Search Results (51,900)

Design optimization is a computational tool that can enable a designer to investigate the effectiveness of a design concept in an organized format. However, this design process requires the design variables, constraints, and objective function to be properly defined and expressed in mathematical forms. Post-optimality analysis thus becomes a necessary step to investigate different variations in the problem formulation and parameters to ensure that optimization produces a stable and trustworthy outcome. One efficient way to achieve this aim is to compute the local derivative of the optimized objective function with respect to the optimization problem parameters, such as bounds on the constraints and the material properties in the state equation. This method is referred to as post-optimality sensitivity analysis. In this study, we derived the post-optimal sensitivity equation to explicitly include the derivatives of state variables with respect to problem parameters and to broaden its applications to minimax and goal attainment design optimization problems. Full article

Background/Objectives: Fertility-sparing treatment is an accepted option for selected women with early-stage endometrial carcinoma, yet comparative evidence on oncologic durability and reproductive success remains limited. This systematic review and meta-analysis aimed to evaluate oncologic and reproductive outcomes across available fertility-preserving strategies in early-stage endometrial carcinoma. Methods: A systematic search of MEDLINE, Embase, Web of Science, CENTRAL, and Scopus was conducted from inception to 13 April 2025. Eligible studies included women of reproductive age with FIGO stage IA endometrial carcinoma undergoing fertility-sparing treatment. Studies enrolling mixed populations were included only if carcinoma-specific outcomes could be clearly identified; cases of atypical endometrial hyperplasia or endometrial intraepithelial neoplasia were not included in the quantitative synthesis. Outcomes included the rates of complete response, recurrence, partial response, no response, pregnancy, and live birth. Meta-analyses with random-effect models were performed, with subgroup analyses by treatment strategy. Risk of bias was assessed using ROBINS-I and RoB 2 tools. The review was preregistered in PROSPERO. Meta-analyses were conducted using R software, v4.4.2 (R Foundation for Statistical Computing, Vienna, Austria). Results: Seventy-six studies involving 2507 women were included. The pooled complete response rate was 74% (95% CI: 69–79%), while recurrence occurred in 35% (95% CI: 28–42%) of complete responders. Strategies incorporating hysteroscopic resection, alongside other combination regimens, consistently demonstrated higher complete response rates (85%) and lower recurrence (14–16%) compared with oral progestin or LNG-IUD monotherapy. Early treatment failure, with pooled partial and no response rates of 6% and 16%, respectively, particularly affecting monotherapy regimens. The pooled pregnancy and live birth rates were 48% (95% CI: 41–54%) and 36% (95% CI: 29–43%), with more consistent reproductive outcomes observed with combination strategies. Conclusions: Fertility-sparing treatment in early-stage endometrial carcinoma is associated with high initial remission rates; however, the durability of response remains limited, while overall reproductive success is moderate. Accumulating evidence suggests that combination strategies, particularly those incorporating hysteroscopic resection, are associated with more favorable oncologic control and reproductive outcomes compared with hormonal monotherapy. Accordingly, fertility preservation should be regarded as a carefully selected, time-limited approach that requires close surveillance and individualized patient counseling. Full article

The pressure-gradient Micro-Electro-Mechanical Systems (MEMS) vector hydrophone is a novel type of sensor capable of simultaneously acquiring both scalar and vectorial information within an acoustic field. Conventional azimuth estimation methods struggle to achieve high-resolution localization using a single pressure-gradient MEMS vector hydrophone. In practical marine environments, the multiple signal classification (MUSIC) algorithm is hampered by significant resolution performance loss. Similarly, the complex acoustic intensity (CAI) method is constrained by a high-resolution threshold for multiple targets, often resulting in inaccurate azimuth estimates. Therefore, a cross-spectral model between the acoustic pressure and the particle velocity for the pressure-gradient MEMS vector hydrophone was established. Integrated with an improved particle swarm optimization (IPSO) algorithm, a high-resolution azimuth estimation method utilizing this hydrophone is proposed. Furthermore, the corresponding Cramér-Rao Bound is derived. Simulation results demonstrate that the proposed algorithm accurately resolves two targets separated by only 5° at a low signal-to-noise ratio (SNR) of 5 dB, boasting a root mean square error of approximately 0.35° and a 100% success rate. Compared with the CAI method and the MUSIC algorithm, the proposed method achieves a lower resolution threshold and higher estimation accuracy, alongside low computational complexity that enables efficient real-time processing. Field tests in an actual seawater environment validate the algorithm’s high-resolution performance as predicted by simulations, thus confirming its practical efficacy. The proposed algorithm addresses key limitations in underwater detection by enhancing system robustness and offering high-resolution azimuth estimation. This capability holds promise for extending to multi-target scenarios in complex marine settings. Full article

Underwater robots demonstrate significant potential for hull biofouling removal. However, achieving uniform and damage-free cleaning remains a persistent challenge. The fixed arrangement of cleaning mechanisms, combined with the inherent non-uniformity of cavitation jet energy distribution, frequently results in inconsistent removal depths, leading to local over-cleaning or under-cleaning. To address this, this paper proposes an optimization framework to coordinate the robot’s motion with its cleaning mechanism. First, the flow field dynamics of the cavitation nozzle are elucidated using the Stress-Blended Eddy Simulation (SBES) turbulence model. Based on the Computational Fluid Dynamic (CFD) data, a Gaussian mapping model is constructed to quantify the relationship between jet erosion efficiency and robotic motion parameters. Furthermore, to resolve the multi-objective coverage parameter optimization problem, an improved hybrid metaheuristic algorithm—the Composite Cycloid Subtraction-Average-Based Optimizer (CCSABO)—is introduced to determine the optimal synchronization of forward and lateral velocities. Numerical simulations demonstrate the framework’s robustness across various fouling thickness scenarios and nozzle parameters. Notably, the CCSABO algorithm achieves a coverage rate of 99% and minimizes the uniformity index to 0.011, demonstrating superior consistency compared to traditional PSO and GWO methods. This improvement effectively mitigates the risk of hull damage while ensuring cleaning quality. Full article

An Improved Two-Stage Rank Reduction (ITS-RARE) algorithm is proposed for the localization of mixed far-field (FF) and near-field (NF) sources under unknown mutual coupling with the uniform linear sensor array. Our algorithm includes two steps: in the first step, the eigenvectors are exploited when the rank reduction occurs at the right DOAs in our method. The eigenvectors corresponding to the smallest eigenvalues inherently represent the mutual coupling coefficient vectors. Based on it, the joint estimation of FF source DOAs and mutual coupling factors is achieved without pre-calibration. In the second step, after the DOA estimation of NF sources (NFSs), the ranges are estimated in closed form. As a result, the computational complexity is significantly reduced compared to existing methods. Furthermore, the full array aperture is preserved through the covariance matrix reconstruction (CMR) method during the FF/NF source classification. The simulation results demonstrate that the proposed algorithm is not only computationally efficient and effective in source classification but also preserves a larger effective aperture, thereby improving estimation accuracy. Full article

Background and Objectives: The purpose of this study was to investigate whether high ethmoid sinus volume (ESV) constitutes a risk factor for the formation of orbital blowout fractures (OBFs) after craniofacial trauma and whether it affects the fracture pattern. Materials and Methods: This retrospective case–control study included patients aged ≥15 years who presented with craniofacial trauma to the emergency department of a Turkish university hospital between 1 October 2022 and 1 September 2023. The predictor variable was the presence of OBF (yes/no). The primary outcome variable was mean ESV, measured on computed tomography using the fully automated 3D Slicer software. Statistical analyses were performed with a significance level set at p < 0.05. Results: The case group consisted of 108 (median age: 41.5 years; 76 males, 70.38%), and the control group consisted of 122 (median age: 38 years; 84 males, 68.85%) subjects. OBFs were more frequent in males (69%), most commonly detected in the orbital floor (68.2%), and were bilateral in two (1.8%) subjects. The mean ESV in the case group (3.91 ± 1.39 cm³) was significantly higher than that in the control group (2.82 ± 0.94 cm³) (p < 0.001). Unlike the cases with medial wall fractures and those with orbital floor fractures, there was no significant difference in mean ESV between the cases with medial wall and orbital floor fractures and the control group (p = 0.562). Conclusions: A large ethmoid sinus not only increases the risk of orbital blowout fracture but also has an impact on the fracture pattern. Based on the data obtained from our study, we demonstrated a significant association between ethmoid sinus volume and the incidence of orbital blowout fracture. Full article

Background/Objectives: Vertical root fractures (VRFs) present significant diagnostic challenges due to their subtle radiographic features and variability across imaging modalities. Artificial intelligence (AI) offers potential to improve detection accuracy, yet evidence regarding its performance across different imaging systems remains fragmented. To critically evaluate current evidence on AI-assisted detection of VRFs across periapical radiography, panoramic radiography, and cone-beam computed tomography (CBCT) and to compare diagnostic performance, methodological strengths, and limitations. Methods: A systematic review of literature up to January 2025 was carried out using databases such as PubMed, Scopus, Web of Science, and the Cochrane Library. The studies included in this review utilized AI-based techniques for detecting VRF through periapical, panoramic, or CBCT imaging. Extracted data encompassed study design, AI models, dataset sizes, preprocessing methods, imaging parameters, validation techniques, and diagnostic metrics. The risk of bias in these studies was evaluated using the QUADAS-2 tool. Results: Ten studies met inclusion criteria; CNN-based models predominated, with performance highly dependent on imaging modality. CBCT-based AI systems achieved the highest diagnostic accuracy (91.4–97.8%) and specificity (90.7–100%), followed by periapical radiography models with accuracies up to 95.7% in controlled settings. Panoramic radiography models demonstrated lower sensitivity (0.45–0.75) but maintained high precision (0.93) in certain contexts. Most studies reported improvements over human performance, yet limitations included small datasets, heterogeneous methodologies, and risk of overfitting. Conclusions: AI-assisted VRF detection shows promising accuracy, particularly with CBCT imaging, but current evidence is constrained by methodological variability and limited clinical validation. Full article

In image super-resolution tasks, methods based on Generative Adversarial Networks (GANs), Transformer models, and diffusion models demonstrate robust global modeling capabilities and outstanding performance. However, their computational costs remain prohibitively high, limiting deployment on resource-constrained devices. Meanwhile, frequency-domain approaches based on convolutional neural networks (CNNs) capture complementary structural information but lack long-range dependencies, resulting in suboptimal perceptual image quality. To overcome these limitations, we propose a micro-Transformer-based architecture. This framework enriches high-frequency image information through wavelet transform-based frequency-domain features, integrates spatio-temporal and frequency-domain cross-feature fusion, and incorporates a discriminator constraint to achieve image super-resolution. Extensive experiments demonstrate that this approach achieves competitive PSNR/SSIM performance while maintaining reasonable computational complexity. Its visual quality and efficiency outperform most existing SR methods. Full article

Large Language Model (LLM) agents depend heavily on multiple external tools such as APIs, databases and computational services to perform complex tasks. However, these tool executions create latency and introduce costs, particularly when agents handle similar queries or workflows. Most current caching methods focus on LLM prompt–response pairs or execution plans and overlook redundancies at the tool level. To address this, we designed a multi-level caching architecture that captures redundancy at both the workflow and tool level. The proposed system integrates four key components: (1) hierarchical caching that operates at both the workflow and tool level to capture coarse and fine-grained redundancies; (2) dependency-aware invalidation using graph-based techniques to maintain consistency when write operations affect cached reads across execution contexts; (3) category-specific time-to-live (TTL) policies tailored to different data types, e.g., weather APIs, user location, database queries and filesystem and computational tasks; and (4) session isolation to ensure multi-tenant cache safety through automatic session scoping. We evaluated the system using synthetic data with 2.25 million queries across ten configurations in fifteen runs. In addition, we conducted four targeted evaluations—write intensity robustness from 4 to 30% writes, personalized memory effects under isolated vs. shared cache modes, workflow-level caching comparison and workload sensitivity across five access distributions—on an additional 2.565 million queries, bringing the total experimental scope to 4.815 million executed queries. The architecture achieved 76.5% caching efficiency, reducing query processing time by 13.3× and lowering estimated costs by 73.3% compared to a no-cache baseline. Multi-tenant testing with fifteen concurrent tenants confirmed robust session isolation and 74.1% efficiency under concurrent workloads. Our evaluation used controlled synthetic workloads following Zipfian distributions, which are commonly used in caching research. While absolute hit rates vary by deployment domain, the architectural principles of hierarchical caching, dependency tracking and session isolation remain broadly applicable. Full article

Reconstructing original cusp dimensions in worn molars represents a fundamental challenge across dentistry, anthropology, and paleontology, as dental wear obscures critical morphological information. In this proof-of-concept study, we present a standardized machine learning pipeline for predicting original cusp height, specifically the horn tips of the enamel–dentine junction (EDJ), in worn lower molars using three-dimensional morphometric data from micro-computed tomography (micro-CT). We analyzed 40 permanent lower first (M1) and second (M2) molars from four hominin groups, systematically evaluated across three wear stages: original, moderately worn (worn1), and severely worn (worn2). Morphometric variables including height, area, and volume were quantified for each cusp, with Random Forest and multiple linear regression models developed individually and combined through ensemble methods. To mimic realistic reconstruction scenarios while preserving a known ground truth, models were trained on unworn specimens (original EDJ morphology) and tested on other teeth after digitally simulated wear (worn1 and worn2). Predictive performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). Our results demonstrate that under moderate wear (worn1), the ensemble models achieved normalized RMSE values between 11% and 17%. Absolute errors typically below 0.25 mm for most cusps, with R² values up to ~0.69. Performance deteriorated under severe wear (worn2), particularly for morphologically variable cusps such as the hypoconid and entoconid, but generally remained within sub-millimetric error ranges for several structures. Random Forests and linear models showed complementary strengths, and the ensemble generally offered the most stable performance across cusps and wear states. To enhance transparency and accessibility, we provide a comprehensive, user-friendly software pipeline including pre-trained models, automated prediction scripts, standardized data templates, and detailed documentation. This implementation allows researchers without advanced machine learning expertise to explore EDJ-based reconstruction from standard morphometric measurements in new datasets, while explicitly acknowledging the limitations imposed by our modest and taxonomically unbalanced sample. More broadly, the framework represents an initial step toward predicting complete crown morphology, including enamel thickness, in worn or damaged teeth. As such, it offers a validated methodological foundation for future developments in cusp and crown reconstruction in both clinical and evolutionary dental research. Full article

Background and Objectives: The quality of recipient vessels is critical for successful microsurgical breast reconstruction, and iatrogenic damage should be minimized. Adjuvant radiotherapy (RTx) and chemotherapy (CTx) are widely used for breast cancer and may induce structural changes in recipient vessels. This study aimed to evaluate changes in recipient vessel diameters for breast reconstruction after adjuvant treatment in patients with breast cancer. Materials and Methods: A total of 167 patients with unilateral breast cancer who underwent surgical resection between 2017 and 2021 were retrospectively reviewed. Patients were classified into four groups: mastectomy only without adjuvant treatment (group A, n = 33), adjuvant RTx only (group B, n = 44), adjuvant CTx only (group C, n = 43), and combined adjuvant CTx and RTx (group D, n = 47). Preoperative and postoperative computed tomography angiography was used to measure the diameters of the thoracodorsal artery (TDA) and internal mammary artery (IMA) on the affected and unaffected sides. Differences in vessel diameters between sides and among groups were analyzed. Results: In groups B and D, the diameters of the affected TDA and IMA were significantly decreased compared with the changes observed on the unaffected side (p < 0.001). In contrast, there were no significant differences in vessel diameters between the affected and unaffected sides in groups A and C (group A: p = 0.644; group C: p = 0.367). Conclusions: Recipient vessel diameters for microsurgical breast reconstruction significantly decreased in patients who received postoperative RTx, with or without CTx. Plastic surgeons planning delayed breast reconstruction should be aware of these adjuvant therapy-related changes in recipient vessels and consider preoperative imaging assessment to accurately counsel patients regarding surgical risks and to support informed decision-making. Full article

Conventional uses of building information modeling (BIM) in existing-building representation tend to prioritize geometric consistency and efficiency, but often at the expense of interpretive depth. This paper challenges BIM’s tendency to promote epistemic closure by proposing a method to foreground relational ambiguity, transforming view reconciliation from a default automated process into a generative act of critical inquiry. The method, implemented in Autodesk Revit, introduces a parametric reference frame within BIM sheets that foregrounds and manipulates reciprocal relationships between orthographic views (e.g., plans and sections) to promote interpretive ambiguity. Through a case study, the paper demonstrates how parameterized view relationships can resist oversimplification and encourage conflicting interpretations. By intentionally sacrificing efficiency for epistemic resilience, the method aims to expand BIM’s role beyond documentation, positioning it as a tool for architectural knowledge production. The paper concludes with implications for software development, pedagogy, and future research at the intersection of critical representation and computational tools. Full article

Ensuring the quality and structural stability of industrial steel buildings requires precise geometric control during the execution stage, in accordance with assembly standards defined by EN 1090-2:2020. In this context, this work proposes a methodology that enables the automatic detection of geometric deviations by comparing the intended design with the actual as-built structure using a Terrestrial Laser Scanner. The integrated pipeline processes the 3D point cloud of the asset by projecting it into 2D images, on which a YOLOv8 segmentation model is trained to detect, classify and segment commercial steel cross-sections. Its application demonstrated improved identification and geometric representation of cross-sections, even in cases of incomplete or partially occluded geometries. To enhance generalisation, synthetic 3D data augmentation was applied, yielding promising results with segmentation metrics measured by mAp@50-95 reaching 70.20%. The methodology includes a systematic segmentation-based filtering step, followed by the computation of Oriented Bounding Boxes to quantify both positional and angular displacements. The effectiveness of the methodology was demonstrated in two field applications during the assembly of industrial steel structures. The results confirm the method’s effectiveness, achieving up to 94% of structural elements assessed in real assemblies, with 97% valid segmentations enabling reliable geometric verification under the standards. Full article

Annex A of EN 1992-1-1:2023—recently revised and amended in the context of the Second Generation of Eurocodes—introduces a method to adjust partial safety factors for the resistance side alongside a set of factors for different conditions and design situations, both for new and existing structures. The method proposed in Annex A is complemented by a set of stochastic models for relevant basic variables and forms a rather simple and objective format to adjust the partial safety factors from the default values offered in EN 1990:2023. Yet, over the last few years, advanced reliability-based methods aligned with modern computational tools have proved to enable rather robust and efficient structural reliability assessments. A thorough comparative analysis is imperative to understand how distinct reliability-based methods can be applied to adjust partial safety factors in the design of new structural components composed of steel-reinforced concrete. This analysis sheds light on the use of different methods to derive partial safety factors for the resolution of common engineering problems and offers inferences regarding possible implications in terms of safety and economic efficiency of design solutions. Full article

Arid and semi-arid regions are highly vulnerable to drought and depend heavily on rainfed agriculture. To minimize the impact of drought, a transition from crisis management to risk management is necessary, which requires a comprehensive risk assessment that accounts for not only drought hazard but also drought vulnerability and population exposure. However, integrated studies that account for socio-economic, agricultural, demographic, and climate factors are currently lacking in Iran. The objective of this study is to comprehensively assess the spatio-temporal changes in drought risk from 2000 to 2019 across Iran. We used the standardized precipitation evapotranspiration index (SPEI) and multiple socio-economic and demographic data to compute drought risk. In particular, we used the SPEI to map drought hazard, an analytical hierarchical process method to assess drought vulnerability, and population density data to compute population exposure. Drought risk increased in 57% of the area of Iran, mainly in the northwest, west, and central regions, at a rate of up to 10% per year. In 21% of the area of Iran, drought risk declined by up to 10% per year, predominantly in the northern and southern regions of the Alborz Mountains, encompassing the provinces of Tehran, Gilan, Mazandaran, and Khorasan Razavi. Our results show that the spatial patterns of drought risk vary across Iran and are modulated by the interaction between climatic and socio-economic factors. The results of this study provide useful information for drought risk management and intervention in Iran. Full article