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Spatiotemporal correlation function is the basic characteristic of dynamic laser speckle and the basis of various applications. The correlation function of the speckle intensity from a 3D target in turbulent atmosphere is derived based on the model of random phase screen and Fresnel Kirchhoff Diffraction Formula, and a fast algorithm based on FFT is developed. The particularity of dynamic speckle in turbulence is numerically analyzed and discussed. The results show that the speckle intensity fluctuates at two independent scales both in space and time domain, which are affected by target size and atmospheric turbulence respectively. In particular, the time scale caused by turbulence is also affected by the target translating velocity and the wind direction. The theory and algorithm developed in this paper can play important roles in applications of laser speckle such as remote detection in atmospheric environment.

Objective: To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preventive medications. Background: Currently, there is no accurate way to predict response to migraine preventive medications, and the standard trial‐and‐error approach is inefficient. Methods: In this cohort study, we analyzed data from the Mayo Clinic Headache database prospectively collected from 2001 to December 2023. Adult patients with migraine completed questionnaires during their initial headache consultation to record detailed clinical features and then at each follow‐up to track preventive medication changes and monthly headache days. We included patients treated with at least one of the following migraine preventive medications: topiramate, beta‐blockers (propranolol, metoprolol, atenolol, nadolol, timolol), tricyclic antidepressants (amitriptyline, nortriptyline), verapamil, gabapentin, onabotulinumtoxinA, and calcitonin gene‐related peptide (CGRP) monoclonal antibodies (mAbs) (erenumab, fremanezumab, galcanezumab, eptinezumab). We pre‐trained a deep neural network, "TabNet," using 145 variables, then employed TabNet‐embedded data to construct prediction models for each medication to predict binary outcomes (responder vs. non‐responder). A treatment responder was defined as having at least a 30% reduction in monthly headache days from baseline. All model performances were evaluated, and metrics were reported in the held‐out test set (train 85%, test 15%). SHapley Additive exPlanations (SHAP) were conducted to determine variable importance. Results: Our final analysis included 4260 patients. The responder rate for each medication ranged from 28.7% to 34.9%, and the mean time to treatment outcome for each medication ranged from 151.3 to 209.5 days. The CGRP mAb prediction model achieved a high area under the receiver operating characteristics curve (AUC) of 0.825 (95% confidence interval [CI] 0.726, 0.920) and an accuracy of 0.80 (95% CI 0.70, 0.88). The AUCs of prediction models for beta‐blockers, tricyclic antidepressants, topiramate, verapamil, gabapentin, and onabotulinumtoxinA were: 0.664 (95% CI 0.579, 0.745), 0.611 (95% CI 0.562, 0.682), 0.605 (95% CI 0.520, 0.688), 0.673 (95% CI 0.569, 0.724), 0.628 (0.533, 0.661), and 0.581 (95% CI 0.550, 0.632), respectively. Baseline monthly headache days, age, body mass index (BMI), duration of migraine attacks, responses to previous medication trials, cranial autonomic symptoms, family history of headache, and migraine attack triggers were among the most important variables across all models. A variable could have different contributions; for example, lower BMI predicts responsiveness to CGRP mAbs and beta‐blockers, while higher BMI predicts responsiveness to onabotulinumtoxinA, topiramate, and gabapentin. Conclusion: We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine‐specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible. Plain Language Summary: Currently, there is no way to predict whether preventive medications, which are medications that may reduce the frequency and severity of headaches, will be helpful to all patients. We used novel artificial intelligence techniques and developed effective tools that can predict how a patient responds to seven different types of migraine preventive medications before the medication is started. We also found that the ways in which patients describe their headache and the types of symptoms they have is important to predict whether the medication will work. [ABSTRACT FROM AUTHOR]

Corrosion of steel bars embedded in reinforced concrete structures considerably reduces the service life and even causes early failure of the structure. This paper investigates the early corrosion behavior of an alloy corrosion-resistant steel bar in seawater–sea sand concrete (SWSSC) using various electrochemical measurements [electrochemical impedance spectroscopy (EIS) linear polarization resistance (LPR)] and surface characterization techniques [scanning electron microscope-energy dispersive spectroscopy (SEM-EDS), atomic force microscope (AFM), and X-ray photoelectron spectroscopy (XPS)], in comparison to low carbon steel bar (LC) and stainless steel bar (SS). The results reveal that the Cr10Mo1 alloy corrosion-resistant steel bar (CR) in SWSSC shows significant increase in the radius of the capacitive arc in later stages, particularly in the low frequency region. This increase in CR can be attributed to the enhanced corrosion resistance of the passivation film, as well as the rise of impedance at the interface between the passivation film and the concrete matrix. These improvements have effectively inhibited the penetration of free chloride ions into the concrete, thereby impeding the initiation and propagation of corrosion in CR In addition, the mechanical properties, pore structure and the hydration production of SWSSC were investigated. Based on the interactions between steel substrate and concrete matrix, the possible resistance mechanism of CR was proposed. This study innovatively provides a comprehensive evaluation of the effectiveness of CR in SWSSC. [ABSTRACT FROM AUTHOR]

Forests are an important part of natural resources and play an important role in carbon sinks. We measured carbon sinks in provincial forest parks using data from four forest inventory surveys in China and the forest stock expansion method. Carbon emissions from forest tourism were also estimated using energy statistics and forest park tourism data. On this basis, spatial analysis was used to summarize the spatial and temporal evolution of the carbon balance and the analysis of influencing factors. The results show the following: (1) With the passage of time, the carbon emissions from forest tourism in all provinces have increased to different degrees, and the national forest tourism carbon emissions have increased from 1,071,390.231 (million tons) in 2003 to 286,255,829.7 (million tons) in 2018; spatially, the distribution of carbon emissions from forest tourism is uneven, with an overall high in the south and low in the north, and a high in the east and a low in the west. (2) The carbon sink of forest parks showed a trend of gradual growth and spatially formed a spatial pattern of high in the northeast and low in the southwest, which is consistent with the distribution of forest resources in China. (3) For forest tourism carbon emissions, the total number of tourists, tourism income, and playing roads are significant influencing factors, and the baseline regression coefficients are 0.595, 0.433, and 0.799, respectively, while for forest park carbon sinks, the number of forest park employees can play a certain positive role in carbon sinks, with the regression coefficient being 1.533. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have made considerable advances in hyperspectral image (HSI) classification. However, most CNN-based methods learn features at a single-scale in HSI data, which may be insufficient for multi-scale feature extraction in complex data scenes. To learn the relations among samples in non-grid data, GCNs are employed and combined with CNNs to process HSIs. Nevertheless, most methods based on CNN-GCN may overlook the integration of pixel-wise spectral signatures. In this paper, we propose a pyramid cascaded convolutional neural network with graph convolution (PCCGC) for hyperspectral image classification. It mainly comprises CNN-based and GCN-based subnetworks. Specifically, in the CNN-based subnetwork, a pyramid residual cascaded module and a pyramid convolution cascaded module are employed to extract multiscale spectral and spatial features separately, which can enhance the robustness of the proposed model. Furthermore, an adaptive feature-weighted fusion strategy is utilized to adaptively fuse multiscale spectral and spatial features. In the GCN-based subnetwork, a band selection network (BSNet) is used to learn the spectral signatures in the HSI using nonlinear inter-band dependencies. Then, the spectral-enhanced GCN module is utilized to extract and enhance the important features in the spectral matrix. Subsequently, a mutual-cooperative attention mechanism is constructed to align the spectral signatures between BSNet-based matrix with the spectral-enhanced GCN-based matrix for spectral signature integration. Abundant experiments performed on four widely used real HSI datasets show that our model achieves higher classification accuracy than the fourteen other comparative methods, which shows the superior classification performance of PCCGC over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions from planting (PCE) is crucial for achieving the "dual carbon" objective within the agricultural sector. This paper aims to investigate the impact of agricultural productive services (APSs) on carbon emissions in the planting industry, providing novel insights into reducing carbon emissions in this field. Based on the panel data of 30 provinces and regions in China from 2005 to 2021, this study measured the PCE and the level of APSs. The internal relationship between the APSs and PCE is subsequently examined empirically using various statistical models, including the fixed effect model, instrumental variable model, mediating effect model, and threshold effect model. The findings suggest that the PCE experienced an initial increase, followed by a subsequent decrease between 2005 and 2021, with the peak observed in 2015. APSs has a significant inhibitory effect on PCE, which remains significant even after conducting various robustness tests. However, the carbon emission reduction effect of APSs varies across different crop-producing areas. The study also revealed that APSs can inhibit PCE by facilitating land transfer. Additionally, the inhibitory effect of APSs on PCE is influenced by the scale of land management and exhibits a U-shaped nonlinear relationship. To effectively reduce PCE and achieve sustainable agricultural development, policymakers should actively promote the integration of APSs with modern agriculture and form moderate-scale operations by facilitating land transfer to maximize APSs' carbon emission reduction effect. [ABSTRACT FROM AUTHOR]

In this study, a series of polyethylene glycol (PEG)/hydrochloric acid-modified sepiolite (HSEP) composite phase change materials (PCMs) are fabricated via vacuum impregnation. HSEP exhibits high adsorption capacity, rendering it superior to natural sepiolite as carriers for PEG. The resulting composite PCMs possess a melting enthalpy of up to 88.9 J/g and maintain stable thermal performances and chemical structures over 100 heating–cooling cycles between room temperature and 65 °C, thus, indicating long-term reliability. Calorimetry studies on cement paste containing 30% composite PCMs reveal a 24.14% reduction in 3-day cumulative hydration heat. However, the mechanical strength and thermal conductivity of the cement paste are adversely affected. Hence, carbon fibers (CFs) are introduced as reinforcement, resulting in a 28-day compressive strength of 45.6 MPa for cement paste containing 20% composite PCMs and 0.6% CFs. The fabricated composite PCMs are promising functional materials for hydration heat control and energy storage in concrete structures. [ABSTRACT FROM AUTHOR]