others-is often impaired among individuals living with schizophrenia and may contribute to difficulties in social and interpersonal functioning. Although the majority of studies assessing metacognition among individuals with schizophrenia use standardized, laboratory-based measurements, an increasing number of studies have measured metacognitive capacity using natural language produced by individuals living with mental illness. At the same time, less is known about how language-derived indices of metacognitive function relate to key social outcomes among people with schizophrenia. The primary objective of this study was to employ a validated language coding system (the Metacognition Assessment Scale, Abbreviated; MAS-A) to assess metacognitive functioning from the spoken life narratives of individuals with schizophrenia (n = 32) and community controls (n = 15). Among individuals with schizophrenia, we also examined the associations between language-derived metacognition and measures of self-reported and performance-based social functioning. Our results suggest that most aspects of metacognition in our sample were not significantly diminished in people with schizophrenia compared to community controls. Unexpectedly, the MAS-A subscale related to one's ability to master psychological difficulties was rated higher among individuals with schizophrenia. Further, our results suggest that among people with schizophrenia, higher metacognitive functioning in the domain of self-reflectivity was associated with poorer self-reported social functioning, while a greater metacognitive awareness of other individuals' minds was associated with better scores on aspects of performance-based social functioning. Collectively, these results underscore the utility of assessing metacognitive functioning via life-story narratives to understand social outcomes and highlight possible aspects of resiliency among individuals who have experienced a serious mental illness.

M5GP: Parallel Multidimensional Genetic Programming with Multidimensional Populations for Symbolic Regression

Machine learning and artificial intelligence are growing in popularity thanks to their ability to produce models that exhibit unprecedented performance in domains that include computer vision, natural language processing and code generation. However, such models tend to be very large and complex and impossible to understand using traditional analysis or human scrutiny. Conversely, Symbolic Regression methods attempt to produce models that are relatively small and (potentially) human-readable. In this domain, Genetic Programming (GP) has proven to be a powerful search strategy that achieves state-of-the-art performance. This paper presents a new GP-based feature transformation method called M5GP, which is hybridized with multiple linear regression to produce linear models, implemented to exploit parallel processing on graphical processing units for efficient computation. M5GP is the most recent variant from a family of feature transformation methods (M2GP, M3GP and M4GP) that have proven to be powerful tools for both classification and regression tasks applied to tabular data. The proposed method was evaluated on SRBench v2.0, the current standard benchmarking suite for Symbolic Regression. Results show that M5GP achieves performance that is competitive with the state-of-the-art, achieving a top-three rank on the most difficult subset of black-box problems. Moreover, it achieves the lowest computation time when compared to other GP-based methods that have similar accuracy scores.

USE OF CANNABIS FOR MEDICINE (JHON FISKE SEMIOTIC ANALYSIS)

Film is an integral part of mass communication, and in its purpose film itself as a message in conveying and offering more than just sound and meaningful images. Recently, the phenomenon of legalizing marijuana has often been discussed and constructed through the media. One of them is the documentary "Musa" by Alexander Sinaga on the YouTube channel LGN_ID TV. The aim of this research is to find out what the struggle of mothers in the use of marijuana for health means. This research uses John Fiske's semiotic analysis and qualitative methodology in a critical perspective. Based on reality patterns with codes of appearance, costume, expression, body movement, and language to the objectives of the research through the pattern of reality. The results of this study show the facts of the three mothers' struggle in the attempt to use marijuana for treatment in the documentary "Musa".

Analysis of hyperbolised phraseology and its cognitive aspect in media literacy

Relevance. The relevance of this study is motivated by a broad public interest in modern media communications, including the use of linguistic means to implement speech strategies and communicative tactics, maximising the impact on the selected audience, and the limited understanding of the functionality of hyperbole-based phraseological expressions in media discourse. Purpose. The purpose of the presented investigation is to comprehend the leading mechanisms of constructing a language paradigm in cognitive terms, linguistic analysis of stable units of speech as means of emotive-evaluation in text, the definition of the main lexical and semantic means, author's intentions and narrativity in media texts. Methodology. The main method employed is a theoretical approach to the study of media literacy in contemporary society, constructing linguistic templates for the perception of reality and its representation in a cognitive manner. Results. This investigation reveals the basic mechanisms of realization of communicative strategies in the media sphere by means of attracting a wide arsenal of linguistic resources, focusing on the functioning of phraseological units built on hyperbole by the example of the national language code (English and Kazakh). Conclusions. The materials presented in this study will contribute to further in-depth study of the frequency of use of such idioms, the functionality of texts using them and the ethical implementation of communication between author and recipient, to analyse the linguistic resources that can and cannot be used in media contexts, to structure the media field depending on the style and targeting, to establish basic rules and criteria to determine the media literacy of speakers of the national speech code. Keywords: phraseological unit; media communication; critical thinking; information space; phraseme; media field

The borders of the borderlands.A case study of the former parish of Usnar in the Grodno region

This article presents a synthesis of the sociolinguistic situation of the Polish–Belarusian borderland, on the territory of the former parish of Usnar. Field studies were conducted systematically in the years 2015–2020 and irregularly in the years 2021–2023. The author employed an original method based on using museum fund. The paper focuses on the influence of the border (the state one, as well as other types of borders) on the sociolinguistic situation of the microarea. Particularly noteworthy is the persistent division into settlements of the petty nobility and villages, along with the corresponding language codes. Keywords: border studies – language codes – methodology of sociolinguistic research – Grodno region.

From ELIZA to ChatGPT: A brief history of chatbots and their evolution

Over the years, chatbots have grown to be used in a variety of industries. From their humble beginnings to their current prominence, chatbots have come a long way. From the earliest chatbot ELIZA in the 1960s to todays popular Chatgpt, chatbot language models, codes, and databases have improved greatly with the advancement of artificial intelligence technology.This paper introduces the development of chatbots through literature review and theoretical analysis. It also analyzes and summarizes the advantages and challenges of chatbots according to the current status of chatbot applications and social needs. Personalized interaction will be an important development direction for chatbots, because providing personalized responses through user data analysis can provide users with a personalized experience, thus increasing user engagement and satisfaction.

M-estimation for common epidemiological measures: introduction and applied examples.

M-estimation is a statistical procedure that is particularly advantageous for some comon epidemiological analyses, including approaches to estimate an adjusted marginal risk contrast (i.e. inverse probability weighting and g-computation) and data fusion. In such settings, maximum likelihood variance estimates are not consistent. Thus, epidemiologists often resort to bootstrap to estimate the variance. In contrast, M-estimation allows for consistent variance estimates in these settings without requiring the computational complexity of the bootstrap. In this paper, we introduce M-estimation and provide four illustrative examples of implementation along with software code in multiple languages. M-estimation is a flexible and computationally efficient estimation procedure that is a powerful addition to the epidemiologist's toolbox.

Functions of Word Play in a Comedy Television Show (Based on Good Omens TV series)

Despite the great interest of scientists in word play, this phenomenon is still insufficiently studied. Many researchers highlight the comic function as the basis for word play. In this study, word play is viewed as the intentional use of a non-standard language code in order to express the inner world of the speaker, which is not always associated with the creation of a comic effect. The subject of this research deals with cases of language play in the Good Omens comedy series. Film discourse can be interpreted as a reflection of the current state of culture and the society, as well as of the language, which became a decisive factor in the choice of research material. The author of the article comes to the conclusion that the comic function does not play a key role in the use of word play in a comedy television series, being exceeded in frequency by such functions as characterological, meaning-forming and expressive. The article contains numerous material from a modern television series, which gives one the ground to realize the relevance of the conclusions.
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Development of A Neural Boosted Model and JSL Code to Identify “Clean” or “Not Clean” Wells - A West Texas Sperry and Oklahoma Woodford Fractured Wells Coiled Tubing Cleaning Case Study

In a previous study, wellbore cleaning coefficient (WCC) correlations for cleaned wellbores out of debris and bridge plug remnants were developed for three conventional coiled tubing sizes (2.375”, 2.625”, and 2.875”). The following key performance indicators (KPIs): (1) slick water density ( ) ρ f , (2) slick water viscosity ( ) µ f , (3) hydraulic diameter c t (d - d ) between casing inner diameter (dc ) and coil tubing outer diameter (dt ), (4) average annular velocity ( ) v and (5) cleaning pressure gradient ∆P across a measured depth (MD) were employed in the empirical models. The models addressed operational conditions under which fractured wells will be identified as whether “clean” or “not clean”. In this study, the database from 150 wells, in the Spraberry formation in West Texas, was used to develop a predictive model to identify status of cleaned fractured wells: whether “clean” or “not clean”? About 70% of the data (99 wells) was used for training and about 30% (51 wells) for validation. 14 wells from the liquids-rich shale Woodford formation (Oklahoma) were utilized for testing. Six predictive modeling tools were designed to validate the derived empirical correlations. These tools are (1) Fit Stepwise, (2) Neural Boosted, (3) Boosted Tree, (4) Decision Tree (Partition), (5) Generalized Regression Lasso, and K-Nearest Neighbors. In the predictive models, independent variables are the annular velocity (AV), the Reynolds’ Number (Re), the Euler’s Number (Eu), and the coiled tubing roughness to internal radius ratio (ε/D). The dependent variable is well status; “clean” or “not clean”. Jump Scripting Language (JSL) code was used to develop user-friendly software. The software would be utilized to identify the fractured wellbore status, whether “clean” or “not clean”. Operators would be able to use the code to identify working conditions for which completed fractured wells are “clean” out of fracturing debris and remnants of bridge plugs or “not clean”. Input parameters to the code are AV, Re, Eu, and ε/D

Linguistic Codes for Security and Social Stability in Urhoboland: Implications for the Translator

This paper posits that every language is characterized by linguistic codes needed by speakers for their security, identity, and social stability in today’s doubtless several security issues. In Urhoboland, such codes manifest in spoken and extra-linguistic forms with varying degree of meanings to different people at various times and places. The paper explores, among other conceivable objectives, some salient linguistic codes which the forebears of the Urhobo people did not pass on the younger generations as well as their translatability. The theoretical framework underlying the discussion hinges on Bernstein (1971) sociolinguistic theory of language codes which is cognate with language use. Taking cognizance of Urhobo believe systems, their social-cultural and religious practices continue to exert considerable challenges in translation studies as well as their importance to the people in contemporary times. Moreover, a large corpus of interviews and participant observation methods subsist in gathering and analysing relevant data for the research. Finally, the paper concludes that all linguistic codes in Urhoboland manifest themselves in socio-cultural contexts through which the people gather experiences for concentration, attention, and as a panacea for staying out of trouble

Numerical Simulation of Heat Pipe Thermal Performance for Aerospace Cooling System Applications

The design of integrated and highly efficient solutions for thermal management is a key capability for different aerospace products, ranging from civil aircraft using hydrogen on board to miniaturized satellites. In particular, this paper discloses a novel numerical tool for the design and thermal performance assessment of heat pipes. To achieve this goal, a numerical Ansys Parametric Design Language code is set up to verify the effective subtractive heat flux guaranteed by the selected heat pipe arrangement. The methodology and related tool show their ability to provide good thermal performance estimates for different heat pipe designs and operating conditions. Specifically, the paper reports two very different test cases: (1) solid metal heat pipes to cool down the crotch leading-edge area of the air intake of a Mach 8 civil passenger aircraft, and (2) a copper-water heat pipe to cool down a Printed Circuit Board of a generic small LEO satellite. The successful application of the methodology and numerical code confirms the achievement of the ambitious goal of developing in-house tools to support heat pipe thermal performance prediction for the entire aerospace domain.

Use of mixed Indonesian-Patani language codes in oral form by Patani (South of Thailand) pupils in Medan City

This research examines code mixing among Patani students in Medan City. The study aims to describe, analyze, interpret, and conclude data on the use of Indonesian-Patani code mixing in oral form by Patani students from southern Thailand in Medan City, focusing on the lexicon form of code mixing. This research aims to investigate code mixing in the interactions of Patani students in the city of Medan. The data was collected using recording and note-taking techniques. The transcribed data was classified, analyzed, and interpreted to draw conclusions. Data analysis results indicate that Patani students in Medan City code-mix verbs, adjectives, nouns, pronouns, numerals, adverbs, and task words. In the research, the causes of code-mixing are divided into four categories: (i) a continued focus on the mother tongue, especially Patani Malay, (ii) difficulty in interacting with an environment that uses both Indonesian and regional languages, (iii) a lack of understanding of Indonesian, resulting in incorrect pronunciation, and (iv) difficulty in understanding new Indonesian vocabulary, resulting in the combination of familiar languages.

Multilevel Ordinal Logit Models: A Proportional Odds Application Using Data from Brazilian Higher Education Institutions

This tutorial delves into the application of proportional odds-type ordinal logistic regression to assess the impact of incorporating both fixed and random effects when predicting the rankings of Brazilian universities in a well-established international academic assessment utilizing authentic data. In addition to offering valuable insights into the estimation of ordinal logistic models, this study underscores the significance of integrating random effects into the analysis and addresses the potential pitfalls associated with the inappropriate treatment of phenomena exhibiting categorical ordinal characteristics. Furthermore, we have made the R language code and dataset available as supplementary resources for the replication.

ChatGPT: A Breakthrough in Developing Human-Like Robots with Natural Language Capabilities

Robotic systems often require engineers to write code to specify the desired behaviour of the robots. This process is slow, costly, and inefficient, as it involves multiple iterations and manual tuning. ChatGPT is a tool that leverages a large language model (LLM) to enable natural language interaction, code generation, and learning from feedback for robotic applications. ChatGPT allows users, who may not have technical expertise, to provide high-level instructions and feedback to the LLM, while observing the robot's performance. ChatGPT can produce code for various scenarios of robots, using the LLM's knowledge to control different robotic factors. ChatGPT can also be integrated with other platforms, such as Snapchat and Duolingo, to enhance the user experience and management. ChatGPT is a novel tool that facilitates a new paradigm in robotics, where users can communicate with and teach robots using natural language. Keywords: ChatGPT, Large Language Model, Natural Language Processing, Human Robot Interaction

Query-oriented two-stage attention-based model for code search

Applying code search models to search through a large-scale codebase can significantly contribute to developers finding and reusing existing code. Researchers have applied deep learning (DL) techniques to code search models, which first compute deeper semantics representation for query and candidate code snippets, and then rank code snippets. However, these models do not well deeply analyze the semantics gap (i.e., the difference and correlation between queries written in natural language and code in programming languages), or suitably apply the correlation to the code search task. Moreover, most DL-based models use complex networks, slowing down code search tasks.To build the correlation of two languages, and apply the correlation well to code search task, we propose a query-oriented code search model named QobCS. QobCS leverage two attention-based stages, which are simple and quick, and the cooperation of the two stages bridges the semantic gap between code and query. Stage1 learns deeper semantics representation for code and query. Stage2 applies their deeper semantic correlation and query’s intention to learn better code representation.We evaluated QobCS on two datasets. On dataset1/dataset2 with 485k/542k code snippets, QobCS achieves the MRRs of 0.701/0.595, outperforming DL-based code search models DeepCS, CARLCS-CNN, UNIF, and our prior study TabCS. For efficiency, our model shows desirable performances on both datasets compared to DL-based models.