Search Results (1,017)

Financial technologies (Fintech) have rapidly reshaped access to financial services, particularly in developing countries where traditional banking remains limited. This study investigates fintech’s role in advancing financial inclusion by analyzing panel data from 89 developing economies gathered from Global Findex reports (2011–2021), complemented by International Monetary Fund (IMF), UNU-WIDER, and PRIO datasets. We applied a random-effects regression model and GMM, incorporating fintech adoption alongside macroeconomic and institutional variables such as education, governance quality, and trade openness. Our results show that fintech is the most significant driver of financial inclusion, especially in expanding account ownership, with education and institutional quality further enhancing outcomes. Conversely, we show that population growth and income disparities constrain progress, while government expenditure and GDP growth display mixed effects. We also find that fintech reduces transaction costs and barriers, yet its impact depends on digital literacy, infrastructure, and governance. In conclusion, our findings highlight that fintech represents a transformative but unevenly utilized tool, capable of fostering broader economic participation and reducing inequality when paired with supportive policies and institutional frameworks. Full article

Tropical forests, despite their critical environmental and socio-economic roles, remain highly vulnerable to deforestation, forest degradation, and climate-related disturbances. There is a growing demand for robust and transparent forest monitoring systems, particularly under REDD+, the Paris Agreement’s Enhanced Transparency Framework (ETF), and emerging climate-finance mechanisms. Conventional approaches based on field inventories and traditional remote sensing are often constrained by limited or uneven field data, persistent cloud cover, complex forest conditions, and limited institutional and technical capacity. This review examines how artificial intelligence (AI) and machine learning (ML) are being integrated into remote sensing–based tropical forest monitoring to address these structural constraints. Using a semi-systematic synthesis of peer-reviewed studies, complemented by operational platforms and grey literature, the review assesses AI/ML approaches, remote sensing datasets, and applications relevant to national and large-scale monitoring. Evidence is synthesized across five analytical dimensions: AI/ML model families and workflows, multi-sensor datasets and training resources, operational monitoring platforms, application domains (including deforestation, degradation, and biomass/carbon estimation), and cross-cutting technical, institutional, and governance barriers. The review finds that AI/ML-enabled remote sensing, particularly those combining optical, radar, and LiDAR time series within cloud-based platforms, has substantially improved the automation, scalability, and speed of tropical forest monitoring. However, effective and equitable adoption remains constrained by limitations in training and validation data, dependence on proprietary platforms and data, uneven technical capacity, and unresolved governance and ethical challenges. Emerging solutions, including open and representative training datasets, platform-agnostic processing infrastructures, long-term capacity building, and inclusive data-governance frameworks, are identified as critical enablers of credible and nationally owned AI/ML-enabled forest-monitoring systems. The review highlights that AI/ML can play a transformative role in supporting climate mitigation, biodiversity conservation, and informed decision-making. This potential, however, depends on transparent data governance arrangements, long-term capacity building, and platform-agnostic infrastructures that support national ownership. Full article

We present a Tagalog lip-reading system designed to enhance communication accessibility for individuals with hearing impairments. Existing lip-reading models focus on English and other major languages and cannot recognize Tagalog visual speech patterns. To address this gap, we implemented 3D Convolutional Neural Network combined with Bidirectional Long Short-Term Memory network, supported by a custom Tagalog dataset of common words. This architecture achieved an average character error rate of 10.09%, word error rate of 24.08%, and overall word accuracy of 76.27%, demonstrating promising recognition accuracy for Tagalog lip movements. By introducing the Tagalog-specific lip-reading framework, the potential of deep learning-based visual speech recognition was validated to support inclusive technologies, with applications in daily communication, education, and assistive tools for the Filipino deaf community. Full article

Introduction: Hospital adverse events are a global patient safety problem that account for avoidable death, long-term disability, extended length of stay, and increased healthcare costs. Underreporting, wherein fewer than 10% of events are indeed recorded, is prevalent and is characterized primarily by cultural and organizational determinants. Artificial intelligence, in the form of machine learning and natural language processing, has been described as a potential tool for enhancing adverse events detection and prediction with the use of large-scale clinical data. Materials and Methods: PRISMA-DTA guidelines were followed in this systematic review. Scopus, PubMed, and Web of Science were searched employing keywords associated with adverse events, artificial intelligence methodologies (e.g., machine learning, deep learning, natural language processing), and healthcare settings. Inclusion criteria included original research on artificial intelligence-based solutions for the detection or prediction of adverse events such as medication errors, hospital-acquired infections, and complications during surgery. Reviews, meta-analyses, and non-artificial intelligence studies were excluded. Following screening, 15 studies were found to meet inclusion criteria. Results: The referenced studies show a shift from rule-based natural language processing models to advanced deep learning and Bidirectional Encoder Representations from Transformers models. Early approaches, i.e., Support Vector Machine classifiers, achieved AUC scores as high as 0.92, while later models (Random Forest, LightGBM, XGBoost) mirrored AUCs of over 0.93. Large language models achieved F1-scores of 0.84 for named entity recognition. Artificial intelligence models even identified unreported incidents. Discussion: Artificial intelligence-powered methods are transforming adverse events detection from retrospective to predictive, proactive monitoring. There remain some challenges, however, including limited external validation, class imbalance, and interpretability of complex models. Future studies must address explainable artificial intelligence, multicenter trials, and high-quality well-annotated datasets to offer secure clinical integration. Full article

Tourism has become a structural driver of land-system transformation, influencing urban restructuring, rural land consumption, coastal development, and housing dynamics. Although tourism sustainability has received growing scholarly attention, less systematic evidence exists on how land governance and spatial planning frameworks mediate tourism-related land-use change. This study presents a systematic review of peer-reviewed journal articles published between 2000 and 2025 examining the relationship between spatial planning, land-use regulation, and tourism development. Following PRISMA guidelines, a structured search strategy and multi-stage screening process were applied using predefined inclusion and quality criteria, resulting in a final dataset of 58 studies. The findings indicate that tourism-driven land transformation is shaped by interconnected governance layers, including statutory planning instruments, institutional coordination mechanisms, and land administration infrastructures. However, these dimensions are rarely analyzed within an integrated framework. By synthesizing tourism planning and land administration scholarship through a land governance perspective, this review clarifies how regulatory tools and administrative systems interact in shaping spatial outcomes across scales. The study offers a structured basis for future comparative research and for more coherent policy responses to tourism-related land governance challenge. Full article

Automated pixel-level detection of steel surface defects is a critical challenge in manufacturing quality control, complicated by the variation in defect size and shape, low contrast with background textures, and the diversity of defect patterns. This paper proposes ID-MSNet, an enhanced version of the UNet3+ architecture, designed specifically for the segmentation of three common steel surface defect types: inclusions, patches, and scratches. The proposed architecture introduces three targeted modifications: (1) a multi-scale feature learning module (MSFLM) in the encoder that uses dilated convolutions at multiple rates to capture contextual features across different scales, combined with DropBlock regularization and batch normalization to improve generalization; (2) an improved down-sampling (IDS) module that replaces standard max-pooling with learnable strided convolutions fused via 1 × 1 convolution, preserving richer feature representations; and (3) a convolutional block attention module (CBAM) integrated into the skip connections to selectively focus the model on spatially and channel-wise relevant defect regions. Experiments on the publicly available SD-saliency-900 dataset demonstrate that ID-MSNet achieved an 86.19% mIoU, outperforming all compared state-of-the-art segmentation models while using only 6.7 million parameters—approximately 75% fewer than the original UNet3+. These results establish ID-MSNet as a strong and efficient baseline for steel surface defect segmentation, with potential applicability to automated quality inspection in broader manufacturing contexts. Full article

Background: Incorporating sex as a biological variable (SBV) is recognized as essential for improving the reliability, reproducibility, and generalizability of pharmacological research. This principle is codified in international policies and guidelines, yet implementation remains uneven, especially in phytomedicine. Phytomedicines are a major component of healthcare worldwide, with 65% of the global population relying on them in both regulated and traditional contexts. Globally, phytomedicines are used by males, females, intersex and non-cis gender persons, all of whom may present specific safety and efficacy considerations and warrant full inclusion in pre-clinical to clinical research pipelines. However, in contemporary settings, phytomedicine lags in SBV best practices relative to Western allopathic standards for research design. Methods: We conducted a non-systematic review and in silico data mining to quantify sex/gender representation in recent preclinical and clinical phytomedicine studies, complemented by targeted case studies of sexually dimorphic safety/efficacy. We also summarize the historical role of women and gender-diverse people as users and providers within Traditional and Integrative Medical Systems (TIMSs). Results: Across rodent and human studies, females are under-represented relative to males, and sex is rarely reported for cell lines. Intentional inclusion of intersex and other gender-diverse populations is largely absent. Case studies illustrate plausible sex-associated differences in pharmacokinetics, pharmacodynamics, and adverse event profiles. TIMSs historically address women’s health needs and include substantial participation by female practitioners; however, contemporary SBV practices remain less standardized than in Western allopathic pipelines. Conclusions: SBV integration in phytomedicine is needed to strengthen safety, efficacy, and regulatory-grade evidence. Practical barriers include legacy datasets without sex metadata, limited intersex animal models, and uneven resources across settings. We outline feasible, stepwise practices to improve SBV adoption in a manner compatible with TIMS contexts and recommend expanding current guidelines to better support diverse research environments while maintaining scientific rigor. Full article

Diabetic retinopathy is a prevalent condition and a major public health concern due to its detrimental impact on eyesight. Diabetes is a root cause of its development and damages small blood vessels caused by prolonged high blood sugar levels. The degenerative consequences of diabetic retinopathy are irrevocable if not diagnosed in the early stages of its progression. This ailment triggers the development of retinal lesions, which can be identified for diagnosis and prognosis. However, lesion detection is challenging due to their similarity in intensity profiles to other retinal features, inconsistent sizes, and random locations. This research evaluates a custom deep learning network for classifying retinal images and compares it with the state-of-the-art classifiers. The novel preprocessing method is introduced to reduce the complexity of the diagnostic process and to enhance classification performance by adaptively enhancing images. Despite being a shallow network, the proposed model yields competitive results with an accuracy of 87.66% and an F1-score of 0.78. The evaluation metrics indicate that class imbalance affects the performance of the proposed model despite using the weighted cross-entropy loss. The future contribution will be the inclusion of generative adversarial networks for generating synthetic images to balance the dataset. This research aims to develop a robust computer-aided diagnostic system as a second interpreter for ophthalmologists during the diagnosis and prognosis stages. Full article

Background: Almost all correlation measures currently available are unable to directly handle missing values. Typically, missing values are either ignored completely by removing them or are imputed and used in the calculation of the correlation coefficient. In either case, the correlation value will be impacted based on the perspective that the missing data represents no useful information. However, missing values occur in real datasets for a variety of reasons. In metabolomics datasets a major reason for missing values is that a specific measurable phenomenon falls below the detection limits of the analytical instrumentation (left-censored values). These missing data are not missing at random, but represent potentially useful information by virtue of their “missingness” at one end of the data distribution. Methods: To include this information due to left-censored missingness, we propose the information-content-informed Kendall-tau (ICI-Kt) methodology. We develop a statistical test and then show that most missing values in metabolomics datasets are the result of left-censorship. Next, we show how left-censored missing values can be included within the definition of the Kendall-tau correlation coefficient, and how that inclusion leads to an interpretation of information being added to the correlation. We also implement calculations for additional measures of theoretical maxima and pairwise completeness that add further layers of information interpretation in the methodology. Results: Using both simulated and over 700 experimental data sets from the Metabolomics Workbench, we demonstrate that the ICI-Kt methodology allows for the inclusion of left-censored missing data values as interpretable information, enabling both improved determination of outlier samples and improved feature–feature network construction. Conclusions: We provide explicitly parallel implementations in both R and Python that allow fast calculations of all the variables used when applying the ICI-Kt methodology on large numbers of samples. The ICI-Kt methods are available as an R package and Python module on GitHub. Full article

In the post-pandemic higher education context, multilingual students, particularly those from widening participation backgrounds, continue to face academic, linguistic, and socio-emotional challenges that can limit their participation and sense of belonging. This study examines the use of Reflective Video Diaries (RVDs) facilitated through Microsoft Flipgrid as an inclusive pedagogical approach to support reflective engagement, communication, and socio-emotional development among multilingual undergraduate students. Adopting a qualitative iterative action research approach, the study was conducted within a UK university module and involved three cycles of implementation, reflection, and pedagogical refinement, capturing students’ lived experiences rather than measuring causal effects. Multiple methods, including RVDs, end-of-module reflective reports, an anonymous survey, and lecturers’ field notes, were deliberately combined to provide complementary perspectives on students’ experiences, allowing triangulation of data and enhancing the validity and richness of findings. Thematic analysis of this longitudinal dataset collected across the three action-research cycles explored how students experienced RVDs as a space for reflection, peer support, and engagement with learning. Findings indicate that Flipgrid-mediated RVDs functioned as a low-anxiety, flexible, and dialogic learning environment that enabled students to articulate challenges, share progress, and develop reflective awareness, confidence, and a sense of connection with peers and lecturers. Improvements in participation and reflective depth were more evident in later cycles, suggesting that benefits emerged through iterative pedagogical adjustment rather than by video technology alone. Both positive experiences and challenges are reported, providing a balanced account of engagement with the RVDs. The study underscores the potential of inclusive digital pedagogies to inform curriculum planning and policy implementation, supporting equitable learning opportunities and socio-emotional development. By conceptualizing RVDs as relational and inclusive pedagogical practices rather than technological interventions, and by demonstrating how reflective engagement developed across successive action-research cycles, this research contributes to understanding how reflective digital practices can support multilingual learners’ academic and socio-emotional development within socially just higher education contexts. Practical implications for designing inclusive reflective learning environments are discussed. Full article

Gender bias embedded in legal and professional texts perpetuates systemic inequality, yet research on bias identification and mitigation remains largely confined to English. Morphologically rich languages such as Greek, where grammatical gender pervades nouns, adjectives, pronouns, and participles, present unique challenges that existing approaches fail to address. This paper elaborates on a systematic methodology primarily focusing on identifying and mitigating gender bias in Greek-language job advertisements and legal documents. To accomplish that task, we define a taxonomy of nine gender bias rules tailored to the linguistic properties of Greek and construct domain-specific annotated datasets comprising 90 expert-curated few-shot examples across both textual domains. Using these resources, we employ XML-structured prompt engineering with in-context learning (ICL)and systematically compare three classes of models: (i) commercial large language models (LLMs), namely Claude Sonnet 4.5 and GPT-5.2, (ii) two open-weight small language models (SLMs), Mistral Small (24B) and Ministral (14B), and (iii) Llama Krikri (8B), a Greek-native language model built on Llama 3.1 and fine-tuned on high-quality Greek corpora. For each input text, the system identifies biased expressions, maps them to specific bias rules, provides explanations, and generates a fully corrected inclusive version. Our experiments reveal substantial performance disparities across model scales and linguistic specialization, with LLMs demonstrating superior contextual reasoning and SLMs exhibiting systematic over-correction and grammatical errors in Greek morphology. We further introduce a critical meta-rule addressing gender agreement with named entities to prevent spurious corrections in legal texts referencing identified individuals. The findings highlight the importance of model scale, language-specific adaptation, and carefully designed prompting strategies for bias mitigation in underrepresented languages. Full article

This study develops and validates an integrated framework that combines UAV multispectral imagery and canopy structural metrics with gridded climatic variables to predict soybean (Glycine max (L.) Merrill) foliar potassium (K) status and grain yield. Field experiments were conducted over three consecutive growing seasons (2022–2023, 2023–2024, and 2024–2025) under different potassium fertilisation strategies and environmental conditions. Machine learning models, particularly the random forest algorithm, were applied to multisource datasets, including UAV-derived canopy structural traits (height and canopy area), spectral indices (NDVI), meteorological variables, and fertilisation information. The foliar K prediction models achieved high accuracy (R² up to 0.85), while the yield prediction models achieved R² values between 0.71 and 0.81. The inclusion of the potassium rate and fertilisation strategy further improved model performance, highlighting the strong influence of potassium supply and fertilisation management on plant physiological responses. Interestingly, compared with those required to stabilise grain yield, foliar potassium saturation occurred at substantially higher K₂O rates, indicating the occurrence of luxury potassium uptake. The association of UAV-derived canopy metrics with this pattern suggests that remote sensing may help detect subtle nutritional dynamics that are not directly reflected in yield responses. Model interpretability using SHAP analysis identified relationships within the analysed dataset that were consistent with physiological expectations, with positive contributions associated with canopy vigour and negative contributions associated with thermal stress. In addition, probabilistic SHAP analysis provided a decision-oriented perspective by quantifying yield probabilities under contrasting potassium management regimes and climate scenarios. Overall, within the experimental conditions studied, the proposed framework enabled a rapid assessment of crop nutritional status, yield prediction, and the evaluation of fertilisation strategies. The integration of UAV data, climatic variables, and machine learning provides an interpretable basis for potassium management and soybean yield forecasting within the experimental conditions studied, while broader transferability requires external validation. Full article

The increasing availability of onboard sensors and digital monitoring platforms has enabled the continuous acquisition of operational and health-related data in aircraft systems. In parallel, advances in Big Data analytics and Artificial Intelligence (AI) have driven significant progress in Predictive Maintenance (PdM), enabling earlier fault detection and more reliable estimations of Remaining Useful Life (RUL). This systematic literature review examines recent developments in AI-driven PdM and fault detection applied to aircraft over the last years. A total of 20 studies were selected based on predefined inclusion criteria and analyzed with respect to research trends, application domains, algorithmic approaches, and expected outputs. The findings indicate a strong research emphasis on civil aviation supported by accessible operational datasets, whereas military aviation research prioritizes fleet readiness and mission continuity, often with limited data transparency. Deep learning approaches, particularly hybrid models combining convolutional and recurrent architectures, dominate recent prognostic methodologies, while optimization and Model-Based Systems Engineering (MBSE) frameworks support decision-making integration. Despite these advancements, the transition from experimental models to operational deployment remains constrained by data heterogeneity, model explainability requirements, and regulatory certification processes. This review highlights current progress and identifies gaps and research opportunities to accelerate the adoption of robust and scalable PdM solutions in aviation. Full article

Background: Total joint arthroplasty (TJA) is one of the most successful surgical procedures for patients to improve the quality of life. In recent years, the use of machine learning (ML) in the setting of arthroplasty decision-making has grown. Methods: This article reviewed studies published between 2020 and 2025 that applied ML to TJA, with a focus on the limitations reported by these studies. A search in ScienceDirect identified 220 articles. After screening and full-text assessment, 17 studies met the inclusion criteria, excluding imaging-based research, to focus on predictive models trained on non-image clinical data. Results: The reviewed studies revealed several common limitations, categorised into four groups, including observations and follow-up (30.3% of the studies), dataset quality and design (27.3%), model transferability and generalisation (27.3%), and outcome measurement and interpretation (15.2%). These limitations impact the reliability and real-world relevance of ML models in the context of arthroplasty. This article also provides suggestions to help researchers address these limitations in future studies. Conclusions: This review provides an overview of the potential limitations associated with the development of ML models within the TJA community in order to identify the gaps and challenges to improve the quality of research and possibly decision-making support systems using joint arthroplasty clinical datasets. Full article

This study focused on the application of supervised learning in the field of renewable energy, specifically for predicting daily solar irradiance in Neiva, department of Huila, Colombia. To this end, decision tree and artificial neural network (DT and ANN, respectively) models were trained and tested using the online tool Google Colab. The main objective was based on the need to optimize energy planning processes at local and regional levels, motivated by the increase in demand for the integration of non-conventional energy sources and the spatial–temporal variability in solar resources in the country. A dataset consisting of 366 daily records for the year 2024 was obtained from the NASA POWER database at the geographic coordinates (2.930079, −75.255650) and used for training and evaluating the proposed models. Statistical and cleaning techniques were used, including the treatment of outliers using the moving-window median for the latter. Metrics, such as mean absolute error ($MAE$), root mean square error ($RMSE$), and coefficient of determination ($R^2$), were used to evaluate the models. Data inclusion and exclusion criteria were applied to ensure the quality and validity of the observations. Model performance was evaluated using a randomized Hold-Out validation strategy (90% training and 10% testing), which was repeated across multiple iterations. The performance metrics reported corresponded to the 10th iteration of the validation process after outlier treatment. Under this configuration, the DT model achieved a higher predictive performance ($R^2$ = 0.8882) compared with the ANN model ($R^2$ = 0.7679), demonstrating its effectiveness as a reliable approach for estimating daily solar irradiance under the studied conditions. This result was also confirmed by the decreased $MAE$ and $RMSE$ for the DT model, which indicated that this model performed better in predicting the real values than the ANN model. Finally, the added value of the study is to consolidate national evidence and open access tools to facilitate the development of sustainable energy policies in intermediate cities such as Neiva. Full article