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Professional Learning Communities (PLCs) assume a vital role in promoting teacher growth and development, while the presence of intrinsic motivation (IM) in the work environment profoundly impacts teacher performance. Unveiling the interconnections between teachers' engagement in PLCs and their intrinsic motivation holds the potential to enhance the overall well-being of both teachers and students. This study used a mixed-methods approach to investigate the relationship between PLCs and IM of Chinese Teachers of Foreign Languages (CTFL) in higher education. The questionnaire survey and semi-structured interviews were conducted among 198 teachers in 19 provinces of China. The quantitative data were analyzed by means of descriptive analysis and regression analyses with SPSS 22.0 and the qualitative data were processed by following thematic analysis. Results demonstrated that the participants' perceptions on their PLCs and IM were at a moderately high level, with mean scores at 3.56 and 3.86 on a 5-point Likert scale, respectively. Regression analyses revealed a significant positive correlation between PLCs and IM. Specifically, Shared Vision (SV), Supportive Environment (SE) and Collaborative Learning (CL) in PLCs were positively correlated with Willingness to Take on Challenges (WTC) and Love for Work (LW) in IM. The qualitative findings highlighted the significance of supportive environment and teacher's work engagement in affecting teacher's perceptions on PLCs. Job Characteristic of foreign language teacher, along with significant others might also influenced teachers' intrinsic motivation. Implications for PLCs leadership and foreign language teachers are discussed.

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Two views contend to account for the processes at play during the early stages of visual word recognition. The first holds that these stages are not modulated by the idiosyncratic properties of different languages. The second maintains that the structural properties of the language determine the weighting of the different domains of linguistic knowledge (e.g., orthographic and morphological domains may be differentially weighted across languages). To explore this question, we focused on orthographic priming in Arabic. In this Semitic language, lexical representations are claimed to be based on morphological similarity, with little or no role for orthographic similarity. We conducted two masked priming experiments using the yes-no and go/no-go versions of the lexical decision task to determine if Arabic target words (e.g., مدير 'mudyr', director) are facilitated by nonword primes that are orthographically but not morphologically related (i.e., pairs share neither a root nor a word pattern; e.g.,ماير 'maAyr') relative to unrelated primes. Results showed faster responses for the orthographically related target words than for the unrelated target words in the two experiments. These findings favor the view that the early phases of visual word processing in Semitic and Indo-European languages are fundamentally the same.

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Cybercriminals have become an imperative threat because they target the most valuable resource on earth, data. Organizations prepare against cyber attacks by creating Cyber Security Incident Response Teams (CSIRTs) that use various technologies to monitor and detect threats and to help perform forensics on machines and networks. Testing the limits of defense technologies and the skill of a CSIRT can be performed through adversary emulation performed by so-called "red teams". The red team's work is primarily manual and requires high skill. We propose SpecRep, a system to ease the testing of the detection capabilities of defenses in complex, heterogeneous infrastructures. SpecRep uses previously known attack specifications to construct attack scenarios based on attacker objectives instead of the traditional attack graphs or a list of actions. We create a metalanguage to describe objectives to be achieved in an attack together with a compiler that can build multiple attack scenarios that achieve the objectives. We use text processing tools aided by large language models to extract information from freely available white papers and convert them to plausible attack specifications that can then be emulated by SpecRep. We show how our system can emulate attacks against a smart home, a large enterprise, and an industrial control system.

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The present paper seeks to explore the contextual factors shaping the emotional labour experiences of secondary school teachers and explain the ways these educators manage their emotions. Data were generated through a series of 20 in-depth, semi-structured interviews with modern language (ML) teachers in the UK. The findings showed that teachers experienced primarily negative forms of emotional labour and these experiences were driven by five interrelated contextual factors: the lack of institutional support, heavy workload, low perceived status of MLs, students' lack of motivation, and classroom misbehaviour. To manage their emotions, the study reveals that teachers used a wide range of coping mechanisms such as suppression, venting, social support, positive reframing, and the development of positive student-teacher relationships. In light of our results, we call for the emotional dimension of teaching to be better integrated into training programmes, an improvement in working conditions and better support mechanisms for teachers.

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Precision public healthcare has been applied to bring about positive change, narrowing the gap in healthcare inequity for Aboriginal peoples. Three such examples include the Mappa, Lyfe Languages, and Pilbra Faces projects, which were all developed through engagement and codesign with Indigenous Australians and each meet a distinct critical need. The Mappa project offers patients and healthcare providers with the necessary geographical information to navigate and maximally utilize available healthcare services. Lyfe Languages is a community driven translational tool that empowers indigenous languages in healthcare. The Pilbara Faces project aims to create a database of clinical measurements enabling better disease diagnosis and monitoring. These three projects have been integrated into a multi-faceted precision public health program, the Healthy Pilbara Project Initiative, acting synergistically to improve the lives of Aboriginal peoples living in Western Australia.

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OBJECTIVE: Given the increasing proportion of patients and caregivers who use languages other than English (LOE) at our institution and across the U.S, we evaluated key workflow and outcome measures in our emergency department (ED) for patients and caregivers who use LOE. METHODS: This was a retrospective, cross-sectional study of patients and caregivers who presented to a free-standing urban pediatric facility. We used electronic health record data (EHR) and interpreter usage log data for our analysis of language documentation, length of stay, and ED revisits. We assessed ED revisits within 72-h using a multivariable logistic regression model adjusting for whether a primary care provider (PCP) was listed in the EHR, whether discharge was close to or on the weekend, and insurance status. We restricted our analysis to low-acuity patient encounters (Emergency Severity Index (ESI) scores of 4 and 5) to limit confounding factors related to higher ESI scores. RESULTS: We found that one in five patients and caregivers who use LOE had incorrect documentation of their language needs in the EHR. Using interpreter usage data to most accurately capture encounters using LOE, we found that patient encounters using LOE had a 38-min longer length of stay (LOS) and twice the odds of a 72-h ED revisit compared to encounters using English. CONCLUSION: These results highlight the need for better language documentation and understanding of factors contributing to extended stays and increased revisits for pediatric patients and caregivers who use LOE.

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Haxe is a general purpose, object-oriented programming language supporting syntactic macros. The Haxe compiler is well known for its ability to translate the source code of Haxe programs into the source code of a variety of other programming languages including Java, C++, JavaScript, and Python. Although Haxe is more and more used for a variety of purposes, including games, it has not yet attracted much attention from bioinformaticians. This is surprising, as Haxe allows generating different versions of the same program (e.g. a graphical user interface version in JavaScript running in a web browser for beginners and a command-line version in C++ or Python for increased performance) while maintaining a single code, a feature that should be of interest for many bioinformatic applications. To demonstrate the usefulness of Haxe in bioinformatics, we present here the case story of the program SeqPHASE, written originally in Perl (with a CGI version running on a server) and published in 2010. As Perl+CGI is not desirable anymore for security purposes, we decided to rewrite the SeqPHASE program in Haxe and to host it at Github Pages (https://eeg-ebe.github.io/SeqPHASE), thereby alleviating the need to configure and maintain a dedicated server. Using SeqPHASE as an example, we discuss the advantages and disadvantages of Haxe's source code conversion functionality when it comes to implementing bioinformatic software.

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Depression is a serious mental state that negatively impacts thoughts, feelings, and actions. Social media use is rapidly growing, with people expressing themselves in their regional languages. In Pakistan and India, many people use Roman Urdu on social media. This makes Roman Urdu important for predicting depression in these regions. However, previous studies show no significant contribution in predicting depression through Roman Urdu or in combination with structured languages like English. The study aims to create a Roman Urdu dataset to predict depression risk in dual languages [Roman Urdu (non-structural language) + English (structural language)]. Two datasets were used: Roman Urdu data manually converted from English on Facebook, and English comments from Kaggle. These datasets were merged for the research experiments. Machine learning models, including Support Vector Machine (SVM), Support Vector Machine Radial Basis Function (SVM-RBF), Random Forest (RF), and Bidirectional Encoder Representations from Transformers (BERT), were tested. Depression risk was classified into not depressed, moderate, and severe. Experimental studies show that the SVM achieved the best result with anaccuracy of 0.84% compared to existing models. The presented study refines thearea of depression to predict the depression in Asian countries.

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The Languages of the Indian subcontinent are less represented in current NLP literature. To mitigate this gap, we present the IndicDialogue dataset, which contains subtitles and dialogues in 10 major Indic languages: Hindi, Bengali, Marathi, Telugu, Tamil, Urdu, Odia, Sindhi, Nepali, and Assamese. This dataset is sourced from OpenSubtitles.org, with subtitles pre-processed to remove irrelevant tags, timestamps, square brackets, and links, ensuring the retention of relevant dialogues in JSONL files. The IndicDialogue dataset comprises 7750 raw subtitle files (SRT), 11 JSONL files, 6,853,518 dialogues, and 42,188,569 words. It is designed to serve as a foundation for language model pre-training for low-resource languages, enabling a wide range of downstream tasks including word embeddings, topic modeling, conversation synthesis, neural machine translation, and text summarization.

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PURPOSE: The purpose of this study was to examine knowledge, attitude, and barriers toward care planning documentation practices with standardized nursing languages (SNLs) of nurses and nursing students at a midwestern healthcare system, comparing student and nurse responses. METHODS: Cross-sectional surveys were given over a 2-month period with nurses and nursing students at different sites in a midwestern healthcare system, using convenience sampling. The Knowledge, Attitude, and Barriers to Using Standardized Nursing Languages and Current Practices Survey was adapted for use and re-tested for validity/reliability (Content Validity Index 0.81-1.00; Cronbach alpha = 0.82-0.99) with 28 Likert scale items measuring knowledge, attitude, and barriers. Descriptive statistics, composite scores, correlations, t-tests, and multiple regression were used to analyze the concepts of the tool. FINDINGS: 134/400 RNs responded (34%); 109/116 students responded (93.9%). Data analyses indicate adequate to superior levels of knowledge related to SNLs and NANDA International, Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC), collectively referred to as NNN (NANDA, NIC, & NOC), positive attitudes toward SNLs/NNN and for adopting SNLs/NNN into documentation practices, but moderate to great barriers for implementation in practice. Barriers included lack of financial resources for change, lack of mentors, and lack of mandates to use SNLs. Students scored significantly higher than nurses in attitude only. CONCLUSIONS: Perceptions of nurses and student nurses for current documentation indicate awareness of inadequacy in existing systems and willingness to change existing systems for standardized languages, with perceived barriers to change/implementation of SNLs. Students were more positive about SNLs than nurses. IMPLICATIONS FOR NURSING PRACTICE: Major implications for nursing are to reevaluate electronic documentation systems and determine how to insert and easily apply SNLs in these systems, such that nursing care documentation is standardized, interoperable, effective, time-saving, and attainable.

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The matter of raising and educating deaf children has been caught up in percepts of development that are persistently inaccurate and at odds with scientific research. These percepts have negatively impacted the health and quality of life of deaf children and deaf people in general. The all too prevalent advice is to raise the child strictly orally and wait to see what happens. Only when the child is seriously behind is a completely accessible language - a sign language - introduced, and that is far too late for protecting cognitive health. The medical profession, along with others, needs to offer parents better advice and better supports so that neither the children nor their parents wait and watch as the oral-only method fails. All must take responsible action to assure an approach that succeeds.

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This study investigated the interrelationship between indigenous languages, agricultural radio programmes and behavioural change towards agricultural practices in Nigeria, focusing on three states (Benue, Nasarawa and Plateau). The following research objectives guided the study: to ascertain farmers' awareness of radio programmes in indigenous languages, identify farmers' sources of information, ascertain farmers' access to agricultural extension workers and the language of engagement, and examine the influence of radio programmes in indigenous languages on farmers' behaviour towards agricultural practices. The study adopted a survey research design with a questionnaire administered to 663 randomly selected farmers in Plateau, Nasarawa, and Benue states using a multistage sampling technique. Findings showed that indigenous languages play an integral role in bringing about behavioural change towards agriculture in Nigeria, with a mean Score of 3.4706 and a Standard Deviation of 1.5668. Further findings also indicate that agricultural extension workers are ready sources of information, with the language of communication being that of the local community. The study concluded that behavioural changes are evident in agricultural practices because these programmes have exposed farmers to innovations that have positively affected their agricultural practices. Thus, it is recommended that agricultural radio programmes aired in local languages should continue to be encouraged and sustained. This will help keep farmers abreast of trends in the farming sector, improving their agricultural practices.

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It is well-known that children with expressive communication difficulties have the right to communicate, but they should also have the right to do so in whichever language they choose, with a voice that closely matches their age, gender, and dialect. This study aimed to develop naturalistic synthetic child speech, matching the vocal identity of three children with expressive communication difficulties, using Tacotron 2, for three under-resourced South African languages, namely South African English (SAE), Afrikaans, and isiXhosa. Due to the scarcity of child speech corpora, 2 hours of child speech data per child was collected from three 11- to 12-year-old children. Two adult models were used to "warm start" the child speech synthesis. To determine the naturalness of the synthetic voices, 124 listeners participated in a mean opinion score survey (Likert Score) and optionally gave qualitative feedback. Despite limited training data used in this study, we successfully developed a synthesized child voice of adequate quality in each language. This study highlights that with recent technological advancements, it is possible to develop synthetic child speech that matches the vocal identity of a child with expressive communication difficulties in different under-resourced languages.

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Many parents express concerns for their children's multilingual development, yet little is known about the nature and strength of these concerns - especially among parents in multilingual societies. This pre-registered, questionnaire-based study addresses this gap by examining the concerns of 821 Quebec-based parents raising infants and toddlers aged 0-4 years with multiple languages in the home. Factor analysis of parents' Likert-scale responses revealed that parents had (1) concerns regarding the effect of children's multilingualism on their cognition, and (2) concerns regarding children's exposure to and attainment of fluency in their languages. Concern strength was moderate to weak, and cognition concerns were weaker than exposure-fluency concerns. Transmission of a heritage language, transmission of three or more languages, presence of developmental issues, and less positive parental attitudes towards childhood multilingualism were associated with stronger concerns. These findings have both theoretical and practical implications: they advance our understanding of parental concerns and facilitate the development of support for multilingual families.

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The RePair compression algorithm produces a context-free grammar by iteratively substituting the most frequently occurring pair of consecutive symbols with a new symbol until all consecutive pairs of symbols appear only once in the compressed text. It is widely used in the settings of bioinformatics, machine learning, and information retrieval where random access to the original input text is needed. For example, in pangenomics, RePair is used for random access to a population of genomes. BigRePair improves the scalability of the original RePair algorithm by using Prefix-Free Parsing (PFP) to preprocess the text prior to building the RePair grammar. Despite the efficiency of PFP on repetitive text, there is a scalability issue with the size of the parse which causes a memory bottleneck in BigRePair. In this paper, we design and implement recursive RePair (denoted as Re 2 Pair ), which builds the RePair grammar using recursive PFP. Our novel algorithm faces the challenge of constructing the RePair grammar without direct access to the parse of text, relying solely on the dictionary of the text and the parse and dictionary of the parse of the text. We compare Re 2 Pair to BigRePair using SARS-CoV-2 haplotypes and haplotypes from the 1000 Genomes Project. We show that our method Re 2 Pair achieves over a 40% peak memory reduction and a speed up ranging between 12% to 79% compared to BigRePair when compressing the largest input texts in all experiments. Re 2 Pair is made publicly available under the GNU public license here: https://github.com/jkim210/Recursive-RePair.

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South Asia is home to almost a quarter of the world's total population and is home to significant ethnolinguistic diversity. Previous studies of linguistic and genetic affiliations of Indian populations suggest that the formation of these distinct groups was a protracted and complex phenomenon involving multiple waves of migration, cultural assimilation, and genetic admixture. The evolutionary processes of migration, mixing and merging of populations thus impact the culture and linguistic diversity of different groups, some of which may retain their linguistic affinities despite genetic admixture with other groups, or vice versa. Our study examines the relationship of genetic and linguistic affinities between Austroasiatic and Indo-European speakers in adjacent geographical regions of Eastern India. We analyzed 224 mitogenomes and 0.65 million SNP genotypes from 40 unrelated individuals belonging to the Bathudi, Bhumij, Ho, and Mahali ethnic groups from the Eastern Indian state of Odisha. These four groups are speakers of Austroasiatic languages who have adopted elements from Indo-European languages spoken in neighbouring regions. Our results suggest that these groups have the greatest maternal genetic affinity with other Austroasiatic-speaking groups in India. Allele frequency-based analyses, genome-wide SNPs, haplotype-based methods and IBD sharing further support the genetic similarity of these East Indian groups to Austroasiatic speakers of South Asia rather than regional populations speaking Indo-European and Dravidian languages. Our study shows that these populations experienced linguistic mixing, likely due to industrialization and modernization that brought them into close cultural contact with neighbouring Indo-European-speaking groups. However, linguistic change in these groups is not reflected in genetic mixing in these populations, as they appear to maintain strict genetic boundaries while simultaneously experiencing cultural mixing.

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To obtain a reliable and accurate automatic speech recognition (ASR) machine learning model, it is necessary to have sufficient audio data transcribed, for training. Many languages in the world, especially the agglutinative languages of the Turkic family, suffer from a lack of this type of data. Many studies have been conducted in order to obtain better models for low-resource languages, using different approaches. The most popular approaches include multilingual training and transfer learning. In this study, we combined five agglutinative languages from the Turkic family-Kazakh, Bashkir, Kyrgyz, Sakha, and Tatar,-in order to provide multilingual training using connectionist temporal classification and an attention mechanism including a language model, because these languages have cognate words, sentence formation rules, and alphabet (Cyrillic). Data from the open-source database Common voice was used for the study, to make the experiments reproducible. The results of the experiments showed that multilingual training could improve ASR performances for all languages included in the experiment, except Bashkir language. A dramatic result was achieved for the Kyrgyz language: word error rate decreased to nearly one-fifth and character error rate decreased to one-fourth, which proves that this approach can be helpful for critically low-resource languages.

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Natural language is frequently employed for information exchange between humans and computers in modern digital environments. Natural Language Processing (NLP) is a basic requirement for technological advancement in the field of speech recognition. For additional NLP activities like speech-to-text translation, speech-to-speech translation, speaker recognition, and speech information retrieval, language identification (LID) is a prerequisite. In this paper, we developed a Language Identification (LID) model for Ethio-Semitic languages. We used a hybrid approach (a convolutional recurrent neural network (CRNN)), in addition to a mixed (Mel frequency cepstral coefficient (MFCC) and mel-spectrogram) approach, to build our LID model. The study focused on four Ethio-Semitic languages: Amharic, Ge'ez, Guragigna, and Tigrinya. By using data augmentation for the selected languages, we were able to expand our original dataset of 8 h of audio data to 24 h and 40 min. The proposed selected features, when evaluated, achieved an average performance accuracy of 98.1%, 98.6%, and 99.9% for testing, validation, and training, respectively. The results show that the CRNN model with (Mel-Spectrogram + MFCC) combination feature achieved the best results when compared to other existing models.

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Introduction: This paper provides an initial exploration of Ukrainian-Russian bilingualism in the context of the war-affected migration from Ukraine to Austria and Germany. While extensive research exists on various aspects of Ukrainian- Russian bilingualism in relation to Ukraine itself, thus far no studies have been conducted on this bilingualism in the diasporic context, i.e., as a language of the first and subsequential generations with a migrant background in Austria and Germany. Methods: To address this research gap, our paper examines the language attitudes of two respondent groups with a Ukrainian background in the two countries: migrants and refugees who left Ukraine after 2014 and those who left after Russia's invasion in February 2022. In the framework of a sociolinguistic survey, we describe their current attitudes regarding the use of Ukrainian and Russian, among others, in relation to the actual and intended use of the language(s) in the multilingual context of migration. The survey eliciting information on demographic information, language proficiency, language attitudes and language use was conducted on 406 Ukrainians in two host countries (Austria: n = 103; Germany: n = 306). First, we compared self-rated proficiency in Ukrainian and Russian as well as attitudes and use of these languages. Second, we applied a network modelling analysis to determine the nature of relationships between these variables. Results and discussion: The results indicated that proficiency in Ukrainian and in Russian were the strongest nodes in the model affecting language use and language attitudes toward the respective languages. Our data analysis focused on the pragmatic and symbolic value of Russian and Ukrainian playing a crucial role in the language vitality in multilingual settings. The paper discusses the imbalanced correlation of the symbolic and pragmatic value of Ukrainian and Russian in the diasporic Ukrainian communities. While Ukrainian has gained a higher symbolic status, Russian maintains a better pragmatic one, despite its negative symbolic status. However, we anticipate that the increasing symbolic value of Ukrainian and the diminishing value of Russian will lead to an increase in the use of Ukrainian also in Russian-dominant bilingual groups of Ukrainian migrants and refugees, even as an insider-code in hermetic minority groups.