Search Results (1,231)

This study investigates how offshore wind farm layout geometry influences farm-level performance using a computationally efficient Jensen–Park wake model combined with directionally resolved Weibull wind-speed statistics. A fixed-capacity 1.8 GW case study, consisting of 120 V236-15.0 MW turbines, is used to examine the effects of grid aspect ratio, inter-turbine spacing, cumulative row skew, and global layout rotation on wake losses, annual energy production (AEP), and capacity factor under representative offshore screening assumptions. Structured layouts with identical turbine count and installed capacity are compared with a regular baseline grid to isolate geometric effects within a consistent modelling framework. For the nominal offshore Jensen wake-expansion coefficient, k = 0.04, the highest sampled AEP is obtained for the 5 × 24 configuration, which produces 8930.69 GWh yr⁻¹ and a capacity factor of 56.64%. The regular baseline produces 7397.50 GWh yr⁻¹ and a capacity factor of 46.91%, corresponding to a 20.73% AEP increase for the highest sampled layout. However, the performance differences among Layouts D–F are small, indicating a high-performing layout plateau rather than a clearly separated optimum. The contribution of this paper is therefore not a new wake model, optimisation algorithm, or general offshore design rule. Instead, this study provides an auditable screening workflow that documents modelling assumptions, parameter bounds, coordinate transformations, convergence checks, sensitivity analyses, and spatial-efficiency indicators for one turbine model, one turbine count, one synthetic wind rose, and a limited set of structured row–column layouts. Full article

Photovoltaic (PV) power exhibits high variability and non-stationarity due to irradiance fluctuations, cloud shading, and seasonal changes, which complicate ultra-short-term multi-step forecasting. This study proposes a Solar-Historical Prior Network (SHPNet) for forecasting at 5 min resolution. SHPNet integrates a solar-geometry clear-sky prior-guided temporal convolutional network (SGCP-TCN), a similar historical window (SHW) branch, and horizon-wise adaptive fusion (HA). SGCP-TCN estimates clear-sky power potential from site coordinates and timestamps and reformulates direct power prediction as clear-sky power ratio forecasting. SHW retrieves training windows from the same intra-day time slot that exhibit similar power–irradiance evolution, thereby constructing a non-parametric historical prior, while HA determines horizon-specific fusion weights based on validation errors. Unlike purely data-driven predictors and conventional similar-day methods, SHPNet combines a physically interpretable power scale with input-window-level historical evolution patterns and adaptively balances the two priors across forecasting horizons. Across the two sites, SHPNet reduced the mean MAE and RMSE by 14.07% and 10.26%, respectively, compared with the original TCN, while increasing the mean R² from 0.8279 to 0.8613. Evaluations under different weather conditions and across seasons demonstrate consistent forecasting performance, while convergence analysis confirms stable training behavior. Full article

Although deep learning has transformed protein structure prediction, the controlled generation of functional and experimentally tractable protein structures remains a major challenge in structural bioinformatics. Diffusion models offer a versatile approach to generating protein backbones, motif-conditioned scaffolds, all-atom structures and biomolecular interaction geometries, while accommodating explicit structural and functional constraints. This review focuses on coordinate- and residue-frame-based diffusion approaches for generating protein structures, paying particular attention to geometric equivariance, conditioning strategies, all-atom modelling and interaction-aware design. We compare representative methods derived from RoseTTAFold, frame-diffusion architectures, and oriented-residue-cloud representations according to their molecular representation, generative objective, and validation strategy. We examine the criteria used to evaluate generated proteins, such as stereochemical quality, structural consistency, designability, novelty, diversity, computational efficiency, and experimental performance. Particular attention is given to the distinction between learned structural distributions and condition-dependent thermodynamic ensembles. Future progress will depend on the integration of generative models with molecular mechanics, conformational sampling, uncertainty estimation, free-energy methods, and experimental design–build–test–learn cycles. Within this framework, diffusion models offer candidate generation and constraint satisfaction capabilities within broader protein engineering workflows. Full article

Two-objective optimization requires both reliable front approximation and explainable non-dominated extraction. This study develops a theoretical and computational method that maps sampled objective vectors to rasterized objective-space images and processes their Pareto structure through supervised neural approximation, a deterministic convolutional extractor, and exploratory reinforcement search. Network I reconstructs a high-density sampled occupancy image from sparse samples, whereas Network II approximates the sampled Pareto-front boundary. The principal algorithmic contribution is a fixed cross-correlation kernel derived from the two-objective dominance quadrant and coupled with a cell archive that preserves original vectors and resolves raster collisions through exact dominance checks. Under the stated coordinate convention, central inversion relates the dominating and dominated displacement quadrants, translation-equivariant cross-correlation applies the same local relation across the grid, and minimization selects only the improvement-directed boundary. Experiments on SCH, FON, POL, KUR, and ZDT synthetic benchmarks assess front-geometry recovery and deterministic extraction on grids from 127 × 127 to 2048 × 2048; the reinforcement-learning results on SCH are interpreted as exploratory feasibility evidence. The present evidence is therefore confined to synthetic benchmarks. The method provides a benchmark-based methodological foundation for future multi-criterion model-selection and calibration research, including option-pricing model research in financial mathematics and quantitative economic analysis. Full article

Uneven straw conveying and unstable throwing can reduce the operational performance of rice combine harvesters. To address these problems, an integrated straw conveying and throwing device combining guided conveying with pneumatic throwing was developed. The brachistochrone principle was introduced into the curved-surface design of the diversion plate as a geometry-guided approach to provide a continuous transition between the straw-falling region and the conveying inlet. Based on the motion characteristics of straw in the diversion and throwing regions, a coordinated feeding–acceleration–throwing process was established. The effects of diversion plate angle, blade rotational speed, and blade installation angle on throwing distance and distribution stability were investigated. A computational fluid dynamics model based on the mixture multiphase approach was used to characterize the macroscopic gas–solid flow field and compare airflow organization under different blade installation angles. A Box–Behnken response surface design was subsequently employed to establish regression models for throwing distance and the coefficient of variation in straw distribution, followed by multi-response numerical optimization. The optimal parameter combination consisted of a blade rotational speed of 2500 r/min, a diversion plate angle of 1.25 rad, and a backward blade installation angle of 15°. Under these conditions, the predicted throwing distance and coefficient of variation were 7.89 m and 14.6%, respectively. Validation tests produced throwing distances of 6.94–8.21 m and coefficients of variation of approximately 13%, showing good agreement with the predicted performance. The developed device and optimization results provide a basis for improving the conveying continuity and throwing uniformity of straw-handling systems in combine harvesters. Full article

Clay slopes are particularly sensitive to variations in soil strength, groundwater conditions, and slope geometry, making their rapid and reliable assessment essential for geotechnical design, landslide prevention, and infrastructure risk management. This study develops and evaluates an empirical express method for estimating the stability of clay slopes based on the relationship between soil mechanical parameters, slip surface geometry, and the factor of safety. The proposed approach derives empirical dependencies for the radius of the potential circular slip surface and the coordinates of its centre as functions of slope height, cohesion, internal friction angle, and water-related conditions. The method is supported by long-term field observations and geotechnical investigations of clay slopes, including dry and water-affected scenarios. Two representative stability conditions are considered: dry slopes and slopes influenced by an elevated depression curve. The method was evaluated for 45° clay slopes with heights up to 60 m, using eight representative cases: four dry scenarios and four water-affected scenarios. The calculated factors of safety were compared with GEO5 SLOPE results obtained using Bishop’s simplified method. The comparison showed that most analysed cases presented differences below 5% between the proposed express method and the Bishop-based numerical benchmark, with larger deviations occurring only in selected boundary cases. The results demonstrate that the proposed method can provide a rapid preliminary assessment of clay slope stability, supporting early-stage geotechnical diagnosis, risk screening, and decision-making in regions where clayey formations and slope instability are recurrent. Full article

Arbitrary-amplitude nonlinear periodic electrostatic waves and the wave-breaking limit are considered in a one-dimensional relativistic electronegative plasma consisting of positive ions, negative ions, and a relativistically degenerate electron-fluid background. A cold, inertial fluid description is adopted for the ions, while the electrons are described by a relativistic Fermi–Dirac equation of state, which provides the required restoring physics through degeneracy rather than ordinary thermal pressure. After transforming to a travelling coordinate system, we reduce the general multicomponent plasma dynamical system to a pseudopotential energy form with a constant of motion. We then determine the allowed potential range, the associated asymmetric pseudopotentials, and the wave-breaking electric field for both linear and nonlinear waves on each of the two admissible branches. We find that arbitrary-amplitude nonlinear periodic waves are intrinsically asymmetric and that the maximum field strength is set by the effective charge-density boundary of each plasma species along the field direction. Parametric analysis suggests that the critical minimum field strength required for nonlinear wave formation may be controlled by increasing either the negative-ion mass ratio, the wave propagation speed, or the negative-ion concentration; however, each of these changes produces a distinct modification of the pseudopotential geometry. This illustrates that relativistic and interspecies effects are not merely responsible for quantitative deviations from the non-relativistic and single-species cases, but instead completely reshape the nonlinear phase space governing the persistence, deformation, and breaking of periodic electrostatic waves. Full article

Bistatic Synthetic Aperture Radar (BiSAR) is an advanced radar imaging system in which the transmitter and receiver platforms are positioned at distinct spatial locations. This separated transmit–receive architecture enables coordinated observation of the target scene. In particular, the highly squinted spaceborne bistatic configuration offers advantages in multi-angle observation, overcoming the insensitivity of conventional spaceborne interferometric SAR (InSAR) to north–south surface deformations, thereby enabling efficient and high-precision measurement of global three-dimensional (3D) surface deformations, which holds significant engineering application value. Focusing on the highly squinted spaceborne BiSAR imaging geometric model, this paper proposes a novel highly squinted imaging method based on a high-order model. Traditional imaging algorithms are founded on straight-line models and employ the method of series reversion (MSR) to achieve imaging. In contrast, the proposed method is specifically tailored to the highly squinted bistatic observation geometry, fully accommodating orbital curvature effects while simultaneously resolving the imaging challenges posed by two-dimensional (2D) spatial variations of imaging parameters. In this method, control points are judiciously distributed within the observation scene, and the imaging parameters are solved via high-order polynomial fitting. Based on this foundation, the 2D spectrum expression for the highly squinted bistatic configuration is rigorously derived, together with the frequency-domain resampling mapping relation that compensates for the 2D spatial variation of imaging parameters, thereby achieving full-scene high-accuracy focused imaging. The proposed approach broadens the applicability of conventional straight-line-model-based algorithms and is well suited for highly squinted bistatic SAR imaging. The validity of the method is ultimately demonstrated via extensive simulation experiments and thorough performance evaluations. Full article

Low-latitude plateau classrooms, such as those in Kunming, experience intense solar radiation that often causes insufficient far-window illumination, excessive near-window brightness, and viewing-direction glare under side-lighting conditions. To investigate this spatial imbalance, field surveys of nine classrooms were used to define realistic parameter ranges, while 100 parametric design cases were evaluated through annual simulation, machine learning, and SHAP analysis. The viewing-direction glare model achieved a test-set R² of 0.819, indicating adequate predictive performance for factor interpretation. Compared with simply increasing the window-to-wall ratio (WWR), coordinated control of classroom geometry, window configuration, and surface reflectance produced a more balanced luminous environment. Daylight availability and excessive illuminance were primarily governed by WWR and window reveal depth, whereas glare was more strongly influenced by seating position, viewing direction, window width, and orientation. Classroom-wide averages may therefore conceal localized glare experienced by students. A moderate WWR of 0.26–0.40 combined with a window reveal depth of 0.75–1.17 m emerged as a preferable strategy within the investigated design space. These findings support desktop-level daylight assessment and student-perspective glare evaluation in the design and renewal of ordinary side-lit classrooms in Kunming and comparable low-latitude plateau regions. Full article

A fully coupled three-dimensional (3D) thermo-magneto-elastic layer-wise formulation is developed for the analysis of multilayered flat and curved panels used in aerospace and aeronautical applications. The model relies on a system of coupled second-order differential equations along the thickness coordinate z, formulated in a mixed orthogonal curvilinear reference system. The governing equations combine the three-dimensional equilibrium equations with the magnetic induction divergence equation and the heat conduction equation, providing a unified multifield framework for thermo-magneto-elastic analyses. Through a suitable definition of the curvature parameters, the same formulation can be directly applied to plates, cylinders, cylindrical panels, and shells with constant radii of curvature. The governing equations are analytically solved by adopting harmonic expansions in the in-plane directions together with the exponential matrix method along the thickness coordinate. The harmonic representation naturally satisfies simply-supported boundary conditions along the panel edges. The multilayered structure is modeled according to a layer-wise strategy, where the continuity of the selected mechanical, magnetic, and thermal variables is enforced across the interfaces between adjacent layers. Different loading boundary conditions can be assigned at the external surfaces by prescribing pressure loads, magnetic potential, transverse magnetic induction, and over-temperature. The numerical investigation is divided into two stages. First, the accuracy of the proposed formulation is verified through comparisons with thermo-magneto-elastic solutions available in the literature. Then, a comprehensive set of new benchmark results is presented by considering different geometries, thickness ratios, and loading boundary conditions. Both tabulated values and through-the-thickness distributions are reported for the most significant field variables. These benchmark results provide useful reference data for the assessment and validation of future two-dimensional and three-dimensional analytical and numerical formulations devoted to coupled thermo-magneto-elastic problems. Full article

This exploratory proof-of-concept study aimed to develop and preliminarily evaluate a 3D-printed holder designed for reuse in cone beam computed tomography (CBCT) in dental research, particularly for comparative studies requiring pre- and post-intervention tooth assessment. The holder was designed using computer-aided design software and fabricated from polylactic acid using 3D printing. A conventional alginate-based holder was used for comparison. The comparison therefore concerned two complete positioning systems with different geometries and modes of adaptation to the CBCT headrest. The two positioning devices were independently placed by two operators with different levels of experience, and all CBCT scans were acquired by the same operator. The procedure was repeated after a 5-day interval. Root canal morphology was assessed using volumetric and surface measurements, as well as voxel counts. The independently segmented canal models were qualitatively displayed together in their native coordinate space to visualize positional consistency between acquisitions. Paired analyses showed substantially lower positional deviations with the 3D-printed holder than with the conventional holder in all four acquisition comparisons (Holm-adjusted p < 0.001). Mean deviations ranged from 0.126 to 0.172 mm for the 3D-printed holder and from 3.626 to 8.318 mm for the conventional holder. Morphometric parameters derived from 3D analysis remained identical across all acquisitions, regardless of operator, time point, or positioning method. Qualitative 3D analysis showed near-complete overlap for the 3D-printed holder and clear spatial discrepancies for the conventional holder. Under the evaluated conditions, the 3D-printed holder exhibited less positional variability than the conventional alginate-based holder. Full article

The receiver autonomous integrity monitoring (RAIM) and Global Navigation Satellite System (GNSS) fault detection literature uses a group of statistical detectors that are often introduced with different names, coordinate systems, and derivation styles. This makes it difficult for new researchers to determine whether two methods use different information or only express the same inconsistency through different statistics. This paper provides a detector-centered tutorial review of statistical snapshot fault detection with a unified whitened linearized model. The chi-squared detector, parity-space detector, Baarda w-test, range comparison detector, jackknife detector, solution separation detector, and generalized likelihood-ratio test are derived with consistent notation. For each detector, the statistic construction, null and alternative distributions, threshold rule, and minimum detectable bias (MDB) are presented. A relationship map was developed to distinguish exact equivalence, projection relations and linear transformations among these detectors. An illustrative validation was then conducted with a real satellite geometry collected in an urban environment and synthetic Gaussian faults. The results verify the relationship checks, detection-probability behavior, and MDB calculations in a reproducible setting. Full article

Over the years, many attempts have been made to mathematically describe an ovoid, both from a purely mathematical standpoint as well as to inform the underlying biological process of egg formation. Most existing formulations are complex and non-predictive, requiring each individual shape to be fitted separately. The aim of this study was to develop a parsimonious, elegant, and predictive model for the full range of avian egg geometry—one based on simple geometric components and a minimal number of parameters. This approach constructs an ovoid from two ellipses and a straight line, governed by only three adjustable parameters. The resulting model is predictive: it can accurately reconstruct the full contour of an avian egg using only the coordinate of maximum breadth and the breadth at one-quarter of the egg’s length from the pointed end. The mean error of this method is 0.047 (RMSE), which is competitive with other comparable methods. This accuracy arises because the geometric invariance of the model closely matches the allometric invariance observed in real avian eggs. Moreover, the ovoids generated by this method mostly conform closely to the Main Axiom, derived from Fermat’s extremum theory, making the model particularly suitable for engineers, designers, and ornithologists who require a straightforward means of constructing realistic ovoid shapes. Full article

This study presents a comprehensive experimental and numerical investigation of a rotating propeller using three-dimensional Digital Image Correlation (3D DIC), Computational Fluid Dynamics (CFD), and Finite Element Method (FEM) analysis. The main objective was to assess the applicability and accuracy of 3D DIC for full-field displacement and strain measurements under centrifugal loading conditions. The propeller geometry was reconstructed using 3D scanning and implemented in a numerical model with material parameters identified through mechanical testing. Experimental measurements were carried out on a dedicated test stand, enabling controlled rotational speed and synchronized image acquisition. Particular attention was devoted to measurement uncertainty, including calibration errors, motion effects, and coordinate system alignment. The obtained displacement and strain fields were compared with FEM predictions after proper spatial transformation for rotational speeds of 4110, 6045, and 6940 rpm. The results demonstrate good agreement between numerical and experimental data in selected regions, confirming the potential of 3D DIC for validation of rotating structures, while also highlighting limitations related to dynamic effects and optical constraints. Full article

Metalloprotein docking is difficult because it requires precise preparation of the grid box, charge assignment, and metal coordination geometry. Many current graphical user interfaces (GUIs) do not support these steps well. Here, we present MBind v2026, a web-based GUI that combines AutoDock Vina, AutoGrid4 and AutoDock4 into a single workflow for standard docking and metalloprotein docking. The interface also has the optional machine learning (ML) docking program GNINA v1.0. The main validated metalloprotein workflow in MBind is based on AutoDock4Zn parameterization for zinc, while preliminary workflows for magnesium, iron, and copper are also included. MBind was benchmarked on eight zinc metalloproteins and eight nonmetalloprotein targets. Its performance was compared with ML pose prediction methods (GNINA v1.0, EquiBind v2026, TankBind v2026, and GAABind v2026), cofolding models (Boltz-2 v2026, and AlphaFold 3 v2026), ML affinity prediction tools (StructureNet v2026, GNNSeq v2026, and PLAIG v2026), and non-ML docking tools (SwissDock v2026, CB-Dock2 v2026, Webina v2026, 1-ClickDock v2026, and MolModa v1.01). Pose accuracy was measured by symmetry-aware ligand RMSD using DockRMSD v1.1 under redocking conditions with co-crystal-defined binding sites. Binding energy trends were evaluated by comparing docking scores with IC50-derived ΔG values using mean absolute error. On the zinc metalloprotein benchmark set, MBind produced a mean RMSD of 0.49 Å. On the nonmetalloprotein set, the mean RMSD was 0.62 Å. These results show that MBind can execute zinc metalloprotein and nonmetalloprotein docking workflows through a web-based interface, while the preliminary Mg, Fe, and Cu workflows require additional validation. The MBind GUI web-based platform is publicly available. Full article