Inverse Method Research Articles

Novel Insights into Causal Effects of Serum Lipids and Apolipoproteins on Cardiovascular Morpho-Functional Phenotypes.

Previous observational studies have explored the association between serum lipids, apolipoproteins, and adverse ventricular/aortic structure and function. However, whether a causal link exists is uncertain. This study employed a two-sample Mendelian randomization (MR), colocalization, reverse, and multivariable MR (MVMR) approach to examine the causal associations among five serum lipids, two apolipoproteins, and 32 cardiac magnetic resonance (CMR) traits. Utilizing single-nucleotide polymorphisms (SNPs) linked to serum lipids and apolipoproteins as instrumental variables. CMR traits from seven independent genome-wide association studies served as preclinical endophenotypes, offering insights into aortic and cardiac structure/function. The primary analysis utilized a random-effects inverse variance method (IVW), followed by sensitivity and validation analyses. In the primary IVW MR analyses, genetically predicted low-density lipoprotein cholesterol (LDL-C) levels were positively correlated with increased descending aorta strain (DAo strain) (β = 0.098; P = 2.69E-07) and ascending aorta strain (AAo strain) (β = 0.079; P = 5.19E-05). Genetically predicted high-density lipoprotein cholesterol (HDL-C) levels were positively correlated with left ventricular radial peak diastolic strain rate (LV-PDSRll) (β = 0.176; P = 2.89E-05) and the left ventricular longitudinal peak diastolic strain rate (LV-PDSRrr) (β = 0.059; P = 2.44E-06), and negatively correlated with left ventricular regional wall thickness (LVRWT). While apolipoprotein B (ApoB) levels were positively correlated with AAo strain (β = 0.076; P = 1.16E-05), DAo strain (β = 0.065; P = 2.77E-05). A shared causal variant was identified to demonstrate the associations of ApoB with AAo strain and DAo strain using colocalization analysis. Sensitivity analyses confirmed the robustness of these associations. Targeting lipid and apolipoprotein levels through interventions may provide novel strategies for the primary prevention of CVDs.

A convolutional neural network to optimize multi-mission satellite altimeter fusion for improving the marine gravity field

Satellite altimetry is the main tool for constructing global or regional marine gravity fields. To improve the accuracy and spatial resolution, it is necessary to fuse multi-mission altimeters. How to determine the weights of multi-mission altimeters is a crucial issue, making the conventional calculation process very complex. In addition, traditional satellite inversion methods are often independent of shipborne gravity, which is used only as validation data, thus not take full advantages of high accuracy and resolution of shipborne gravity. In this study, we introduce a convolutional neural network (CNN) to merge the vertical deflections (DOVs) obtained from multi-altimeter missions to construct a marine gravity model in the South China Sea. High-accuracy shipborne gravity and a dataset comprising DOVs and geo-locations are employed as input data for neural network training. For the validation of CNN method, the gravity model is also computed by conventional Inverse Vening Meinesz (IVM) method. Independent shipborne gravity measurements and SIO V32.1, DTU17 models are used as validation data. The evaluation results show that the CNN-derived model achieves a higher level of accuracy, yielding a standard deviation (STD) of 3.21 mGal, with an improvement of 36.56% compared to IVM-derived model. More than 92% of the differences between the CNN-derived model and shipborne gravity are less than 5 mGal. In addition, spectral analysis results further show that the CNN-derived model has stronger energy at short wavelengths (less than 25 km) compared to other models. These findings reveal that CNN method is feasible for marine gravity recovery and the CNN-derived model can achieve higher accuracy. The CNN method can improve the accuracy and spectral characteristics of the constructed gravity model by taking advantage of the high accuracy and high resolution of shipborne gravity.Graphical

Relationships between emotional states, bipolar disorder, and gastrointestinal disorders: A two-sample Mendelian randomization study

BackgroundFunctional gastrointestinal symptoms are frequently observed in patients with bipolar affective disorder, but the causal relationship between these conditions remains unclear. To establish causality, this study utilized Mendelian randomization (MR) with data from large-scale genomic research. The investigation aimed to elucidate the relationship between emotional states, bipolar disorder, and functional gastrointestinal disorders (FGIDs). MethodsSummary statistics from GWAS were utilized for mood swings (UK biobank, N = 451,619), irritable mood (UK biobank, N = 373,733), bipolar disorder (UK biobank, N = 352,006),functional dyspepsia (FinnGen biobank, N = 194,071), and irritable bowel syndrome (UK biobank, N = 486,601). All GWAS summary statistics were derived from individuals of European ancestry. The primary analysis employed the inverse variance-weighted method for Mendelian randomization (MR). Additionally, we conducted tests for heterogeneity and pleiotropy to ensure the robustness of our results. ResultsA suggestive positive causal relationship was identified between mood swings-related conditions and IBS using the inverse variance-weighted (IVW) method [mood swings-IBS: OR/95%CI: 3.221(2.417,4.294), P(1.42E−15); irritable mood-IBS: OR/95%CI: 1.881(1.615,2.191), P(4.56E−16); bipolar disorder-IBS: OR/95%CI: 1.003(1.001,1.006), P(0.009)]. For functional dyspepsia, a suggestive positive causal relationship was observed with mood swings [mood swings-FD: OR/95%CI: 2.827(1.124,7.109), P(0.027)]. In the reverse analysis, causal relationship was observed between IBS and emotional states [IBS and mood swings: OR/95%CI: 1.030(1.021,1.040), P(1.10E−10); IBS and irritable mood: OR/95%CI: 1.064(1.041,1.087), P(2.58E−08)]. ConclusionsMood swings, irritable mood, and bipolar disorder were associated with an increased risk of functional gastrointestinal disorders (FGIDs). Additionally, reverse analysis revealed a causal relationship between irritable bowel syndrome (IBS) and both mood swings and irritable mood. These findings suggest that targeted emotional interventions may be beneficial for patients with FGIDs. Further research is warranted to explore the relationship between mood instability-related disorders and FGIDs, particularly IBS.

Does achieving apical patency increase postoperative pain during endodontics?

PubMed, Scopus, Embase, Web of Science, Cochrane Library, and gray literature up to May 2023. Twelve studies were included for meta-analysis. Two authors screened databases to identify suitable studies, with a third investigator resolving disagreements. Inclusion criteria were randomized controlled trials (RCTs) involving individuals without systemic diseases, who underwent root canal treatment in a permanent tooth with or without apical patency. Outcomes included pain, number of patients with pain and analgesic use. Exclusion criteria included patients who had undergone tooth extraction, incomplete data reports and studies which were not RCTs. There were no limits on publication language. A search of PubMed, Scopus, Embase, Web of Science, Cochrane Library, and gray literature from Google Scholar, ProQuest, OpenAire, and BASE identified 92 articles until May 2023. After removing 42 duplicates, 50 article full texts were assessed. Ultimately, 12 studies were included, seven of which were new relative to previous systematic reviews. Data were analysed using RevMan 5.4 software. Dichotomous categorical data were analysed through the Cochran-Mantel-Haenszel test, with the inverse variance method used for continuous data. Following assessment of heterogeneity with the χ2 test, fixed- or random- effects modeling was used. Pain scores were reported within one week by ten studies, but three were excluded from meta-analysis due to methodological issues. Studies involving participants aged 14-65 years, with pulp necrosis, apical periodontitis, or pulpitis, were included for analysis. Irrigation solutions varied from saline to sodium hypochlorite (2.25-5.25%), EDTA, citric acid, or chlorhexidine. Types of teeth treated included molars and anterior teeth. All studies used a 10# K-file for apical patency and various hand and nickel-titanium instruments for canal preparation. Generally, there was slightly reduced post-operative pain reported through the maintenance of patency. This was evident in the initial post-operative period of 24 h (OR = 1.69, P = 0.002), 1-day (MD = -1.69, P = 0.03) and 2-days (MD = -0.85, P = 0.04), but became non-significant over the remainder of the 7-day monitoring period. There was no significant difference in analgesic use between groups (OR = 0.82, P = 0.42). Studies were classified into three groups based on quality: four high quality with low risk of bias; three moderate quality with some concerns; and five low quality with high risk of bias. Evidence quality varied; pain scores on days 1 and 2 were of low quality, with very low quality for 12 h and days 3-6. High heterogeneity and imprecision were noted, especially in pain measurements and analgesic use. The meta-analysis indicates that during the initial post-operative period, apical patency significantly reduces postoperative pain. However, the findings should be interpreted cautiously due to high heterogeneity and variable evidence quality. Given that apical patency is seen as desirable for achieving the biological aims of endodontic treatment, the fact that it also appears to reduce post-operative pain helps to align patient reported outcome measures with traditional measures indicating success.

Urban nighttime aerosol optical depth retrieval methods from SDGSAT-1 glimmer data

ABSTRACT Aerosol Optical Depth (AOD) is vital for assessing air quality and climate. Nighttime lights (NTL) provide a new data source for urban AOD inversion, but current NTL data suffer from low spatial resolution, limiting detailed AOD studies in smaller urban areas and hindering high-precision AOD algorithms. This study introduces methods for retrieving AOD using Glimmer Imager (GLI) data from the newly launched SDGSAT-1. Three methods were proposed: the standard deviation method, look-up-table method, and collaborative inversion method with VIIRS Day Night Band (DNB) data. Applied to Beijing, these methods produced a 10 m resolution AOD product. The look-up-table method achieved the highest correlation (R = 0.8876) compared to the standard deviation (R = 0.8224) and collaborative inversion (R = 0.8322) methods. The look-up-table method performed best with ample data, while the collaborative inversion method proved more reliable with limited data. These findings demonstrate the effectiveness of SDGSAT-1 GLI data in accurately retrieving nighttime AOD, offering significant potential for monitoring urban pollution.

Spatial and frequency identification of the dynamic properties of thin plates with the Frequency-Adapted Virtual Fields Method

Vibroacoustic inverse methods use the measured response of a vibrating structure to identify a structural parameter or a dynamic load. Two inverse methods are considered, the Force Analysis Technique (FAT) and the Virtual Fields Method (VFM). The Corrected Force Analysis Technique (CFAT) is a variant of FAT that corrects its singularity. This correction allows the method to be applied in the high-frequency domain, when the number of measurement points per flexural wavelength becomes small. In this study, the proposed novelty is the development of a Frequency-Adapted VFM (FA VFM) to the case of a Love–Kirchhoff plate. Thanks to this method, the VFM can now be applied to identify the equivalent bending stiffness and structural damping of a thin plate when the number of measurement points per wavelength is small. The method has previously been developed for an Euler–Bernoulli beam. An experimental identification of the complex bending stiffness of an locally damped aluminium plate using Laser Doppler Velocimetry (LDV) data and the developed method is performed. The experimental study shows for the first time that the FA VFM can be used to map the equivalent bending stiffness and structural damping as a function of position on a plate and identify these parameters as a function of frequency over a large frequency band. The results of the Frequency-Adapted VFM are compared with those of CFAT and the classical VFM approach. FA VFM results are more accurate than those of classical VFM and similar to those of CFAT.

Structural Modifications due to Bi-Doping in MAPbBr3 Single Crystals and Their Impact on Electronic Transport and Stability.

Doping strategy in lead halide perovskites is essential to enhance its optoelectrical properties and expand the potential applications. In this work, the mechanisms, for how dopants affect the overall structural, optical, electrical, and chemical properties and stability of lead halide perovskite materials, are investigated. This is done by specifically considering various bismuth (Bi)doping concentrations in MAPbBr3 single crystals grown using the inverse temperature crystallization method. The resultant doped single crystals exhibit a saturation point when Bi concentration exceeds 0.063% which is considered an optimum doping point. The highest thermal stability is also achieved at this doping concentration among the doped single crystals. This study clearly identifies how Bi doping affects the properties of MAPbBr3 and extends to consider stability, which has not been fully considered for MA-based perovskites previously. This will provide a clear understanding of evaluating doped perovskite materials for enhanced material properties, device performance, and stability.

Preparation and characterization of waterborne silicone modified epoxy resin

AbstractSilicone modified epoxy resin was synthesized with bisphenol‐A epoxy resin (E51) and dimethyldichlorosilane (DMDCS), and waterborne silicone modified epoxy resin (WSER) was prepared. The WSER emulsion exhibited good stability with particle size ranging from 0.5 to 2 μm. Compared with waterborne epoxy resin (WER) cured film, the water resistance of WSER cured film was enhanced by the excellent hydrophobicity of DMDCS. Furthermore, the WSER cured film with 6% DMDCS showed better mechanical properties, and Shore hardness and tensile strength of that were greatly increased. Moreover, the corrosion resistance and ultraviolet (UV) aging resistance of WSER cured film were obviously improved compared to WER cured film.Highlights Dimethyldichlorosilane (DMDCS) was employed to modify bisphenol‐A epoxy resin (E51). Waterborne silicone modified epoxy resin (WSER) was prepared by phase inversion method. WSER cured films did better than WER cured film in water resistance and mechanical properties. WSER cured films showed good corrosion resistance and ultraviolet aging resistance.

One‐dimensional deep learning inversion of marine controlled‐source electromagnetic data

AbstractThis paper explores the application of machine learning techniques, specifically deep learning, to the inverse problem of marine controlled‐source electromagnetic data. A novel approach is proposed that combines the convolutional neural network and recurrent neural network architectures to reconstruct layered electrical resistivity variation beneath the seafloor from marine controlled‐source electromagnetic data. The approach leverages the strengths of both convolutional neural network and recurrent neural network, where convolutional neural network is used for recognizing and classifying features in the data, and recurrent neural network is used to capture the contextual information in the sequential data. We have built a large synthetic dataset based on one‐dimensional forward modelling of a large number of resistivity models with different levels of electromagnetic structural complexity. The combined learning of convolutional neural network and recurrent neural network is used to construct the mapping relationship between the marine controlled‐source electromagnetic data and the resistivity model. The trained network is then used to predict the distribution of resistivity in the model by feeding it with marine controlled‐source electromagnetic responses. The accuracy of the proposed approach is examined using several synthetic scenarios and applied to a field dataset. We explore the sensitivity of deep learning inversion to different electromagnetic responses produced by resistive targets distributed at different depths and with varying levels of noise. Results from both numerical simulations and field data processing consistently demonstrate that deep learning inversions reliably reconstruct the subsurface resistivity structures. Moreover, the proposed method significantly improves the efficiency of electromagnetic inversion and offers significant performance advantages over traditional electromagnetic inversion methods.

Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating and the virtual fields method, MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods—specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models—we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations. Commensurate extensometer strain data from tensile dog bones is provided as well for comparison. We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods.

Spiralling Inverse Method: A new inverse method to estimate ocean mixing

Abstract Here we introduce the Spiralling Inverse Method (SIM) that provides estimates of the small-scale and mesoscale mixing strength. The SIM uses a vertical integral over a balance between the watermass transformation equation and the thermal wind equation. The result is an equation where all terms, except for the mixing strengths, can be obtained from hydrographic data of temperature and salinity. As an advantage, the SIM estimates the mixing strengths without the need of further knowledge of a reference velocity or streamfunction. Here we apply the SIM to a small region in the North Atlantic. We find that the estimates obtained by the SIM compare well to observations and other (inverse) estimates of the mixing strength. The SIM therefore has potential to improve and constrain parameterizations used for climate and ecosystem modelling using readily available hydrographic data.

Polysulfone (Psf) Mixed Matrix Membrane incorporating Titanium Dioxide (TiO2)/Polyethylene Glycol (PEG) for the removal of copper

The global increasing contamination of water resources with toxic metals such as copper (Cu), poses severe threats to human health and aqueous ecosystems. Therefore, the ultrafiltration mixed matrix membranes (UF MMMs) possess an applicable approach for the removal of copper ions. This novel fabricated technology can be applied in various wastewater treatment systems for the removal of heavy metals, especially copper. MMMs were fabricated by blending polysulfone (Psf) with additives into the dope solution via the phase inversion method by incorporating titanium dioxide (TiO2) and polyethylene glycol (PEG) in Psf MMMs. Seven Psf MMMs samples labelled M0 to M6, each with its own formulation, were prepared and tested for density, porosity, and degree of Cu retention. MMMs were further characterized via Fourier transform infrared spectroscopy (FTIR), which revealed the range of the IR spectrum of Psf polymer membrane from 1319 cm-1 to 1600 cm-1, 1650 cm-1 to 3400 cm-1 for PEG, and 800 cm-1 to 3600 cm-1 for TiO2 NPs. As for the scanning electron microscopy (SEM), M6 (Psf/TiO2/PEG 6000) was found to be the most dense and highest porous morphology asymmetric Psf MMM. The retained percentage of Cu and flux for M6 attained the highest value of 80.3% and 136.99 L/m2 .h respectively, whereas for the neat Psf membrane, M0 exhibited the lowest retained percentage of Cu and flux, about 25.8% and 61.64 L/m2.h. The inclusion of pore former and additives has shown an improvement of 54.5% in the copper rejection. Moreover, M6 displayed the highest antifouling properties compared to other Psf MMMSs. This study proves that PEG and TiO2 additives have significant potential to improve membrane performance due to the highest percentage of Cu retained on the surface of the membrane as adsorptive separation on Psf MMMs.

Research on defect image inversion method based on multiple lift-off values magnetic memory signals

Metal magnetic memory (MMM) technique can detect macroscopic defects and stress concentration zones of ferromagnets, and it can identify the approximate positions of these flaws, while a little further information about defects characteristics can be provided. To promote study in this area, defects were modeled as located magnetic dipoles whose strength and locations should be determined, and the technique of truncated generalized inverse and reduced space inversion were used to image the dipole strength. Through the simulation experiment, we found that the inversion image quality was influenced by the lift-off value. When the magnetic field data for inversion was measured with a small lift-off value, the inner dipoles contributions were easily buried and the inversion image can only shown the surface dipole well. In contrast, if the magnetic field data was measured with a greater lift-off value, it was possible to inverse the deep dipoles while the image was somewhat out of focus. To overcome this limitation, we composed the magnetic field data measured under different lift-off values together for inversion. We used the same model to test this approach and applied it on real crack defects magnetic field data. It demonstrates that the new inversion approach shows better accuracy than the single lift-off value and it was possible to infer the location and size of defect by the evaluation of preset dipoles.

Ensemble Machine-Learning-Based Framework for Estimating Surface Soil Moisture Using Sentinel-1/2 Data: A Case Study of an Arid Oasis in China

Soil moisture (SM) is a critical parameter in Earth’s water cycle, significantly impacting hydrological, agricultural, and meteorological research fields. The challenge of estimating surface soil moisture from synthetic aperture radar (SAR) data is compounded by the influence of vegetation coverage. This study focuses on the Weigan River and Kuche River Delta Oasis in Xinjiang, employing high-resolution Sentinel-1 and Sentinel-2 images in conjunction with a modified Water Cloud Model (WCM) and the grayscale co-occurrence matrix (GLCM) for feature parameter extraction. A soil moisture inversion method based on stacked ensemble learning is proposed, which integrates random forest, CatBoost, and LightGBM. The findings underscore the feasibility of using multi-source remote sensing data for oasis moisture inversion in arid regions. However, soil moisture content estimates tend to be overestimated above 10% and underestimated below 5%. The CatBoost model achieved the highest accuracy (R2 = 0.827, RMSE = 0.014 g/g) using the top 16 feature parameter groups. Additionally, the R2 values for Stacking1 and Stacking2 models saw increases of 0.008 and 0.016, respectively. Thus, integrating multi-source remote sensing data with Stacking models offers valuable support and reference for large-scale estimation of surface soil moisture content in arid oasis areas.

Hydrochemical characterization and health risk assessment of shallow groundwater in a northern coalfield of Anhui Province, China

In a global context, the hydrochemical characteristics of shallow groundwater in coalfields exhibit high degrees of diversity and complexity that are rooted in the intricate interplay of geological variations, diverse climatic conditions, and extensive human activities. The specific types and concentrations of ions, such as Ca2+, Cl−, and NO3−, show stark differences across geographical regions. Given the crucial roles of coalfields as energy suppliers, the potential environmental contamination risks posed by mining activities to groundwater cannot be overlooked as such pollution directly impacts human health and ecological safety. This study focuses on the Huainan coal mining area in northern Anhui Province (China), where shallow groundwater samples were systematically collected and analyzed to determine the hydrochemical characteristics and ascertain the water quality status. By integrating hydrogeochemical analysis techniques with inverse modeling methods, it was revealed that the groundwater in this region is predominantly HCO3-Ca type, exhibiting weak alkaline characteristics. The formation mechanisms are primarily governed by silicate rock weathering and mineral dissolution–precipitation processes, albeit with discernible influences from human activities. PHREEQC simulations were used to further confirm the precipitation tendencies of minerals like calcite, dolomite, and fluorite as well as the significant dissolution characteristics of halite. The inverse modeling pathway analysis reveals specific hydrochemical processes along different paths: paths I and IV are notably dominated by Ca2+ dissolution–precipitation and cation exchange–adsorption processes, whereas paths II and III are closely associated with the precipitation of calcium montmorillonite as well as dissolution of kaolinite, calcite, quartz, and mineral incongruents. Moreover, evaluations based on the entropy-weighted water quality index (EWQI) indicated overall positive trends of the groundwater quality indicators within the Huainan mining area, reflecting the effectiveness of regional water quality management efforts and providing a scientific basis for future water quality protection and improvement strategies. In summary, this study not only deepens our understanding of the groundwater chemistry in the Huainan coal mining area but also underscores the importance of scientifically assessing and managing groundwater resources to address the environmental challenges potentially arising from coal mining activities.

Joint Inversion of DC Resistivity and Gravity Data with Undulating Terrain Based on Deformed Hexahedral Mesh

In the field of mineral resource exploration, accurate imaging of subsurface structures is key to discovering and assessing potential mineral deposits. Traditional single geophysical methods, limited by terrain variations and their own constraints, can lead to divergent solutions and structural inconsistencies, affecting the reliability of exploration outcomes. To address these challenges, this paper presents a joint inversion method for three-dimensional direct current (DC) resistivity and gravity data based on a deformed hexahedral mesh. The article begins by outlining the current state of development of the method under study and proposes a research plan, followed by a detailed explanation of the theoretical basis and algorithmic implementation of the proposed method. Model tests confirm the advantages of the deformed hexahedral mesh in reducing terrain impacts and enhancing model resolution, demonstrating the optimization and complementarity of the resolution between the two methods after joint inversion. Finally, applying this method to actual data from the Huaniu Mountain area shows that joint inversion not only improves the consistency of the ore belt structure but also provides a more precise analysis for the quantitative interpretation of the distribution of underground mineral resources. This confirms the method’s effectiveness and potential in practical geological exploration.

Interferometry of ionospheric E-region irregularities based on Kunming VHF radars

There is a long history of using VHF radar systems to detect ionospheric irregularities based on the theory of coherent scattering. According to previous work, there is a high occurrence of field-aligned irregularities (FAIs) in the ionospheric E-region over Kunming, China. In this paper, the VHF coherent scattering radar at Kunming is used to study the FAIs in the ionospheric E-region. Different arrangement of VHF radar antenna arrays, interferometry, and FAI echo parameter inversion methods are designed and tested. The measurement results show that the temporal and spatial characteristics of the irregularities can be obtained using these methods, as well as more refined spatial three-dimensional structure information. It is indicated that the new arrangement of the VHF radar antenna array is feasible to operate interferometry detection of E-region FAIs with the Kunming VHF radar.

Design and experimental verification of an S-shaped continuous weft insertion system for soft–hard blended preform narrow channels

To address the limitations of the existing weft insertion technology in inserting carbon fiber bundles into the narrow channels of soft–hard blended preform, a proposed solution is to use a continuous weft insertion trajectory based on an S-shaped path for narrow channels. A weft insertion system that meets the process requirements has been designed. The analysis of the functional requirements for the continuous weft insertion system has identified five key nodes for the system, and the motion paths of each mechanism have been established. Through design methods such as analytical methods, rigid inversion methods, and mechanical analysis, the modular design of the system and the planning of the timing sequence for multiple mechanisms have been completed. Weft insertion experiments have been conducted on a continuous weft insertion system experimental platform, and the results demonstrate that the weft insertion system can effectively introduce carbon fiber bundles into the narrow channels, forming the expected S-shaped continuous weft insertion trajectory. This study verifies the feasibility of the designed weft insertion system and the accuracy of the continuous weft insertion theory, providing a viable solution for continuous weft insertion in the narrow channels of soft–hard blended preforms.

Efficient Inverse Design of Large-Scale, Ultrahigh-Numerical-Aperture Metalens

Efficient design methods for large-scale metalenses are crucial for various applications. The conventional phase-mapping method shows a weak performance under large phase gradients, thus limiting the efficiency and quality of large-scale, high-numerical-aperture metalenses. While inverse design methods can partially address this issue, existing solutions either accommodate only small-scale metalenses due to high computational demands or compromise on focusing performance. We propose an efficient large-scale design method based on an optimization approach combined with the adjoint-based method and the level-set method, which first forms a one-dimensional metalens and then extends it to two dimensions. Taking fabrication constraints into account, our optimization method for large-area metalenses with a near-unity numerical aperture (NA = 0.99) has improved the focusing efficiency from 42% to 60% in simulations compared to the conventional design method. Additionally, it has reduced the deformation of the focusing spot caused by the ultrahigh numerical aperture. This approach retains the benefits of the adjoint-based method while significantly reducing the computational burden, thereby advancing the development of large-scale metalenses design. It can also be extended to other large-scale metasurface designs.

Association Between P-Wave Duration, Dispersion, and Interatrial Block and Atrial High-Rate Episodes in CIED Patients.

Atrial high-rate episodes (AHRE) have been linked to increased thromboembolic risk and all-cause mortality in patients with cardiac implantable electronic devices (CIEDs). Various predictors of AHRE development have been identified, emphasizing the need for close monitoring and the potential transition to clinical atrial fibrillation (AF). However, the predictive value of P wave characteristics on AHRE development remains conflicting. This meta-analysis aims to summarize existing data to investigate this association. We examined studies from MEDLINE and EMBASE databases up to May 2024 to investigate the association of baseline P-wave duration (PWD), P-wave dispersion (PWDIS), and interatrial block (IAB) with the risk of developing AHRE. We extracted the mean and standard deviations of PWD and PWDIS to calculate the pooled mean difference (MD). Risk ratios (RR) and 95% confidence intervals (CIs) were used to assess the association between IAB and AHRE risk, using the generic inverse variance method for combination. The meta-analysis included nine studies. Patients with AHRE had longer PWD and PWDIS compared to those without AHRE, with a pooled MD for PWD of 9.17ms (95% CI: 4.74-13.60; I2 = 47%, p < 0.001) and a pooled MD for PWDIS of 20.56ms (95% CI: 11.57-29.56; I2 = 57%, p < 0.001). Additionally, patients with IAB had a higher risk of developing AHRE, with a pooled RR of 3.33 (95% CI: 2.53-4.38; I2 = 0%, p < 0.001), compared to those without IAB. Our meta-analysis found that patients with AHRE had higher PWD and PWDIS than those without AHRE. Additionally, IAB was associated with a higher risk of developing AHRE. These findings emphasize the importance of close monitoring and risk stratification, particularly for patients with P wave abnormalities.