Search Results (466)

We modelled, for the first time, the seasonal dynamics and long-term trends of Abies marocana forests (Rif Mountains, northern Morocco) using remote-sensing-derived vegetation indices. Using the MODIS Terra Vegetation Indices product MOD13Q1 (enhanced vegetation index, EVI; 16-day frequency; 250 m spatial resolution) from 2000 to 2024 (575 images over 25 years), we applied a robust seasonal trend analysis (RSTA) workflow, representing an inferential extension of classical seasonal trend analysis (STA) through the explicit control of Type I error under serial and spatial correlation. This approach combined: (i) harmonic regression to capture the annual and semi-annual cycles of A. marocana forests, estimating seasonal amplitudes and phases while filtering out low-frequency noise; (ii) an iterative trend-free prewhitening (TFPW) procedure following Wang and Swail, applied only to time series with significant serial autocorrelation according to the Durbin–Watson test; (iii) the Theil–Sen slope (TS) estimator, a robust non-parametric method, to quantify the magnitude and direction of seasonality trends; (iv) the contextual Mann–Kendall (CMK) test to assess the statistical significance of seasonality trends, while correcting for spatial autocorrelation and accounting for cross-correlation among neighbouring pixels; (v) the Benjamini–Hochberg (BH) procedure to control the false discovery rate (FDR), ensuring that only statistically robust seasonality trends were retained; and (vi) reconstruction of seasonal curves representing the beginning and end of the study period and derivation of phenological metrics from the statistically significant seasonal trends retained after inferential filtering. After applying the complete analytical workflow, statistically significant trends were detected in 79.2% of pixels within A. marocana forests, compared with 86.4% when prewhitening and false discovery rate control were not applied. All Theil–Sen slopes retained by the RSTA workflow were positive, with a mean slope of approximately 0.00175 EVI year⁻¹, corresponding to an average annual increase of roughly 0.7% and an overall increase of approximately 15% over the 2000–2024 study period relative to the initial mean EVI conditions. Browning trends identified by classical STA were not supported after inferential filtering and FDR control, indicating that all these patterns were spurious or only marginal, and confined to limited areas and edge zones. The reconstructed seasonal trend curves were consistent with a longer growing season, although this inference is based on land-surface vegetation dynamics rather than direct phenological observations. The long-term ecological consequences of these changes in seasonal vegetation activity will hinge on the interactions among warming, rising water demand, and potential disturbance regimes under future climatic conditions. Full article

In this study, a detailed analytical-numerical study of the complex modified Korteweg–De Vries (mKdV) model with truncated M-fractional derivative is carried out to investigate the effects of the fractional order on nonlinear wave propagation. The fractional partial differential equation is solved by an appropriate fractional traveling wave transformation, which transforms it into a nonlinear ordinary differential equation. Two very powerful analytical methods are then used: the modified sub-equation method and the Kumar–Malik method, which give the exact closed-form solutions. The obtained semi-analytical numerical approximations are then obtained from the Differential Transformation Method (DTM). Bright and dark solitons, kink-type waves, periodic and rational solutions, exponential solutions, and Jacobi elliptic functions are found for a variety of parametric regimes. Explicit compatibility conditions and parametric constraints, which control the amplitude, width, and propagation, are derived. The DTM approximations are found to converge to the exact solutions with good accuracy, and the absolute errors are almost negligible, which validates the accuracy of the approximations and reliability of the solution. The three-dimensional visualizations of surface plots, two-dimensional profiles, and contour visualization further illustrate the dispersive dynamics and stability properties. Significance: This study shows that the truncated M-fractional derivative is a good operator to model memory-dependent nonlinear wave propagation. A new precise solution and reliable validation methods have been obtained for high-dimensional fractional nonlinear evolution equations in the hybrid analytical-numerical framework, which can be useful in plasma physics, nonlinear optics, and complex media. The present study contains restrictions for constant coefficients, a specific parametric regime, one fractional derivative definition, and experimental validation is not included. Future directions are limitations on constant coefficients, specific parametric regimes, one fractional derivative definition, and experimental validation is not included. The approach is to be extended in the future to variable coefficients, other fractional operators (Caputo, Riemann–Liouville), and to higher-order nonlinearities, and then to be experimentally tested in optical or plasma systems. Full article

This paper investigates machine learning (ML)-supplemented workflows integrated within a modular parametric modelling framework derived from a typological analysis of stadiums. The objective of the research is to address a gap between numerous isolated computational studies and the realities of early stadium design within the Royal Institute of British Architects (RIBA) Plan of Work (PoW) Stages 0–3. From a practical perspective, the proposed design framework aims to embed supervised learning, semi-supervised learning, and evolutionary optimisation into stadium design development to support site appraisal, brief preparation, concept development, spatial coordination, and stadium bay or stand optimisation based on quantifiable design characteristics. The framework addresses the inefficiencies and limitations of the traditional stadium design process by allowing rapid design space exploration defined by typological drivers, evaluation of a large set of solutions based on performance metrics such as circulation distances, sightline quality, and layout distribution, and the validation of concepts against benchmarks. Within the applicable design pipelines, and where labels are derived from deterministic performance criteria, the supervised approaches achieved prediction accuracies above 85%, while evolutionary optimisation reduced the number of seats with restricted views by approximately 95%. The value of the study is that it demonstrates that the integration of parametric modelling based on shared typological characteristics and the mapping of ML methods to the RIBA PoW has the potential to support stadium design in a novel way. Full article

Prediction-powered inference (PPI) studies statistical inference when a small set of gold-standard labels is combined with a much larger pool of machine-generated predictions. The central difficulty is that predictions can substantially reduce variance, yet naive substitution of predictions for outcomes generally changes the estimand and invalidates uncertainty quantification. The basic remedy in the literature is rectification: predictions are used to construct a low-variance plug-in term, while labeled observations are used to estimate and correct the inferential distortion induced by prediction substitution. We review PPI as a family of rectified plug-in procedures for hybrid measurement regimes. The survey develops a common statistical template based on mean estimation, estimating equations, and loss-based formulations, and then uses that template to compare modern variants according to the component they modify: the rectification engine, the label-acquisition design, predictor dependence, or the validity target. We also position PPI relative to model-assisted survey sampling, post-prediction correction, surrogate-outcome methods, classical measurement-error models, and semiparametric augmentation. Throughout, we distinguish questions of validity from questions of efficiency, robustness, and computation, and we emphasize that valid use of prediction assistance does not require a correct predictive model but does depend on how rectification, dependence, and sampling design are handled. The survey closes with recurrent failure modes, practical reporting recommendations, and open problems in finite-sample theory, heterogeneous proxy quality, and protocol-aware deployment. Full article

Higher construction costs (CCs) linked to carbon reduction methods have hindered the adoption of low-carbon approaches in the built environment. The simultaneous minimisation of upfront embodied carbon (EC) and CCs has not received much attention in building design optimisation (BDO) research; most studies prioritise operational energy, operational carbon, and operational cost reduction. This paper develops an integrated conceptual framework for low-carbon, cost-effective BDO, particularly targeting upfront EC and CCs, to fill this research gap and meet industry demands. A systematic literature review was conducted following PRISMA guidelines, synthesising 41 peer-reviewed articles published between 2015 and 2026. Thematic and content analyses were employed to extract and categorise key methodological components, including optimisation problem characterisation, objective-driven design variable selection, constraint modelling, algorithm selection, and evaluation and validation approaches. Subsequently, the developed conceptual framework was validated through semi-structured expert interviews with participants comprising BDO researchers and building designers in the construction field. A cross-mapping of optimisation objectives, optimised parameters, and design variables was developed to clarify their interrelationships, alongside structured criteria for optimisation algorithm selection. Based on these insights, a conceptual framework named “ICCO-BD (Integrated Upfront Carbon and Construction Cost Optimisation for Building Design) framework” is proposed and validated, integrating problem formulation, parametric modelling, multi-objective optimisation, and systematic Pareto-based evaluation into a coherent end-to-end workflow, enabling improved time efficiency through reduced redesign iterations, enhanced solution quality via better pareto front exploration, and more robust decision-making through clearer trade-off interpretation. While expert feedback indicated strong conceptual relevance and practical applicability, the framework remains conceptual in nature and requires further empirical verification through real-world case studies and optimisation applications before broader industry implementation. Full article

This study introduces a spatiotemporal framework at the district level in Indonesia to examine and forecast stunting prevalence. The empirical analysis draws on data from 514 districts observed over 2022–2024, with short-term projections extended to 2025–2027 in line with the SDG 2.2 agenda. The modeling methodology is based on a Bayesian spatiotemporal formulation with the SPDE-INLA method. Instead of handling spatial and temporal lags separately, the model simultaneously incorporates them to reflect dependencies that change across both dimensions. This structure facilitates a more flexible representation of underlying risk dynamics. To improve prediction performance, we augment the baseline model with a hybrid component. Specifically, residual variation from the Bayesian specification is further explored using machine learning methods, providing an additional layer of adjustment. Spatial dependence is assessed through three alternative weighting schemes—KNN, Queen contiguity, and distance-based matrices—which are compared prior to selecting the final specification. The empirical specification includes nine key predictors within a semi-parametric framework. Several covariates are allowed to depart from strict linearity by accommodating time-varying effects. Three algorithms were evaluated during the prediction process to determine their abilities to capture the residual structure: XGBoost, Random Forest, and Elastic Net. Spatiotemporal clustering is examined through exceedance probabilities, resulting in the identification of seven unique cluster patterns. The findings consistently indicate that poverty is the main factor influencing stunting dynamics, with evident regional spillovers and temporal variations. Persistent hotspots are primarily located in eastern Indonesia. From a predictive standpoint, the hybrid specification—particularly the variant based on XGBoost—delivers the most stable performance. The forecast results indicate a gradual reduction in stunting prevalence throughout the forecast period. This study establishes persistent geographic inequalities in child nutrition risk and translates them into district-specific intervention priorities, providing decision-support information to further SDG Target 2.2 and its relationships with SDGs 1, 3, 4, and 6. Full article

The promotion of rapidly scaling technology startups has become a major policy priority. Sustainable startups are increasingly viewed as potential contributors to resilient and environmentally responsible economies, as they may combine economic growth with environmental and social objectives. Based on entrepreneurial ecosystem theory, the resource-based view, and Schumpeterian creative destruction, this study identifies innovation ecosystem conditions associated with sustainable startup growth. Turnover growth is used as a proxy for the economic pillar of the Triple Bottom Line framework and as a measure of startup scaling capacity. K-means clustering is applied to identify distinct growth profiles. To analyse relationships between startup growth and innovation ecosystem variables, the study employs a multi-method semiparametric framework. The results show multifaceted associations between ecosystem factors and startup growth. Market access and human capital are positively associated with global business models and innovation, while sectoral relatedness and knowledge spillovers may show negative associations, potentially through stronger competition and higher talent acquisition costs. Venture capital is positively associated with startup growth, whereas public R&D investment and direct government funding show no consistent positive relationship. The study is limited by using financial growth as a proxy for economic sustainability and by focusing on four European innovation ecosystems. Full article

While existing mixed-effects models address multivariate outcomes, nonlinearity, or non-normality separately, their joint integration remains limited in medical research. This study proposes a flexible semi-parametric multivariate mixed-effects model that simultaneously accounts for correlated outcomes, nonlinear covariate effects, and skewness, providing a more comprehensive framework for analyzing complex longitudinal data. The proposed approach is illustrated using longitudinal data on fasting blood sugar (FBS) and systolic blood pressure (SBP) among individuals with type 2 diabetes (T2D) and hypertension. A simulation study was also conducted to assess model performance. Results indicate that the proposed model outperforms conventional approaches by effectively capturing nonlinear patterns and asymmetry in the data. The findings further show a strong and direct relationship between FBS and SBP over time. Overall, the proposed model provides a robust and flexible framework for analyzing complex multivariate longitudinal data in medical research. Full article

The Inner Mongolia reach of the Yellow River faces compound “low flow, high sediment” extremes under reservoir regulation, threatening flood and ice-flood safety in ways that traditional mean-based or correlation-based methods fail to quantify. This study integrates POT-GPD extreme value theory with a vine copula-CoVaR framework using daily data (1951–2023) from four stations. The financial CoVaR concept was adapted to rivers through three hydrological modifications: a 5-day hydrodynamic lag, redefinition of the baseline to the downstream unconditional VaR, and semi-parametric tail modeling. Bootstrap confidence intervals (n = 1000) and a sensitivity analysis to the upstream–downstream lag (τ = 3–7 days) and the period cutoff (1984–1990) were used to assess robustness. Bayangol exhibits the highest Expected Shortfall (ES₉₅ = 0.0329 kg·s·m⁻⁶). The Bayangol → Toudaoguai path is the only persistent positive risk transmission link, with ΔCoVaR showing a directionally consistent increase of 253% from the natural period (1951–1986) to the regulated period (1987–2023); by contrast, ΔCoVaR from Dengkou to Toudaoguai remains near zero or negative when assessed under the conventional bivariate framework. A three-dimensional vine copula analysis, conducted independently for the pre- and post-reservoir periods, reveals a qualitative reversal of compound extreme spillover that is masked when the two periods are pooled. While the bivariate analysis identifies Bayangol → Toudaoguai as the only persistent positive spillover route at the annual scale, the 3D vine analysis unpacks the compound extreme mechanism at the daily scale. Under the joint compound extreme condition (upstream Q and S each ≥ Q₉₀), the conditional VaR₉₅ of downstream sediment concentration shifts from systematically negative in P1 (ΔVaR₉₅ = −4.75 kg·m⁻³ at the 90th-percentile threshold, indicating natural attenuation) to systematically positive in P2′ (ΔVaR₉₅ = +4.70 kg·m⁻³, +86.9% relative increase, indicating amplification). The same reversal is observed for the tail mean (ΔES₉₅), is preserved across four compound extreme thresholds (Q₇₅–Q₉₀), and is robust to the choice of period cutoff (28/28 cases reverse across seven candidate cutoffs). Bidirectional counterfactual simulations indicate that the copula shift from tail independence (Clayton) to tail dependence (Gaussian) alone elevates extreme concurrence probability by 58% (from 2.21% to 3.49%), while marginal distribution changes contribute negligibly (≤0.1 percentage points). Structural deterioration of water–sediment coordination therefore dominates risk amplification. The copula-CoVaR framework offers a candidate tool that requires further validation with large samples for tail risk assessment in heavily regulated fluvial systems. Full article

Background: Infective endocarditis (IE) is characterized by marked heterogeneity in microbiological etiology, clinical presentation, valvular involvement, and patient complexity, which complicates risk stratification. Unsupervised machine learning has been proposed to identify latent clinical phenotypes in complex diseases; however, whether IE exhibits a natural cluster structure remains unclear. Methods: In a cohort of 739 patients undergoing surgery for IE, unsupervised clustering was performed using K-medoids based on Gower distance to account for mixed-type variables, which is a common scenario in clinical settings. The optimal number of clusters was selected by maximizing the average silhouette width and the gap statistic. Density and semi-parametric algorithms (K-prototypes, KAMILA, hierarchical clustering, and HDBSCAN) were applied as a sensitivity analysis. Differences in postoperative outcomes across clusters were explored using logistic regression. Results: K-medoids clustering identified three patient groups; however, the average silhouette width was low (0.129), indicating very weak separation between clusters. Sensitivity analysis confirmed the absence of a natural cluster structure. Despite this, a descriptive comparison of forced clusters revealed a gradient of clinical severity, with one group characterized by older age, higher comorbidity burden, complex infection features, and worse postoperative outcomes. Conclusions: Unsupervised clustering did not identify natural clinical phenotypes in surgically treated IE, likely reflecting the extreme intrinsic heterogeneity of the disease. Although forced clustering highlighted clinically interpretable gradients of risk, these groups should not be considered true latent phenotypes. Alternative approaches, such as continuous risk modeling, may be more appropriate for patient stratification in IE. Full article

The governing reaction–diffusion model for carbohydrate oxidation catalyzed by an immobilized bienzyme system glucose dehydrogenase and laccase within a spherical porous microreactor is adapted from Baronas et al. and extended here to the non-steady-state regime. The model consists of coupled non-linear partial differential equations based on non-Michaelis–Menten kinetics. The principal novelty of this work lies in the derivation of closed-form semi-analytical expressions for transient and steady-state concentrations of the carbohydrate substrate, oxygen, and product, as well as for the effectiveness factor, using the Laplace Homotopy Perturbation Method (LHPM). The LHPM solutions are validated against MATLAB R2026a numerical simulations (maximum error $<0.009\%$) and demonstrate superior accuracy compared to previously reported Adomian Decomposition Method (ADM) and Taylor Series Method (TSM) solutions. Parametric analysis reveals that the Thiele modulus, saturation parameters, and dimensionless time strongly influence the internal concentration profiles and reactor effectiveness. These analytical results provide rapid, closed-form predictive tools for optimizing catalyst particle size, enzyme loading, and operating conditions in immobilized enzyme microreactor systems. Full article

Against the backdrop of the digital transformation in engineering education, this study developed and implemented a Learning Analytics (LA)-driven and Artificial Intelligence (AI)-supported blended learning model to address structural challenges in the “Combustion” course, including highly abstract theories, experimental safety risks, and compressed instructional hours. Moving beyond mere technical stacking, the model establishes a closed-loop data ecosystem that integrates “pre-class adaptive diagnosis, in-class contextualized internalization, and post-class personalized transfer,” while deeply embedding engineering ethics and sustainability issues related to carbon neutrality. A one-semester quasi-experimental study (Experimental N = 60, Control N = 60) was conducted, utilizing a triangulated assessment of final exam scores, platform-based behavioral trajectories, and semi-structured interviews. The results showed that the experimental group achieved significantly higher final assessment scores than the control group (82.4 ± 5.7 vs. 73.2 ± 6.9), with normality tests supporting the use of parametric analysis and Analysis of Covariance (ANCOVA) indicating a significant instructional effect after controlling for Grade Point Average (GPA) and pre-test scores. Furthermore, behavioral analysis confirms that the LA mechanism significantly enhances students’ self-regulated learning and engagement by increasing the visibility of the learning process. This study provides an evidence-based reform paradigm for engineering curricula to achieve the synergistic cultivation of knowledge acquisition, competency development, and value alignment within constrained instructional timeframes. Full article

Cold bending provides a cost-effective method for fabricating triangular glass units for free-form architectural envelopes. Replacing conventional continuous edge constraints with discrete point clamps reduces over-constraint but introduces pronounced bending–membrane coupling in the unsupported spans between adjacent clamps. Consequently, the mechanisms governing local instability and optical-quality degradation remain insufficiently understood. In this study, cold-bending tests were performed on isosceles triangular fully toughened glass plates to measure out-of-plane deflection and surface-strain evolution. The experimental data were then used to establish and validate an Abaqus finite element model for systematic parametric analysis. Based on von Kármán’s large-deflection theory, a semi-empirical reduced-order framework that combines modal superposition with the response-surface method was developed to identify instability-sensitive configurations. The results show that, under weak constraints and large vertex angles, the panel response changes from a bending-dominated regime to a strongly nonlinear large-deflection regime governed by membrane effects; this transition is marked by a reversal of mid-span deflection and a compressive-to-tensile stress transition. Increasing the number of clamps from two to four substantially suppresses both global and local distortion by shortening the free spans and redistributing membrane strain energy, reducing peak mid-span deflection by 47–68%, and satisfying the EN 12150-1 limits for both bow deformation and local distortion. The height-to-base ratio is the dominant geometric parameter controlling instability. Under two-point support, a critical response turning point occurs at a height–base ratio of approximately 0.5 before the material fracture limit is reached, defining a geometric boundary below which optical serviceability failure accelerates. These findings provide a theoretical basis and quantitative engineering guidance for optimizing the cold-bending process of isosceles triangular fully toughened glass plates. Full article

Cyclone separators remain widely used for gas–solid separation, yet analytical prediction of cut size and pressure drop remains challenging. This study presents an explicit semi-empirical model for the cut size (d₅₀) of reverse-flow cyclones based on the radial particle equation of motion in cylindrical coordinates, with d₅₀ obtained by equating radial migration time and residence time. A closed-form solution is derived in the Stokes regime, whereas non-Stokes behavior is handled numerically through the Schiller–Naumann drag correction. Turbulence is incorporated through a phenomenological correction, and the grade–efficiency curve is represented by a logistic relation. The model was implemented in MATLAB R2025a and applied in a parametric study covering inlet velocity, particle density, cyclone diameter, and gas viscosity. A Euler-type pressure drop relation was included to examine the separation–energy trade-off. Validation on the Kim et al. benchmark using one calibration point per cyclone family and six independent verification cases yielded a mean absolute percentage error of 13.5% and a root mean square error of 0.22 μm for d₅₀; the paired pressure drop check gave a 2.8% mean absolute percentage error. A complementary benchmark based on Wang et al. using 15 cm 1D3D and 2D2D cyclones under actual-air and standard-air conditions further supported the family-calibrated use of the model. A separate scale-up test showed that constant swirl intensity similarity is not transferable across large diameter changes. The formulation provides a transparent reduced-order tool for preliminary design and sensitivity analysis. Full article

Deepwater shallow gas sediments and the weakly consolidated overburden are sensitive to depletion-induced effective stress redistribution. Since deepwater shallow gas has only recently begun to be treated as a commercially available natural gas resource, it lacks models to quantify the coupled flow and geomechanical behaviors in such environments. In this study, we propose a semi-analytical model for a shallow gas layer and its overburden sediments, where pore pressure evolution is described by vertical transient diffusion and the stress response is represented by an OCR-dependent (overconsolidation ratio-dependent) in situ stress field with depletion-induced effective stress increments. Pre-yield compressibility is characterized by a stress-dependent nonlinear elastic law, and post-yield deformation is approximated by a Mohr–Coulomb-based yield-controlled plastic correction for engineering purposes. The formulation is used in the base case and during a parametric sensitivity analysis. In the base case, the final settlement is 0.597 m, of which 45.3% is elastic and 54.7% is plastic. The sediments begin to yield after approximately 115 d of production, and the final yielded-thickness fraction reaches 0.268. The sensitivity analysis shows that friction angle, maximum drawdown, gas-layer thickness, and OCR magnitudes predominantly affect the final settlement and yielded-thickness response, while gas-layer permeability has an insignificant effect. Furthermore, the comparison reveals that the depletion timescale governs the stress evolution rate, while depletion pressure drawdown magnitude dictates deviatoric stress evolution and long-term settlement. Considering the engineering condition for the development of typical deepwater shallow sediments, the feasible production parameters should be in the low-to-moderate drawdown and slow depletion range. A practical operating window is approximately 3.6~4.0 MPa maximum drawdown with a depletion timescale of about 340~400 d. This study can provide quantitative insights into the potential commercial production of gas layers in deepwater shallow sediments. Full article