Search Results (242)

The rapid expansion of AI applications in various domains demands models that balance predictive power with human interpretability, a requirement that has catalyzed the development of hybrid algorithms combining high accuracy with human-readable outputs. This study introduces a novel neuro-symbolic framework, OIKAN (Optimized Interpretable Kolmogorov–Arnold Network), designed to integrate the representational power of feedforward neural networks with the transparency of symbolic regression. The framework employs Gaussian noise-based data augmentation and a two-phase sparse symbolic regression pipeline using ElasticNet, producing analytical expressions suitable for both classification and regression problems. Evaluated on 60 classification and 58 regression datasets from the Penn Machine Learning Benchmarks (PMLB), OIKAN Classifier achieved a median accuracy of 0.886, with perfect performance on linearly separable datasets, while OIKAN Regressor reached a median R² score of 0.705, peaking at 0.992. In comparative experiments with ElasticNet, DecisionTree, and XGBoost baselines, OIKAN showed competitive accuracy while maintaining substantially higher interpretability, highlighting its distinct contribution to the field of explainable AI. OIKAN demonstrated computational efficiency, with fast training and low inference time and memory usage, highlighting its suitability for real-time and embedded applications. However, the results revealed that performance declined more noticeably on high-dimensional or noisy datasets, particularly those lacking compact symbolic structures, emphasizing the need for adaptive regularization, expanded function libraries, and refined augmentation strategies to enhance robustness and scalability. These results underscore OIKAN’s ability to deliver transparent, mathematically tractable models without sacrificing performance, paving the way for explainable AI in scientific discovery and industrial engineering. Full article

Diesel engines power much of the heavy-duty equipment used in underground mines, where exhaust emissions pose acute environmental and occupational health challenges. However, predicting the amount of air required to dilute these emissions is difficult because exhaust mass flow and pollutant concentrations vary nonlinearly with multiple operating parameters. We apply deep learning to predict the total exhaust mass flow and carbon monoxide (CO) concentration of a six-cylinder gas–diesel (dual-fuel) turbocharged KAMAZ 910.12-450 engine under controlled operating conditions. We trained artificial neural networks on the preprocessed experimental dataset to capture nonlinear relationships between engine inputs and exhaust responses. Model interpretation with Shapley additive explanations (SHAP) identifies torque, speed, and boost pressure as dominant drivers of exhaust mass flow, and catalyst pressure, EGR rate, and boost pressure as primary contributors to CO concentration. In addition, symbolic regression yields an interpretable analytical expression for exhaust mass flow, facilitating interpretation and potential integration into control. The results indicate that deep learning enables accurate and interpretable prediction of key exhaust parameters in dual-fuel engines, supporting emission assessment and mitigation strategies relevant to underground mining operations. These findings support future integration with ventilation models and real-time monitoring frameworks. Full article

Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies. Full article

Accurate prediction of soil–structure interface shear strength (τ_max) is critical for reliable geotechnical design. This study combines experimental testing with interpretable machine learning to overcome the limitations of traditional empirical models and black-box approaches. Ninety large-displacement ring shear tests were performed on five sands and three interface materials (steel, PVC, and stone) under normal stresses of 25–100 kPa. The results showed that particle morphology, quantified by the regularity index (RI), and surface roughness (R_t) are dominant factors. Irregular grains and rougher interfaces mobilised higher τ_max through enhanced interlocking, while smoother particles reduced this benefit. Harder surfaces resisted asperity crushing and maintained higher shear strength, whereas softer materials such as PVC showed localised deformation and lower resistance. These experimental findings formed the basis for a hybrid symbolic regression framework integrating Genetic Programming (GP) with Shapley Additive Explanations (SHAP), Fourier feature augmentation, and physics-informed constraints. Compared with multiple linear regression and other hybrid GP variants, the Physics-Informed Neural Fourier GP (PIN-FGP) model achieved the best performance (R² = 0.9866, RMSE = 2.0 kPa). The outcome is a set of five interpretable and physics-consistent formulas linking measurable soil and interface properties to τ_max. The study provides both new experimental insights and transparent predictive tools, supporting safer and more defensible geotechnical design and analysis. Full article

The aim of the present paper is to further develop a theory on the flow of linguistic variables making a sentence, namely, the transformation of (a) characters into words; (b) words into word intervals; and (c) word intervals into sentences. The relationship between two linguistic variables is studied as a communication channel whose performance is determined by the slope of their regression line and by their correlation coefficient. The mathematical theory is applicable to any field/specialty in which a linear relationship holds between two variables. The signal-to-noise ratio $\Gamma$ is a figure of merit of a channel being “deterministic”, i.e., a channel in which the scattering of the data around the regression line is negligible. The larger $\Gamma$ is, the more the channel is “deterministic”. In conclusion, humans have invented codes whose sequences of symbols that make words cannot vary very much when indicating single physical or mental objects of their experience (larger $\Gamma )$. On the contrary, large variability (smaller $\Gamma )$ is achieved by introducing interpunctions to make word intervals, and word intervals make sentences that communicate concepts. This theory can inspire new research lines in cognitive science research. Full article

In this study, we provide, to our knowledge, one of the first multidimensional, data-driven evaluations of green education performance in Turkish higher education, combining the THE Education Score, THE Impact Score, and the UI GreenMetric Education & Research Score (GM-ED) with institutional characteristics, and situating the analysis within SDG 4 (Quality Education). While universities worldwide increasingly integrate sustainability into their missions, systematic evidence from middle-income systems remains scarce. To address this gap, we compile a dataset of 50 Turkish universities combining three global indicators—the Times Higher Education (THE) Education Score, THE Impact Score, and the UI GreenMetric Education & Research Score (GM-ED)—with institutional characteristics such as ownership and student enrollment. We employ descriptive statistics; correlation analysis; robust regression models; composite indices under equal, PCA, and entropy-based weighting; and exploratory k-means clustering. Results show that integration of sustainability into curricula and research is the most consistent predictor of SDG-oriented performance, while institutional size and ownership exert limited influence. In addition, we propose composite indices (GECIs). GECIs confirm stable top performers across methods, but mid-ranked universities are volatile, indicating that governance and strategic orientation matter more than structural capacity. The study contributes to international debates by framing green education as both a measurable indicator and a transformative institutional practice. For Türkiye, our findings highlight the need to move beyond symbolic initiatives toward systemic reforms that link accreditation, funding, and governance with green education outcomes. More broadly, we demonstrate how universities in middle-income contexts can institutionalize sustainability and provide a replicable framework for assessing progress toward SDG 4. Full article

This study investigates whether the presence of an ESG committee in promoting sustainable practices is symbolic or substantive to creditors when setting costs. With unbalanced panel data, the study used 1518 company-year observations from non-financial firms listed on the IDX period 2018 to 2023. The hypothesis testing of this study was conducted by using moderated regression analysis (MRA). Hypothesis testing using a fixed effects model indicates that ESG disclosure can significantly lower the cost of debt. The role of the ESG committee is to act as a quasi-moderator for the relationship between ESG disclosure and the cost of debt. While the presence of an ESG committee can significantly reduce the cost of debt, the committee itself weakens the relationship between ESG disclosure and the cost of debt. Therefore, these findings suggest that the role of the ESG committee in promoting ESG disclosure to creditors in determining the cost of debt is becoming more substantive, moving away from a merely symbolic role that focuses on maintaining the company’s reputation and strengthening substantive management to control governance risk. The results of this study are expected to contribute to formulating policies that strengthen the role of ESG committees in improving corporate governance and sustainability practices by providing stakeholders with important and relevant ESG disclosure information for investment and funding decisions. Full article

Open Ground-Storey (OGS) buildings, widely adopted for functional openness, are highly vulnerable to seismic collapse due to stiffness irregularity at the ground storey (GS). The magnification factor (MF), defined as the amplification applied to GS column design forces, acts as a practical strengthening measure to enhance GS stiffness and thereby mitigate the soft storey failure mechanism. While earlier studies recommended fixed MF values, their lack of adaptability often left stiffness deficiencies unresolved. This study develops a rational framework to quantify and predict the required MF for OGS columns, enabling safe yet functionally efficient design. A comprehensive set of three-dimensional reinforced concrete OGS models was analyzed under seismic loads, covering variations in plan geometry, ground-to-upper-storey height ratio (Hr), and GS infill percentage. Iterative stiffness-based evaluations established the MF demand needed to overcome stiffness deficiencies. To streamline prediction, advanced machine learning (ML) models were applied. Among these, black-box models achieved high predictive accuracy, but Symbolic Regression (SR) offered an interpretable closed-form equation that balances accuracy with transparency, making it suitable for design practice. A sensitivity analysis confirmed the Hr as the most influential parameter, with additional contributions from other variables. Validation on additional OGS configurations confirmed the reliability of the SR model, while seismic response comparisons showed that Modified OGS (MOGS) frames with the proposed MF achieved improved stiffness, reduced lateral displacements, uniform drift distribution, and shorter fundamental periods. The study highlights the novelty of integrating interpretable ML into structural design, providing a codifiable and practical tool for resilient OGS construction. Full article

Recent decades have witnessed contractors’ increasing investment in corporate social responsibility (CSR) to build constructive stakeholder relationships and better corporate reputation. However, due to the uncertain benefits of engaging in CSR matters and the information asymmetry between contractors and stakeholders, corporate hypocrisy (CH), which refers to a disconnect between “talking” and “walking” in CSR, has widely surfaced in the international construction industry. This study used substantive corporate social responsibility (CSRS) and symbolic corporate social responsibility (CSRR) to deconstruct corporate hypocrisy. Both qualitative and quantitative data are used to analyze the relationship between internationalization and corporate hypocrisy, based on CSR reports from international contractors, as well as data from Thomson Reuters DataStream and Refinitiv, covering the period from 2011 to 2020. Multiple regression models serve as the analytical tool for assessing the impacts of internationalization on corporate hypocrisy. The results found that internationalization can promote both substantive corporate social responsibility and symbolic corporate social responsibility, and contractors are more prone to corporate hypocrisy with a higher degree of internationalization. The findings suggest that international contractors should avoid corporate hypocrisy as far as possible to mitigate operational risks. To achieve this, contractors can implement strategies such as ensuring transparency in CSR reporting, aligning their CSR actions with actual practices, and engaging with local stakeholders to better understand and meet their expectations. By distinguishing and examining the differential manifestations of substantive and symbolic CSR in the process of internationalization, this study reveals the mechanism through which internationalization affects corporate hypocrisy, thereby filling the gap in the existing literature regarding the relationship between internationalization and corporate hypocrisy in the construction industry. Full article

Objective: We aimed to explore associations between plasma neurofilament light chain (pNfL) and cognition through patient-reported outcomes (PROs) in multiple sclerosis (MS). Methods: In this cross-sectional study, we included 211 people with MS (PwMS) and collected data from pNfL (fully automated chemiluminescent enzyme immunoassay), EDSS, education, cognition (the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test-II (CVLT II), and Brief Visuospatial Memory Test–Revised (BVMT-R)), the Modified Fatigue Impact Scale (MFIS), Beck Depression Inventory (BDI-II), Beck Anxiety Inventory (BAI), and Pittsburgh Sleep Quality Index (PSQI). Results: On multivariate linear regression models, higher educational attainment was significantly associated with lower pNfL (high school: Coeff = −0.22, 95% CI = −0.41 to −0.04, p = 0.019; university: Coeff = −0.22, 95% CI = −0.42 to −0.02, p = 0.030). In logistic regression models, the likelihood of having pNfL levels above normal thresholds increased by 56% for each one-point increment in the EDSS score (OR = 1.56, 95% CI = 1.23 to 1.98, p < 0.001) and was 2.5 times greater in individuals with impaired SDMT (OR = 2.50, 95% CI = 2.20 to 5.21, p = 0.014). No statistically significant associations were observed between pNfL and CVLT-II, BVMT-R, BDI-II, MFIS, BAI, or PSQI. Conclusions: Neuro-axonal damage in people with MS manifests clinically as increased disability and reduced attention and processing speed. However, these effects may be mitigated by greater brain resilience, as suggested by the protective role of higher educational attainment. The PROs assessed in this study showed no significant associations with pNfL levels, possibly due to measurement errors and heterogeneity, with limited sensitivity to neuro-axonal damage. Full article

This study examines how educational attainment and creative-class identity influence public library use among Black homeowners in Ward 8, Washington, D.C., a historically disinvested, yet resilient, Black community. Using an adapted theoretical framework (Chatman’s Small World Theory, Florida’s creative class theory, and Crenshaw’s intersectionality), the research investigates how symbolic capital informs institutional engagement in a racially homogeneous but economically stratified setting. A survey of 56 Black homeowners examined the relationships among education, income, creative-class identity, and library use. Logistic regression analysis revealed that higher educational attainment was a significant predictor of identification with the Black Creative Class^TM. However, neither income nor creative-class identity significantly predicted public library use. These findings challenge the assumption that middle-class status or creative-class affiliation ensures participation in educational or cultural institutions. Instead, they suggest that deeper dynamics, such as cultural relevance, perceived alignment, and trust, may shape engagement with public libraries. The study advances knowledge in library and information science (LIS) and urban studies by demonstrating how spatial context and class distinctions within Black communities shape library engagement. The results underscore the need for culturally responsive library strategies that recognize class-based variation within racial groups, moving beyond monolithic models of community outreach. Full article

Proper estimation of flowing bottomhole pressure at coiled tubing depth (BHP-CTD) is crucial in optimization of nitrogen lifting operations in oil wells. Conventional estimation techniques such as empirical correlations and mechanistic models may be characterized by poor generalizability, low accuracy, and inapplicability in real time. This study overcomes these shortcomings by developing and comparing sixteen machine learning (ML) regression models, such as neural networks and genetic programming-based symbolic regression, in order to predict BHP-CTD with field data collected on 518 oil wells. Operational parameters that were used to train the models included fluid flow rate, gas–oil ratio, coiled tubing depth, and nitrogen rate. The best performance was obtained with the neural network with the L-BFGS optimizer (R² = 0.987) and the low error metrics (RMSE = 0.014, MAE = 0.011). An interpretable equation with R² = 0.94 was also obtained through a symbolic regression model. The robustness of the model was confirmed by both k-fold and random sampling validation, and generalizability was also confirmed using blind validation on data collected on 29 wells not included in the training set. The ML models proved to be more accurate, adaptable, and real-time applicable as compared to empirical correlations such as Hagedorn and Brown, Beggs and Brill, and Orkiszewski. This study does not only provide a cost-efficient alternative to downhole pressure gauges but also adds an interpretable, data-driven framework to increase the efficiency of nitrogen lifting in various operational conditions. Full article

Jordan’s aging population faces a critical challenge: a strong cultural preference for aging at home, rooted in Islamic ethics of familial care (birr al-wālidayn), conflicts with housing stock that is largely unsafe and inaccessible. This first national mixed-methods study examines the intersection of home modifications, socio-economic barriers, and cultural constraints to aging in place. Data from 587 surveys and 35 interviews across seven governorates were analyzed using chi-square tests, linear regression, and thematic coding. Results indicate that while physical modifications significantly improve accessibility to key spaces like kitchens and reception areas (majlis) (χ² = 341.86, p < 0.001), their adoption is severely limited. Socio-economic barriers are paramount, with 34% of households unable to afford the median modification cost of over $1500. Cultural resistance is equally critical; 22% of widows avoid modifications like grab bars to prevent the ‘medicalization’ of their home, prioritizing aesthetic and symbolic integrity over safety. The study reveals a significant gendered decision-making dynamic, with men controlling 72% of structural modifications (β = 0.27, p < 0.001). We conclude that effective policy must integrate universal design with Islamic care ethics. We propose three actionable recommendations: (1) mandating universal design in building codes (aligned with SDG 11), (2) establishing means-tested subsidy programs (aligned with SDG 10), and (3) launching public awareness campaigns co-led by faith leaders to reframe modifications as preserving dignity (karama) (aligned with SDG 3). This approach provides a model for other rapidly aging Middle Eastern societies facing similar cultural-infrastructural tensions. Full article

Under the pressure of global low-carbon transformation, the sustainable development initiative of the United Nations has gradually become an essential orientation of corporate Environmental, Social, and Governance (ESG) performance. Based on the integrated theoretical framework of sustainable development finance, this work explores the relationships among corporate ESG performance, its financing constraints in China, and its influencing mechanism, as well as the role played by green innovation in this relationship. Using a comprehensive panel dataset of 1038 A-share listed companies from 2013 to 2023, totaling 11,418 observations, we find that corporate ESG performance and financing constraints exhibit a significant negative relationship, indicating that strong corporate ESG performance can effectively alleviate corporate financing constraints. To address endogeneity concerns, we employ a systematic generalized method of moments (GMM) and a two-stage least squares regression using lagged instrumental variables. The results of the mechanism test show that ESG performance mitigates financing constraints by reducing perceived financial risks, improving information transparency, and increasing access to government green subsidies. Furthermore, moderating effect analysis reveals that green innovation strengthens the mitigating effect of corporate ESG performance on financing constraints in this process, based on SDG 9. Heterogeneity analysis reveals that this mitigating effect of corporate ESG performance on financing constraints is more pronounced for firms in China’s economically advanced eastern region, for companies facing harder budget constraints, and in the period following the implementation of the stringent new Environmental Protection Law. Distinguishing between genuine and symbolic corporate actions, we provide evidence that only substantive ESG improvements, as opposed to “greenwashing,” are rewarded by capital providers. The findings provide insights for the formulation of government policies and corporate sustainability strategies in emerging markets. Full article

Kolmogorov–Arnold Networks (KANs) have recently emerged as a powerful alternative to traditional Artificial Neural Networks (ANNs), offering superior accuracy and interpretability, which are two critical requirements in healthcare applications. This study investigates the effectiveness of KANs across a range of clinical tasks by applying them to diverse medical datasets, including structured clinical data and time-series physiological signals. Compared with conventional ANNs, KANs demonstrate significantly improved performance, achieving higher predictive accuracy even with smaller network architectures. Beyond performance gains, KANs offer a unique advantage: the ability to extract symbolic expressions from learned functions, enabling transparent, human-interpretable models—a key factor in clinical decision-making. Through comprehensive experiments and symbolic analysis, our results reveal that KANs not only outperform ANNs in modeling complex healthcare data but also provide interpretable insights that can support personalized medicine and early diagnosis. There is nothing specific about the datasets or the methods employed, so the findings are broadly applicable and position KANs as a compelling architecture for the future of AI in healthcare. Full article