Search Results (88)

The rapid expansion of Generative Artificial Intelligence (GAI) is transforming higher education systems, particularly public institutions seeking to advance toward smart governance models and digital transformation. In this context, digital teaching competence emerges as a strategic factor for the effective, ethical, and pedagogically sound adoption of these technologies. This study assesses the level of digital competence among public higher education faculty in Paraguay and examines its predictive capacity regarding the adoption of GAI tools using machine learning models. A nationwide quantitative study was conducted with a sample of 800 faculty members from public universities across Paraguay. Data were collected through a structured questionnaire based on international digital competence frameworks, incorporating additional variables such as attitudes toward GAI, technological experience, institutional infrastructure, and perceived organizational support. Data analysis involved the application of machine learning techniques, including Logistic Regression, Random Forest, and Gradient Boosting, to identify the variables with the strongest predictive power regarding faculty readiness and willingness to integrate GAI into teaching practices. Model performance was evaluated using metrics such as accuracy, F1-scores, and the AUC-ROC. The findings identify key predictors of technological readiness and structural gaps within Paraguay’s public higher education system. This research provides empirical evidence from Latin America on the factors influencing GAI adoption in public sector educational contexts and contributes to the design of educational policies aimed at fostering smart universities and digitally sustainable academic ecosystems. Full article

We explore a phenomenological extension of the Polyakov–Nambu–Jona-Lasinio (PNJL) model by introducing a curvature-sensitive effective contribution to the Polyakov-loop potential, motivated by the hypothesis that the non-perturbative QCD vacuum in the confined phase may retain a residual sensitivity to cosmic expansion. In a spatially flat FLRW background, this modification reduces to a term proportional to $\alpha {(H/H_0)}^{d}f(\Phi ,{\Phi }^{*})$, which naturally vanishes in the deconfined regime and behaves as an effective dynamical vacuum component at late times, without invoking a fundamental cosmological constant. The construction provides an effective thermodynamic description of the QCD sector within an adiabatic framework and introduces a minimal phenomenological extension characterized by the exponent d and the amplitude parameter $\alpha$. We analyze the cosmological implications at the background level and compare the model with low-redshift observations, including cosmic chronometers, Type Ia supernovae, HII galaxies, and quasars. Using Bayesian Monte Carlo techniques, we constrain the model parameters and compare its performance with the ΛCDM. Our results indicate that the modified PNJL cosmology provides a statistically competitive fit to current data while allowing small departures from the ΛCDM within observational uncertainties. We also investigate the impact of the coupling on the QCD phase diagram and the critical end point. The framework offers a tractable effective approach to connect confinement physics with late-time cosmology and suggests directions for further theoretical development in QCD under curved backgrounds. Full article

In this work, measurements of thermal diffusivity, heat capacity and thermal expansion of 40HM (42CrMo4, 1.7225, AISI 4140) steel manufactured conventionally and via Laser Powder Bed Fusion (L-PBF) were carried out in the temperature range from room temperature (RT) to 1000 °C. Thermophysical properties were tested using specialized test stands from NETZSCH. Thermal diffusivity was studied using both the LFA 427 laser flash apparatus and the LFA 467 xenon flash apparatus. Specific heat capacity was investigated using DSC 404 F1 Pegasus differential scanning calorimeter, and thermal expansion was investigated using the DIL 402 C. Inconel 600 and A310 steel were selected as the reference materials during the thermal diffusivity test using LFA467 in the RT÷500 °C range. The conventionally manufactured 40HM steel, in the form of hot-rolled bar stock, was subjected to standard heat treatment for this steel grade—quenching followed by high-temperature tempering. The additively manufactured 40HM steel was subjected to stress-relief annealing. The results revealed no significant differences between the thermophysical properties of the L-PBF-produced samples in the out-of-plane and in-plane build orientations. Furthermore, no substantial differences were observed between the thermophysical properties of the conventionally produced material and the material manufactured using the L-PBF technique. Full article

Despite the rapid expansion of AI and machine-learning techniques in bankruptcy prediction, classical statistical methods such as discriminant analysis and logistic regression remain relevant because of their transparency and interpretability. These characteristics are crucial for stakeholders who require understandable decision-making tools, especially in NACE Rev. 2 Section I—Accommodation and Food Service Activities, a sector characterized by high operating leverage, vulnerability to economic shocks, and strong macroeconomic importance. The study aims to evaluate and compare the predictive performance of discriminant analysis and logistic regression for bankruptcy prediction and to identify key predictors that can serve as managerial early-warning signals for companies in crisis across B9 countries. The sample of 4395 companies was used. The classification ability of all models is assessed using multiple performance metrics, including overall accuracy, sensitivity, specificity, precision, the F1-score, the F2-score, the Matthews correlation coefficient, and the area under the receiver operating characteristic curve. The results show that both approaches achieve consistently high predictive performance, with all major metrics exceeding 0.92 on the test sample of prosperous and non-prosperous enterprises. Six significant bankruptcy predictors are identified for each method, with three common indicators: financial leverage, total liabilities to assets, and return on costs. The comparative analysis results in a methodological “draw,” confirming comparable predictive power. These findings reaffirm the relevance of classical prediction models and identify key financial indicators that can be used as practical early-warning signals by managers in the sector. Full article

Background/Objectives: Dental diseases represent a great problem for oral health care, and early diagnosis is essential to reduce the risk of complications. Panoramic radiographs provide a detailed perspective of dental structures that is suitable for automated diagnostic methods. This paper aims to investigate the use of an advanced deep learning (DL) model for the multiclass classification of diseases at the sub-diagnosis level using panoramic radiographs to resolve the inconsistencies and skewed classes in the dataset. Methods: To classify and test the models, rich data of 10,580 high-quality panoramic radiographs, initially annotated in 93 classes and subsequently improved to 35 consolidated classes, was used. We applied extensive preprocessing techniques like class consolidation, mislabeled entry correction, redundancy removal and augmentation to reduce the ratio of class imbalance from 2560:1 to 61:1. Five modern convolutional neural network (CNN) architectures—InceptionV3, EfficientNetV2, DenseNet121, ResNet50, and VGG16—were assessed with respect to five metrics: accuracy, mean average precision (mAP), precision, recall, and F1-score. Results: InceptionV3 achieved the best performance with a 97.51% accuracy rate and a mAP of 96.61%, thus confirming its superior ability for diagnosing a wide range of dental conditions. The EfficientNetV2 and DenseNet121 models achieved accuracies of 97.04% and 96.70%, respectively, indicating strong classification performance. ResNet50 and VGG16 also yielded competitive accuracy values comparable to these models. Conclusions: Overall, the results show that deep learning models are successful in dental disease classification, especially the model with the highest accuracy, InceptionV3. New insights and clinical applications will be realized from a further study into dataset expansion, ensemble learning strategies, and the application of explainable artificial intelligence techniques. The findings provide a starting point for implementing automated diagnostic systems for dental diagnosis with greater efficiency, accuracy, and clinical utility in the deployment of oral healthcare. Full article

To address the data quality challenges in ship maintenance data, such as high missing rates, anomalous noise, and multi-source heterogeneity, this paper proposes a data quality enhancement method based on a multi-strategy enhanced Harris Hawks Optimization algorithm for optimizing the Kolmogorov–Arnold Stacked Denoising Autoencoder. First, leveraging the Kolmogorov–Arnold theory, the fixed activation functions of the traditional Stacked Denoising Autoencoder are reconstructed into self-learnable B-spline basis functions. Combined with a grid expansion technique, the KASDAE model is constructed, significantly enhancing its capability to represent complex nonlinear features. Second, the Harris Hawks Optimization algorithm is enhanced by incorporating a Logistic–Tent compound chaotic map, an elite hierarchy strategy, and a nonlinear logarithmic decay mechanism. These improvements effectively balance global exploration and local exploitation, thereby increasing the convergence accuracy and stability for hyperparameter optimization. Building on this, an IHHO-KASDAE collaborative cleaning framework is established to achieve the repair of anomalous data and the imputation of missing values. Experimental results on a real-world ship maintenance dataset demonstrate the effectiveness of the proposed method: it achieves an 18.3% reduction in reconstruction mean squared error under a 20% missing rate compared to the best baseline method; attains an F1-score of 0.89 and an AUC value of 0.929 under a 20% anomaly rate; and stabilizes the final fitness value of the IHHO optimizer at 0.0216, which represents improvements of 31.7%, 25.6%, and 12.2% over the Particle Swarm Optimization, Differential Evolution, and the original HHO algorithm, respectively. The proposed method outperforms traditional statistical methods, deep learning models, and other intelligent optimization algorithms in terms of reconstruction accuracy, anomaly detection robustness, and algorithmic convergence stability, thereby providing a high-quality data foundation for subsequent applications such as maintenance cost prediction and fault diagnosis. Full article

Freeze-thaw (F-T) cycles in seasonally frozen regions induce progressive volumetric strains leading to degradation of soils’ mechanical properties and performance of earthen infrastructure. Conventional chemical stabilization techniques often are not adaptive to cyclic thermal stresses and do not address the fundamental phase changes of porous media, underscoring the need for sustainable alternatives. This study explores the potential of extracellular polymeric substances (EPS) produced by the psychrophilic bacterium Polaromonas hydrogenivorans as a bio-mediated soil treatment to enhance freeze-thaw durability. Two EPS formulations were examined—EPS 1 (high ice-binding activity) and EPS 2 (low ice-binding activity)—to evaluate their effectiveness in improving volumetric stability and thawing strength of silty soil subjected to ten F-T cycles. Tests were conducted at four moisture contents (12%, 18%, 24%, and 30%) and three EPS concentrations (3, 10, and 20 g/L). Volumetric strain measurements quantified freezing expansion and thawing contraction, while unconfined compressive strength assessed post-thaw mechanical integrity. The untreated soils exhibited maximum net volumetric strains (γ_Net) of 5.62% and only marginal strength recovery after ten F-T cycles. In contrast, EPS 1 at 20 g/L mitigated volumetric changes across all moisture contents and increased compressive strength to 191.2 kPa. EPS 2 yielded moderate improvements, reducing γ_Net to 0.98% and enhancing strength to 183.9 kPa at 30% moisture. Lower EPS concentrations (3 and 10 g/L) partially mitigated volumetric strain, with performance strongly dependent on moisture content. These results demonstrate that psychrophilic EPS, particularly EPS 1, effectively suppresses ice formation within soil pores and preserves mechanical structure, offering a sustainable, high-performance solution for stabilizing frost-susceptible soils in cold-regions. Full article

The increasing global connectivity, driven by the expansion of the Internet of Things (IoT), is generating a significant increase in system vulnerabilities. Cyberattackers exploit the computing and processing limitations of typical IoT devices and take advantage of inherent vulnerabilities in wireless networks and protocols to attack networks, compromise infrastructure, and cause damage. This paper presents a shallow learning multiclassifier approach for detecting and classifying cyberattacks on IoT networks. Specifically, it addresses MQTT networks, widely used in the IoT, to detect Denial-of-Service (DoS) and Intrusion attacks, using inter-device communication data as a basis. The use of shallow learning techniques allows this cybersecurity system to be implemented on resource-constrained devices, enabling local network monitoring and, consequently, increasing security and incident response capabilities by detecting and identifying attacks. The proposed system is validated on a real dataset obtained from an IoT system over MQTT, demonstrating its correct operation by achieving an accuracy greater than 99% and F1-score greater than 80% in the detection of Intrusion attacks. Full article

This interdisciplinary pilot study examines the use of Natural Language Processing (NLP) techniques, specifically Large Language Models (LLMs) with Prompt Engineering (PE), to analyze economic vulnerability from qualitative self-narratives. Seventy narratives from twenty-five women in the Palk Bay coastal region of Rameshwaram, India were analyzed using a schema adapted from a contextual empowerment framework. The study operationalizes theoretical constructs into structured Information Extraction (IE) templates, enabling systematic identification of multiple vulnerability aspects, contributing factors, and experiential expressions. Prompt templates were iteratively refined and validated through dual-annotator review, achieving an F1-score of 0.78 on a held-out subset. Extracted elements were examined through downstream analysis, including pattern grouping and graph-based visualization, to reveal co-occurrence structures and recurring vulnerability configurations across narratives. The findings demonstrate that LLMs, when aligned with domain-specific conceptual models and supported by human-in-the-loop validation, can enable interpretable and replicable analysis of self-narratives. While findings are bounded by the pilot scale and community-specific context, the approach supports translation of narrative evidence into community-level program design and targeted grassroots outreach, with planned expansion to multi-site, multilingual datasets for broader applicability. Full article

We establish precise exponential tail estimates for lacunary trigonometric sums of the form $f_N\left(x\right)={\sum }_{k=1}^N{c}_k\cos \left(2\pi n_{k}x\right)$, under the Hadamard gap condition. Using cumulant expansions and moment-generating function techniques, we obtain non-asymptotic upper bounds for the tail probabilities, including third-order corrections that refine the classical central limit theorem estimates. Furthermore, several examples illustrate these bounds for various choices of coefficients, highlighting the transition from subgaussian to stretched-exponential tail behavior. Full article

This study examines the nonlinear dynamical behavior of a Van der Waals system in the viscosity–capillarity regularization form. The solitary wave solutions of the proposed model are investigated using advanced analytical techniques, including the generalized Arnous method, the modified generalized Riccati equation mapping method, and the modified F-expansion approach. Additionally, we use mathematical simulations to enhance our comprehension of wave propagation. Moreover, a machine learning algorithm known as the multilayer perceptron regressor neural network was adopted to predict the performance results of our soliton solutions. Another important aspect of this study is the exploration of the chaos of the studied model by introducing a perturbed system. Chaotic analysis is supported by different techniques, such as return maps, power spectra, a bifurcation diagram, and a chaotic attractor. This multifaceted investigation not only emphasizes the rich dynamical pattern of the studied model but also presents a robust mathematical framework for studying nonlinear systems. The studied model also presents a robust mathematical framework for studying nonlinear systems. This study offers novel insights into nonlinear dynamics and wave phenomena by assessing the effectiveness of modern methodologies and clarifying the distinctive characteristics of a system’s nonlinear dynamics. Full article

The Bagley–Torvik equation (BTE) is an important model in mathematical physics and mechanics, but obtaining its analytical solution remains challenging. For its numerical treatment, the presence of composite functions in the generalized BTE poses additional difficulties, and efficient approaches for handling nonlinear terms are still lacking in the literature. This study proposes an improved numerical method for the fractional BTE with integral boundary conditions. By employing an integration technique, the original problem is transformed into a weakly singular Fredholm–Hammerstein (F–H) integral equation of the second kind. To address the nonlinear terms, an enhanced piecewise Taylor expansion scheme is developed to construct the discrete form, while the uniqueness of the solution is proven using the contraction mapping theorem in Banach spaces. The convergence and error analyses are rigorously carried out, and numerical experiments confirm the accuracy and efficiency of the proposed approach. Full article

With the rapid expansion of the Internet of Things (IoT), interconnected systems are becoming increasingly vulnerable to cyberattacks, making intrusion detection essential but difficult. The marked imbalance between regular traffic and attacks, as well as the redundancy of variables from multiple sensors and protocols, greatly complicates this task. The study aims to improve the robustness of IoT intrusion detection systems by reducing the risks of overfitting and false negatives through appropriate rebalancing and variable selection strategies. We combine two data rebalancing techniques, Synthetic Minority Over-sampling Technique (SMOTE) and Random Undersampling (RUS), with two feature selection methods, LASSO and Mutual Information, and then evaluate their performance on two classification models: CatBoost and a Simple Neural Network (SNN). The experiments show the superiority of CatBoost, which achieves an accuracy of 82% compared to 80% for SNN, and confirm the effectiveness of SMOTE over RUS, particularly for SNN. The CatBoost + SMOTE + LASSO configuration stands out with a recall of 82.43% and an F1-score of 85.08%, offering the best compromise between detection and reliability. These results demonstrate that combining rebalancing and variable selection techniques significantly enhances the performance and reliability of intrusion detection systems in the IoT, thereby strengthening cybersecurity in connected environments. Full article

Landslides pose a significant threat to Taiwan’s mountainous regions, particularly after extreme weather events such as typhoons. This study introduces a machine learning framework for post-disaster land use-land cover (LULC) classification and landslide detection in Nanfeng Village, central Taiwan, following Typhoon Khanun in August 2023. Using high-resolution Pléiades imagery and 22 environmental and spectral factors, a Random Forest classifier was developed. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was systematically evaluated across multiple variants. The Distance_SMOTE method yielded the best results, increasing overall accuracy from 74% to 85% and the Kappa coefficient from 0.69 to 0.82. F1-scores for landslides, roads, and grassland improved markedly, reaching 0.97, 0.85, and 0.78, respectively. The optimized model produced accurate pre- and post-typhoon LULC maps, revealing significant expansion of landslide zones after the event. This study demonstrates the practical value of combining SMOTE-based resampling with Random Forest for rapid, reliable post-disaster assessment, offering actionable insights for disaster response and land management in data-imbalanced conditions. By enabling timely mapping of hazard-affected areas and informing targeted recovery actions, the approach supports disaster risk reduction, sustainable land use planning, and ecosystem restoration. These outcomes contribute to the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by strengthening community resilience, promoting climate adaptation, and protecting terrestrial ecosystems in hazard-prone regions. Full article

Accurate tumor segmentation is crucial for cancer diagnosis and treatment planning. We developed a hybrid framework combining complementary convolutional neural network (CNN) models and advanced post-processing techniques for robust segmentation. Model 1 uses contrast-limited adaptive histogram equalization preprocessing, CNN predictions, and active contour refinement, but struggles with complex tumor boundaries. Model 2 applies noise-augmented preprocessing and iterative detection to enhance the segmentation of subtle and irregular regions. The outputs of both models are merged and refined with edge correction, size filtering, and a spatial intensity metric (SIM) expansion to improve under-segmented areas, an approach that achieves higher F1 scores and intersection over union scores. The developed framework highlights the potential in combining machine learning and image-processing techniques to develop reliable diagnostic tools. Full article