Search Results (994)

Background/Objectives: Speech perception typically takes place against a background of other speech or noise. The present study investigates the effectiveness of segregating speech streams within a competing speech signal, examining whether cues such as pitch, which typically denote a difference in talker, behave in the same way as cues such as speaking rate, which typically do not denote the presence of a new talker. Methods: Native English speakers listened to English target speech within English two-talker babble of a similar or different pitch and/or a similar or different speaking rate to identify whether mismatched properties between target speech and masker babble improve speech segregation. Additionally, Dutch and French masker babble was tested to identify whether an unknown language masker improves speech segregation capacity and whether the rhythm patterns of the unknown language modulate the improvement. Results: Results indicated that a difference in pitch or speaking rate between target and masker improved speech segregation, but when both pitch and speaking rate differed, only a difference in pitch improved speech segregation. Results also indicated improved speech segregation for an unknown language masker, with little to no role of rhythm pattern of the unknown language. Conclusions: This study increases the understanding of speech perception in a noisy ecologically valid context and suggests that there is a link between a cue’s potential to denote a new speaker and its ability to aid in speech segregation during competing speech perception. Full article

Background and Objectives: The management of type 2 diabetes (T2D) extends beyond glycemic control, requiring a more global strategy that includes optimization of body composition, even more so in the context of sarcopenia and visceral adiposity, as they contribute to poor outcomes. Past reviews have typically been focused on weight reduction or glycemic effectiveness, with limited inclusion of new therapies’ effects on muscle and fat distribution. In addition, the emergence of incretin-based therapies and dual agonists such as tirzepatide requires an updated synthesis of their impacts on body composition. This review attempts to bridge the gap by taking a systematic approach to how current blood-glucose lowering therapies affect lean body mass, fat mass, and the risk of sarcopenia in T2D patients. Materials and Methods: Between January 2015 and March 2025, we conducted a narrative review by searching the PubMed, Scopus, and Web of Science databases for English-language articles. The keywords were combinations of the following: “type 2 diabetes,” “lean body mass,” “fat mass,” “body composition,” “sarcopenia,” “GLP-1 receptor agonists,” “SGLT2 inhibitors,” “tirzepatide,” and “antidiabetic pharmacotherapy.” Reference lists were searched manually as well. The highest precedence was assigned to studies that aimed at adult type 2 diabetic subjects and reported body composition results. Inclusion criteria for studies were: (1) type 2 diabetic mellitus adult patients and (2) reporting measures of body composition (e.g., lean body mass, fat mass, or muscle function). We prioritized randomized controlled trials and large observational studies and excluded mixed diabetic populations, non-pharmacological interventions only, and poor reporting of body composition. Results: Metformin was widely found to be weight-neutral with minimal effects on muscle mass. Insulin therapy, being an anabolic hormone, often leads to fat mass accumulation and increases the risk of sarcopenic obesity. Incretin-based therapies induced substantial weight loss, mostly from fat mass. Notable results were observed in studies with tirzepatide, demonstrating superior reduction not only in fat mass, but also in visceral fat. Sodium-glucose cotransporter 2 inhibitors (SGLT2 inhibitors) promote fat loss but are associated with a small yet significant decrease in lean muscle mass. Conclusions: Blood-glucose lowering therapies demonstrated clinically relevant effects on body composition. Treatment should be personalized, balancing glycemic control, cardiovascular, and renal benefits, together with optimal impact on muscle mass along with glycemic, cardiovascular, and renal benefits. Full article

This review addresses the urgent need for alternative strategies to combat drug-resistant mycobacterial infections, including multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis, as well as non-tuberculous mycobacterial (NTM) diseases. Traditional antibiotics are increasingly limited by resistance, toxicity, and poor efficacy, particularly in immunocompromised patients. A comprehensive literature search was conducted using PubMed, Scopus, and Google Scholar, covering publications primarily from 2000 to 2025. Only articles published in English were included to ensure consistency in data interpretation. Search terms included “mycobacteriophages,” “phage therapy,” “drug-resistant mycobacteria, “diagnostic phages,” and “phage engineering.” The review examines the therapeutic and diagnostic potential of mycobacteriophages—viruses that specifically infect mycobacteria—focusing on their molecular biology, engineering advances, delivery systems, and clinical applications. Evidence suggests that mycobacteriophages offer high specificity, potent bactericidal activity, and adaptability, positioning them as promising candidates for targeted therapy. Although significant obstacles remain—including immune interactions, limited host range, and regulatory challenges—rapid progress in synthetic biology and delivery platforms continues to expand their clinical potential. As research advances and clinical frameworks evolve, mycobacteriophages are poised to become a valuable asset in the fight against drug-resistant mycobacterial diseases, offering new precision-based solutions where conventional therapies fail. Full article

This paper aims to discuss the transition of new graduate nurses into the workforce, the preparation provided to equip them through the novice–beginner stage, and the theory–practice conundrum. Background: In Australia, new graduate transition programs have been in existence since the 1990s. While there is widespread acknowledgment that this period is pivotal for new graduate nurses entering the profession, there is a lack of consensus on the definition of best practice to achieve optimal preparation for new graduate nurses transitioning into the workforce. Methods: This discussion paper integrates the nursing literature on this topic with the extensive professional experiences of the authors, who are currently working as clinicians in metropolitan hospitals and hold academic positions at universities. Their insights are informed by the literature sourced from peer-reviewed English language journals, including reviews, empirical studies, and national and international reports. Discussion: Recruiting and retaining nurses presents a multifaceted challenge that requires the development of effective tools and strategies to build a sustainable workforce. Both the literature and the authors’ experiences highlight several key factors influencing the preparedness of new graduates. These factors include workplace culture, the demands placed on new graduates, and the support, education, and training they receive. The perspectives shared in this article offer valuable discussion points that can deepen our understanding of the current issues and contribute to the development of more effective solutions. Full article

Social network platforms have a big impact on the development of companies by influencing clients’ behaviors and sentiments, which directly affect corporate reputations. Analyzing this feedback has become an essential component of business intelligence, supporting the improvement of long-term marketing strategies on a larger scale. The implementation of powerful sentiment analysis models requires a comprehensive and in-depth examination of each stage of the process. In this study, we present a new comparative approach for several feature extraction techniques, including TF-IDF, Word2Vec, FastText, and BERT embeddings. These methods are applied to three multilingual datasets collected from hotel review platforms in the tourism sector in English, French, and Arabic languages. Those datasets were preprocessed through cleaning, normalization, labeling, and balancing before being trained on various machine learning and deep learning algorithms. The effectiveness of each feature extraction method was evaluated using metrics such as accuracy, F1-score, precision, recall, ROC AUC curve, and a new metric that measures the execution time for generating word representations. Our extensive experiments demonstrate significant and excellent results, achieving accuracy rates of approximately 99% for the English dataset, 94% for the Arabic dataset, and 89% for the French dataset. These findings confirm the important impact of vectorization techniques on the performance of sentiment analysis models. They also highlight the important relationship between balanced datasets, effective feature extraction methods, and the choice of classification algorithms. So, this study aims to simplify the selection of feature extraction methods and appropriate classifiers for each language, thereby contributing to advancements in sentiment analysis. Full article

Nested named entity recognition (NER) is a task that identifies hierarchically structured entities, where one entity can contain other entities within its span. This study introduces a nested NER model for few-shot learning environments, addressing the difficulty of building extensive datasets for general named entities. We enhance the Biaffine nested NER model by modifying its output layer to incorporate label semantic information through a novel label description embedding (LDE) approach, improving performance with limited training data. Our method replaces the traditional biaffine classifier with a label attention mechanism that leverages comprehensive natural language descriptions of entity types, encoded using BERT to capture rich semantic relationships between labels and input spans. We conducted comprehensive experiments on four benchmark datasets: GENIA (nested NER), ACE 2004 (nested NER), ACE 2005 (nested NER), and CoNLL 2003 English (flat NER). Performance was evaluated across multiple few-shot scenarios (1-shot, 5-shot, 10-shot, and 20-shot) using F1-measure as the primary metric, with five different random seeds to ensure robust evaluation. We compared our approach against strong baselines including BERT-LSTM-CRF with nested tags, the original Biaffine model, and recent few-shot NER methods (FewNER, FIT, LPNER, SpanNER). Results demonstrate significant improvements across all few-shot scenarios. On GENIA, our LDE model achieves 45.07% F1 in five-shot learning compared to 30.74% for the baseline Biaffine model (46.4% relative improvement). On ACE 2005, we obtain 44.24% vs. 32.38% F1 in five-shot scenarios (36.6% relative improvement). The model shows consistent gains in 10-shot (57.19% vs. 49.50% on ACE 2005) and 20-shot settings (64.50% vs. 58.21% on ACE 2005). Ablation studies confirm that semantic information from label descriptions is the key factor enabling robust few-shot performance. Transfer learning experiments demonstrate the model’s ability to leverage knowledge from related domains. Our findings suggest that incorporating label semantic information can substantially enhance NER models in low-resource settings, opening new possibilities for applying NER in specialized domains or languages with limited annotated data. Full article

Social media has become a platform for the rapid spread of persuasion techniques that can negatively affect individuals and society. Propaganda detection, a crucial task in natural language processing, aims to identify manipulative content in texts, particularly in news media, by assessing propagandistic intent. Although extensively studied in English, Arabic propaganda detection remains challenging because of the language’s morphological complexity and limited resources. Furthermore, most research has treated propaganda detection as an isolated task, neglecting the influence of sentiments and emotions. The current study addresses this gap by introducing the first multi-task learning (MTL) models for Arabic propaganda detection, integrating sentiment analysis and emotion detection as auxiliary tasks. Three MTL models are introduced: (1) MTL combining all tasks, (2) PSMTL (propaganda and sentiment), and (3) PEMTL (propaganda and emotion) based on transformer architectures. Additionally, seven task-weighting schemes are proposed and evaluated. Experiments demonstrated the superiority of our framework over state-of-the-art methods, achieving a Macro-F1 score of 0.778 and 79% accuracy. The results highlight the importance of integrating sentiment and emotion for enhanced propaganda detection; demonstrate that MTL improves model performance; and provide valuable insights into the interaction among sentiment, emotion, and propaganda. Full article

This research addresses two specific knowledge gaps. The first regards the influence of domestic low-carbon technology (LCT) installation approaches and occupier status on user acceptance. The second is to demonstrate the role of machine learning techniques in producing an enhanced model-based understanding of domestic LCT acceptance. Together, these two approaches provide new insights into LCT acceptance through the theory of planned behaviour and demonstrate the value of machine learning for modelling such acceptance. Our aim is therefore to contribute to model-based knowledge about the acceptance of domestic LCTs. Specifically, we contribute new knowledge of the acceptance of LCTs according to the theory of planned behaviour and of the value of machine-learning techniques for modelling this acceptance. Through empirical research using an online quasi-experiment with 3813 English residents, we developed a model of low-carbon technology adoption and evaluated machine learning for model analysis. The design factors were the installation approach and occupier status, with main outcomes including adoption intention, willingness to accept, willingness to pay, attitude, subjective norm, and perceived behavioural control. To examine residents’ technology acceptance, we created two virtual reality models of technology implementation, differing in installation approach. For machine learning analysis, we employed nine techniques for model validation and predictor selection: linear regression, LASSO regression, ridge regression, support vector regression, regression tree (decision tree regression), random forest, XGBoost, k-NN, and neural network. LASSO regression emerged as the best technique in terms of predictor selection, with (near-)optimal model fit (R² and MSE). We found that attitude, subjective norm, and perceived behavioural control significantly predicted the intention to adopt low-carbon technologies. The installation approach influenced willingness to accept, with higher intention for new-build installations than retrofits. Homeownership positively predicted perceived behavioural control, while age negatively predicted several outcomes. This study concludes with implications for policy and future research, a specific emphasis upon contemporary UK policy towards Future Homes Standards, and public information campaigns targeted to specific demographic user groups. This research demonstrates the value of an extended theory of planned behaviour model to study the acceptance of LCTs and the value of machine learning analysis in acceptance modelling. Full article

In 2021, the National Centre for Religious Studies in New Zealand published the new religious education curriculum for Catholic schools in Aotearoa New Zealand. While in many ways, very like other religious education curricula, from its naming in Māori before English, Tō Tātou Whakapono Our Faith shines a light on the role of culture and language in the transmission and expression of faith. This paper is written in two parts. Part 1 of this paper provides an examination of the key curriculum documents and website to find that Tō Tātou Whakapono Our Faith is unique in three ways. First, it enjoys a level of security in the dominant presence of Catholics in the Catholic school, guaranteed by the Integration Act of 1975. Second, it offers flexibility in approach, necessary for a curriculum with national status, and finally, it demonstrates an extraordinary commitment to the inclusion of Māori culture and language. Part 2 of this paper takes up the inclusion of Māori culture and language to offer a response to the call that Māori need to be allowed to develop a theology from within their own culture and language. It proposes that the introduction of a new hermeneutical lens in the study of scripture, one that would replicate the practice of the Bible authors who drew freely on their own experience and language to speak of God, could provide a simple but effective way of developing such a theology. It is in Part 2 that the significance of the subtitle of this paper will become apparent. Full article

The framing of water infrastructure in the news influences how the public perceives future infrastructure development and associated social-environmental risks. This study examines English-language newspaper coverage of the Ken-Betwa river link, the first component of India’s National River Linking Program (INRLP) to receive approval. Data for this analysis comprised 316 newspaper articles, collected via a keyword search in LexisNexis API, from seven Indian English-language newspapers (Free Press Journal (India), Hindustan Times, Indian Express, The Economic Times, The Hindu, The Times of India (TOI), and Times of India (Electronic Edition)) published between 2004 and 2022. By applying LDA topic modeling, a type of generative probabilistic model, to this dataset, this study examines how evolving media narratives frame water infrastructure in India. Our results identify 23 distinct topics and three dominant frames: (1) a government policy frame, (2) INRLP comparative frame, and (3) environmental conservation frame. We find that these frames evolve, with early coverage emphasizing feasibility and government-led negotiations, and later articles highlighting environmental risks. Our analysis shows how media discourse reflects institutional logic and infrastructure milestones. This study demonstrates the value of computational methods for longitudinal media analysis, has the potential to reveal shifts in public discourse, and highlights power dynamics in environmental reporting. Full article

Climate change is profoundly impacting human health in South Africa, aggravating existing health challenges and creating new threats, particularly in vulnerable populations. This scoping review aims to comprehensively map existing evidence of climate change and diverse human health impacts to assist in the equipping of health systems to address evolving challenges of climate change. The scoping review will inform the development of evidence-based policy, improve public health preparedness, and ensure that adaptation strategies are effectively tailored to South Africa’s socio-economic and environmental conditions. This scoping review protocol will be conducted using the Joanna Briggs Institute (JBI) methodology, following five steps: (1) defining the research question, (2) search strategy, (3) setting inclusion criteria, (4) extracting data, (5) assessing, summarizing, and presenting findings. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) tool will be used. A comprehensive peer-reviewed literature search, including PubMed, Scopus, ScienceDirect, and Google Scholar, will be conducted by two independent reviewers. The review will be conducted over eight weeks, focusing on English studies published between 2015 and 2025, and conducted within South Africa. A two-stage screening process will determine article eligibility. Disagreements will be resolved through consensus and consultation of a third reviewer. The results of this review will be presented as tables, including a narrative synthesis of the findings. Full article

Research on language contact outcomes, such as code-switching, continues to face theoretical and methodological challenges, particularly due to the difficulty of comparing findings across studies that use divergent data collection methods. Accordingly, scholars have emphasized the need for publicly available and comparable bilingual corpora. This paper introduces the El Paso Bilingual Corpus, a new Spanish–English bilingual corpus recorded in El Paso (TX) in 2022, designed to be methodologically comparable to the Bangor Miami Corpus. The paper is structured in three main sections. First, we review the existing Spanish–English corpora and examine the theoretical challenges posed by studies using non-comparable methodologies, thereby underscoring the gap addressed by the El Paso Bilingual Corpus. Second, we outline the corpus creation process, discussing participant recruitment, data collection, and transcription, and provide an overview of these data, including participants’ sociolinguistic profiles. Third, to demonstrate the practical value of methodologically aligned corpora, we report a comparative case study on diminutive expressions in the El Paso and Bangor Miami corpora, illustrating how shared collection protocols can elucidate the role of community-specific social factors on bilinguals’ morphosyntactic choices. Full article

Telemedicine in emergency contexts presents unique challenges, particularly in multilingual and low-resource settings where accurate, clinical understanding and triage decision support are critical. This paper introduces TriagE-NLU, a novel multilingual natural language understanding system designed to perform both semantic parsing and clinical intervention classification from emergency dialogues. The system is built on a federated learning architecture to ensure data privacy and adaptability across regions and is trained using TriageX, a synthetic, clinically grounded dataset covering five languages (English, Spanish, Romanian, Arabic, and Mandarin). TriagE-NLU integrates fine-tuned multilingual transformers with a hybrid rules-and-policy decision engine, enabling it to parse structured medical information (symptoms, risk factors, temporal markers) and recommend appropriate interventions based on recognized patterns. Evaluation against strong multilingual baselines, including mT5, mBART, and XLM-RoBERTa, demonstrates superior performance by TriagE-NLU, achieving F1 scores of 0.91 for semantic parsing and 0.89 for intervention classification, along with 0.92 accuracy and a BLEU score of 0.87. These results validate the system’s robustness in multilingual emergency telehealth and its ability to generalize across diverse input scenarios. This paper establishes a new direction for privacy-preserving, AI-assisted triage systems. Full article

Expatriates and missionaries in China played a significant role in the development and transformation of Chinese architecture in the Late Qing period. However, a systematic comparison of their discourses and proposals on Chinese architecture has been hindered by a lack of historical literature and the complexities of fragmented data and methodologies. This article examines and compares the two most influential non-native newspapers: The North-China Daily News in English, edited by expatriates, and The Review of the Times in Chinese, founded by missionaries. By analyzing these two groups’ discourses and narratives on Chinese architecture, the study explores their similarities and distinctions, revealing the characteristics, strategies, attitudes, interests, and opinions of expatriates, missionaries, and non-missionaries in China on the transmission and transformation of architecture knowledge. The research highlights differences in their preferences for specific text types, subjects, and themes on Chinese architecture, as well as their attitudes toward native and foreign architecture, professional education, and architecture regulations in individual and official spheres. Despite these differences, overlapping characteristics and proposals existed among the three groups. The study further investigates the underlying reasons and mechanisms for their similar or divergent mindsets and behavioral patterns, drawing on human responsive psychology rather than relying on postcolonial or cultural theories. Full article

Background/Objectives: The Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway is a critical mediator of immune regulation, inflammation, and cancer progression. Although implicated in colorectal cancer (CRC) pathogenesis, its molecular heterogeneity and clinical significance remain insufficiently characterized—particularly within early-onset CRC (EOCRC) and across diverse treatment and demographic contexts. We present AI-HOPE-JAK-STAT, a novel conversational artificial intelligence platform built to enable the real-time, natural language-driven exploration of JAK/STAT pathway alterations in CRC. The platform integrates clinical, genomic, and treatment data to support dynamic, hypothesis-generating analyses for precision oncology. Methods: AI-HOPE-JAK-STAT combines large language models (LLMs), a natural language-to-code engine, and harmonized public CRC datasets from cBioPortal. Users define analytical queries in plain English, which are translated into executable code for cohort selection, survival analysis, odds ratio testing, and mutation profiling. To validate the platform, we replicated known associations involving JAK1, JAK3, and STAT3 mutations. Additional exploratory analyses examined age, treatment exposure, tumor stage, and anatomical site. Results: The platform recapitulated established trends, including improved survival among EOCRC patients with JAK/STAT pathway alterations. In FOLFOX-treated CRC cohorts, JAK/STAT-altered tumors were associated with significantly enhanced overall survival (p < 0.0001). Stratification by age revealed survival advantages in younger (age < 50) patients with JAK/STAT mutations (p = 0.0379). STAT5B mutations were enriched in colon adenocarcinoma and correlated with significantly more favorable trends (p = 0.0000). Conversely, JAK1 mutations in microsatellite-stable tumors did not affect survival, emphasizing the value of molecular context. Finally, JAK3-mutated tumors diagnosed at Stage I–III showed superior survival compared to Stage IV cases (p = 0.00001), reinforcing stage as a dominant clinical determinant. Conclusions: AI-HOPE-JAK-STAT establishes a new standard for pathway-level interrogation in CRC by empowering users to generate and test clinically meaningful hypotheses without coding expertise. This system enhances access to precision oncology analyses and supports the scalable, real-time discovery of survival trends, mutational associations, and treatment-response patterns across stratified patient cohorts. Full article