Search Results (4,491)

This study investigates the development of a predictive model in simulations for assessing steel corrosion in determining corrosion-affected zones in reinforced concrete. A series of reinforced concrete cubes with varying degrees of corrosion were tested using a four-probe Wenner configuration. The experimental data showed a clear inverse relationship between ER and steel mass loss, with a strong negative correlation, highlighting the potential of ER as a corrosion indicator. A third-degree polynomial model was developed to predict the diameter of the corrosion-affected region based on steel mass loss and concrete cover, achieving high predictive accuracy. This model was validated using numerical simulation conducted in COMSOL Multiphysics, which replicated the experimental setup under steady-state conditions. Parametric studies further examined the effects of electrical conductivity ($\sigma$) and electrode spacing on the simulated results. The findings confirm that while $\sigma$ has a moderate impact, electrode spacing significantly influences the measured ER values. The study underscores the importance of incorporating variable parameters into simulation models to improve the accuracy and field applicability of ER-based corrosion assessments. Furthermore, the simulation framework developed in this study demonstrates how numerical modeling can enhance the interpretive value of ER measurements, supporting the advancement of non-destructive testing techniques aimed at improving corrosion monitoring and maintenance strategies. Full article

This paper proposes an advanced three-dimensional optical encryption technique based on double random phase encryption for the simultaneous encryption of two primary datasets. While conventional double random phase encryption offers high-speed encryption, it suffers from low data efficiency. To address this issue, the proposed method assigns the first primary dataset to the amplitude and the second to the phase. However, this approach faces a critical limitation: the phase information becomes undefined or lost when the amplitude is zero. Therefore, we introduce a biased amplitude encoding scheme for double random phase encryption to ensure the mathematical recoverability of the phase component. In the proposed method, a biased value $ϵ$ is added to the amplitude part during the double random phase encryption encryption process and subsequently subtracted from the decrypted data to recover the two primary datasets. To verify the effectiveness of our approach, we employ synthetic aperture integral imaging and volumetric computational reconstruction. The experimental results show that while the first dataset remains lossless, the lossy characteristics of the second dataset are significantly mitigated. Full article

This study investigates the characterisation of particulate matter (PM) across different size fractions (TSP, PM₁₀, PM₄, PM_2.5, and PM₁) in Porto, Portugal, over a 2-year period. Sampling was conducted at two heights (ground level and rooftop), integrating real-time measurements and filter-based analyses to evaluate seasonal and spatial variations. Elemental composition was determined using Inductively Coupled Plasma–Mass Spectrometry (ICP-MS), enabling detailed assessments of 30 chemical elements. Meteorological parameters, including temperature, precipitation, wind speed, and direction, were analysed to understand their influence on PM concentrations. Results indicate that significant seasonal trends, with higher PM concentrations observed during autumn and winter, were associated with low boundary layer height, promoting greater mixing of particles, enhanced deposition, and higher anthropogenic emissions, with average seasonal TSP values ranging from 0.001 to 0.059 µg m⁻³. Elemental analysis revealed distinct profiles at ground and rooftop levels, with Ba, Cu, Pb, Mg, and Na among the most frequently detected elements; ground-level samples showed stronger contributions from local sources, such as traffic, while rooftop samples reflected regional and long-range transport. Meteorological factors, such as precipitation and wind speed, exhibited negative correlations with PM concentrations, underscoring their role in atmospheric washing. These findings highlight the complex interplay of local and regional factors in shaping PM dynamics and emphasise the importance of multi-level monitoring for effective air-quality management. Full article

Multi-walled carbon nanotubes (MWCNTs) are increasingly incorporated into industrial and consumer products, raising concerns about potential carcinogenicity because their physicochemical properties vary widely among materials. Although Mitsui-7 has been classified as possibly carcinogenic to humans (IARC, Group 2B), the carcinogenic potential of domestically manufactured MWCNTs and the determinants underlying material-specific differences remain insufficiently characterized. Here, we applied an adverse outcome pathway (AOP)-oriented integrated testing strategy (ITS) to compare four domestically manufactured MWCNTs with Mitsui-7 using human bronchial epithelial BEAS-2B cells. Acute responses were assessed by measuring cytotoxicity and intracellular reactive oxygen species (ROS). Exposure concentrations for long-term studies were selected using range-finding assays, and cells were then exposed for four weeks at non-cytotoxic concentrations. Following chronic exposure, transformation-related phenotypes were evaluated using anchorage-independent growth, anchorage-dependent clonogenicity, wound healing migration, and Transwell–Matrigel invasion assays, and tumorigenic potential was examined in xenograft models using colony-derived cells. Highly aggregated MWCNTs elicited stronger oxidative stress and were associated with increased proliferation/clonal expansion, enhanced anchorage-independent colony formation, and increased tumor formation in vivo, whereas other materials showed more limited or endpoint-specific responses. Overall, the results indicate that MWCNT-associated carcinogenic potential is material-dependent rather than a uniform class effect and support the utility of an AOP-aligned ITS for nanosafety assessment and hazard differentiation of carbon-based nanomaterials. Full article

Polymer films and polymer blend films are widely used as functional materials; however, their photophysical behavior cannot be fully explained solely by bulk properties such as relative permittivity or glass transition temperature. In this study, we investigate how local polymer microenvironments regulate fluorescence responses by employing two strongly emissive solvatochromic dyes—FπPCM, a D–π–A-type π-conjugation-extended fluorene dye, and PK, a D–π–A-type pyrene dye—as molecular probes. The photophysical properties of these dyes were systematically examined in a series of transparent polymer matrices, including polystyrene, polycarbonate, poly(methyl methacrylate), poly(vinyl chloride), triacetylcellulose, poly(butyl methacrylate), and poly(2-ethyl-2-oxazoline). Polymer films containing the dyes were prepared by solution casting from homogeneous polymer–dye solutions onto quartz substrates followed by solvent evaporation. Both dyes exhibited polymer-dependent variations in fluorescence wavelength, quantum yield, and lifetime, reflecting not only differences in polymer polarity but also local chain packing and specific dye–polymer interactions. Fluorescence lifetime analysis of PS/POz blend films revealed microscopic heterogeneity even in miscible systems, quantitatively captured using averaged lifetime parameters. Temperature-dependent fluorescence measurements further demonstrated that thermal history and structural relaxation significantly influence local polymer environments. In particular, ratiometric fluorescence analysis of PMMA/PBMA blend films enabled reproducible temperature sensing over a wide range from 30 to 120 °C, despite an overall negative temperature response. These results establish solvatochromic dyes as versatile optical probes for evaluating local polymer microenvironments and highlight their potential for polymer-state monitoring and fluorescence-based temperature-sensing applications. Full article

Smart cities utilise advanced technology to enhance the quality of life, economic efficiency, and environmental sustainability of citizens. This transformation is both vital and complex in Africa due to rapid urbanisation and socio-economic challenges. This paper examines the prospects, challenges, and pathways toward smart city development in African cities. The study was conducted through a systematic literature review and case study analyses of initiatives for smart city development in Africa. The findings indicate that infrastructure deficits, financial constraints, weak policy frameworks, limited expertise, and socio-economic inequalities are the key challenges. The high use of mobile technologies, innovation hubs, and increasing policy support have created opportunities. Strategic actions for transforming African cities include strengthening infrastructure through public–private partnerships, developing financial mechanisms, creating coherent policies, promoting inclusivity, and building technical capacity. Technologies such as Information and Communication Technology (ICT) and Artificial Intelligence (AI) are among the key enablers, supporting the growth of Small and Medium-Sized Enterprises (SMEs), improving infrastructure, fostering inclusive governance, managing resources sustainably, and enhancing public services such as healthcare and education. The study also proposes a conceptual framework for smart cities in Africa and outlines a pathway to unlock the continent’s potential for smart cities. It is argued that African cities need to address systemic challenges, leverage unique opportunities, and ensure inclusivity at the urban level. An integrated approach that utilises advanced technologies and prioritises sustainability and resilience is essential for developing smart and inclusive cities. Full article

Industry 5.0 emphasizes human-centered integration of artificial intelligence in industrial contexts, yet successful adoption depends critically on workforce perception and acceptance. This research develops and validates a machine learning framework for predicting AI-related perceptions and expected impacts in the construction industry under small sample constraints typical of specialized industrial surveys. Specifically, the study aims to develop and empirically validate a predictive AI decision support model that estimates the expected impact of AI adoption in the construction sector based on digital competencies, ICT utilization, AI training and experience, and AI usage at both individual and organizational levels, operationalized through a composite AI Impact Index and two process-oriented outcomes (perceived task automation and perceived cost reduction). Using a dataset of 51 survey responses from Slovak construction professionals collected in 2025, we implement a methodologically rigorous approach specifically designed for limited-data regimes. The framework encompasses ordinal target simplification from five to three classes, dimensionality reduction through theoretically grounded composite indices reducing features from 15 to 7, exclusive deployment of low variance regularized models, and leave-one-out cross-validation for unbiased performance estimation. The optimal model (Lasso regression with recursive feature elimination) predicts cost reduction perception with R² = 0.501, MAE = 0.551, and RMSE = 0.709, while six classification targets achieve weighted F1 = 0.681, representing statistically optimal performance given sample constraints and perception measurement variability. Comparative evaluation confirms regularized models outperform high variance alternatives: random forest (R² = 0.412) and gradient boosting (R² = 0.292) exhibit substantially lower generalization performance, empirically validating the bias-variance trade-off rationale. Key methodological contributions include explicit bias-variance optimization preventing overfitting, feature selection via RFE reducing input space to six predictors (personal AI usage, AI impact on budgeting, ICT utilization, AI training, company size, and age), and demonstration that principled statistical approaches achieve meaningful predictions without requiring large-scale datasets or complex architectures. The framework provides a replicable blueprint for perception and impact prediction in data-constrained Industry 5.0 contexts, enabling targeted interventions, including customized training programs, strategic communication prioritization, and resource allocation for change management initiatives aligned with predicted adoption patterns. Full article

International trade policy constitutes a challenging subject for undergraduate students, as it requires the integration of historical, institutional, and quantitative perspectives. This study presents a digital learning framework designed to support the teaching of Ecuador’s trade policy trajectory between 1979 and 2024 through the use of open macroeconomic datasets, interactive visualizations, and guided data-analysis tasks. The framework combines historical interpretation with structured digital inquiry, allowing students to explore policy cycles, export composition, and institutional shifts using empirical evidence. A small-scale classroom implementation with economics and social science students (n = 48) indicates that the proposed approach supports students’ ability to recognize long-term economic trends and to relate policy decisions to broader development patterns. Rather than offering causal claims, the study provides exploratory evidence of how data-driven digital environments can enhance analytical engagement in policy-oriented courses. The framework is intended as a transferable pedagogical model for contexts where economic history, public policy, and digital learning intersect. Full article

Background: Cervical cancer (CC) and ovarian cancer (OC) are among the most prevalent and lethal gynecological malignancies in women, necessitating the identification of reliable biomarkers for early diagnosis and prognosis. Methods: This study integrates bioinformatics and Healthcare 4.0 to identify key biomarkers associated with these cancers. Differentially expressed genes (DEGs) were identified from two microarray datasets. mRMR followed by SVM-RFE was applied to the identified DEGs to extract the most significant ML-based DEGs (MDEGs). The predictive ability of the selected gene subsets was further evaluated via multiple classifiers, where attention-based long short-term memory (AttLSTM) consistently achieved the best performance across both datasets. In parallel, WGCNA was conducted to identify coexpression-associated genes (CAGs) from significant modules in each dataset. A PPI network (PPIN) was constructed using the genes common to MDEGs and CAGs and was analyzed via Cytoscape. Results: Four hub genes, MCM3, FOXM1, SH3BP5, and PAPSS2, were identified via the degree method. mRNA expression analysis revealed that FOXM1 and MCM3 were upregulated, whereas SH3BP5 and PAPSS2 were downregulated in cancer tissues compared with normal tissues. ROC curve analysis demonstrated the high prognostic significance of these hub genes, with substantial AUC scores indicating strong discriminatory power. Furthermore, molecular docking analysis with an FDA-approved drug compound confirmed the significant binding affinity between these genes and the drug molecules. Conclusions: These findings suggest that FOXM1, MCM3, SH3BP5, and PAPSS2 could serve as biomarkers for early prognosis, diagnosis, and targeted therapy in patients with cervical and ovarian cancer. Full article

This paper presents a systematic comparative study of optical character recognition (OCR) architectures for Korean license plate recognition under identical detection conditions. Although recent automatic license plate recognition (ALPR) systems increasingly adopt Transformer-based decoders, it remains unclear whether performance differences arise primarily from sequence modeling strategies or from backbone feature representations. To address this issue, we employ a unified YOLOv12-based license plate detector and evaluate multiple OCR configurations, including a CNN with an Attention-LSTM decoder and a MobileNetV3 with a Transformer decoder. To ensure a fair comparison, a controlled ablation study is conducted in which the CNN backbone is fixed to ResNet-18 while varying only the sequence decoder. Experiments are performed on both static image datasets and tracking-based sequential datasets, assessing recognition accuracy, error characteristics, and processing speed across GPU and embedded platforms. The results demonstrate that the effectiveness of sequence decoders is highly dataset-dependent and strongly influenced by feature quality and region-of-interest (ROI) stability. Quantitative analysis further shows that tracking-induced error accumulation dominates OCR performance in sequential recognition scenarios. Moreover, Korean license plate–specific error patterns reveal failure modes not captured by generic OCR benchmarks. Finally, experiments on embedded platforms indicate that Transformer-based OCR models introduce significant computational and memory overhead, limiting their suitability for real-time deployment. These findings suggest that robust license plate recognition requires joint consideration of detection, tracking, and recognition rather than isolated optimization of OCR architectures. Full article

This study examines the effectiveness of a purposefully constructed Foreign Language Learning Environment (FLLE) in developing foreign language communicative competence within Kazakhstani higher education. Focusing on four interrelated components—pedagogical resources, physical learning space, motivational strategies, and ICT integration—the study addresses the limited opportunities for authentic English communication characteristic of EFL contexts. A quasi-experimental design involving 69 undergraduate students was employed, with participants divided into experimental and control groups. Statistical analysis using the Mann–Whitney U test revealed significantly higher post-test results in the experimental group, particularly in speaking performance. The findings demonstrate that communicative competence development can be significantly enhanced when (1) instructional materials prioritize authentic, task-based communication, (2) classroom spaces are organized to facilitate face-to-face interaction, (3) motivational support is provided through speaking activities and extracurricular activities, and (4) ICT tools, including conversational AI, are used to extend communicative interaction beyond classroom time. Full article

This paper presents LinguoNER, a practical and extensible framework for bootstrapping Named Entity Recognition (NER) in extremely low-resource languages, demonstrated on Yambeta, a Bantu language spoken by a minority community in Cameroon. Due to scarce digital resources and the absence of annotated corpora, Yambeta has remained largely underrepresented in Natural Language Processing (NLP). LinguoNER addresses this gap by providing a methodologically transparent end-to-end workflow that integrates corpus acquisition, gazetteer-driven automatic annotation, tokenizer training, transformer fine-tuning, and multi-level evaluation in settings where large-scale manual annotation is infeasible. Using a Bible-derived corpus as a linguistically stable starting point, we release the first publicly available Yambeta NER dataset (≈25,000 tokens) annotated with the CoNLL BIO scheme and a restricted entity schema (PER/LOC/ORG). Because labels are generated via dictionary-based annotation, the corpus is best characterized as silver-standard; credibility is strengthened through recorded dictionaries, transparency logs, expert-in-the-loop validation on sampled subsets, and complementary qualitative error analysis. We additionally train a dedicated Yambeta WordPiece tokenizer that preserves tone markers and diacritics, and fine-tune a bert-base-cased transformer for token classification. On a held-out test split, LinguoNER achieves strong token-level performance (Precision = 0.989, Recall = 0.981, F1 = 0.985), substantially outperforming a dictionary-only gazetteer baseline ($\Delta$F1 ≈ 0.36). Per-entity-type evaluation further indicates improvements beyond surface-form matching, while remaining errors are linguistically motivated and primarily involve multi-word entity boundaries, agglutinative constructions, and tone-/diacritic-sensitive tokenization. We emphasize that results are restricted to a Bible domain and a limited label space, and should be interpreted as proof-of-concept evidence rather than claims of broad out-of-domain generalization. Overall, LinguoNER provides a reproducible blueprint for bootstrapping NER resources in underrepresented languages and supports future work on broader corpora sources (e.g., news, OPUS, JW300), additional African languages (e.g., Yoruba, Igbo, Bassa), and the iterative creation of expert-refined datasets and gold-standard subsets. Full article

Traditional innovation studies frequently highlight the role of geographic clustering in fostering competitive advantages. However, the sustainability of innovation cannot be understood solely through spatial proximity and requires a broader, multidisciplinary perspective. This study examines the Information and Communication Technology (ICT) sector in Quito by applying the CRI (Capabilities, Results, and Impacts) model of innovation potential, adapted to the local organisational and institutional context. By combining normalised CRI data with georeferenced firm information, spatial autocorrelation was assessed using Moran’s Index and local indicators of spatial association (LISA). The results reveal random spatial patterns and no statistically significant evidence of innovation clustering among firms, academia, and the public sector. Notably, the few high-concentration clusters identified lie outside the traditional economic and institutional hubs of the Quito Innovation System. These findings demonstrate that innovation capacities, outputs, and impacts in Quito’s ICT sector are driven more by organisational capabilities, institutional linkages, and sectoral dynamics than by geographic location. This study contributes to the debate on sustainable innovation systems by challenging the assumption of spatial dependence and emphasising the need for policies and managerial practices that strengthen resilient innovation networks beyond clusters. Full article

The modern smart grid integrates information and communication technology (ICT) with electronic devices, but this integration introduces cybersecurity risks. Load measurements, crucial for grid operation, are vulnerable to attacks, particularly Load Redistribution Attacks (LRAs). LRAs maliciously alter load readings to mislead control systems without being detected by conventional methods. This paper first introduces two advanced LRA variants: a stealthy-enhanced LRA designed to bypass sophisticated data-driven detectors, and an impact-enhanced LRA engineered to cause significant operational disruptions, such as increased generation costs. To address these evolving threats, we propose a novel Sensor-oriented Temporal Detector (STD). Unlike existing methods that often rely on aggregate data or labeled attack examples, our STD focuses on the unique temporal patterns of individual sensor measurements. It achieves this by combining principal subspace projection to identify normal data subspaces with sequential change extraction to detect subtle deviations over time. This approach allows the STD to identify various LRA types effectively, even without prior knowledge of attack signatures. Extensive simulations validate the destructive impact of our proposed LRA variants and demonstrate the superior detection performance of the STD against these sophisticated attacks. Full article

The agricultural sector in the world and Sub-Saharan Africa faces the pressing challenge of meeting the growing food demand driven by the exponential population growth. With smallholder poultry farming playing a critical role in food and nutritional security, this systematic review synthesizes literature from the past two decades to assess the adoption of Information and Communication Technologies (ICTs) among smallholder poultry farmers in Sub-Saharan Africa (SSA). The review focuses on the benefits and barriers impacting this adoption. Following the PRISMA methodology, 19 peer-reviewed studies were analyzed to explore how ICT facilitates market participation, enhances information exchange, and improves producer livelihoods. The included studies in this review were sourced from four major academic databases: Science Direct, Web of Science, Wiley online library, and EBSCOhost. The findings reveal that ICT adoption significantly reduces information asymmetry, enables farmers to access market and production knowledge, and thus improves their profitability and inclusion in informal and formal market platforms. The review underscores the potential of targeted policy interventions and digital platforms to empower smallholder poultry farmers, enhance their commercialization, and contribute towards agricultural sustainability in the region. This study highlights the critical need for increased ICT accessibility, capacity building, and infrastructural improvements to support the digital transformation of smallholder poultry farming in Sub-Saharan Africa. Full article