Search Results (143)

Accurately predicting glass density is crucial for designing novel materials. This study aims to develop a robust predictive model for the density of oxide glasses and, more importantly, to investigate how physically informed feature engineering can create accurate and interpretable models that reveal underlying physical principles. Using a dataset of 76,593 oxide glasses from the SciGlass database, three machine learning (ML) models (ElasticNet, XGBoost, MLP) were trained and evaluated. Four distinct feature sets were constructed with increasing physical complexity, ranging from simple elemental composition to the advanced Magpie descriptors. The best model was further analyzed for interpretability using feature importance and SHapley Additive exPlanations (SHAP) analysis. A clear hierarchical improvement in predictive accuracy was observed with increasing feature sophistication across all models. The XGBoost model combined with the Magpie feature set provided the best performance, achieving a coefficient of determination (R²) of 0.97. Interpretability analysis revealed that the model’s predictions were overwhelmingly driven by physical attributes, with mean atomic weight being the most influential predictor. The model learns to approximate the fundamental density equation using mean atomic weight as a proxy for molar mass and electronic structure features to estimate molar volume. This demonstrates that a data-driven approach can function as a scientifically valid and interpretable tool, accelerating the discovery of new materials. Full article

International efforts are underway to implement carbon neutrality policies in rapidly changing climate conditions. This situation has strongly demanded the discovery of novel carbon sinks. The Salix genus has attracted attention as a promising carbon sink owing to its rapid growth and efficient use as a biofuel in short-rotation cultivation. The present study aims to derive an allometric equation and conduct stem analysis as fundamental tools for estimating net primary productivity (NPP) in Salix pierotii Miq. stand, which is increasingly acknowledged as an important emerging carbon sink. The allometric equations derived showed a high explanatory rate and fitness (R² ranged from 0.74 to 0.99). The allometric equations between DBH and stem volume and biomass derived in the process of stem analysis also showed a high explanatory rate and fitness (R² ranged from 0.87 to 0.94). The NPPs calculated based on the allometric equation derived and stem analysis were 11.87 tonC∙ha⁻¹∙yr⁻¹ and 15.70 tonC∙ha⁻¹∙yr⁻¹, respectively. These results show that the S. pierotii community, recognized as the representative riparian vegetation, could play an important role as a carbon sink. In this context, an assessment of the carbon absorption capacity of riparian vegetation such as willow communities could contribute significantly to achieving carbon neutrality goals. Full article

This paper investigates the well-posedness of the inverse problem for the time-fractional Burgers equation, which aims to reconstruct initial conditions from terminal observations. Such equations are crucial for the modeling of hydrodynamic phenomena with memory effects. The inverse problem involves inferring initial conditions from terminal observation data, and such problems are typically ill-posed. A framework based on optimal control theory is proposed, addressing the ill-posedness via $H^1$ regularization. Three substantial results are achieved: (1) a rigorous mathematical framework transforming the ill-posed inverse problem into a well-posed optimization problem with proven existence of solutions; (2) theoretical guarantee of solution uniqueness when the regularization parameter is $\alpha >0$ and the stability is of order O($\delta$) with respect to observation noise ($\delta$); and (3) the discovery of a “super-stability” phenomenon in numerical experiments, where the actual stability index (0.046) significantly outperforms theoretical expectations (1.0). Finally, the theoretical framework is validated through comprehensive numerical experiments, demonstrating the accuracy and practical effectiveness of the proposed optimal control approach for the reconstruction of hydrodynamic initial conditions. Full article

The chiral nonlinear Schrödinger equation (CNLSE) serves as a simplified model for characterizing edge states in the fractional quantum Hall effect. In this paper, we leverage the generalization and parameter inversion capabilities of physics-informed neural networks (PINNs) to investigate both forward and inverse problems of 1D and 2D CNLSEs. Specifically, a hybrid optimization strategy incorporating exponential learning rate decay is proposed to reconstruct data-driven solutions, including bright soliton for the 1D case and bright, dark soliton as well as periodic solutions for the 2D case. Moreover, we conduct a comprehensive discussion on varying parameter configurations derived from the equations and their corresponding solutions to evaluate the adaptability of the PINNs framework. The effects of residual points, network architectures, and weight settings are additionally examined. For the inverse problems, the coefficients of 1D and 2D CNLSEs are successfully identified using soliton solution data, and several factors that can impact the robustness of the proposed model, such as noise interference, time range, and observation moment are explored as well. Numerical experiments highlight the remarkable efficacy of PINNs in solution reconstruction and coefficient identification while revealing that observational noise exerts a more pronounced influence on accuracy compared to boundary perturbations. Our research offers new insights into simulating dynamics and discovering parameters of nonlinear chiral systems with deep learning. Full article

The implementation of chaotic behavior and a sensitivity assessment of the newly developed M-fractional Kuralay-II equation are the foremost objectives of the present study. This equation has significant possibilities in control systems, electrical circuits, seismic wave propagation, economic dynamics, groundwater flow, image and signal denoising, complex biological systems, optical fibers, plasma physics, population dynamics, and modern technology. These applications demonstrate the versatility and advantageousness of the stated model for complex systems in various scientific and engineering disciplines. One more essential objective of the present research is to find closed-form wave solutions of the assumed equation based on the $({\displaystyle \frac{G^{\prime }}{G^{\prime }+G+A}})$-expansion approach. The results achieved are in exponential, rational, and trigonometric function forms. Our findings are more novel and also have an exclusive feature in comparison with the existing results. These discoveries substantially expand our understanding of nonlinear wave dynamics in various physical contexts in industry. By simply selecting suitable values of the parameters, three-dimensional (3D), contour, and two-dimensional (2D) illustrations are produced displaying the diagrammatic propagation of the constructed wave solutions that yield the singular periodic, anti-kink, kink, and singular kink-shape solitons. Future improvements to the model may also benefit from what has been obtained as well. The various assortments of solutions are provided by the described procedure. Finally, the framework proposed in this investigation addresses additional fractional nonlinear partial differential equations in mathematical physics and engineering with excellent reliability, quality of effectiveness, and ease of application. Full article

A significant advancement in Neural Network (NN) research is the integration of domain-specific knowledge through custom loss functions. This approach addresses a crucial challenge: How can models utilize physics or mathematical principles to enhance predictions when dealing with sparse, noisy, or incomplete data? Physics-Informed Neural Networks (PINNs) put this idea into practice by incorporating a forward model, such as Partial Differential Equations (PDEs), as soft constraints. This guidance helps the networks find solutions that align with established laws. Recently, researchers have expanded this framework to include Bayesian NNs (BNNs) which allow for uncertainty quantification. However, what happens when the governing equations of a system are not completely known? In this work, we introduce methods to automatically select PDEs from historical data in a parametric family. We then integrate these learned equations into three different modeling approaches: PINNs, Bayesian-PINNs (B-PINNs), and Physical-Informed Bayesian Linear Regression (PI-BLR). To assess these frameworks, we evaluate them on a real-world Multivariate Time Series (MTS) dataset related to electrical power energy management. We compare their effectiveness in forecasting future states under different scenarios: with and without PDE constraints and accuracy considerations. This research aims to bridge the gap between data-driven discovery and physics-guided learning, providing valuable insights for practical applications. Full article

This study introduces a novel mathematical model tailored to the unique fluid dynamics of paper-based microfluidic devices (PBMDs), focusing specifically on the transport behavior of human blood plasma, albumin, and heat. Unlike previous models that depend on generic commercial software, our custom-developed computational incorporates the Richards equation to extend Darcy’s law for more accurately capturing capillary-driven flow and thermal transport in porous paper substrates. The model’s predictions were validated through experimental data and demonstrated high accuracy in both two- and three-dimensional simulations. Key findings include new analytical expressions for uniform paper wetting after sudden geometric expansions and the discovery that plasma and albumin preferentially migrate along paper edges—a phenomenon driven by surface tension and capillary effects that varies with paper type. Additionally, heat transfer analysis indicates that a one-minute equilibration period is necessary for the reaction zone to reach ambient temperature, an important parameter for assay timing. These insights provide a deeper physical understanding of PBMD operation and establish a robust modeling tool that bridges experimental and computational approaches, offering a foundation for the optimized design of next-generation diagnostic devices for biomedical applications. Full article

With advancements in cloud computing and machine learning algorithms, an increasing number of cropland datasets have been developed, including the China land-cover dataset (CLCD) and GlobeLand30 (GLC). The unique climatic conditions of the Tibetan Plateau (TP) introduce significant differences and uncertainties to these datasets. Here, we used a quantitative and visual integrated assessment approach to assess the accuracy and spatial consistency of five cropland datasets around 2020 in the TP, namely the CLCD, GLC30, land-use remote sensing monitoring dataset in China (CNLUCC), Global Land Analysis and Discovery (GLAD), and global land-cover product with a fine classification system (GLC_FCS). We analyzed the impact of terrain, climate, population, and vegetation indices on cropland spatial consistency using structural equation modeling (SEM). In this study, the GLAD cropland area had the highest fit with the national land survey (R² = 0.88). County-level analysis revealed that the CLCD and GLC_FCS underestimated cropland areas in high-elevation counties, whereas the GLC and CNLUCC tended to overestimate cropland areas on the TP. Considering overall accuracy, GLC and GLAD performed the best with scores of 0.76 and 0.75, respectively. In contrast, CLCD (0.640), GLC_FCS (0.640), and CNLUCC (0.620) exhibited poor overall accuracy. This study highlights the significantly low spatial consistency of croplands on the TP, with only 10.60% consistency in high and complete agreement. The results showed substantial differences in spatial accuracy among zones, with relatively higher consistency observed in low-altitude zones and notably poorer accuracy in zones with sparse or fragmented cropland. The SEM results indicated that elevation and slope directly influenced cropland consistency, whereas temperature and precipitation indirectly affected cropland consistency by influencing vegetation indices. This study provides a valuable reference for implementing cropland datasets and future cropland mapping studies on the TP region. Full article

This paper aimed to analyze the effect of green supply chain integration (GSCI) on sustainable supply chain performance (SSCP), as well as consider the mediating and moderating effects of green innovation (GInv) and green value co-creation (GVCc). This empirical study is based on a survey of 317 senior managers from agro-based companies involved in manufacturing and extractive industries in Nigeria. The empirical research model is examined utilizing partial least squares structural equation modeling (PLS-SEM). The discovery entails that GSCI positively and substantially impacted SSCP and GInv. The research suggests that green innovation serves as a mediator in the relationship between GSCI and SSCP. Moreover, green value co-creation exerts a detrimental moderating influence on the GSCI-SSCP paradigm. Meanwhile, the originality of this study emanated from being the first to objectively explore the simultaneous moderating and mediating effects of GInv and GVCc on the relationship between GSCI and SSCP. Full article

Spaceborne radar is a powerful tool for probing planetary subsurface structures. Earlier radar studies of the Moon have primarily examined large-scale horizontal structures. However, recent discoveries of vertical holes suggesting the existence of lava tubes and theoretically predicted subsurface gas voids formed by volatiles in magma have highlighted the importance of small-scale subsurface structures. We developed a method using SELENE Lunar Radar Sounder (LRS) data to detect small-scale subsurface echoes (hundreds of meters). Surface scattering simulations incorporating incoherent scattering from sub-resolution roughness were performed using a high-resolution digital elevation model generated by a generative adversarial network. Detection thresholds for subsurface echo candidates (SECs) were determined from the histograms of difference intensities between LRS and simulation B-scans. Results show that some SECs exist in the extension area of the analyzed graben. SECs were also detected continuously across multiple LRS ground tracks in areas unrelated to grabens. Using the radar equation analysis, the echo intensities of SECs could be explained for subsurface structures with 50–600 m widths and dielectric constants of 1–4. This suggests the existence of either subsurface voids or materials with a high porosity of more than 35%. Among the SECs detected continuously across multiple LRS ground tracks, those that are more or less aligned in the downward elevation direction are likely indicative of lava tubes. On the other hand, the SECs distributed along the extension of the graben are aligned parallel to the contour lines. These SECs likely suggest gas voids at the tip of the intrusive magma that formed the graben. Full article

The Schrödinger equation, Heisenberg’s uncertainty principles, and the Boltzmann constant represent transformative scientific achievements, the impacts of which extend far beyond their original domain of physics. As we celebrate the centenary of these fundamental quantum mechanical formulations, this review examines their evolution from abstract mathematical concepts to essential tools in contemporary drug discovery and development. While these principles describe the behavior of subatomic particles and molecules at the quantum level, they have profound implications for understanding biological processes such as enzyme catalysis, receptor–ligand interactions, and drug–target binding. Quantum tunneling, a direct consequence of these principles, explains how some reactions occur despite classical energy barriers, enabling novel therapeutic approaches for previously untreatable diseases. This understanding of quantum mechanics from 100 years ago is now creating innovative approaches to drug discovery with diverse prospects, as explored in this review. However, the fact that the quantum phenomenon can be described but never understood places us in a conundrum with both philosophical and ethical implications; a prospective and inconclusive discussion of these aspects is added to ensure the incompleteness of the paradigm remains unshifted. Full article

This paper proposes a semi-analytical solution for one-dimensional consolidation of viscoelastic unsaturated soil considering a variable permeability coefficient under exponential loading. The governing equations of excess pore air pressure (EPAP) and excess pore water pressure (EPWP) were acquired by introducing the Merchant viscoelastic model. By employing Lee’s correspondence principle and the Laplace transform, the solutions for EPAP and EPWP were derived under the boundary conditions of the permeable top surface and impermeable bottom surface. Crump’s method was then used to execute the inverse Laplace transform, yielding a semi-analytical solution in the time domain. Through typical examples, the dissipation of EPAP and EPWP and the change of the average degree of consolidation over time under the influence of different elastic moduli, viscoelastic coefficients, and air-to-water permeability ratios were studied. The variation of the permeability coefficient and its influence on consolidation were also analyzed. The findings of this research show that the consolidation rate of viscoelastic unsaturated soil is slower than that of elastic unsaturated soil; however, an acceleration in the consolidation of the soil is observed when changes in the permeability coefficient are considered. These discoveries enhance our comprehension of the consolidation behaviors exhibited by viscoelastic unsaturated soil, thereby enriching the knowledge base on its consolidation traits. Full article

The phenomenon of augmented gravity on the scale of galaxies, conventionally attributed to dark matter halos, is shown to possibly result from the incremental growth of galactic masses and radii over time. This approach elucidates the cosmological origins of the acceleration scale $a_0\approx c{H}_0/2\pi \approx {10}^{-10}$ ms⁻² at which galaxy rotation curves deviate from Keplerian behavior, with no need for new particles or modifications to the laws of gravity, i.e., it constitutes a new explanatory path beyond Cold Dark Matter (CDM) and Modified Newtonian Dynamics (MOND). Once one formally equates the energy density of the universe to the critical value ($\rho ={\rho }_c$) and the cosmic age to the reciprocal of the Hubble parameter ($t=H^{-1}$), independently of the epoch of observation, the result is the Zero-Energy condition for the cosmic fluid’s equation of state, with key repercussions for the study of dark energy since the observables can be explained in the absence of a cosmological constant. Furthermore, this mass-energy evolution framework is able to reconcile the success of CDM models in describing structure assembly at $z\lesssim 6$ with the unexpected discovery of massive objects at $z\gtrsim 10$. Models that feature a strong coupling between cosmic time and energy are favored by this analysis. Full article

Thrombotic thrombocytopenia purpura is a serious disease that can involve complex symptomatology, prolonged hospitalization, and a high risk of mortality if treatment is delayed. This disease is rare, but it is even rarer among pediatric patients. Even though it was first described 100 years ago, the earliest documented case was a pediatric patient. The last three decades have seen the discovery of the pathological mechanisms responsible for its clinical presentation. Symptoms/signs characteristic of microangiopathic hemolytic anemia with significant thrombocytopenia characterize the vast majority of patients. Its pathology centers on the accumulation of ultra-large von Willebrand factor multimers due to an enzyme deficiency that prevents their breakdown. Currently, in pediatric patients, two forms of the disease are known: congenital due to a mutation in the enzyme’s gene and immune-mediated due to enzyme depletion or neutralization secondary to autoantibody formation. With the advent of therapeutic plasma exchanges, immunosuppression, and, more recently, a TTP-specific nanobody, there is reason for optimism that the disease does not necessarily equate to a bad outcome. Thus, the aim of this review is to contrast the congenital and immune-mediated forms of the disease in pediatric patients while presenting them in the context of their pathologic mechanisms, diagnosis, and treatment. Full article

The phenomenon of informative cluster size (ICS) emerges when the number of repeated measurements is correlated with the outcome variable. In such scenarios, the prevailing generalized estimating equation (GEE) method often yields biased estimates due to nonignorable cluster size. This study proposes an integrated methodology that explicitly accounts for ICS and provides a robust solution to mitigate its effects. Our approach combines within-cluster resampling (WCR) with a penalized likelihood framework, ensuring consistent model selection and parameter estimation across resampled datasets. Additionally, we introduce a penalized mean regression method to aggregate the estimators from multiple resampled datasets, producing a final estimator that improves the true positive discovery rate while controlling false positives. The proposed penalized likelihood method via WCR (${\mathrm{PL}}_{\mathrm{WCR}}$) is evaluated through extensive simulations and an application to yeast cell-cycle gene expression data. The results demonstrate its robustness and superior performance in high-dimensional longitudinal data analysis with ICS. Full article