Composite Model Research Articles

P. gingivalis in oral-prostate axis exacerbates benign prostatic hyperplasia via IL-6/IL-6R pathway.

Benign prostatic hyperplasia (BPH) is the most common disease in elderly men. There is increasing evidence that periodontitis increases the risk of BPH, but the specific mechanism remains unclear. This study aimed to explore the role and mechanism of the key periodontal pathogen Porphyromonas gingivalis (P. gingivalis) in the development of BPH. The subgingival plaque (Sp) and prostatic fluid (Pf) of patients with BPH concurrent periodontitis were extracted and cultured for 16S rDNA sequencing. Ligature-induced periodontitis, testosterone-induced BPH and the composite models in rats were established. The P. gingivalis and its toxic factor P. gingivalis lipopolysaccharide (P.g-LPS) were injected into the ventral lobe of prostate in rats to simulate its colonization of prostate. P.g-LPS was used to construct the prostate cell infection model for mechanism exploration. P. gingivalis, Streptococcus oralis, Capnocytophaga ochracea and other oral pathogens were simultaneously detected in the Pf and Sp of patients with BPH concurrent periodontitis, and the average relative abundance of P. gingivalis was found to be the highest. P. gingivalis was detected in both Pf and Sp in 62.5% of patients. Simultaneous periodontitis and BPH synergistically aggravated prostate histological changes. P. gingivalis and P.g-LPS infection could induce obvious hyperplasia of the prostate epithelium and stroma (epithelial thickness was 2.97- and 3.08-fold that of control group, respectively), and increase of collagen fibrosis (3.81- and 5.02-fold that of control group, respectively). P. gingivalis infection promoted prostate cell proliferation, inhibited apoptosis, and upregulated the expression of inflammatory cytokines interleukin-6 (IL-6; 4.47-fold), interleukin-6 receptor-α (IL-6Rα; 5.74-fold) and glycoprotein 130 (gp130; 4.47-fold) in prostatic tissue. P.g-LPS could significantly inhibit cell apoptosis, promote mitosis and proliferation of cells. P.g-LPS activates the Akt pathway through IL-6/IL-6Rα/gp130 complex, which destroys the imbalance between proliferation and apoptosis of prostate cells, induces BPH. P. gingivalis was abundant in the Pf of patients with BPH concurrent periodontitis. P. gingivalis infection can promote BPH, which may affect the progression of BPH via inflammation and the Akt signaling pathway.

Feasibility of using superelastic shape memory alloy in plastic hinge regions of steel bridge columns for seismic applications

AbstractThis paper contributes to the further development of seismic resilient infrastructure by introducing a novel prototype bridge which integrates tubular steel piers with superelastic shape memory alloy (SMA). The proposed bridge pier is composed of a circular steel tube which is bonded to a superelastic SMA tube in regions of high stress. The pier is distinguished by its ability to minimize residual drifts following inelastic deformations induced by cyclic loading. Three‐dimensional continuum finite element (FE) models are utilized to examine its lateral behavior. Experimental data is used to demonstrate the effectiveness of the continuum FE procedure in replicating the cyclic response and capturing both global and localized behaviors. A novel composite material model is proposed to represent the degradation of strength and accumulation of irreversible strains in the cyclic response of superelastic SMAs. An iterative procedure for the calibration of this material is presented. Investigations, employing the calibrated FE procedure, focus on the quasi‐static cyclic response of steel piers with superelastic SMA in the plastic hinge zone, aiming to identify the optimal length of the SMA tube for achieving a self‐centering response with reduced residual deformation. The study is then further expanded to examine the seismic response of a bridge structure incorporating such piers. Development of the FE model for the prototype bridge includes the modelling of the piers using continuum elements, while the superstructure, bearing units, abutment walls, and backfill material are modelled using discrete elements. Nonlinear time history analyses are undertaken to investigate the effects of column wall thickness and materials used in the plastic hinge zones of the piers. Dynamic FE study results indicate that bridges employing such piers are capable of returning to their original position, provided the SMA tube is of adequate length.

Application of Composite Frequency Data Analysis Model to Forecast the Growth of Vietnam's Coffee Exports

In recent years, mixed-frequency data analysis models have received much attention and application in the fields of economics and finance, especially in forecasting national macroeconomic indicators. However, in Vietnam, there are almost no studies applying these models. Vietnam is a country with a long-standing agricultural production background and the workforce mainly makes a living from farming. There are many types of Vietnamese agricultural products with high export value, bringing a significant source of foreign currency to the country. Coffee is Vietnam's main export product, a product that earns a lot of foreign currency, contributing to the country's socio-economic development. Therefore, applying models with mixed frequency data such as MIDAS to forecast coffee export growth is a promising new research direction.

Naturalness-motivated composite Higgs model for generating the top Yukawa coupling

The large top Yukawa coupling results in the top quark contributing significantly to the quantum correction of the Higgs mass term. Traditionally, this effect is canceled by the presence of top partners in symmetry-based models. However, the absence of light top partners poses a challenge to the naturalness of these models. In this paper, we study a model based on composite Higgs with the top Yukawa coupling originating from dimension-six four-fermion operators. The low cutoff scale of the top quark loop required by the naturalness principle can be realized with a light gauge boson Eμ that connects the hyperfermions and top quarks. A scalarless dynamical model with weakly coupled extended SU(4)EC gauge group is presented. The model features an Eμ boson and a ZE′ boson both at the sub-TeV scale, which lead to a rich phenomenology, especially in the top quark physics. Published by the American Physical Society 2024

A linearly polarized light emission with a composite nanowire grating in whole white band

To obtain a highly linearly polarized light, a composite model consisting of white light emission, anti-reflection film, and metal-dielectric-metal nanowire grating was designed, analyzed, optimized, and fabricated. Based on the finite-difference time-domain method, the impacts of material, period, height, and incidence angle on the polarization performance of the composite model were discussed. The metal-dielectric-metal nanowire grating was fabricated on blue chip and fluorescent ceramics using nanoimprint technology. The employed materials of metal-dielectric-metal nanowire grating were aluminum and PMMA, with the period of 200 nm, wire width of 100 nm, and the height of metal and dielectric were 100 nm and 120 nm. Additionally, the anti-reflection film consisting of PMMA with the thickness of 45 nm was incorporated on fluorescent ceramics to enhance energy efficiency. Finally, through a series of test experiments, the composite model can be realized by the extinction ratio of 40 dB, while the transmittance of TM mode exceeds 50% at 450–750 nm. The theoretical analysis of this study is verified by experiments, and it has significant potential in the pursuit of high brightness, ultra-thin micro displays.

Optimization of the extrusion rate for different layer thicknesses to achieve controlled mechanical properties in MEX 3D printing of low-alloy steel

Composite extrusion modeling is an advanced material extrusion additive manufacturing process that requires fine-tuning of printing parameters for printing injection molding feedstocks. In order to achieve comparable high-quality parts at different layer thicknesses, the parameters must be optimized for the respective layer thicknesses. In this work, the printing parameters for AISI 8740 low-alloy steel injection molding material are optimized for four different layer thicknesses. The extrusion multiplier values for each layer thickness are tuned, allowing fine adjustment of the printer's screw rotation speed at every layer thickness, resulting in all final parts' maximum densities (≥ 98 %) with high accuracies. Scanning electron microscope images, surface roughness measurements, and printing precision tests proved the good and comparable high-quality of all optimized printed parts. Young's modulus, tensile and yield strengths also raised by ∼24 %, ∼16 % and ∼15 %, respectively, compared to the non-optimized printed sintered part. The achieved tensile strength range of (∼888–947) MPa and yield strength range of (∼574–586) MPa of all optimized printed sintered parts were also higher than those provided by the feedstock manufacturer. This promising approach significantly reduces printing time and manufacturing costs without compromising the printed final parts' quality and mechanical properties. The adjusting screw rotation speed methodology allows broader applicability across diverse material extrusion processes.

Formation and evolution of apple (Malus pumila Mill.) bruising based on high-speed camera verification and finite element method

To analyse the characteristics and internal mechanical changes of bruising formed on apples under different working conditions, this paper explored the formation mechanism of bruising using finite element method. A composite model of peel and flesh was established by reverse engineering, and the accuracy of the apple model was validated two aspects: drop velocity and bruise volume, with a maximum error of 2.51 %. The collision velocity, contact force, stress, and energy of apple bruising were analysed using commonly used contact materials (wood and rubber) at falling heights of 600, 900, 1200, 1500, 1800, and 2100 mm. The characteristics of apple bruising after formation were analysed and the susceptibility of apple bruising were explored. The findings suggest that as the height increased, so does the maximum stress and bruise susceptibility. At a height of 2100 mm, the apple’s bruise susceptibility was measured at 24955.87 mm3 J−1 (wood) and 24710.18 mm3 J−1 (rubber), respectively. This study offers valuable insights into understanding the mechanism behind bruising in fruits, and has a guiding role in the control of bruising and the optimal design of related machinery.

Experimental and theoretical investigation of shear lag effect in twin box‐shaped composite girders of long‐span cable‐stayed bridges

AbstractSteel‐concrete composite box girders offer notable advantages such as high stiffness, excellent torsional performance, and light self‐weight, making them prevalent in large‐span bridges, particularly in cable‐stayed configurations. However, a persistent challenge arises from the shear lag effect in the concrete slab within the flanges induced by shearing interactions between the webs and flanges. Limited attention has been devoted to investigating the experimental and theoretical aspects of shear lag in long‐span cable‐stayed bridges with twin box‐shaped composite girders. This research addresses this gap by designing a segmental scale twin box‐shaped composite model specimen to simulate the cable spacing of the main girder in a cable‐stayed bridge, specifically targeting the shear‐lag effect. The study successfully captures the non‐uniform distribution of cross‐sectional strain in the top concrete slab. Subsequently, analytical strain solutions for the composite girder under mid‐span concentrated load and axial load are derived using the energy variation method. The theoretical model's applicability is then calibrated against experimental results. The investigation extends to the in‐situ testing of the Chibi Yangtze River Bridge, the world's largest steel‐concrete composite girder cable‐stayed bridge, boasting a main span of up to 720 m. An established finite element (FE) analysis model for the Chibi Yangtze River Bridge simulates the transverse stress distribution under typical construction conditions of CC6. Comparative analyses between the simulated results, calculated values from elementary beam theory, and the theoretical model against field‐measured values reveal that the FE model provides a more accurate estimation of the shear lag effect in the concrete slab. Notably, the FE model effectively reflects the bridge's characteristics and serves as a baseline for subsequent studies. This comprehensive investigation contributes significantly to the understanding and analysis of shear lag phenomena in twin box composite girders of cable‐stayed bridges. The integration of experimental, analytical, and construction site validations establishes robust tools for engineering design and the construction of safe and serviceable cable‐stayed bridges. The research augments knowledge regarding shear lag behavior and underscores the necessity to appropriately consider its effects in bridge analysis and design.

Intelligent design of reconfigurable flexible assembly fixture for aircraft panels based on smart composite jig model and knowledge graph

The current demand for aircraft panel assembly fixtures necessitates agile manufacturing solutions. Traditional welding fixtures struggle to meet diverse panel types, high quantities, and short manufacturing cycles. Conversely, reconfigurable flexible assembly fixtures offer universality, efficiency and ease of maintenance but heavily rely on designers' experience, hindering knowledge reuse. Existing research on computer-aided fixture design (CAFD) primarily addresses specialized fixtures, neglecting reconfigurable flexible ones, lacking fixture design knowledge systems. Aiming at the above problems, the study proposes an intelligent design method for the reconfigurable flexible assembly fixture for aircraft panels based on the smart composite jig model (SCJM) and knowledge graph (KG). Firstly, based on the SCJM of the aircraft panel type assembly fixture (PSCJM), a configuration design method is proposed to carry out the multi-level design of reconfigurable assembly fixture to instantiate PSCJM containing fixture configuration rules and a module library based on model definition (MBD). Then, a knowledge retrieval method is proposed to search the suitable fixture design knowledge based on KG, assisting designers in achieving the flexible design of aircraft panel reconfigurable assembly fixtures. Finally, practical engineering design validation confirms the method's feasibility and effectiveness in obtaining the reconfigurable flexible assembly fixture design scheme for aircraft panels.

Application of carbon fiber composite materials in aircraft

Carbon fiber composite is a material with excellent mechanical properties. Compared with other high-performance fiber materials, carbon fiber composites have the advantages of high strength and high modulus even at ultra-high temperatures. In addition, the carbon fiber composite material also has excellent electrical and thermal conductivity and electromagnetic shielding performance. Carbon fiber composites are widely used in the aircraft manufacturing industry due to their incomparable performance with other composite materials. In recent years, the amount of carbon fiber composite materials in newly developed aircraft products has reached more than 50%. This paper aims to explore the application of carbon fiber in aircraft, and to reflect the importance of carbon fiber composite materials by comparing models that use composite materials and models that do not use composite materials. The study explores the application of carbon fiber composite materials in aircraft through a comprehensive literature analysis and review. Key findings indicate significant advantages of these materials in enhancing aircraft performance, including reduced weight and increased strength. The paper also discusses the challenges in manufacturing and environmental impacts, offering insights into future research directions for sustainable aviation technologies.

Monte Carlo Simulation of the CO2 Flooding Efficiency at a Core Scale for Different Oil Compositions

The evaluation of water-alternating-gas (WAG) efficiency and profitability is complicated by a large number of reservoir, operating, and economic parameters and constraints. This study aims at understanding the influence of the oil composition on different WAG injections. By employing compositional reservoir modeling and the Monte Carlo method to characterize the diversity of oils occurring in nature, we simulate the microscopic displacement efficiency of CO2 flooding when it is applied to both light- and heavy-oil reservoirs. We find that the economic performance of WAG in both miscible and immiscible scenarios is mainly characterized by the dimensionless injection rate and the oil density at surface conditions. Neither the bubble point pressure nor the minimum miscibility pressure can be used for the quantification of the optimal WAG parameters. We present our estimates of the best strategies for the miscible and immiscible injections and verify some of our previous results for randomly sampled oils. In particular, we demonstrate that CO2 flooding is better to apply at higher-dimensionless injection rates. We show that the injection of CO2 organized at a light-oil reservoir results in a higher profitability of WAG, although this comes at the cost of lower carbon storage efficiency.

Robustness and classification capabilities of MRI radiomic features in identifying carotid plaque vulnerability.

To assess how radiomic features may be combined with plaque morphological and compositional features identified by multi-contrast magnetic resonance imaging (MRI) to improve upon conventional risk assessment models in determining culprit lesions. Fifty-five patients (mean age: 62.6; 35 males) with bilateral carotid stenosis who experienced transient ischaemic attack (TIA) or stroke were included from the CARE-II multi-centre carotid imaging trial (ClinicalTrials.gov Identifier: NCT02017756). They underwent MRI within 2 weeks of the event. Classification capability in distinguishing culprit lesions was assessed by machine learning. Repeatability and reproducibility of the results were investigated by assessing the robustness of the radiomic features. Radiomics combined with a relatively conventional plaque morphological and compositional metric-based model provided incremental value over a conventional model alone [area under curve (AUC), 0.819 ± 0.002 vs. 0.689 ± 0.019 respectively, p = 0.014]. The radiomic model alone also provided value over the conventional model [AUC, 0.805 ± 0.003 vs. 0.689 ± 0.019 respectively, p = 0.031]. T2-weighted imaging-based radiomic features had consistently higher robustness and classification capabilities compared with T1-weighted images. Higher-dimensional radiomic features outperformed first-order features. Grey Level Co-occurrence Matrix (GLCM), Grey Level Dependence Matrix (GLDM) and Grey Level Size Zone Matrix (GLSZM) sub-types were particularly useful in identifying textures which could detect vulnerable lesions. The combination of MRI-based radiomic features and lesion morphological and compositional parameters provided added value to the reference-standard risk assessment for carotid atherosclerosis. This may improve future risk stratification for individuals at risk of major adverse ischemic cerebrovascular events.

Abstract PS04-08: Sociodemographic Risk Factors and Prediction of Aromatase Inhibitor Non-Adherence in Women with Breast Cancer Enrolled in SWOG S1105

Abstract Background: Non-adherence to aromatase inhibitors (AIs) for breast cancer is common and increases risk of recurrence. Few prospective studies have systematically evaluated non-adherence and non-clinical factors associated with it. We analyzed baseline sociodemographic characteristics and financial factors associated with non-adherence to create a baseline composite risk-score for AI non-adherence. Methods: Patients enrolled in S1105 were required to have been on an AI for ≥30 days at enrollment. Patients were assessed for non-adherence of AIs every 3 months for 36 months, with non-adherence defined as urine AI metabolite assay results satisfying any of the following: < 10 ng/mL, undetectable, specimen submitted outside of the ± 21-day follow-up appointment window, or no submitted specimen. Factors included race, Hispanic ethnicity, age, education, income, health insurance type, and prescription cost. We also included socioeconomic deprivation and rural or urban residence using trial participants’ residential zip codes linked to the Area Deprivation Index (ADI) and Rural Urban Continuum codes (RUCC) respectively. For numeric factors and categorical factors with more than two levels, we used cutpoint analysis to determine the highest risk range by optimizing the Chi-squared statistic. For each resulting binary factor, we performed a logistic regression, adjusting for the study stratification factors and stratifying by treatment arm. Factors associated with adherence at the alpha=0.10 level were combined into a composite risk score, with study participants given one point for each baseline adverse risk factor. Secondarily, we examined a composite risk score including all factors, regardless of statistical significance. Results: In total, 724 patients were registered from 40 institutions between May 2012 and September 2013. The median age was 60.9 years, and 64.5% were on AI < 12 months prior to registration. Only 8% had out of pocket (OOP) cost >$30. Observed adherence at 36 months was 35.9%. Patients living in metro areas with populations ≥ 250,000 (RUCC ≤ 3) were more likely to be non-adherent (37.5% vs 28.3%, OR=1.50, p=.08); as were those who were younger, those without a college education, and those with limited (≤$5) OOP cost. ADI, race and ethnicity were not associated with non-adherence. The composite risk score was strongly associated with 36-month adherence. For each 1 unit increase in risk level, the risk of non-adherence increased 47% (OR=1.47, p< .001); the risk increased 64% for those with >2 vs. ≤2 risk factors (OR=1.64, p=.002). Results using all factors regardless of statistical significance were consistent. Conclusions: A composite model comprised of sociodemographic risk factors can identify patients on AI’s at much greater risk of long-term AI non-adherence. In addition to mitigating side effects, targeted interventions to improve adherence should focus on structural barriers among those at highest risk. Non-adherence (+/- 6 months) = factor + AI duration + AI type, stratified by arm Citation Format: Dawn Hershman, Anna Moseley, Kathryn Arnold, Julie Gralow, Alfred Neugut, Scott Ramsey, N. Lynn Henry, Joseph Unger. Sociodemographic Risk Factors and Prediction of Aromatase Inhibitor Non-Adherence in Women with Breast Cancer Enrolled in SWOG S1105 [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS04-08.

Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands

Background and purposeRecently, a comprehensive xerostomia prediction model was published, based on baseline xerostomia, mean dose to parotid glands (PG) and submandibular glands (SMG). Previously, PET imaging biomarkers (IBMs) of PG were shown to improve xerostomia prediction. Therefore, this study aimed to explore the potential improvement of the additional PET-IBMs from both PG and SMG to the recent comprehensive xerostomia prediction model (i.e., the reference model). Materials and methodsTotally, 540 head and neck cancer patients were split into training and validation cohorts. PET-IBMs from the PG and SMG, were selected using bootstrapped forward selection based on the reference model. The IBMs from both the PG and SMG with the highest selection frequency were added to the reference model, resulting in a PG-IBM model and a SMG-IBM model which were combined into a composite model. Model performance was assessed using the area under the curve (AUC). Likelihood ratio test compared the predictive performance between the reference model and models including IBMs. ResultsThe final selected PET-IBMs were 90th percentile of the PG SUV and total energy of the SMG SUV. The additional two PET-IBMs in the composite model improved the predictive performance of the reference model significantly. The AUC of the reference model and the composite model were 0.67 and 0.69 in the training cohort, and 0.71 and 0.73 in the validation cohort, respectively. ConclusionThe composite model including two additional PET-IBMs from PG and SMG improved the predictive performance of the reference xerostomia model significantly, facilitating a more personalized prediction approach.

Research on optimization of improved short-term load composite forecasting model based on AM–CNN–Bi–LSTM

Accurate load prediction is a prerequisite for the design, operation, scheduling, and management of energy systems. In the context of the development of smart grids, the extensive integration of highly volatile distributed energy generation into the power system has brought new challenges to the accuracy, reliability, real-time performance, and intelligence of short-term load forecasting. Therefore, this article proposes a novel short-term power load composite prediction model based on AM–CNN–Bi–LSTM. First, CNN is used to extract relevant feature quantities of power load coupling characteristics. Then, AM is used to evaluate the importance of the feature data, highlighting the features that have a greater impact on the prediction results. Finally, the Bi-LSTM network captures bidirectional temporal information from multiple time steps for prediction. Taking one year of measured data as an example, the error comparison of the prediction results of the composite prediction model overlay shows that compared with other models, the composite prediction model has improved prediction accuracy, feature extraction, generalization ability, and other aspects. The research results improve the accuracy of short-term power load forecasting while providing effective model references for decision-making in power system optimization scheduling, safe operation, and reasonable pricing.

Development of a machine learning model to classify mineral deposits using sphalerite chemistry and mineral assemblages

Sphalerite is a commonly occurring mineral in natural systems and a prominent indicator mineral used in resource exploration. Its chemistry and associated mineral assemblages are both controlled by the physicochemical conditions of the local environment, such as temperature and sulfur fugacity. Accordingly, the chemistry of sphalerite and the nature of the associated minerals provide valuable clues about the classification of mineral deposits and their environment of formation. Nevertheless, exclusive reliance on the trace-element chemistry of sphalerite for deposit classification has its limitations given the multitude of factors (For example, deposit type, temperature, pressure, and background concentrations of elements) that affect its chemistry. To address this challenge, we develop machine learning models using the SHapley Additive exPlanations (SHAP) method to assess the importance of sphalerite trace-element chemistry and mineral assemblage information to distinguishing between five petrogenetically distinct mineral deposit types. This contribution demonstrates that a composite model that incorporates both data types markedly improve the accuracy of deposit type discrimination. The composite model is composed of two sub-models, Random Forest (RF) and Extra Random Trees (ERT), which are specifically employed for processing trace-element data and mineral assemblage data, respectively, due to their superior performance in handling these two distinct types of datasets. To simplify the end-user experience, we provide an executable file of the machine learning-based classifier, allowing it to be readily applied as an exploration tool using simple inputs. In summary, this work substantially enhances the confidence of Pb-Zn deposit type classification using sphalerite and introduces an innovative perspective on the application of machine learning to resolving complex geological problems.

Landscape features influencing gene flow and connectivity of an endangered passerine.

Dispersal of individuals and gene flow are crucial aspects to maintain genetic diversity and viability of populations, especially in the case of threatened species. Landscape composition and structure may facilitate or limit individual movement within and among populations. We used a landscape genetics approach to assess the connectivity patterns of the threatened Dupont's lark (Chersophilus duponti subsp. duponti), considering their genetic patterns and the landscape features associated with its gene flow in Spain. We analysed the genetic relatedness based on 11 species-specific polymorphic microsatellites on 416 Dupont's lark individuals sampled across peninsular Spain between 2017 and 2019, covering most of the European distribution of the species. To assess the relationship between the landscape composition and the species gene flow, we estimated genetic distance at the individual level (Dps). Next, we built a set of environmental surfaces from two time periods (years 1990 and 2018), based on factors such as land use and topography, influencing individuals' movement. We then obtained resistance surfaces from an optimization process on landscape variables. Landscape genetics analyses were done for single and composite surface models for each year separately. Our findings from both time points show that scatter or mosaic-structured vegetation composed by low agricultural and tree cover and high presence of sclerophyllous shrubs favoured Dupont's lark dispersal, while dense and continuous tree cover, as well as areas of intensive agriculture, were limiting factors. Our results suggest the importance of steppe habitat patches for the species' establishment and dispersal. In addition, our results provide key information to develop conservation measures, including conserving and restoring steppe habitats as scattered and/or mosaic-structured vegetation that could warrant the connectivity and persistence of Dupont's lark populations.

Conditional generative adversarial networks for image-based sunlight analysis of residential blocks

Sunlight simulation is often used to analyze whether buildings receive sufficient sunlight in complex design cases. However, it is time-consuming to establish geometrical and physical models, and non-negligible computation time (from a few seconds to minutes) leads to high time cost of iterative design optimization. Therefore, this study builds surrogate models to instantaneously detect problematic buildings (those receiving insufficient sunlight) using images of residential blocks. Different conditional generative adversarial networks (CGANs), including Pix2Pix and conditional attention GAN (CAGAN), are employed to train surrogate models. The attention mechanisms and the multi-scale vision give CAGAN an edge in capturing the global structure of buildings and learning high-level knowledge related to the occlusion relationships between buildings. In addition, this study also proposes a stepwise strategy to construct composite surrogate models. The first sub-model provides more informative images to help the second sub-model learn the features of problematic buildings. This study focuses on evaluating the models based on the detection performance in addition to image structural similarity and pixel similarity, which have been used in most studies. The CAGAN models outperform the Pix2Pix models in the average detection accuracy (ADA) and the 100 % successful detection probability (SDP) on the same dataset. Moreover, the stepwise strategy increases the ADA and SDP to 89.88 % and 71.07 %, respectively. The proposed methods can be extended to other urban environmental performance diagnoses, such as thermal comfort or the wind environment.

Fatigue damage evolution of modified recycled aggregate concrete

Basalt fiber (BF) and nano-silica (NS) are used to improve the properties of recycled aggregate concrete (RAC) and produce modified RAC (MRAC). The fatigue failure patterns of the MRAC were observed through a uniaxial compression fatigue test, and the reasons for the different patterns were explained. The effects of the stress level, replacement rate, and loading frequency on the fatigue life were investigated, and the effects of BF and NS on the fatigue strain, damage evolution, and energy dissipation evolution of MRAC were analyzed. The enhancement mechanism of BF and NS on RAC was revealed using microstructural analysis techniques, such as scanning electron microscopy (SEM) and computed tomography (CT). The results showed that the fatigue failure modes of the MRAC could be divided into split, shear, and mixed failures. The fatigue life and fatigue strain of RAC exhibited a pattern of initially increasing and subsequently decreasing with an increase in the BF content (from 0kg·m-3 to 3kg·m-3 and then to 6kg·m-3). At stress level of 0.80, the average fatigue life of the specimens increased by 76.15% compared to RAC when the BF doping was 3kg·m-3; however, when the BF doping was increased to 6kg·m-3, the average fatigue life of the specimens increased by only 47.68% compared to RAC. The mixed BF and NS improved the fatigue life of RAC more than the single mixed BF or NS. Fatigue equations and unified single logarithmic fatigue equations, including fiber parameters, were established to accurately describe the relationship between the fatigue stress level, fatigue life, and failure probability. The fatigue strain gradually decreases with increasing NS content (0% to 2%); with increasing loading frequency (from 1Hz to 10Hz and then to 15Hz), the fatigue strain gradually decreases. The fatigue life of the MRAC decreased dramatically at low loading frequencies (1Hz) and then steadily increased as the loading frequency increased. BF and NS have an improving effect on the porosity of microstructure, improve the fatigue life of RAC, and BF exhibits a more significant improvement than NS. A composite fatigue damage model that comprehensively considers the contribution rates of NS and BF was established, and the fatigue damage evolution of the MRAC was predicted.

The Effect of Starspots on Spectroscopic Age and Mass Estimates of Nonaccreting T Tauri Stars in the Taurus–Auriga Star-forming Region

Accurate age and mass determinations for young pre-main-sequence stars are made challenging by the presence of large-scale starspots. We present results from a near-infrared spectroscopic survey of 10 T-Tauri Stars in Taurus-Auriga that characterize spot-filling factors and temperatures, the resulting effects on temperature and luminosity determinations, and the consequences for inferred stellar masses and ages. We constructed composite models of spotted stars by combining BTSettl-CIFIST synthetic spectra of atmospheres to represent the spots and the photosphere along with continuum emission from a warm inner disk. Using a Markov Chain Monte Carlo algorithm, we find the best-fit spot and photospheric temperatures, spot-filling factors, as well as disk-filling factors. This methodology allowed us to reproduce the 0.75–2.40 μm stellar spectra and molecular feature strengths for all of our targets, disentangling the complicated multicomponent emission. For a subset of stars with multiepoch observations spanning an entire stellar rotation, we correlate the spectral variability and changes in the filling factors with rotational periods observed in K2 and AAVSO photometry. Combining spot-corrected effective temperatures and Gaia distances, we calculate luminosities and use the Stellar Parameters of Tracks with Starspots models to infer spot-corrected masses and ages for our sample of stars. Our method of accounting for spots results in an average increase of 60% in mass and a doubling in age with respect to traditional methods using optical spectra that do not account for the effect of spots.