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Exploring the Future of Corpus Linguistics: Innovations in AI and Social Impact
Full text linkThis paper explores the evolving landscape of corpus linguistics, focusing on the impact of artificial intelligence (AI) and its social implications. Over the past two decades, the study of language through corpus linguistics has evolved significantly, prompting ongoing reflection on the field's transformation. These reflections naturally give rise to pressing questions related to how corpus linguistics will evolve in a world defined by rapid technological progress and changing societal priorities. To validate the suppositions and reflections addressed in this contribution, the study explores a corpus that comprises scholarly papers from scientific journals, and a collection of AI-related articles taken from the media. This dual corpus enables a comparative analysis of how AI-driven corpus linguistics is represented, in order to explore how the integration of artificial intelligence is transforming corpus linguistics, and hence the methodological, theoretical, and socio-political implications of this shift. The methodological framework combines quantitative corpus analysis with qualitative discourse analysis. Collocation and keyword frequency retrieval is applied to identify prevalent themes. As expected academic literature emphasizes methodological advancements and data-driven rigor, while media discourse highlights ethical concerns and societal implications. These findings support the overview and contribute to understanding how AI is shaping both the practice and perception of corpus linguistics in contemporary society
Comprehensive Overview of Preterm Developmental Supportive Care: Narrative Review
Full text linkBackground: Preterm infants face significant health challenges due to the underdevelopment of their organ systems. The neonatal intensive care unit (NICU) is considered a stressful environment, which can disrupt self-regulation and maturation. Developmental supportive care strategies have been shown to enhance neurodevelopmental outcomes in neonates by mimicking intrauterine conditions, reducing stress, and providing appropriate sensory stimulation. Nursing interventions, particularly developmental care, are essential in improving neonatal health, with nurses playing a pivotal role in implementing these strategies.
Aim: This article aimed to provide a comprehensive narrative synthesis of recent research on the impact of developmental supportive care on the health outcomes of preterm infants.
Methods: An extensive literature search was conducted across PubMed, Scopus, Web of Science, Google Scholar, and the Cochrane Library. Quantitative studies, quasi-experimental research, observational studies, and meta-analyses of randomized clinical trials published in English within the past decade were included, with a particular emphasis on studies from the last five years.
Results: The findings indicated that the application of developmental supportive care strategies in the neonatal intensive care unit significantly improved health outcomes for preterm infants, as reported in previous studies.
Conclusion: This narrative overview demonstrated that developmental supportive care effectively contributes to the management and enhancement of preterm infants' health. The study recommends promoting and standardizing these practices in neonatal intensive care units, conducting a collective review of developmental supportive care techniques, and establishing training programs to improve health outcomes for preterm infants and identify effective interventions
Effect of School Based Cognitive behavioral Therapy on Multidimensional Level of Anxiety among Adolescents with Intellectual Disability: A Randomized Controlled Trial
No full textIntroduction: Anxiety is a common issue faced by adolescents with mild intellectual disabilities, but there's still limited research on interventions designed specifically for them. While Cognitive Behavioral Therapy (CBT) has proven effective for managing anxiety in the general population, we need to understand better how well it works for young people with intellectual disabilities.
Purpose/Objectives: This study set out to explore how effective school-based CBT is at reducing different types of anxiety in adolescents with mild intellectual disabilities. It also looked at whether gender and age play a role in how well the therapy works.
Methodology: We carried out a pretest-posttest randomized controlled trialstudy with 150 adolescents randomly placed into either a CBT treatment group or a control group. The anxiety measurement tool was carefully adapted to fit the cultural context and tested to ensure it was reliable. We analyzed the results using Multivariate Analysis of Covariance (MANCOVA), while accounting for anxiety levels before treatment.
Results: The findings showed that CBT significantly eased symptoms across five types of anxiety: separation anxiety, social phobia, generalized anxiety, panic anxiety, and obsessive-compulsive disorder. The therapy worked equally well for both boys and girls. Most age groups responded similarly, but there was a notable difference in how separation anxiety improved, suggesting that a young person's developmental stage may affect their response to CBT.
Conclusion/Implications: These results reinforce that cognitive-behavioral therapy can be effective for adolescents with mild intellectual disabilities. They also highlight the importance of adjusting therapy to fit different developmental stages. Overall, the study encourages incorporating CBT into school mental health programs with tailored support to best meet the needs of this vulnerable group, ensuring that treatment is both practical and fair for all
Leveraging Artificial Intelligence to Enhance Inclusive Teaching for Students with Intellectual Disabilities in Nigerian Universities
Full text linkBackground: Artificial Intelligence (AI) offers significant opportunities for advancing inclusive teaching, particularly for students with intellectual disabilities in Nigerian universities. By leveraging AI tools, educators can help close learning gaps, boost student engagement, and promote equitable learning experiences. This study explores the extent to which lecturers use AI tools to support inclusive teaching and examines how these tools influence the academic engagement of students with intellectual disabilities.
Methods: A descriptive survey design was employed, involving 600 lecturers drawn through accidental sampling from universities across Nigeria. Data were collected using a structured questionnaire that assessed the use of AI tools, lecturers’ perceptions of their effectiveness, and their impact on students’ academic engagement. Descriptive statistics (mean and standard deviation) were used to summarize the responses. At the same time, independent t-tests and multiple regression analyses were employed to test the hypotheses at the 0.05 level of significance.
Results: Findings indicated that lecturers’ overall use of AI tools in inclusive teaching was low. Nevertheless, in cases where AI was applied, it was perceived to have a strong positive effect on students’ engagement. Specifically, lecturers noted improvements in students’ attention, participation, interest in learning tasks, and task completion when AI tools were integrated into instruction. Further analysis revealed that factors such as gender, teaching experience, and the type of university did not significantly affect how AI was adopted. Instead, what mattered most was how actively lecturers integrated AI tools into their teaching. This level of engagement proved to be the strongest factor linked to improved student participation and learning outcomes.
Conclusions: The study highlights AI’s transformative potential in fostering inclusive education in Nigerian universities. Although current utilization remains limited, the reported benefits suggest that greater investment in lecturer training, technological infrastructure, and supportive policies could expand the role of AI in inclusive teaching. These insights contribute to global efforts toward Sustainable Development Goal 4, which emphasizes inclusive and equitable quality education for all
Toward a Holistic Planning Culture Framework: Integrating Individual, Collective, and Societal Dimensions
Full text linkPlanning culture scholarship has evolved significantly over six decades, yet fundamental analytical tensions persist: between systematic comparison and contextual specificity, between structural determinism and agential capacity, and between cultural continuity and institutional change. Existing frameworks, particularly the influential Culturised Planning Model, struggle to address these tensions because they inadvertently reproduce the structure-agency dualism they seek to transcend, treating institutional contexts as relatively fixed parameters that shape but are not shaped by planning practice. This paper presents a holistic planning culture framework that bridges this divide by drawing on sociological institutionalism’s insights into how actors simultaneously work within, reshape, and transform institutional arrangements. The framework distinguishes three interdependent cultural layers: individual attitudes (encompassing knowledge, beliefs, and actions), collective practices (including procedural rules and actors’ constellations), and societal environment (reflected in steering styles). Unlike hierarchical models that imply unidirectional causation, this framework conceptualises these layers as mutually constitutive – simultaneously structuring and being structured by planning practice. This recursive character explains both cultural reproduction (through mutually reinforcing relationships) and transformation (through contradictions creating openings for institutional entrepreneurship). The framework enables more nuanced analysis of how planning cultures operate across multiple scales, how identical formal instruments produce divergent outcomes, and how endogenous change emerges through everyday planning practice. By treating planning culture as a dynamic institutional field rather than a static context, the framework supports more reflexive, culturally-informed planning practice
Novel Insights into Lectin Binding Patterns in the Nasopharyngeal Tonsil of Buffaloes (Bubalus bubalis)
Full text linkBackground: The present study investigates the specificity of lectin binding in the nasopharyngeal tonsil of six healthy adult buffaloes (Bubalus bubalis), a species not extensively studied regarding its immune system. Lectins, proteins that bind specifically to carbohydrates, are used to identify and characterize different cell types that may have roles in immune responses. This study explores how lectins bind to various cells within the nasopharyngeal tonsil, shedding light on cellular differentiation, interactions, and the potential functional roles of these cells in mucosal immunity.
Method: A total of 21 biotinylated lectins, grouped into five categories based on their carbohydrate specificity (N-acetylglucosamine, N-acetylgalactosamine, galactose, glucose/ mannose, and fucose), were used to probe the nasopharyngeal tonsil tissue. Lectin histochemistry was applied to identify the binding patterns of these lectins to different cell types within the tissue, including epithelial cells, lymphoid cells, and specialized structures such as M-cells and P-cells. The study also involved the detection of vimentin filaments to explore potential immune responses within the tissue.
Results: Lectin histochemistry revealed a dynamic epithelial composition of the nasopharyngeal tonsil, consisting of pseudostratified columnar ciliated epithelium and lymphoepithelium, with distinct adaptations in the follicle-associated epithelium (FAE). The FAE exhibited M-cells, which are believed to play a role in antigen processing. Additionally, a new class of cells, termed P-cells, was identified based on their lectin-binding patterns, which share similarities with M-cells but are distinct in their function. Lectins targeting N-acetylglucosamine exhibited varying affinities for M- and P-cells, while lectins recognizing N-acetylgalactosamine selectively bound to cilia and goblet cells. Lectins targeting galactose produced complex staining patterns in mucous glands and lymphoid tissues. Specific binding was also observed in lymphoid cells with lectins recognizing glucose/mannose and fucose groups. Vimentin filaments in lymphocytes and specialized epithelial cells suggest an involvement in immune response mechanisms.
Conclusion: This study provides new insights into structural organisation landscape of the buffalo nasopharyngeal tonsil, highlighting the role of lectin-binding patterns in identifying specialized cells and tissues. The M-cells and discovery of P-cells and the detailed lectin-binding profiles may contribute to understanding the cellular dynamics of mucosal immunity. Additionally, the structural details uncovered in this study may serve as a valuable reference for comparative research on mucosal immunity across different species, advancing our understanding of antigen recognition and immune responses at mucosal surfaces
Buffalo Disease Diagnosis Using Machine Learning: A Symptom-Based Text Classification Approach
Full text linkBackground: Buffaloes play a crucial role in the agricultural economy, especially in regions dependent on dairy and draught animals. However, research specifically targeting disease detection in buffaloes remains limited despite their susceptibility to several infectious diseases. Early and accurate diagnosis is vital for managing disease outbreaks and ensuring herd health. This study uses machine learning (ML) and deep learning (DL) models to emphasize buffalo-specific disease classification. Five commonly occurring diseases, anthrax, blackleg, foot and mouth disease, lumpy skin disease, and pneumonia, were investigated using symptom-based textual descriptions, focusing on enhancing diagnostic accuracy for buffaloes.
Method: Textual symptom data were collected and pre-processed using Term Frequency-Inverse Document Frequency (TF-IDF) to convert unstructured text into numerical feature representations. The study explored three different classification algorithms: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and XGBoost. Each model was trained and evaluated on species-specific subsets, with particular attention given to buffalo disease data. Performance was measured using classification accuracy and disease-wise detection effectiveness to assess the suitability of each model for buffalo diagnostics.
Results: MLP consistently outperformed the other models in classifying diseases in buffaloes, particularly for anthrax and blackleg, which exhibit distinct symptoms. CNN demonstrated robust handling of complex symptom patterns, while XGBoost provided stable and generalized results. However, the classification accuracy declined for diseases with overlapping clinical features, such as pneumonia and lumpy skin disease. These patterns highlight the challenges in differentiating symptomatically similar diseases and indicate the need for enhanced symptom representation in future research.
Conclusion: Based on textual symptom data, the study demonstrates the feasibility and effectiveness of using ML and DL models for automated disease classification in buffaloes. MLP, in particular, shows promise for integrating into intelligent decision-support tools to improve diagnostic accuracy and response time in Buffalo Healthcare. The findings contribute to species-specific veterinary informatics and support the development of targeted surveillance systems for managing buffalo health more effectively
Cryptocurrencies, Blockchain, and Financial Crimes
Full text linkCryptocurrencies and blockchain technology have revolutionized the financial sector, offering decentralized, secure, and efficient transaction mechanisms. However, these innovations have also introduced new challenges, particularly in the realm of financial crimes such as money laundering, illicit trade, and fraud. This paper explores the dual-use nature of cryptocurrencies, examining their potential for both financial innovation and criminal exploitation, with over $20 billion in illicit transactions recorded in 2023 (Chainalysis, 2023). By reviewing case studies, regulatory responses, and technological solutions, this paper provides a comprehensive analysis of the risks and opportunities presented by cryptocurrencies and blockchain technology. Current regulatory frameworks, such as the EU’s MiCA Regulation (2023) and FATF recommendations and guidelines, have significantly influenced cryptocurrency adoption by balancing innovation with risk mitigation. The paper concludes with actionable recommendations for enhancing regulatory frameworks, fostering international cooperation, leveraging AI and other technological advancements, and creating educational initiatives to mitigate financial crimes in the digital age
The Impact COVID-19 Pandemic on Coronary Heart Disease Deaths: Using Bayesian Lorenz Curve and Gini-Index Distribution
Full text linkAim: The aim to investigate and assess role of COVID-19 on Coronary Heart Disease (CHD) mortality using Bayesian Lorenz Curve and associated Gini Index
Statistical Method: Bayesian estimation was applied to analyze CHD mortality rates, focusing on both gender and age group differences.
Application: A total of 341,467 patients were treated during 2-year period from 2020 to 2021during COVID-19 in Turkey. 195,413 females and 146,054 males were diagnosed and 155,211 deaths where 88,824 were males and 66,387 were females with CHD, and hence were studied to evaluate whether female gender was an independent predictor for poor prognosis.
Results: Mortality rates increase with age for females compared to males. The model suggests that males have higher risks or proportions across all groups compared to females, particularly in older age categories. The Lorenz curves for both genders show that a significant portion of deaths is concentrated in a relatively small subset of age groups, particularly older adults. The Gini Index regarding mortality for males is found to be 0.123 compared to value of 0.384 associated with female's age distribution. Meanwhile, the Gini Index regarding morbidity for males (0.146) and females (0.394) are very similar, suggesting that the patterns of inequality in morbidity distribution are comparable across genders.
Conclusion: The study highlights the effectiveness of empirical Bayesian techniques in estimating CHD-related COVID-19 mortality rates across Turkey. It suggests that such statistical methods can help allocate resources more efficiently to high-risk areas and to ensure fair resource distribution and healthcare interventions
A Hybrid Time Series–Regression Model for Tuberculosis Forecasting in Resource-Limited Settings
Full text linkTuberculosis (TB) is still a serious public health issue in Sudan, especially in Gedaref State, because of limited medical facilities and inadequate disease reporting. This experiment develops a forecasting model by employing Seasonal Trend decomposition using LOESS (STL) and linear regression in combination, relying on the weekly tests to improve TB prediction. The model improves the accuracy of its forecasts by combining time series information with the details of the daily operations of the health system. Weekly data from Gedaref showed that the STL + regression approach performed better than ARIMA, reducing the root mean squared error (RMSE) from 2986.85 to 540.95, an improvement of about 81.9%. The model also remained flexible to fluctuations in testing volume. The findings illustrated that hybrid statistical methods have been proved to be reliable and practical in forecasting TB cases in situations where limited resources exist, providing a strong base for overseeing TB and other communicable diseases