Search Results (226)

Background/Objectives: The aim of the present investigation was to validate the diagnostic performance of the AI-based dental cloud software Diagnocat^® AIS (Version 1.0 (UDI: 860010268018), DGNCT LLC, Miami, FL, USA) regarding the detection possibilities of seven different endodontic findings on cone-beam computed tomography (CBCT) against a multi-rater consensus reference standard, and to characterize its calibration, threshold-optimized performance and clinical utility. Methods: 358 root-canal-treated teeth from 167 CBCT scans (167 patients) were retrospectively evaluated at a single private dental practice. From initially included 383 root-canal-treated teeth from 177 patients, 358 (93.5%) were recognized by the AI tool and entered the primary analysis. Two experienced dentists with a clinical focus on endodontics independently graded each tooth and disagreements were adjudicated by a senior expert. Seven different endodontic findings were evaluated: (i) apical (periapical) lesion; (ii) short root-canal filling (apical filling end >2 mm short of the radiographic apex); (iii) voids/lacunae in the root-canal filling; (iv) missed (un-instrumented/un-filled) canal; (v) overfilled root-canal filling (apical extrusion); (vi) apicoectomy (resected root apex with or without retrograde filling); and (vii) coronal restoration with a full-coverage crown. Diagnocat^® output was binarized at the manufacturer-fixed 0.50 probability threshold; sensitivity, specificity, predictive values, accuracy, area under the curve AUC (ROC), Cohen κ and Gwet AC1 were computed with 95% cluster-bootstrap confidence intervals (cluster = scan). Threshold optimization, probability calibration, GEE-based subgroup analyses, and decision-curve analysis were pre-specified. Results: Diagnostic performance varied by finding. AUCs were 0.984 for missed canal, 0.917 for overfilled root canal, 0.902 for short root filling, 0.893 for crown, 0.864 for apical lesion, 0.857 for apicoectomy and 0.761 for voids in the root filling. Apical-lesion sensitivity rose from 33.6% for sub-millimeter lesions to ≥80% for lesion measuring 1–5 mm. Re-tuning the decision threshold raised missed-canal sensitivity from 69.6% to 97.5%. Decision-curve analysis confirmed positive benefits for missed canal and root-filling-quality findings. Conclusions: The AI tool Diagnocat^® can be recommended as a focused screening adjunct in CBCT-based endodontic interpretation for missed canals, crowns, and gross root-filling-quality flaws. Sub-millimeter apical lesions and several less common findings (resorption, instrument fragment, retrograde filling) remain outside the reliable performance envelope of the current platform. Full article

Phase change materials (PCMs) are increasingly incorporated into façades and wall systems to enhance passive thermal regulation; however, their fire safety remains poorly understood. While PCMs effectively reduce cooling loads, limited data exist on their behaviour under real fire exposure. In this study, the thermal response of PCM-integrated concrete panels was investigated through two-dimensional finite element modelling using an apparent heat-capacity formulation that accounts for phase change, latent-heat absorption, and encapsulation softening. Simulations were performed under the ISO 834 standard fire curve and constant furnace exposures between 200 °C and 800 °C for 60 min to evaluate insulation performance and encapsulation stability. Results show that PCM melting at approximately 31 °C provides a 20–25 min delay in rear-face temperature rise under moderate fire exposure (≤400 °C), maintaining the rear-face temperature increase below 180 °C for one hour. Beyond 500 °C, the acrylonitrile butadiene styrene (ABS) encapsulation softens near 95 °C, suppressing latent-heat storage and leading to rear-face temperatures between 260 °C and 360 °C. Comparative analyses indicate that organic PCMs lose effectiveness rapidly unless protected by at least a 25 mm concrete cover, whereas inorganic PCMs exhibit superior stability owing to their non-combustibility and endothermic dehydration behaviour. The results identify performance trends, thermal limitations, and design considerations for the investigated PCM–ABS–concrete assembly under the studied fire exposure conditions. The validated experimental–numerical framework provides insight into the thermal response of PCM-integrated concrete assemblies and supports future development of fire-resilient building-envelope components. Full article

Railway screw couplings are safety-critical, yet service failures show fatigue cracking at geometric discontinuities. This work assesses the response of two UIC screw-coupling components—the shackle and trunnion—under longitudinal forces from Traction–Emergency Braking (TEB) manoeuvres. A linear-elastic 3D finite element model was built for 42CrMo4/AISI 4140 steel, idealising the threaded load transfer with an RBE2 condensation and the hook–shackle interface with a tied contact to provide a repeatable baseline. Longitudinal force histories were generated in TrainDy for a freight consist and mapped to Regions of Interest; fatigue was evaluated in Altair HyperLife using rainflow counting, Goodman mean-stress correction, and Palmgren–Miner accumulation on a uniaxial S-N curve. For the 636 kN envelope case, the model predicts an axial displacement of 0.985 mm and von Mises stresses in several relevant regions near the nominal yield strength. Fatigue results rank the trunnion pin fillet as the governing hotspot: representative TEB sequences yield damage indices greater than 1 (often of order 20), while a lower-amplitude braking block shows negligible damage. Overall, the analysed spectra leave little endurance margin for the current geometry and support redesign of critical radii and more realistic contact/boundary modelling. Full article

Architectural planning often involves balancing multiple and potentially conflicting objectives, such as safety, economy, functionality, and aesthetics. However, conventional benchmarking approaches typically focus on single performance dimensions and provide limited support for multi-objective decision-making. To address this limitation, this study proposes a benchmark learning framework for multi-objective street house planning that explicitly incorporates architects’ planning preferences. The framework integrates fuzzy sets to define preference functions, indifference curves to represent trade-offs and derive preference weights, and utility functions to quantify satisfaction levels. In addition, Data Envelopment Analysis (DEA) and efficient frontier theory are employed to evaluate planning efficiency and identify optimal benchmark cases. Using empirical data from 627 street houses, the results indicate that the proposed approach effectively captures architects’ subjective preferences while providing an objective assessment of planning efficiency. The integration of indifference curves and the efficient frontier enables explicit visualization of trade-offs, whereas the combination of utility functions and efficiency analysis facilitates the identification of benchmark learning cases. The proposed framework provides a systematic approach to multi-objective optimization in architectural planning by bridging subjective decision-making with quantitative performance evaluation. It offers practical guidance for architects and planners and contributes to the advancement of benchmark-based methodologies in complex design environments. 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

Lithium-ion battery (LIB) cells are available in various sizes, formats, and chemistries. Should a LIB be exposed to conditions outside its operating parameters, each variation affects the cell failure mechanisms and any resultant fire dynamic. Battery fires can be dynamic events that differ significantly from those solid-, liquid- or gas-based fire curves often used in standard building material fire resistance tests. This preliminary research aimed to investigate how standard building materials, sometimes used as a compartment fire envelope, such as gypsum plasterboard, react when exposed to a dynamic battery fire. The research explored batteries that produced jet fires, could act as projectiles, or produced overpressures when they failed. The results showed that cylindrical cells can travel at significant speeds and distances due to expulsing the cell’s contents through the cell’s vent or ejected end cap. These cells were shown to be capable of piercing plasterboard and remain hot enough to present a fire risk where they fall on the far side of the plasterboard. It was also found that the overpressures produced by failing prismatic cells affected the structural integrity of some building materials. The results show a need for further research into the effectiveness of standard building fire controls when exposed to LIB fires. Full article

Tropical coastal regions exhibit pronounced spatial and temporal variability in rainfall driven by seasonal atmospheric circulation and coastal–orographic interactions. Accurate representation of the temporal distribution of rainfall is essential for hydrologic modeling and infrastructure design. This study develops regionalized Huff curves for the Department of Magdalena, Colombia, addressing a critical gap in the characterization of rainfall temporal patterns in tropical coastal regions. A total of 270 short-duration (5–6 h) rainfall events from automatic stations were converted into normalized cumulative mass curves. The resulting curves were grouped into homogeneous temporal patterns using clustering algorithms. Three dominant storm types were identified: early-peak (Curve 1), intermediate (Curve 2), and uniform (Curve 3), reflecting the region’s coastal, lowland, and orographic influences. Probability envelopes and representative design hyetographs were derived to quantify intra-event variability. Rainfall–runoff simulations for a 100-km² watershed showed peak-flow differences of up to 132% between storm types, highlighting the sensitivity of hydrologic response to rainfall temporal distributions. The resulting regionalized Huff curves provide a practical and transferable framework for hydrologic modeling, flood-risk assessment, and infrastructure planning in tropical regions with limited high-resolution rainfall data. Full article

Reliable detection of internal defects in pressure vessel structures remains essential for structural safety and condition-based maintenance. This study presents a low-complexity structural health monitoring framework based on fiber Bragg grating (FBG) sensing and multiresolution wavelet analysis for void detection in curved pressure vessel structures under guided wave excitation. Guided waves are introduced using piezoelectric actuators, while the FBG sensors capture the resulting strain-induced wavelength variations. Due to the limited bandwidth of the optical interrogator, the recorded signals represent the strain envelope response associated with guided wave interaction rather than the resolved ultrasonic carrier waveform. To characterize defect-induced changes, the acquired signals are analyzed using continuous wavelet transform (CWT) for time–frequency interpretation, and discrete wavelet transform (DWT) and wavelet packet transform (WPT) for energy-based multiresolution feature extraction. Experimental results show that void defects lead to consistent redistribution of wavelet-domain energy and increased non-stationarity in the measured strain responses. These trends are further supported by finite-element simulations, which reproduce similar energy redistribution patterns between intact and damaged cases. The proposed framework provides a physically interpretable and computationally efficient approach for defect detection using low-bandwidth FBG sensing, without reliance on high-speed acquisition or data-intensive learning models. The results demonstrate the feasibility of using energy-based multiresolution analysis of FBG strain signals for practical and scalable structural health monitoring of pressure vessel systems. Full article

The detection and identification of unmanned aerial vehicles (UAVs) using radio frequency (RF) signals becomes particularly challenging in congested spectral environments, where conventional approaches relying solely on spectral characteristics often prove limited. This work introduces a novel technique for both UAV detection and classification based on temporal representations derived directly from the envelope of received RF signals. The proposed system follows a two-stage architecture: first, binary detection of UAV presence in a given RF channel, and second, identification of the specific UAV model among several commercial platforms. For the first stage, two signal representation methodologies are employed—Gramian Angular Fields and Hilbert curves—both generated from short-time RF windows and subsequently used as inputs to convolutional neural networks. Experimental evaluation demonstrates that the detection stage achieves accuracy rates exceeding 94% for the non-UAV class and approaching 99% for the UAV class with both approaches. In the identification stage, the system attains an accuracy above 90% for most considered UAV models, reaching up to 100% for certain platforms. These results confirm the effectiveness of the envelope-based approach for analyzing UAV-related RF signals. Full article

Corrosion of reinforcing steel is a key cause of deterioration in reinforced concrete (RC) structures exposed to coastal environments with chloride presence. The loss of reinforcing steel cross-sectional area, cracking of the concrete cover, and reduction in confinement progressively decrease both strength and ductility of structural elements. This study provides a reproducible, open-access dataset, compiling input parameters and numerical results of the cyclic behaviour of isolated RC columns and RC frames, specifically addressing their nonlinear cyclic response under moderate corrosion (η < 25%), as well as in the non-corroded (baseline) conditions, generated through conventional nonlinear modelling. In terms of modelling, the methodology applies fibre-section modelling for columns and concentrated plastic hinges for beams. Furthermore, the corrosion effects are incorporated by reducing the steel area and ultimate strain, while also accounting for the decrease in compressive strength of the cracked concrete cover. Therefore, the cyclic response is represented by a Pivot-type hysteretic model. It is worth noting that the dataset provides model input information, such as material stress–strain relationships and backbone curves reflecting corrosion-induced deterioration. It also includes structural outputs, such as force–displacement relationships, and envelopes of quasi-static hysteretic cycles for the analyzed columns and frames. Overall, the dataset facilitates the calibration and validation of numerical models for RC structures affected by corrosion. In conclusion, the contribution enhances the reliability of computational simulations and supports the development of predictive tools for structural performance under degradation scenarios. Full article

Despite their excellent torsional and bending strength, the economical production of hypotrochoidal profiles (H-profiles) remains an obstacle to their use. Due to the tool clearance angle, the commercially available twin-spindle turning process has limited ability to manufacture many of the profiles standardized according to DIN 3689 (Deutsches Institut für Normung). On the other hand, the manufacturing of cycloidal as a non-involute special geometry using generating processes (hobbing or continuous generating grinding) depends critically on the accuracy of the tool geometry—whether a hobbing cutter or a grinding worm. Conventional tool design methods—based on approximations, involute-derived profiles, or iterative trial-and-error corrections—face fundamental limitations: unpredictable cutting force variations, elevated surface roughness, and limited process capability. However, if the exact tool geometry has been determined analytically, the same machine achieves significantly better performance. In this work, the exact tool geometry conjugated to the H-profile for profile manufacturing is determined based on the gearing law. This provides modular H-profile manufacturing without deviations. Consequently, a design concept that enables the implementation of all existing rolling processes—including gear hobbing, gear shaping, gear planning, and other variants such as gear grinding—is presented. For profile shaping of hollow contours, the transfer ratio is considered and a curve conjugated to the profile contour is determined for the tool. A CAD-based simulation shows very good consistency with the analytically determined tool geometry. Full article

Giving the definitions of one-parameter family of frontals in lightcone and one-parameter family of spacelike Legendrian curves in ${\Delta }_2$ and ${\Delta }_3$, and further using the variability condition and the tangency condition, the definitions of envelopes of these geometric objects are presented. The aim of this work is to explore the criterion conditions on the envelopes of a one-parameter family of frontals related to ${\Delta }_2$ and ${\Delta }_3$ in three pseudo-spheres. Thereby the characterizations of these envelopes are described via Envelope Theorems. The geometric relations among these envelopes are discussed in detail. It is demonstrated that the ${\Delta }_2$-duality or the ${\Delta }_3$-duality of one-parameter family of frontals among three pseudo-spheres leads to the fact that the one-parameter family of frontals that are ${\Delta }_2$-duality or ${\Delta }_3$-duality each other share the same envelope. In addition, the hyperbolic and de Sitter parallels of the one-parameter family of spacelike frontals are also defined, and the existence conditions of the envelopes of such parallels are investigated correspondingly. Finally, two examples are provided to understand the theoretical results. Full article

Reliable production forecasting is a critical task for evaluating asset valuation and commercial performance in oil and gas reservoirs. Conventional short-term forecasting methods, such as Arps’ decline curve analysis, rely on simple mathematical curve fitting and often oversimplify reservoir performance. On the other hand, long-term forecasting requires complex multidisciplinary models that integrate geophysics, reservoir engineering, and production engineering, but these approaches are time-consuming and have high turnaround times. To bridge the gap between long and short-term production forecasts, reduced-physics models such as Blasingame type curves have been developed, incorporating transient well behaviour derived from diffusivity equations and Darcy’s law. These models assume homogeneity and uniform reservoir properties, enabling faster results while honouring pressure performance. However, despite their efficiency, they still face limitations in reliability, particularly when extended to long-term forecasts. This paper proposes a hybrid modelling approach that integrates flowing material balance (FMB) concepts into physics-informed neural networks (PiNNs) and machine learning models to improve the accuracy and reliability of production forecasting. The proposed methodology introduces two hybrid strategies: physics-informed models enriched with FMB feature, and PiNNs. The first proposed hybrid model uses a created FMB-derived feature as input to neural networks. The second PiNN model embeds data-driven loss functions with a physics-based envelope to reflect reservoir response into the machine learning model. The primary loss function is mean squared error, ensuring minimization of data misfit between predicted and observed production rates. The study validates both proposed physically informed neural network models through performance metrics such as RMSE, MAE, MAPE, and R². Results application on field data shows that the integration of FMB into neural network models using the PiNN concept guides the neural network models to predict the production rates with higher reliability over the full span of the tested data period, which was the last year of unseen production data. Additionally, the proposed PiNN model is able to predict the well productivity index via hyper-tuning of the PiNN model. Furthermore, the PiNN is not improving the metric performance of conventional neural networks, as it has to satisfy an additional material balance equation. This is due to a lower degree of freedom in the PiNN models. Full article

This study addresses a critical methodological gap in evaluating building envelope performance in hot, arid climates, the overreliance on annual energy indicators, which fail to capture transient thermal behavior during peak-load periods. In such environments, instantaneous heat gains, their intensity, and temporal distribution are decisive factors for cooling demand, occupant comfort, and grid stability. To overcome this limitation, a dynamic evaluation framework—the Thermal Adaptation Rating (TAC) system—is proposed. TAC integrates three interrelated indices—peak temperature reduction (ΔT_peak), relative peak cooling load reduction (ΔP_peak, %), and peak thermal delay (Δt_delay), representing thermal damping, load intensity mitigation, and temporal redistribution, respectively. A typical residential building in Karbala was modeled in DesignBuilder using the EnergyPlus engine, with inputs documented and calibration performed against real consumption data following ASHRAE standards (MBE and CV(RMSE)) to ensure reliability. The study examined advanced envelope systems, including thermochromic glass (TG), phase-change materials (PCMs), aerogel materials (AMs), and hybrid combinations. Results revealed that while AM achieved the greatest annual energy savings, its impact on instantaneous cooling load was limited. PCM, by contrast, effectively mitigated and delayed peak loads, enhancing thermal comfort (PMV/PPD). Hybrid systems, particularly TG-PCM, delivered the most balanced performance, simultaneously reducing peak cooling load and shifting its occurrence to reshape the cooling demand curve during critical periods. These findings demonstrate that annual indices alone are insufficient for evaluating envelope performance in extreme climates. Peak-condition analysis, expressed in terms of instantaneous cooling load, as operationalized through TAC, provides a more accurate representation of thermal behavior and offers a practical tool to guide envelope design decisions in hot, dry regions. Full article

Additive manufacturing in construction is expanding production possibilities for housing, however its integration into architectural design workflows remains limited. This research proposes a computational workflow for the early-stage form-finding of housing volumes intended for additive construction. A parametric design system was developed to generate a wide range of residential volumetric configurations based on geometric parameters derived from conventional housing typologies and emerging 3D-printed construction practices. The design space was explored through user-driven experimentation and automated evolutionary optimization targeting predefined surface area conditions. Besides design alternatives were visualized using AI-assisted image generation to support comparative evaluation, translated into BIM models for further architectural development, and tested through physical 3D-printed scale models to assess material expression and constructability. Five design exploration activities involving architects and graduate students produced nearly 200 volumetric alternatives, in order to review its use and possibilities. The results show that the parametric system enables efficient exploration of both conventional and novel housing forms potentially compatible with additive construction. Vertically articulated volumes with curved envelopes and spatial variation emerged as promising alternatives. The study demonstrates the potential of integrating parametric modeling, evolutionary search, AI-assisted visualization, and physical prototyping to support architectural decision-making and facilitate the incorporation of 3D printing into housing design processes. Full article