Search Results (5,592)

As demand for plant-based beverages grows, analytical tools are needed to classify and understand their structural and compositional diversity. This study applied a multi-analytical approach to characterize 41 commercial almond-, oat-, rice- and soy-based beverages, evaluating attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, protein secondary structure proportions, colorimetry, and microscopic image texture analysis. A total of 26 variables, derived from ATR-FTIR and protein secondary structure assessment, were employed in multivariate models, using partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) to evaluate classification performance. The results indicated clear group separation, with soy and rice beverages forming distinct clusters while almond and oat samples showing partial overlap. Variable importance in projection (VIP) scores revealed that β-turn and α-helix protein structures, along with carbohydrate-associated spectral bands, were the key features for beverages’ classification. Textural features derived from microscopy images correlated with sugar and carbohydrate content and color parameters were also employed to describe beverages’ differences related to sugar content and visual appearance in terms of homogeneity. These findings demonstrate that combining ATR-FTIR spectral data with protein secondary structure data enables the effective classification of plant-based beverages, while microscopic image textural and color parameters offer additional extended product characterization. Full article

This article presents an empirical evaluation of the technical and economic performance of a building-integrated photovoltaic (PV) system implemented at the Faculty of Architecture and Urbanism of the University of Cuenca, Ecuador. This study explores both stages of deployment, beginning with a 7.7 kWp pilot system and later scaling to a full 75.6 kWp configuration. This hourly monitoring of power exchanges with utility was conducted over several months using high-resolution instrumentation and cloud-based analytics platforms. A detailed comparison between projected energy output, recorded production, and real energy consumption was carried out, revealing how seasonal variability, cloud cover, and academic schedules influence system behavior. The findings also include a comparison between billed and actual electricity prices, as well as an analysis of the system’s payback period under different cost scenarios, including state-subsidized and real-cost frameworks. The results confirm that energy exports are frequent during weekends and that daily generation often exceeds on-site demand on non-working days. Although the university benefits from low electricity tariffs, the system demonstrates financial feasibility when broader public cost structures are considered. This study highlights operational outcomes under real-use conditions and provides insights for scaling distributed generation in institutional settings, with particular relevance for Andean urban contexts with similar solar profiles and tariff structures. Full article

The growing demand for the production of food has led to the development of new analytical techniques in the food industry, enabling innovative strategies to streamline food production and ensure its physicochemical and microbiological quality. In this work, a smart sensor was developed using the electrical impedance spectroscopy (EIS) technique. The system is based on discrete Fourier transform (DFT) and incorporates a Howland current source. The experimental results showed that the sensor was able to detect the moisture content in potatoes (Solanum tuberosum L.). Favorable responses were obtained by exciting the system with two frequency intervals: 0–100 Hz and 500–5000 Hz. An exhaustive analysis of the frequency response was performed to identify the most linear behavior in the moisture measurement, with an R-squared of 0.786 and signals in intervals from 500 to 5000 Hz. Moreover, the linearity remained stable across most frequencies, resulting in consistent measurements, even with the implementation of low-cost components. Full article

This study proposes a computational framework that transforms eye-tracking analysis from statistical description to cognitive structure modeling, aiming to reveal the organizational features embedded in the viewing process. Using the designers’ observation of a traditional Chinese landscape painting as an example, the study draws on the goal-oriented nature of design thinking to suggest that such visual exploration may exhibit latent structural tendencies, reflected in patterns of fixation and transition. Rather than focusing on traditional fixation hotspots, our four-dimensional framework (Region, Relation, Weight, Time) treats viewing behavior as structured cognitive networks. To operationalize this framework, we developed a data-driven computational approach that integrates fixation coordinate transformation, K-means clustering, extremum point detection, and linear interpolation. These techniques identify regions of concentrated visual attention and define their spatial boundaries, allowing for the modeling of inter-regional relationships and cognitive organization among visual areas. An adaptive buffer zone method is further employed to quantify the strength of connections between regions and to delineate potential visual nodes and transition pathways. Three design-trained participants were invited to observe the same painting while performing a think-aloud task, with one participant selected for the detailed demonstration of the analytical process. The framework’s applicability across different viewers was validated through consistent structural patterns observed across all three participants, while simultaneously revealing individual differences in their visual exploration strategies. These findings demonstrate that the proposed framework provides a replicable and generalizable method for systematically analyzing viewing behavior across individuals, enabling rapid identification of both common patterns and individual differences in visual exploration. This approach opens new possibilities for discovering structural organization within visual exploration data and analyzing goal-directed viewing behaviors. Although this study focuses on method demonstration, it proposes a preliminary hypothesis that designers’ gaze structures are significantly more clustered and hierarchically organized than those of novices, providing a foundation for future confirmatory testing. Full article

Critical components in support structures for wind turbines, flange joints, are fundamental to ensure the structural integrity of mechanical assemblies under varying operational conditions. This paper investigates the structural performance of L-type flange joints, focusing on the influence of bolt grades and bolt pretension through a finite element analysis (FEA) study of its key performance indicators, including stress distribution, deformation, and force–displacement behaviors. This paper studies two high-strength bolt grades, Grade 10.9 and Grade 12.9, and two main steps—first, bolt pretension and, second, external loading (tower shell tensile load)—to investigate the influence on joint reliability and safety margins. The novelty of this study lies in its specific focus on static axial loading conditions, unlike the existing literature that emphasizes fatigue or dynamic loads. Results show that the specimen carrying a higher bolt grade (12.9) has 18% more ultimate load carrying capacity than the specimen with a lower bolt grade (10.9). Increased pretension increases the stability of the joint and reduces the micro-movements between A and B (on model specimen), but could result in material fatigue if over-pretensioned. Comparative analysis of the different bolt grades has provided practical guidance on material selection and bolt pretension in L-type flange joints for wind turbine support structures. The findings of this work offer insights into the proper design of robust flange connections for high-demand applications by highlighting a balance among material properties, bolt pretension, and operational conditions, while also proposing optimized pretension and material recommendations validated against classical analytical models. Full article

This study investigates the fabrication, nanomechanical behavior, and tribological performance of nanostructured superlattice coatings (NSCs) composed of alternating TiAlSiNb-N/TiCr-CN bilayers. Deposited via High-Power Ion-Plasma Magnetron Sputtering (HiPIPMS) onto 100Cr6 steel substrates, the coatings achieved nanohardness values of ~25 GPa and elastic moduli up to ~415 GPa. A novel empirical method was applied to extract stress–strain field (SSF) gradient and divergence profiles from nanoindentation load–displacement data. These profiles revealed complex, depth-dependent oscillations attributed to alternating strain-hardening and strain-softening mechanisms. Fourier analysis identified dominant spatial wavelengths, DWL, ranging from 4.3 to 42.7 nm. Characteristic wavelengths WL1 and WL2, representing fine and coarse oscillatory modes, were 8.2–9.2 nm and 16.8–22.1 nm, respectively, aligning with the superlattice period and grain-scale features. The hyperfine structure exhibited non-stationary behavior, with dominant wavelengths decreasing from ~5 nm to ~1.5 nm as the indentation depth increased. We attribute the SSF gradient and divergence spatial oscillations to alternating strain-hardening and strain-softening deformation mechanisms within the near-surface layer during progressive loading. This cyclic hardening–softening behavior was consistently observed across all NSC samples, suggesting it represents a general phenomenon in thin film/substrate systems under incremental nanoindentation loading. The proposed SSF gradient–divergence framework enhances nanoindentation analytical capabilities, offering a tool for characterizing thin-film coatings and guiding advanced tribological material design. Full article

Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an Explainable AI (XAI) framework to achieve both high predictive accuracy and interpretability in turnover forecasting. Methods: Two publicly available HR datasets (IBM HR Analytics, Kaggle HR Analytics) were preprocessed with label encoding and MinMax scaling. Class imbalance was addressed via GAN-based synthetic data generation. A three-layer Transformer encoder performed binary classification, and SHapley Additive exPlanations (SHAP) analysis provided both global and local feature attributions. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. Results: On the IBM dataset, the Generative Adversarial Network (GAN) Transformer model achieved 92.00% accuracy, 96.67% precision, 87.00% recall, 91.58% F1, and 96.32% ROC AUC. On the Kaggle dataset, it reached 96.95% accuracy, 97.28% precision, 96.60% recall, 96.94% F1, and 99.15% ROC AUC, substantially outperforming classical resampling methods (ROS, SMOTE, ADASYN) and recent literature benchmarks. SHAP explanations highlighted JobSatisfaction, Age, and YearsWithCurrManager as top predictors in IBM and number project, satisfaction level, and time spend company in Kaggle. Conclusion: The proposed GAN Transformer SHAP pipeline delivers state-of-the-art turnover prediction while furnishing transparent, actionable insights for HR decision-makers. Future work should validate generalizability across diverse industries and develop lightweight, real-time implementations. Full article

The increasing demand for data-driven decision making to maintain the innovations and competitiveness of organizations highlights the need for data science educations across academia and industry. At its core is a solid understanding of statistics, which is necessary for conducting a thorough analysis of data and deriving valuable insights. Unfortunately, conventional statistics learning often lacks practice in real-world applications using computer programs, causing a separation between conceptual knowledge of statistics equations and their hands-on skills. Integrating statistics learning into Python programming can convey an effective solution for this problem, where it has become essential in data science implementations, with extensive and versatile libraries. In this paper, we present a self-learning method for fundamental statistics through Python programming for data science studies. Unlike conventional approaches, our method integrates three types of interactive problems—element fill-in-blank problem (EFP), grammar-concept understanding problem (GUP), and value trace problem (VTP)—in the Programming Learning Assistant System (PLAS). This combination allows students to write code, understand concepts, and trace the output value while obtaining instant feedback so that they can improve retention, knowledge, and practical skills in learning statistics using Python programming. For evaluations, we generated 22 instances using source codes for fundamental statistics topics, and assigned them to 40 first-year undergraduate students at UPN Veteran Jawa Timur, Indonesia. Statistics analytical methods were utilized to analyze the student learning performances. The results show that a significant correlation ($\rho <0.05$) exists between the students who solved our proposal and those who did not. The results confirm that it can effectively assist students in learning fundamental statistics self-learning using Python programming for data science implementations. Full article

This study investigates the relationship between sovereign credit rating transitions and domestic equity market performance, focusing on Greece from 2004 to 2024. Although credit ratings are central to sovereign risk assessment, their immediate influence on financial markets remains contested. This research adopts a multi-method analytical framework combining algebraic combinatorics and time-series econometrics. The methodology incorporates the construction of a directed credit rating transition graph, the partially ordered set representation of rating hierarchies, rolling-window correlation analysis, Granger causality testing, event study evaluation, and the formulation of a reward matrix with optimal rating path optimization. Empirical results indicate that credit rating announcements in Greece exert only modest short-term effects on the Athens Stock Exchange General Index, implying that markets often anticipate these changes. In contrast, sequential downgrade trajectories elicit more pronounced and persistent market responses. The reward matrix and path optimization approach reveal structured investor behavior that is sensitive to the cumulative pattern of rating changes. These findings offer a more nuanced interpretation of how sovereign credit risk is processed and priced in transparent and fiscally disciplined environments. By bridging network-based algebraic structures and economic data science, the study contributes a novel methodology for understanding systemic financial signals within sovereign credit systems. Full article

Evaluating performance is a necessary and specific process across all sectors and organizational levels, shaped by context, indicators, and purpose. Considering global sustainability transitions, understanding financial performance entails a deeper perspective on technical accuracy, conceptual clarity, and systemic integration. This study investigates how financial performance is assessed and interpreted in sustainability-focused research, drawing on a bibliometric analysis of 490 articles indexed in the Web of Science from 2007 to 2023. Using SciMAT, we traced thematic evolutions and revealed a fragmented research landscape marked by competing theoretical, methodological, and practical orientations. To address this conceptual dispersion, we applied the Analytic Hierarchy Process (AHP) to evaluate five key alternatives to financial-performance assessment (quantitative measurement, definition-oriented reasoning, theoretical frameworks, experiential comparison, and integration with sustainability and ethics) against three conceptual criteria (philosophical depth, holistic scope, and multidisciplinary relevance). The results highlight a strong preference for holistic and integrative models of financial performance, with quantitative measurement ranking highest in practical terms, followed by experiential and sustainability-driven approaches. These results underscore the need to align financial evaluation more closely with sustainability values, bridging short-term metrics with long-term societal impact. By combining diachronic thematic mapping with structured decision analysis, this study advances a more reflective and forward-looking framework for performance research. It contributes to sustainability research by identifying underexplored epistemological pathways and supporting the development of financial evaluation models that are inclusive, ethically grounded, and aligned with sustainable development goals. Full article

The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. To address the scarcity of in situ observational data, this study developed a satellite remote sensing-driven multi-parameter coupled model and reconstructed the WJ’s seasonal variations using the XGBoost machine learning algorithm. The results revealed that wind stress components, sea surface temperature, and wind stress curl serve as the primary drivers of its seasonal dynamics. The XGBoost model demonstrated superior performance in reconstructing WJ’s seasonal variations, achieving coefficients of determination (R²) exceeding 0.97 across all seasons and maintaining root mean square errors (RMSE) below 0.2 m/s across all seasons. The reconstructed currents exhibited strong consistency with the Ocean Surface Current Analysis Real-time (OSCAR) dataset, showing errors below 0.05 m/s in spring and autumn and under 0.1 m/s in summer and winter. The proposed multi-feature integrated modeling framework delivers a high spatiotemporal resolution analytical tool for tropical Indian Ocean circulation dynamics research, while simultaneously establishing critical data infrastructure to decode monsoon current coupling mechanisms, advancing early warning systems for extreme climatic events, and optimizing regional marine resource governance. Full article

The Zhurong rover of the Tianwen-1 mission has detected sulfates in its landing area. The analysis of these sulfates provides scientific evidence for exploring past hydration conditions and atmospheric evolution on Mars. As a non-contact technique with long-range detection capability, Laser-Induced Breakdown Spectroscopy (LIBS) is widely used for elemental identification on Mars. However, quantitative analysis of anionic elements using LIBS remains challenging due to the weak characteristic spectral lines of evaporite salt elements, such as sulfur, in LIBS spectra, which provide limited quantitative information. This study proposes a quantitative analysis method for sulfur in sulfate-containing Martian analogs by leveraging spectral line correlations, full-spectrum information, and prior knowledge, aiming to address the challenges of sulfur identification and quantification in Martian exploration. To enhance the accuracy of sulfur quantification, two analytical models for high and low sulfur concentrations were developed. Samples were classified using infrared spectroscopy based on sulfur content levels. Subsequently, multimodal deep learning models were developed for quantitative analysis by integrating LIBS and infrared spectra, based on varying concentrations. Compared to traditional unimodal models, the multimodal method simultaneously utilizes elemental chemical information from LIBS spectra and molecular structural and vibrational characteristics from infrared spectroscopy. Considering that sulfur exhibits distinct absorption bands in infrared spectra but demonstrates weak characteristic lines in LIBS spectra due to its low ionization energy, the combination of both spectral techniques enables the model to capture complementary sample features, thereby effectively improving prediction accuracy and robustness. To validate the advantages of the multimodal approach, comparative analyses were conducted against unimodal methods. Furthermore, to optimize model performance, different feature selection algorithms were evaluated. Ultimately, an XGBoost-based feature selection method incorporating prior knowledge was employed to identify optimal LIBS spectral features, and the selected feature subsets were utilized in multimodal modeling to enhance stability. Experimental results demonstrate that, compared to the BPNN, SVR, and Inception unimodal methods, the proposed multimodal approach achieves at least a 92.36% reduction in RMSE and a 46.3% improvement in R². Full article

Traditional project scheduling tools like Gantt charts struggle with dynamic adjustments and real-time optimization in complex construction projects, leading to inefficiencies and delays. This study addresses this challenge by proposing a dynamic optimization framework that integrates Building Information Modeling (BIM) and Artificial Intelligence (AI) to enhance schedule management. The framework comprises three layers: a data layer for collecting BIM and real-time site data, an analysis layer powered by AI algorithms for predictive analytics and optimization, and an application layer for visualizing progress and supporting decision-making. Through a case study on a large-scale water reservoir tunnel project in China, the framework demonstrated significant improvements in identifying schedule risks, optimizing resource allocation, and enabling real-time adjustments. Key innovations include a 4-in-1 Network Diagram Engine and a Blueprint Engine, which facilitate intuitive progress monitoring and automated task management. However, limitations in personnel skill matching, interface complexity, and mobile system performance were identified. This research advances the theoretical foundation of BIM-AI integration and provides practical insights for improving scheduling efficiency and project outcomes in the construction industry. Future work should focus on enhancing human resource management modules and refining system usability for broader adoption. Full article

The oil and gas industry is subject to significant fire hazards due to the flammability of hydrocarbons and the extreme conditions of operational facilities. Intumescent coatings (ICs) serve as a crucial passive fire protection strategy, forming an insulating char layer when exposed to heat, thereby reducing heat transfer and delaying structural failure. This review article provides an overview of recent developments in the effectiveness of ICs in mitigating fire risks, enhancing structural resilience, and reducing environmental impacts within the oil and gas industry. The literature surveyed shows that analytical techniques, such as thermogravimetric analysis, scanning electron microscopy, and large-scale fire testing, have been used to evaluate the thermal insulation performances of the coatings. The results indicate significant temperature reductions on protected steel surfaces that extend critical failure times under hydrocarbon fire conditions. Recent advancements in nano-enhanced and bio-derived ICs have also improved thermal stability and mechanical durability. Furthermore, numerical modeling based on heat transfer, mass conservation, and kinetic equations aids in optimizing formulations for real-world applications. Nevertheless, challenges remain in terms of standardizing modeling frameworks and enhancing the environmental sustainability of ICs. This review highlights the progress made and the opportunities for continuous advances and innovation in IC technologies to meet the ever-evolving challenges and complexities in oil and gas industry operations. Consequently, the need to enhance fire protection by utilizing a combination of tools improves predictive modeling and supports regulatory compliance in high-risk industrial environments. Full article

Quantum Dots (QDs) are small semiconductor nanoparticles (<10 nm) with strong, relatively stable, and tunable luminescent properties, which are increasingly applied in the sensing and detection of various analytes, including metal ions, biomarkers, explosives, proteins, RNA/DNA fragments, pesticides, drugs, and pollutants. In this review, we critically assess recent developments and advancements in luminescent QD-based sensors from an analytical perspective. We collected, tabulated, and analyzed relevant data reported in 124 peer-reviewed articles. The key analytical figures of merit, including the limit of detection (LOD), excitation and emission wavelengths, and size of the particles were extracted, tabulated, and analyzed with graphical representations. We calculated the geometric mean and median LODs from those tabulated publications. We found the following geometric mean LODs: 38 nM for QD-fluorescent-based sensors, 26 nM for QD-phosphorescent-based sensors, and an impressively low 0.109 pM for QD-chemiluminescent-based sensors, which demonstrate by far the best sensitivity in QD-based detection. Moreover, AI-based sensing methods, including the ATTBeadNet model, optimized principal component analysis(OPCA) model, and Support Vector Machine (SVM)-based system, were reviewed as they enhance the analytical performance of the detection. Despite these advances, there are still challenges that include improvements in recovery values, biocompatibility, stability, and overall performance. This review highlights trends to guide the future design of robust, high-performance, QD-based luminescent sensors. Full article