Search Results (48)

In this paper, machine learning techniques and risk factor analyses are applied to a marine hazard potential map of the Taiwan Strait. The waters surrounding Taiwan are characterized by dense maritime traffic, including commercial cargo transportation and fishing operations. Marine accidents caused by severe weather conditions are frequently reported, leading to irreversible loss of life and property. To mitigate these risks, this study utilizes the XGBoost machine learning model in conjunction with oceanic parameters and historical accident statistics to map the risk potential distribution of maritime accidents across the Taiwan Strait on a monthly basis. To address the challenge of limited historical accident data, this research employs a TVAE (Tabular Variational Autoencoder) to generate synthetic maritime accident data. The quality of such synthetic data is evaluated by comparing the similarity of probability distributions between the original and synthetic datasets. The resulting risk potential maps indicate that risk levels are significantly higher during the winter and lower during the summer. Furthermore, the SHAP (SHapley Additive exPlanations) model is applied to analyze key risk factors, identifying wave height as the primary driver, followed by meridional (north–south) wind speed and the primary spatial modes of wave height. These findings are validated using the National Ocean Database and Sharing System (NODASS) data, providing a comprehensive explanation of the underlying physical mechanisms. This study has successfully utilized the XGBoost machine learning model together with the TVAE generative technique to develop monthly marine hazard potential distribution maps for the Taiwan Strait. The novel research flowchart employed in this study can be applied to many other marine problems. Full article

Metal-composite joints, leveraging the high specific strength/stiffness and superior fatigue resistance of carbon fiber reinforced polymers (CFRP) alongside metallic materials’ excellent toughness and formability, have become prevalent in aerospace structures. Fastener flexibility serves as a critical parameter governing load distribution prediction and fatigue life assessment, where accurate quantification directly impacts structural reliability. Current approaches face limitations: experimental methods require extended testing cycles, numerical simulations exhibit computational inefficiency, and conventional machine learning (ML) models suffer from “black-box” characteristics that obscure mechanical principle alignment, hindering aerospace implementation. This study proposes an integrated framework combining numerical simulation with explainable ML for fastener flexibility analysis. Initially, finite element modeling (FEM) constructs a dataset encompassing geometric features, material properties, and flexibility values. Subsequently, a random forest (RF) prediction model is developed with five-fold cross-validation and residual analysis ensuring accuracy. SHapley Additive exPlanations (SHAP) methodology then quantifies input features’ marginal contributions to flexibility predictions, with results interpreted in conjunction with theoretical flexibility formulas to elucidate key parameter influence mechanisms. The approach achieves 0.99 R2 accuracy and 0.11 s computation time while resolving explainability challenges, identifying fastener diameter-to-plate thickness ratio as the dominant driver with negligible temperature/preload effects, thereby providing a validated efficient solution for aerospace joint optimization. Full article

Despite Türkiye’s high agricultural potential, consumer interest in organic foods remains limited. Understanding the motivations of Turkish consumers who prefer organic foods is crucial for expanding the domestic organic market. This research aims to explain consumers’ attitudes and purchasing behaviors toward organic agricultural products by utilizing the Theory of Planned Behavior (TPB) and Value-Belief-Norm (VBN) theories and examining the impact of health, environmental, economic, and social motivations on these attitudes and behaviors. Research data were obtained from online surveys conducted with 952 adult consumers across Türkiye. Partial Least Squares Structural Equation Modeling was used in the analysis of the data. Research results show that females purchase more organic agricultural products than males, and consumer aged 36 and over purchase more organic agricultural products than those aged 18–25. In the research, health-related, environmental, economic, and social motivations were found to be statistically significant in terms of consumer attitudes. It has been determined that social motivations are statistically significant in the purchasing behavior of organic agricultural products. The results show that a positive attitude towards organic agricultural products has been formed, but only social motivations can motivate consumers to purchase. The results indicate that the attitude and perceived behavioral control dimensions of the TPB, when considered in conjunction with the value- and norm-based explanations of the VBN, provide a more holistic explanation of organic product consumption. These findings highlight the importance of developing marketing strategies that center on social motivations and value-based communication. Furthermore, Turkish consumers’ economic constraints and product price differences also influence their purchasing decisions. In this context, incentives for low-income groups, such as discount campaigns and promotions, are recommended. Full article

Root cause analysis (RCA) identifies the faults and vulnerabilities underlying software failures, informing better design and maintenance decisions. Earlier approaches typically framed RCA as a classification task, predicting coarse categories of root causes. With recent advances in large language models (LLMs), RCA can be treated as a generative task that produces natural language explanations of faults. We introduce RCEGen, a framework that leverages state-of-the-art open-source LLMs to generate root cause explanations (RCEs) directly from bug reports. Using 298 reports, we evaluated five LLMs in conjunction with human developers and LLM judges across three key aspects: correctness, clarity, and reasoning depth. Qwen2.5-Coder-Instruct achieved the strongest performance (correctness ≈ 0.89, clarity ≈ 0.88, reasoning ≈ 0.65, overall ≈ 0.79), and RCEs exhibited high semantic fidelity (CodeBERTScore ≈ 0.98) to developer-written references despite low lexical overlap. The results demonstrated that LLMs achieve high accuracy in root cause identification from bug report titles and descriptions, particularly when reports contained error logs and reproduction steps. Full article

Diabetic Macular Edema (DME) poses a significant threat to vision, often leading to permanent blindness if not detected and addressed swiftly. Existing manual diagnostic methods are arduous and inconsistent, highlighting the pressing necessity for automated, accurate, and personalized solutions. This study presents a novel methodology for diagnosing DME and categorizing choroidal neovascularization (CNV), drusen, and normal conditions from fundus images through the application of transfer learning models and bio-inspired optimization methodologies. The methodology utilizes advanced transfer learning architectures, including VGG16, VGG19, ResNet50, EfficientNetB7, EfficientNetV2-S, InceptionV3, and InceptionResNetV2, for analyzing both binary and multi-class Optical Coherence Tomography (OCT) datasets. We combined the OCT datasets OCT2017 and OCTC8 to create a new dataset for our study. The parameters, including learning rate, batch size, and dropout layer of the fully connected network, are further adjusted using the bio-inspired Particle Swarm Optimization (PSO) method, in conjunction with thorough preprocessing. Explainable AI approaches, especially Shapley additive explanations (SHAP), provide transparent insights into the model’s decision-making processes. Experimental findings demonstrate that our bio-inspired optimized transfer learning Inception V3 significantly surpasses conventional deep learning techniques for DME classification, as evidenced by enhanced metrics including the accuracy, precision, recall, F1-score, misclassification rate, Matthew’s correlation coefficient, intersection over union, and kappa coefficient for both binary and multi-class scenarios. The accuracy achieved is approximately 98% in binary classification and roughly 90% in multi-class classification with the Inception V3 model. The integration of contemporary transfer learning architectures with nature-inspired PSO enhances diagnostic precision to approximately 95% in multi-class classification, while also improving interpretability and reliability, which are crucial for clinical implementation. This research promotes the advancement of more precise, personalized, and timely diagnostic and therapeutic strategies for Diabetic Macular Edema, aiming to avert vision loss and improve patient outcomes. Full article

Urban heat islands are intensifying under the dual pressures of global climate change and rapid urbanization, posing serious challenges to ecological sustainability and human well-being. Among the factors influencing urban thermal environments, vegetation and green spaces play a critical role in mitigating heat accumulation through canopy cover, evapotranspiration, and ecological connectivity. In this study, a comparative analysis of Seoul and Busan—two representative metropolitan areas in South Korea—was conducted using land surface temperature (LST) data derived from Landsat 8 and a set of multi-source spatial indicators. The nonlinear effects and interactions among built environment, socio-economic, and ecological variables were quantified using the Extreme Gradient Boosting (XGBoost) model in conjunction with Shapley Additive Explanations (SHAP). Results demonstrate that vegetation, as indicated by the Normalized Difference Vegetation Index (NDVI), consistently exerts significant cooling effects, with a pronounced threshold effect observed when NDVI values exceed 0.6. Furthermore, synergistic interactions between NDVI and surface water availability, measured by the Normalized Difference Water Index (NDWI), substantially enhance ecological cooling capacity. In contrast, areas with high building and population densities, particularly those at lower elevations, are associated with increased LST. These findings underscore the essential role of green infrastructure in regulating urban thermal environments and provide empirical support for ecological conservation, urban greening strategies, and climate-resilient urban planning. Strengthening vegetation cover, enhancing ecological corridors, and integrating greening policies across spatial scales are vital for mitigating urban heat and improving climate resilience in rapidly urbanizing regions. Full article

This note presents a series of contrasts pertaining to Vietnamese polar questions: (i) The subject can be definite but not quantificational; (ii) the subject can be plain but not only-focused; (iii) the modal adverb chắc chắn (‘certainly’) can follow but not precede verum focus. I argue that a monoclausal analysis, advocated in several previous works, will have difficulties accounting for these contrasts and propose a bi-clausal analysis that explains them in a natural way. The explanation relies on the assumption of a general condition on questions, Partition by Exhaustification (PbE), in conjunction with some other independently motivated semantic and pragmatic constraints. Full article

The study of neuronal electrical activity and spatial organization is essential for uncovering the mechanisms that regulate neuronal electrophysiology and function. Mathematical models have been utilized to analyze the structural properties of neuronal networks, predict connectivity patterns, and examine how morphological changes impact neural network function. In this study, we aimed to explore the role of the actin cytoskeleton in neuronal signaling via primary cilia and to elucidate the role of the actin network in conjunction with neuronal electrical activity in shaping spatial neuronal formation and organization, as demonstrated by relevant mathematical models. Our proposed model is based on the polygamma function, a mathematical application of ramification, and a geometrical definition of the actin cytoskeleton via complex numbers, ring polynomials, homogeneous polynomials, characteristic polynomials, gradients, the Dirac delta function, the vector Laplacian, the Goldman equation, and the Lie bracket of vector fields. We were able to reflect the effects of neuronal electrical activity, as modeled by the Van der Pol equation in combination with the actin cytoskeleton, on neuronal morphology in a 2D model. In the next step, we converted the 2D model into a 3D model of neuronal electrical activity, known as a core-shell model, in which our generated membrane potential is compatible with the neuronal membrane potential (in millivolts, mV). The generated neurons can grow and develop like an organoid brain based on the developed mathematical equations. Furthermore, we mathematically introduced the signal transduction of primary cilia in neurons. Additionally, we proposed a geometrical model of the neuronal branching pattern, which we described as ramification, that could serve as an alternative mathematical explanation for the branching pattern emanating from the neuronal soma. In conclusion, we highlighted the relationship between the actin cytoskeleton and the signaling processes of primary cilia. We also developed a 3D model that integrates the geometric organization unique to neurons, which contains soma and branches, such that the mathematical model represents the interaction between the actin cytoskeleton and neuronal electrical activity in generating action potentials. Next, we could generalize the model into a cluster of neurons, similar to an organoid brain model. This mathematical framework offers promising applications in artificial intelligence and advancements in neural networks. Full article

Industry 4.0 technologies offer significant opportunities to enhance sustainable production and circular economy practices in the face of challenges arising from climate change. Considering the growing interest in this field, the literature review exposed that, particularly in the case of the Internet of Things (IoT), there is a need for empirical assessments of the impact of this technology on sustainability and circularity. This paper presents the validation process of an original assessment tool that evaluates IoT’s alignment with the socioenvironmental and circular context of manufacturing organizations and their supply chains. Emphasis is placed on the construct titled “IoT Technology Expectations”. After systematically conducting a literature review, this study employed the Delphi method in conjunction with statistical analyses to refine or formulate new indicators or statements based on expert consensus, validating the proposed assessment tool. The findings of this research contribute to management practices by providing an instrument to assess the current stance of top management and other key managers (production, project and supply chain) on IoT use in manufacturing operations or supply chains from a socioenvironmental and circular perspective. The instrument serves as a starting point for exploring IoT’s potential in circular economy practices. Academically, it provides a detailed explanation of the Delphi method and its application and outcomes. Full article

In addition to its flavor and nutritional value, the origin of kelp has become a crucial factor influencing consumer choices. Nevertheless, research on kelp’s origin traceability by volatile organic compound (VOC) analysis is lacking, and the application of deep learning in this field remains scarce due to its black-box nature. To address this gap, we attempted to identify the origin of kelp by analyzing its VOCs in conjunction with explainable deep learning. In this work, we identified 115 distinct VOCs in kelp samples using gas chromatography coupled with ion mobility spectroscopy (GC-IMS), of which 68 categories were discernible. Consequently, we developed a comprehensible one-dimensional convolutional neural network (1D-CNN) model that incorporated 107 VOCs exhibiting significant regional disparities (p < 0.05). The model successfully discerns the origin of kelp, achieving perfect metrics across accuracy (100%), precision (100%), recall (100%), F1 score (100%), and AUC (1.0). SHapley Additive exPlanations (SHAP) analysis highlighted the impact of features such as 1-Octen-3-ol-M, (+)-limonene, allyl sulfide-D, 1-hydroxy-2-propanone-D, and (E)-2-hexen-1-al-M on the model output. This research provides deeper insights into how critical product features correlate with specific geographic information, which in turn boosts consumer trust and promotes practical utilization in actual settings. Full article

Remote tower technology is an important development direction for air traffic control to reduce the construction and operation costs of small or remote airports. However, its digital and virtualized working environment poses new challenges to controllers’ situational awareness (SA). In this study, a dataset is constructed by collecting eye-tracking (ET) and heart rate variability (HRV) data from participants in a remote tower simulation control experiment. At the same time, probe questions are designed that correspond to the SA hierarchy in conjunction with the remote tower control task flow, and the dataset is annotated using the scenario presentation assessment method (SPAM). The annotated dataset containing 25 ET and HRV features is trained using the LightGBM model optimized by a Tree-structured Parzen Estimator, and feature selection and model interpretation are performed using the SHapley Additive exPlanations (SHAP) analysis. The results show that the TPE-LightGBM model exhibits excellent prediction capability, obtaining an RMSE, MAE and adjusted R² of 0.0909, 0.0730 and 0.7845, respectively. This study presents an effective method for assessing and predicting controllers’ SA in remote tower environments. It further provides a theoretical basis for understanding the effect of the physiological state of remote tower controllers on their SA. Full article

This paper investigates points of vulnerability in the decisions made by backers and campaigners in crowdfund pledges in an attempt to facilitate a sustainable entrepreneurial ecosystem by increasing the rate of good projects being funded. In doing so, this research examines factors that contribute to the success or failure of crowdfunding campaign pledges using eXplainable AI methods (SHapley Additive exPlanations and Counterfactual Explanations). A dataset of completed Kickstarter campaigns was used to train two binary classifiers. The first model used textual features from the campaigns’ descriptions, and the second used categorical, numerical, and textual features. Findings identify textual terms, such as “stretch goals”, that convey both elements of risk and ambitiousness to be strongly correlated with success, contrary to transparent communications of risks that bring forward worries that would have otherwise remained dormant for backers. Short sentence length, in conjunction with high term complexity, is also associated with campaign success. We link the latter to signaling theory and the campaigners’ projection of knowledgeability of the domain. Certain numerical data, such as the project’s duration, frequency of post updates, and use of images, confirm previous links to campaign success. We enhance implications through the use of Counterfactual Explanations and generate actionable recommendations on how failed projects could become successful while proposing new policies, in the form of nudges, that shield backers from points of vulnerability. Full article

Overbreak induced by tunnel blasting is a harmful phenomenon. Accurate assessment of overbreak can effectively reduce investment and ensure operational safety. In this study, a hybrid intelligent model for assessing blasting-induced overbreak is proposed which can accurately predict overbreak and effectively evaluate the importance of feature parameters. To ensure accurate prediction of overbreak, hyperparameters of four machine learning algorithms are optimized using a whale optimization algorithm. Their performance is compared based on three regression metrics: R², RMSE, and VAF. Given the limitations of traditional feature importance analysis methods, the Shapley Additive Explanation method is used in conjunction with the random forest algorithm. After accurately predicting overbreak caused by different sections of the tunnel, the impact of each input parameter on overbreak is analyzed, and recommendations for design values of certain significant parameters are provided. The research indicates that the proposed method can accurately predict overbreak caused by actual engineering blasts and provide insights into the selection of design parameter values. Full article

Pomelo fruit pulp mainly is consumed fresh and with very little processing, and its peels are discarded as biological waste, which can cause the environmental problems. The peels contain several bioactive chemical compounds, especially essential oils (EOs). The content of a specific EO is important for the extraction process in industry and in research units such as breeding research. The explanation of the biosynthesis pathway for EO generation and change was included. The chemical bond vibration affected the prediction of EO constituents was comprehensively explained by regression coefficient plots and x-loading plots. Visible and near-infrared spectroscopy (VIS/NIRS) is a prominent rapid technique used for fruit quality assessment. This research work was focused on evaluating the use of VIS/NIRS to predict the composition of EOs found in the peel of the pomelo fruit (Citrus maxima (J. Burm.) Merr. cv Kao Nam Pueng) following storage. The composition of the peel oil was analyzed by gas chromatography–mass spectrometry (GC-MS) at storage durations of 0, 15, 30, 45, 60, 75, 90, 105 and 120 days (at 10 °C and 70% relative humidity). The relationship between the NIR spectral data and the major EO components found in the peel, including nootkatone, geranial, β-phellandrene and limonene, were established using the raw spectral data in conjunction with partial least squares (PLS) regression. Preprocessing of the raw spectra was performed using multiplicative scatter correction (MSC) or second derivative preprocessing. The PLS model of nootkatone with full MSC had the highest correlation coefficient between the predicted and reference values (r = 0.82), with a standard error of prediction (SEP) of 0.11% and bias of 0.01%, while the models of geranial, β-phellandrene and limonene provided too low r values of 0.75, 0.75 and 0.67, respectively. The nootkatone model is only appropriate for use in screening and some other approximate calibrations, though this is the first report of the use of NIR spectroscopy on intact fruit measurement for its peel EO constituents during cold storage. Full article

The purpose of this article is to give a historical and reasoned overview of the phenomenon of emergence according to the various authors involved, with particular emphasis on its openness to the dimension of the mystery of the real, which can lead the scientist to humility in scientific research. The evidence, the curiosity and then the study of this concept, which is so pervasive in the complexity of cosmic dynamics, in fact requires an investigation that must be extended not only to different disciplines, but through them. In fact, the cross-disciplinary method enriches the quality of this research, giving reason to both the unity and the complexity of reality. The phenomenon of emergence is particularly concerned with this cross-disciplinary scientific approach, which transcends any reductionism in favour of a network of meanings specifically nourished by the possibility of conjunctive explanations involving empirical science, philosophy, metaphysics and theology. Faced with this perspective offered by emergence, science discovers the mystery of the cosmos in a new light, thereby opening the door to an ever deeper understanding and new avenues of research. An essential characteristic of this revised scientific method, inspired by cross-disciplinarity, is thus humility, which allows, on the one hand, a deeper relationship between disciplines and persons and, on the other hand, a heightened awareness of the depth of reality, as a complex and intelligible gift of a Trinitarian God, revealed as Logos in Jesus Christ. Full article