Search Results (2,571)

This dataset compiles continuous hydraulic and water quality observations from the combined sewer overflow structure at the outlet of the Graz-West R05 catchment in Austria, covering the period from 2008 to 2011. It integrates high-resolution in-sewer measurements of flow rate, water level, flow velocity and water quality parametres (COD, TSS, temperature), complemented by laboratory analyses of discrete grab samples. Water quality parametres were monitored using an in situ UV/VIS spectrometer installed on a floating pontoon. Additional locally calibrated COD values derived from laboratory measurements are included. The in-sewer data were acquired at 1 or 3 min intervals depending on flow conditions. Flow rates, water levels and overflow discharges were monitored using radar and ultrasonic sensors. Three nearby tipping-bucket rain gauges provided time-stamped precipitation increments, enabling the detailed reconstruction of wet-weather dynamics. A hydrodynamic SWMM model of the catchment, including geospatial information and dry-weather calibration, is included to support modelling applications. This combination of long-term measurements and a calibrated hydrodynamic model supports the development, testing and validation of process-based, statistical or data-driven approaches for simulating combined sewer system behaviour and pollutant dynamics. Full article

This paper investigates an Organic Rankine Cycle (ORC) system for low-to-medium temperature heat recovery using comparative thermodynamic, exergoeconomic and economic modelling. A working-fluid study considering environmental and thermodynamic perspectives is conducted. A 20 kW ORC unit is tested and used as a feasibility and trend-consistency reference to support the modelling assumptions and practical operating bounds. A parametric study then examines the effects of evaporator pressure, condensation temperature, superheat, subcooling and heat-exchanger pinch-point temperature differences on net power output, first- and second-law efficiencies, total product cost and total capital investment under prescribed boundary conditions. Multi-objective optimization is applied to identify Pareto-optimal trade-offs and representative compromise solutions. Results show an intermediate evaporator pressure maximizes net power output, while lower condensation temperature generally improves efficiency; superheat has limited efficiency impact but should ensure safe operation, and a small subcooling margin (around 3 °C) mitigates cavitation risk. The best overall performance is obtained with an evaporator pinch of 3 °C and a condenser pinch of 5–9 °C; tightening pinch constraints increases required heat-transfer area and makes heat exchangers the main cost bottleneck for high-efficiency solutions. Full article

Current onboard hydrogen storage systems are volumetrically inefficient and represent a major constraint on the driving range of heavy-duty fuel cell vehicles. This work presents a conceptual model of an internally reinforced Type I rectangular-shaped pressure vessel as a solution to enhance the volumetric efficiency of hydrogen storage in heavy-duty vehicles. The pressure vessel’s geometry incorporates an internal reinforcing structure to ensure both the structural integrity of the vessel and compliance with the standards for onboard hydrogen storage. Initially, an analytical approach was employed to determine the base parameters of the wall and the internal structure of the reinforced pressure vessel. Finite element analysis was then conducted to validate the analytical solutions and assess the structural integrity of the pressure vessel under design pressure conditions. This was followed by a parametric optimisation study in which the design parameters were systematically varied to identify an optimal pressure vessel design. The 35 MPa reinforced titanium pressure vessel offers 29% more volumetric capacity than the conventional Type IV storage system. The gravimetric capacity of the titanium pressure vessel is low, 2.9 wt%; despite this, the mass of the vessel is applicable in HDVs. This design increases hydrogen storage capacity, offering a range increase of approximately 29% for the same design space. Full article

Aiming at the insufficient integration of real-gas effects and the unclear parameter influence mechanisms in predicting high-pressure hydrogen leakage flame length, this paper proposes a refined predictive model that systematically incorporates the real-gas critical flow factor ($C_r^{*}$). By dynamically correcting the mass flow rate calculation under high-pressure conditions, the model significantly improves prediction accuracy (relative error in mass flow rate < 3%). A parametric analysis reveals that the flame length is approximately three times more sensitive to the leakage orifice diameter than to the storage pressure ($L\propto D^{1.041}{P}_0^{0.347}$), providing a quantitative basis for inherent safety design. Validated by experimental datasets, the model demonstrates good accuracy. It can be employed for safety distance demarcation and risk assessment at hydrogen refueling stations and storage facilities. Full article

Gas ionization chambers face significant challenges in diagnosing laser-accelerated proton beams due to severe space charge effects induced by high peak currents and broad energy dispersion. These effects typically cause electric field distortion, signal saturation, and non-linear responses. In this study, we propose an optimized ionization chamber design that effectively mitigates space charge through a rigorous co-simulation approach. We combined ANSYS for macroscopic electrostatic field optimization with Garfield++ for microscopic charge transport modeling, explicitly incorporating ionization (Heed++) and electron drift/diffusion (Magboltz) processes. A systematic finite element modeling workflow—including gas volume meshing and the removal of dielectric components—was implemented to eliminate field non-uniformities and dielectric charging effects. Crucially, we validated the design’s performance against Boag’s theoretical recombination model. While theoretical calculations predict severe saturation (<80% efficiency) for standard chambers under high-flux conditions (10⁷ protons/pulse), our simulation results demonstrate a strictly linear response with charge collection efficiency consistently exceeding 99.85%. Parametric studies further confirm that the optimized geometry and operational parameters (high bias, low pressure) successfully suppress space charge accumulation, providing a robust solution for laser-driven beam diagnostics. Full article

This study investigates the development of a predictive model in simulations for assessing steel corrosion in determining corrosion-affected zones in reinforced concrete. A series of reinforced concrete cubes with varying degrees of corrosion were tested using a four-probe Wenner configuration. The experimental data showed a clear inverse relationship between ER and steel mass loss, with a strong negative correlation, highlighting the potential of ER as a corrosion indicator. A third-degree polynomial model was developed to predict the diameter of the corrosion-affected region based on steel mass loss and concrete cover, achieving high predictive accuracy. This model was validated using numerical simulation conducted in COMSOL Multiphysics, which replicated the experimental setup under steady-state conditions. Parametric studies further examined the effects of electrical conductivity ($\sigma$) and electrode spacing on the simulated results. The findings confirm that while $\sigma$ has a moderate impact, electrode spacing significantly influences the measured ER values. The study underscores the importance of incorporating variable parameters into simulation models to improve the accuracy and field applicability of ER-based corrosion assessments. Furthermore, the simulation framework developed in this study demonstrates how numerical modeling can enhance the interpretive value of ER measurements, supporting the advancement of non-destructive testing techniques aimed at improving corrosion monitoring and maintenance strategies. Full article

The 1626 nm laser is an essential component for conducting superlattice research on the strontium atomic clock platform. The superlattice constructed with the 1626 nm and 813 nm lasers will facilitate cutting-edge quantum information research focused on topological quantum states transport. We demonstrate an idler-resonant optical parametric oscillator that achieves 1626 nm laser output based on pump enhancement technology. Through a well-designed external cavity, a laser output of 127 mW at 1626 nm has been achieved, with a corresponding pump quantum conversion efficiency of 50% and a pump threshold of 110 mW. The long-term power stability of the output laser is ±1.5% per hour. Variations in the pump cavity modes under different experimental conditions have been measured, and the impedance matching process of the pump light within the cavity has been discussed. The 1626 nm laser and the associated technologies reported in this manuscript will provide optical support for the investigation of superlattice physics on the strontium optical lattice clock platform. Full article

This paper presents a comprehensive study of statistical tests for comparing temporal point processes in general, with a particular focus on Poisson processes. We explore three key approaches: (1) an intensity-based test specifically for Poisson processes, (2) general parametric tests using the notion of maximum likelihood estimation, and (3) a general nonparametric test using the Isometric Log-Ratio (ILR) transformation. The first approach adopts a three-step procedure for comparing inhomogeneous Poisson processes by testing total and normalized intensities separately and then combining the corresponding p-values using Fisher’s method. The second method proposes a likelihood-based parametric test to examine the conditional intensity functions in point processes, emphasizing the application to Hawkes processes. Lastly, the third approach introduces a nonparametric test for general point processes, by transforming inter-event times into a Euclidean space via the ILR transformation, followed by conventional depth-based methods on multivariate data. We then conduct thorough studies on simulations as well as real-world data to illustrate these testing procedures and demonstrate their effectiveness. Full article

Current national-scale building stock models mainly focus on structural materials, overlooking the significant resource potential of Mechanical, Electrical, and Plumbing (MEP) systems. These systems are resource-intensive and contain standardized components with high-value materials such as copper and steel, yet their potential remains largely untapped due to fragmented data. This study introduces the novel systematic framework to estimate MEP components at high granularity and national scale. It integrates harmonized public data, machine-learning imputation (>90% accuracy under sparse conditions), and parametric rules reflecting building type, energy system, and construction decade. A Swiss case study yields scalable material stock estimates and lifespan-based turnover projections, showing strong consistency with existing GHG benchmarks. The framework highlights contrasting patterns across regions and building types, indicating where policy and industry can upscale reuse and recovery. Its modular design enables transferability and integration with circular economy planning and material-efficiency targets. Full article

In this study, a comprehensive mathematical method for modeling the operational reliability of mine hoisting equipment under conditions of incomplete and heavily censored data is developed. The analyzed dataset includes 259 observations collected over a five-year period for six critical components, with the overall level of censoring reaching 62% and exceeding 70% for long life mechanical subsystems. Considering right, left, and interval censoring, the paper proposes a unified statistical procedure that combines empirical estimation of failure rates with parametric identification using Weibull, exponential, normal, and lognormal distributions. Model parameters are estimated using censored data–aware fitting procedures, while model selection is performed based on likelihood-based criteria, supplemented by correlation analysis to assess agreement between empirical and fitted reliability curves. The methodology is implemented computationally in the Mathcad Prime environment and is supplemented with mathematical tools for reconstructing survival curves, analyzing parameter sensitivity, and evaluating robustness at different censoring levels. In addition, an economic optimization model is formulated to determine cost-effective maintenance intervals by minimizing an integral functional that accounts for preventive maintenance, repair, and downtime costs. The results demonstrate that the proposed approach provides stable reliability estimates and reliable forecast intervals, enabling the construction of generalized life cycle curves for individual subsystems. The study establishes a rigorous mathematical basis for the transition from fixed-interval maintenance to adaptive, reliability-oriented maintenance strategies in industrial mine hoisting systems. Full article

Stock return prediction for quantitative trading in U.S. equity markets has evolved from parametric econometric modeling toward data-driven deep learning systems that must jointly capture temporal dynamics, discontinuous jumps, and evolving cross-asset dependencies. Existing approaches still face three key challenges in deep learning-based stock return prediction: jump-aware temporal modeling is often missing or handled by ad hoc heuristics; higher-order stock relations are frequently encoded by static graphs/hypergraphs that do not adapt across market conditions, and temporal and relational learning are commonly implemented as sequential blocks with limited bidirectional interaction. We propose LévyHyper, an end-to-end framework that unifies jump-aware temporal encoding with regime-adaptive dynamic hypergraph learning and multi-scale hypergraph reasoning. LévyHyper integrates a neural jump-aware temporal layer motivated by Lévy jump-diffusion modeling, a regime-weighted fusion of predefined and learned hyperedges via a differentiable constructor, and a multi-scale hypergraph convolution module for hierarchical temporal aggregation. Experiments on S&P 500 data (463 stocks, 10 evaluation phases, prediction horizon $\tau =5$ trading days) show that LévyHyper improves IC/RankIC and portfolio-level Sharpe ratio over strong baselines on average. We additionally report uncertainty estimates, significance tests, and transaction-cost sensitivity to support robust conclusions. Full article

The integration of Foamed Concrete (FC) into 3D Concrete Printing (3DCP) processes facilitates the design of energy-efficient building envelopes. However, strategies for optimizing material porosity and printing topology to balance winter and summer performance remain underexplored. This study presents a 2D numerical thermal analysis of an innovative 3D-printed building envelope block characterized by sinusoidal internal partitions. Through a parametric variation in porosity (ranging from 10% to 50%) and internal geometry (amplitude and period of the partitions), 45 distinct configurations were simulated. Performance was evaluated by calculating the steady-state thermal transmittance (U) and the periodic thermal transmittance (Y_ie) under dynamic climatic conditions. The results demonstrate that porosity is the governing parameter; increasing porosity from 10% to 50% reduces U by 31% and, contrary to traditional assumptions for massive structures, also improves Y_ie by 12.3%. These outcomes are physically driven by the drastic reduction in thermal conductivity, which overcompensates for the loss of thermal mass, leading to a net reduction in overall thermal diffusivity. While internal topology plays a secondary role, its optimization allows for fine-tuning dynamic damping without compromising insulation. The study confirms that 3D printing with foamed concrete enables the overcoming of the traditional trade-off between insulation and thermal inertia. High-porosity configurations (50%) with optimized internal topology emerge as the most effective solution, simultaneously guaranteeing beneficial steady-state and dynamic thermal performance for sustainable buildings. Full article

The expansion of urban rail transit has exacerbated environmental issues related to low-frequency noise (LFN), yet the impact of geometric symmetry breaking on structure-borne noise remains underexplored. This study aims to quantify the mechanism by which cross-sectional asymmetry influences the vibro-acoustic coupling of viaducts. A 2.5D Hybrid Finite Element-Boundary Element Method (FEM-BEM) was employed to model a parametric box girder under eccentric track loading, and the numerical framework was validated against analytical benchmarks. The “Modal Symmetry Index” (MSI) and “Acoustic Asymmetry Indicator” (AAI) were defined to evaluate the effects of the asymmetry parameter ($\alpha$) on sound field distribution. Numerical results reveal a nonlinear “V-shaped” relationship between geometric asymmetry and acoustic directivity. While severe asymmetry ($\alpha >0.15$) exacerbates noise deflection via flexural–torsional coupling, a critical “self-balance zone” exists. Specifically, moderate asymmetry ($\alpha \approx 0.07$) effectively neutralizes load eccentricity, reducing the AAI from 1.5 dB (in strictly symmetric designs) to nearly 0 dB. Robustness analysis under right-side loading conditions further confirms a “reverse deflection” phenomenon, verifying that the proposed self-balance design minimizes directional sensitivity. These findings challenge the traditional assumption that geometric symmetry is acoustically optimal. A “competition–compensation” mechanism is identified, suggesting that deliberate, slight geometric asymmetry can serve as an effective passive noise control strategy for viaducts. Full article

Background: Pain remains one of the most debilitating and prevalent symptoms in cancer patients. However, assessment based solely on subjective self-report tools is limited by cognitive impairment and the heterogeneous nature of cancer pain. Since evidence on the ability of physiological biosignals to discriminate cancer pain intensity and pain phenotypes in real clinical settings remains limited, this study explored the potential of biosignals to discriminate between pain intensity and pain type. Methods: Electrodermal activity (EDA) and electrocardiogram (ECG) signals were recorded in cancer patients using the BITalino (r)evolution board (sampling frequency 1000 Hz). EDA was processed to extract skin conductance responses (SCRs) using continuous decomposition analysis (CDA) and trough-to-peak (TTP) methods. Heart rate variability (HRV) features were extracted in both time and frequency domains, including low frequency (LF), high frequency (HF), and the LF/HF ratio. Non-parametric Kruskal–Wallis tests were performed to compare biosignal parameters across pain intensity (Numeric Rating Scale, NRS: low 1–3; medium 4–6; and high 7–10) and pain types (nociceptive, neuropathic, mixed, and breakthrough cancer pain—BTCP). Results: Data from 61 patients were analyzed. For EDA, the maximum skin conductance response amplitude (MaxCDA) significantly differed across intensity groups (p = 0.037). Post hoc analysis showed a significant difference between the low- and high-intensity groups (p = 0.015), with the low-intensity group exhibiting a higher mean MaxCDA (0.063 µS) than the high-intensity group (0.024 µS). Several EDA parameters were significantly associated with pain type. The number of SCRs (TTP) (p = 0.015) and maximum SCR amplitude (TTP) (p = 0.040) were significantly lower in the mixed pain group compared with the nociceptive and neuropathic groups. No HRV parameters showed significant associations with pain intensity or pain type. BTCP did not significantly affect any biosignal parameters. Subgroup analyses showed that EDA features discriminating mixed pain were preserved in patients without bone metastases, BTCP, or high opioid burden, whereas no clinical variable modified the association between biosignals and pain intensity and type. Conclusions: In this investigation, selected EDA parameters were associated with cancer pain intensity and pain type, whereas heart rate variability measures did not show significant discrimination under the present methodological conditions. These findings suggest that EDA may provide complementary information on pain-related autonomic alterations in oncology patients. However, biosignals should not be considered standalone indicators of pain, and their interpretation requires integration with clinical variables and pharmacological context. Further studies adopting multimodal and longitudinal approaches are needed to clarify their role in automatic pain assessment in cancer care. Full article

In the design of shaking table tests concerning saturated sand–pile interactions, quantitatively achieving similarity in liquefaction responses between the model and the prototype has long been a challenging task. In addition, the dynamic shear modulus of the prepared model soil often fails to satisfy the ideal similarity conditions, which further exacerbates the difficulty of realizing liquefaction response similarity. To address the above issues, the authors have proposed a liquefaction similarity law for saturated sand–pile shaking table tests under horizontal excitation, considering the dynamic shear modulus error of the model soil. To further verify the accuracy of the proposed liquefaction similarity law, investigate its simulation capability, and evaluate its applicability under different conditions, this paper establishes and validates numerical models of saturated sand–pile dynamic interaction systems based on shaking table test results and conducts a series of parametric analyses via numerical simulation. The results indicate that when the proposed similarity law is applied, the acceleration similarity ratio should be set to 1, which can satisfy both gravity similarity and elastic force similarity simultaneously. A comparison with the artificial mass similarity law demonstrates the distinct advantages of the proposed similarity law. Finally, the applicability of the proposed similarity law under different parametric conditions is verified, and the influence of various parameters on the accuracy of the back-calculated results using the similarity law is investigated. Full article