Search Results (1,119)

This study investigates the shear damage mechanisms in reinforced concrete (RC) beams through non-linear numerical modeling. Using the Finite Element Method (FEM) in ABAQUS, a Concrete Damaged Plasticity (CDP) framework was validated against experimental results and subsequently utilized for a 36-model parametric investigation. The study isolated the influence of stirrup spacing, diameter, and yield strength to evaluate their roles in ultimate shear capacity. The results indicated that while increasing stirrup diameter yielded modest capacity enhancements of approximately 7%, the impact of increasing yield strength was negligible, as the failure modes were primarily governed by concrete web crushing before reinforcement yielding could occur. These physical limit states were compared against the linear Truss Analogy adopted by major design standards—including ACI 318-19, Eurocode 2, and TS 500—to quantify discrepancies in heavily reinforced sections. The findings reveal that, strictly within the investigated parameter space (a/d = 2.67, f’_c = 28.5 MPa), current linear equations can significantly overestimate the physical capacity gains provided by reinforcement modifications. These observations are configuration-specific and highlight the need for cautious application of linear models in heavily reinforced scenarios. Furthermore, the study suggests that utilizing 3D beam elements for transverse reinforcement provides a more nuanced representation of shear transfer mechanisms, such as dowel action, compared to standard truss models. Full article

In the context of subtropical cities, the slow-moving environment of HOD (Hospital-Oriented Development) faces the dual challenges of spatial fragmentation and an extreme hot and humid climate, which also restricts the outdoor space’s thermal environment performance. Taking the Macau Light Rapid Transit (LRT) Union Hospital Station as an example, this study constructs a “topology-climate” dual quantitative assessment framework that integrates space syntax and parametric universal thermal climate index (UTCI) simulation. In response to the current problems of mixed pedestrian and vehicular traffic and high-intensity heat radiation, a comprehensive intervention strategy combining three-dimensional stitching and spatial optimization is proposed. The results show that: (1) The implantation of three-dimensional corridors improved the spatial integration of the core area of the site by 67.0%, significantly optimizing network connectivity. (2) During the extreme high-temperature period of daytime (9:00–18:00) in summer and autumn, the intervention strategy precisely opened up a continuous low-heat-stress linear shade zone through the synergistic mechanism of building projection shadows, physical shading of connecting corridors, (landscape shading effect, original evaporation removed). (3) The study confirms that landscape-coupled shading layout is the most effective method, reducing potential pedestrian heat exposure across the entire area, while the three-dimensional connecting corridors precisely control the thermal environment of core walkways. Together, these two elements construct a “topology-climate” optimization framework, achieving a synergistic improvement in spatial accessibility and simulated thermal comfort performance under standard meteorological input and quantitatively verifying the optimization effectiveness of the tiered intervention scheme. This study provides a data-driven decision-making basis for optimizing potential walking thermal conditions for vulnerable groups and reshaping the space’s potential to improve microclimate via shading design of medical hub areas and also provides a scientific paradigm for TOD microclimate planning focused on shading-based thermal environment optimization. Full article

The ocean–atmosphere turbulent heat exchange plays a critical role in the energy and moisture budgets of the Tropical Atlantic Ocean (TAO) and in weather and climate forecasts. However, its estimation strongly depends on the choice of bulk parameterization, as direct in situ measurements are sparse. This study evaluates sensible (Hs) and latent (Hl) heat fluxes derived from three bulk parameterization schemes used operationally in models at the Brazilian Center for Weather Forecast and Climate Studies (CPTEC) of the National Institute for Space Research (INPE), Brazil: the Brazilian Atmospheric Model (BAM), the Modular Ocean Model version 6 (MOM6), and the Weather Research and Forecasting (WRF) model. Using daily in situ observations from seven Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) buoys across the TAO during 1997–2023, we computed monthly mean fluxes and compared them against the Coupled Ocean–atmosphere Response Experiment (COARE) algorithm version 3.0b (COARE 3.0b) reference. COARE version 3.6 (COARE 3.6) and European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis 5th generation (ERA5) data were included as additional benchmarks. All offline schemes were forced with identical buoy data, isolating differences in internal physical assumptions. Hl is approximately one order of magnitude larger than Hs across all sites, and inter-scheme differences are substantially larger for Hl (±50 W∙m⁻²) than for Hs (±5 W∙m⁻²). All schemes reproduce the seasonal cycle linked to the Intertropical Convergence Zone (ITCZ) migration and trade-wind variability, with correlations generally exceeding 0.8 (p < 0.001) for most buoys. However, systematic magnitude biases remain. The Coordinated Ocean Research Experiments (CORE) bulk formulation implemented in MOM6 (MOM6-CORE) shows high temporal correlation (often r ≈ 1.0) but a persistent negative bias for both Hs and Hl (e.g., B1 Hl bias = −24.0 W∙m⁻²), indicating weaker turbulent exchange relative to COARE 3.0b. BAM overestimates Hs (by 1–3 W∙m⁻²) and underestimates Hl at most northern and southern sites, while the parametrization of the Yonsei University (YSU) implemented in the WRF model (WRF-YSU) amplifies Hs variability intermittently, particularly at the equator (B4). As expected, COARE 3.6 remains the closest to the reference (differences < 1 W∙m⁻² for Hs and <7 W∙m⁻² for Hl; r ≈ 0.99). ERA5 captures temporal variability well (r ≈ 0.7–0.9) but systematically overestimates Hl (positive bias up to +47.6 W∙m⁻² at B7), implying stronger evaporative cooling. Buoy-specific regimes modulate skill. The choice of bulk formulation thus remains a first-order source of uncertainty in turbulent heat flux estimates over the TAO, with direct implications for mixed-layer heat budgets, SST evolution, and coupled ocean–atmosphere variability. MOM6-CORE provides the most consistent performance relative to the COARE reference and emerges as the most robust option for operational applications at CPTEC/INPE. The findings also provide guidance for improving the representation of ocean–atmosphere turbulent exchanges in MONAN (Model for Ocean-Land-Atmosphere Prediction), the new Brazilian Earth System Model under development for weather and climate prediction. Full article

The development of additive manufacturing in steel construction opens new possibilities for shaping structurally efficient and geometrically differentiated load-bearing systems. At the same time, the viability of such solutions depends strongly on their material rationality, especially at the scale of larger structural typologies. This paper presents a computational comparative screening of spatial steel roof typologies that may be relevant for future large-scale metal additive manufacturing, focusing on how global geometry, support arrangement, curvature, and structural depth influence mass efficiency under a unified structural modelling framework. Using computational modelling and comparative evaluation, the study examines how variations in structural form influence the performance of spatial systems developed within a unified design framework. The analysis demonstrates that the potential for material rationalisation of such structures is not limited to local modification of member dimensions, but is fundamentally linked to the configuration of the overall structural geometry. More than 40 structural configurations were analysed, covering seven typological variants, three rise levels, two support strategies, and two section-sizing approaches. An additional threshold sensitivity check was performed for representative variants to examine whether the main typological ranking remained stable under an alternative four-group utilisation classification. The obtained masses varied by more than one order of magnitude between the most and least favourable configurations, confirming the strong influence of global typology and support arrangement on material demand. The results highlight the importance of structural typology, support arrangement, and geometric organisation in achieving material-efficient solutions. The study therefore argues that, in the context of steel structures considered for future additive manufacturing, global form should be treated as a primary design variable rather than as a secondary outcome of local member sizing. Full article

Stroke-related gait impairments are frequently associated with deficits in trunk control, movement coordination, and dynamic stability. Although robotic-assisted gait rehabilitation has shown promising clinical benefits, phase-specific biomechanical adaptations following rehabilitation remain incompletely understood. This study investigated phase-specific biomechanical adaptations following robotic-assisted gait rehabilitation in individuals with stroke using sensor-derived waveform analysis. Rehabilitation was performed three times per week over approximately 5–6 weeks using treadmill-based robotic gait training under dynamic body-weight support conditions. Pre- and post-intervention kinematic data were collected using a sensor-based motion analysis system. Joint kinematics, trunk motion, and center of gravity (COG) displacement were analyzed across the normalized gait cycle using waveform-based effect size analysis, statistical parametric mapping, principal component analysis, and k-means clustering to explore inter-individual adaptation patterns. Thirteen post-stroke hemiplegia patients (10 males; age = 63.9 ± 13.8 years), including six subacute and seven chronic stroke survivors, completed 16 rehabilitation sessions. The most prominent improvements were observed in trunk lateral flexion, particularly during loading response (d = 0.47, p < 0.01), indicating enhanced frontal plane trunk stability. Trunk flexion–extension showed reduced compensatory motion, whereas hip and knee adaptations were smaller and phase-dependent. COG displacement decreased across the gait cycle, reflecting improved dynamic stability. Step length increased significantly on both hemiplegic (Δ = +5.73 cm, p = 0.024) and intact sides (Δ = +8.83 cm, p = 0.007), while cadence and load symmetry remained unchanged. Clustering analysis revealed heterogeneous adaptation profiles rather than distinct responder groups. Chronic participants demonstrated greater variability within the Principal Component Analysis space compared to subacute participants, suggesting more variable and individualized biomechanical reorganization patterns rather than clearly separable recovery categories. Overall, robotic rehabilitation induced inter-individual biomechanical adaptations, predominantly involving proximal trunk control and stabilization strategies. Full article

This study aims to identify the defining characteristics of pocket parks and evaluate their ecological and socio-economic significance by analyzing their contribution to sustainable development, in alignment with the 17 United Nations Sustainable Development Goals (SDGs). This research highlights the benefits of green spaces and pocket parks in relation to the three core pillars of sustainability, mapping them directly onto specific SDG Targets and indicators. This framework informs the creation of a streamlined, early design indicators toolkit. The toolkit’s practical utility is then evaluated and validated through its application to four real-world case studies, where the performance of pocket parks is assessed regarding their contributions to urban sustainability. The selected case studies represent diverse morphological typologies and operational attributes. To embed sustainability benefits into the active planning process, their spatial design criteria were cross-examined to identify structural interconnections, which were subsequently translated into a parametric model. Each design parameter is analyzed with emphasis on the relationships among spatial elements rather than on their absolute metric values. The study develops a procedural design sequence that, when applied to any site boundary, generates the essential spatial characteristics defining a pocket park. The results demonstrate that this parametric approach establishes the adaptability and effectiveness of pocket parks as versatile urban green spaces, regardless of available plot size or geometric configuration. Full article

Implied volatility surfaces describe option-implied volatilities across strikes, and maturities and play a central role in derivative pricing and risk management. However, in practice, they are often incomplete due to illiquidity or sparse trading, requiring reliable reconstruction of missing regions. Existing approaches typically rely on parametric assumptions or latent space optimisation methods, which may be restrictive or computationally intensive. This study proposes a data-driven framework based on conditional generative adversarial networks (GANs) to map partially observed surfaces to completed ones in a single forward pass. The approach is evaluated in a controlled setting using synthetic data generated from the Heston stochastic volatility model, with varying levels of missingness (10–96%). The generator objective incorporates penalty terms enforcing the absence of call-spread, butterfly-spread, and calendar-spread arbitrage, together with a smoothness regulariser on the implied risk-neutral density. Compared with a conditional variational autoencoder (VAE), the Bates model, and the stochastic volatility-inspired (SVI) parameterisation, the proposed approach achieves lower reconstruction errors across all levels of missingness, including unseen cases, while preserving the no-arbitrage properties. An ablation study shows that the conditional GAN implicitly learns no-arbitrage behaviour, with density smoothness regularisation being the only constraint that meaningfully improves reconstruction quality. Full article

Optimizing flapping-foil kinematics for underwater propulsion is challenging due to strong temporal coupling and nonlinear fluid–structure interactions. Most existing approaches rely on parameterized motion profiles, which restrict the accessible design space. A non-parametric kinematic optimization framework based on the discrete adjoint method is developed, enabling direct optimization of time-resolved motions without predefined functional forms. A Morison-based low-order hydrodynamic model, calibrated against Computational Fluid Dynamics (CFD), is employed for efficient evaluation within a validated regime. Results show that optimized motions substantially enhance propulsion performance over conventional sinusoidal motions, yielding non-sinusoidal, high-efficiency kinematics. In thrust-maximization cases, the optimized kinematics achieve a 50.29% increase in mean thrust by redistributing heave and pitch amplitudes and timing. Under balanced thrust–power conditions, the optimized motions consistently outperform sinusoidal counterparts. In power-minimization cases, a “generator-like” regime emerges, indicating a reversal of net energy transfer enabled by the non-parametric formulation. These results demonstrate that non-parametric optimization provides enhanced design flexibility and improved propulsion performance, offering a practical framework for biomimetic underwater propulsion design. Full article

A numerical investigation of forced convection heat transfer in a three-dimensional T-shaped bifurcating channel with an upstream rotating cylinder and a downstream vibrating wavy wall is presented. The working fluid is a ternary hybrid nanofluid (Fe₂O₃, CuO, MoS₂ in water) exhibiting Casson rheology under an inclined magnetic field. The novelty of this work lies in the first integrated configuration combining these simultaneous mechanical, magnetic, and non-Newtonian effects. Using COMSOL Multiphysics, 413 parametric combinations of Reynolds number, Hartmann number, Casson parameter, nanoparticle shape and volume fraction, magnetic field angle, cylinder rotation speed, wall amplitude (Am), and period were solved. Average Nusselt and Bejan numbers quantified heat transfer enhancement and thermodynamic irreversibility. To interpret the high-dimensional parameter space and to circumvent the prohibitive computational cost of additional 3D magnetohydrodynamics simulations, machine learning (XGBoost) models were developed to rank feature importance and provide fast, accurate surrogate predictions (R² > 0.99). Cylinder rotation dominates heat transfer, increasing the Nusselt number by over 980% (feature importance 0.42) with a modest entropy penalty. Nanoparticle volume fraction reduces the Nusselt number via viscous damping. Magnetic field parameters negligibly affect heat transfer but strongly influence entropy generation; a perpendicular field recovers up to 97% thermal efficiency at high Hartmann numbers. Full article

Space-based optical detection is a critical capability for Space Situational Awareness, yet the scarcity of real on-orbit observation data significantly hampers the development and validation of object detection and tracking algorithms. To address this need, this paper proposes a high-fidelity image simulation method designed to provide reliable data sup-port for algorithm development and evaluation. The method systematically integrates or-bit propagation, high-precision astrometric corrections, imaging visibility constraints, and multi-source noise modeling. A unified Point Spread Function convolution streak model is established to consistently represent the motion blur of both stars and space objects during exposure. Additionally, simplified parametric stray light background models covering the Sun, Moon, and Earth airglow are constructed. Quantitative comparison with real image data from the Kaiyun-1 satellite demonstrates good agreement in star positions, streak morphology, and centroid localization accuracy. Preliminary validation against real data demonstrates that the proposed simulation framework can provide effective image data for testing and performance assessment of space-based situational awareness algorithms. Full article

Federal post-quantum cryptography migration is scoped around three categories of cryptographic assets: libraries, protocols, and key stores. We argue that this scoping is incomplete. Cryptographic functions and key material can be realized in the parameters of machine-learning models, and the current open-source serialization-focused scanners we evaluated do not detect them. We provide an existence proof: a 30-layer feed-forward ReLU network that realizes AES-128 exactly, with the master key and all eleven round keys resident directly in the layer bias vectors and recoverable by parsing. The construction validates bit-exactly against FIPS 197 and the NIST CAVP AESAVS known-answer subsets across 10⁴ random plaintext-key pairs, including under float32 quantization. We argue analytically—by a sizing analysis rather than empirical construction—that ML-KEM and ML-DSA private keys hide more comfortably in modern weight tensors than AES keys do. The basis is twofold: larger key sizes amortize the construction’s fixed parameter overhead, and the lattice arithmetic underlying these primitives admits more architectural variation than the rigid AES key schedule. Under the harvest-now-decrypt-later threat model, the consequence is direct: any long-lived cryptographic key embedded in an open-weights model artifact distributed today is recoverable by any future party with knowledge of the embedding scheme, with no quantum capability required. We propose an audit primitive—a parameter-space cryptographic recognizer—that screens model artifacts at ingestion through four stages: structural matching against cipher fingerprints, a parametric analysis for bias-and-sign coupling signatures, functional probing for cryptographic input–output behavior, and the integration with cryptographic bill-of-materials tooling as a parameter-resident cryptographic content emission class extending the MBOM-PQC schema. The recognizer is defense-in-depth: it closes the gap for known constructions and architectural fingerprints without claiming completeness against adaptive adversaries. We make no claim that any deployed model contains such an embedding; the contribution is the existence of the capability, the absence of detection in the scanners we evaluated, and the migration-scope consequence. Full article

Facing the harsh offshore environment characterized by severe space constraints and continuous platform motion, this study develops an optimized rotating packed bed (RPB) for compact and robust triethylene glycol dehydration. Through integrated experimental and computational investigation, the concentric-ring rotor was identified as superior among four configurations, consistently achieving dehydration equilibrium above 80% under lean TEG conditions. CFD analysis revealed its fundamental mechanism: synergistic matching between the centrifugal force field and annular flow paths yields the most uniform liquid distribution. This enabled the establishment of a strong predictive correlation (R² = 0.935) between simulated liquid uniformity and experimental dehydration performance. Guided by flow field diagnostics, targeted structural optimizations increased dehydration equilibrium from 86.1% to 92.25% while reducing system pressure drop by 73%. Parametric studies defined an optimal operating envelope at a gas-to-liquid ratio of 60:1 and system pressure of 2 MPa, achieving peak efficiency of 96.42% with robust performance across 50–150% load variations. This work demonstrates a simulation-guided pathway for intensifying separation processes, providing a validated framework for designing marine-adapted dehydration technology. Full article

We study nearest-neighbor-based estimators of Tsallis entropy associated with Poisson and binomial point processes on general metric measure spaces. In this study, by combining existing stabilization methods with the validation of the estimator’s local k-nearest-neighbor structure, we investigate nearest-neighbor-based Tsallis entropy estimators under Poisson and binomial distributed input data. Rather than proposing a new second-order Poincaré inequality, this paper details and clearly presents stabilization-based normal approximation bounds for Tsallis-type k-NN functionals. We establish asymptotic normality and derive explicit convergence rates for the Kolmogorov distance. Our analysis avoids explicit score-function decompositions and instead relies on flexible localizations of add-one costs, which simplify the treatment of higher-order terms. Under natural stabilization and moment conditions, the resulting bounds recover the classical normal approximation rates $s^{-1/2}$ and $n^{-1/2}$ and extend corresponding results for Shannon and Rényi entropy estimators. We further illustrate the scope of the framework through examples involving Tsallis entropy functionals, weighted k-NN Shannon entropy estimators. The examples provided highlight the benefits of stabilization-based normal approximations for non-parametric statistical inference in complex spatial and high-dimensional settings. Full article

In this paper, we deal with the canal hypersurfaces that are formed as the envelope of a family of pseudo-hyperspheres or pseudo-hyperbolic hyperspheres with centers lying on a pseudo-null curve with Bishop vector fields in four-dimensional Lorentz–Minkowski space. We give main theorems which contain the parametric expressions of these canal hypersurfaces along with their Gaussian, mean, and principal curvatures and important geometric characterizations. We also provide these characterizations for tubular hypersurfaces. Finally, we construct an example to allow for better understanding and comprehension of the results. Full article

The containment structure serves as the final leak-tight barrier of nuclear power plants, making it critical to nuclear safety. The stable performance of the steel liner anchorage system is the fundamental prerequisite for maintaining the structural integrity of the containment. However, existing studies lack systematic quantification of the mechanical behavior and bearing capacity contributions of key components (angle steels, studs) in such anchorage system, creating significant uncertainties to the engineering design of steel liner plates. In this study, a high-fidelity three-dimensional numerical model of the steel liner composite anchorage system is established. A systematic parametric analysis is conducted to investigate the influence of angle steel quantity and stud row number on the system’s mechanical response. Three typical failure modes are identified for the steel liner anchorage system, and the individual contributions of long angle steels, short angle steels and studs to the global load-bearing capacity are quantified independently. The rationality of the angle steel spacing design employed in the prototype structure is confirmed, and the nonlinear contribution characteristics of angle steels and studs are elucidated. The findings provide a crucial theoretical foundation for the optimal design and safety assessment of the steel liner anchorage system in nuclear plants. Full article