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New advances in top-down construction are developing with the construction of supertall buildings in China. This study presents a finite element analysis for high-rise structural construction with a top-down method (TDM) considering complex environmental conditions. Based on this analysis model, the forces and the deformation of the diaphragm wall, beams, and soldier piles at various stages of construction are computed. Taking a super high-rise building with a 5-story basement in Nanjing as an example, the reliability and accuracy of the model is verified by comparing the measured and simulated results of displacement and stress values at various locations. The research results reveal the relationship between excavation depth, soil settlement and pile displacement, which is convenient for finding the optimal construction critical surface. It lays a foundation for the study of the critical height of the subsequent construction and facilitates the prediction of the weak link in the process. At present, this project is under construction, so this study has reference value for subsequent construction projects. [ABSTRACT FROM AUTHOR]

As a key component of electronic skins, flexible pressure sensors have attracted more and more attention because of the increasingly growing demand. Stability is a key parameter to evaluate pressure sensors, while relatively few reports have focused on it. Here, a paper-based piezoresistive sensor is developed, in which, the airlaid paper based sensing layer is modified with silver nanowires (AgNWs) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and sandwiched in between two convex electrodes. Due to the cross bonding of PEDOT:PSS membrane, the conductive paths of AgNWs networks are strengthened and stabilized, thus the stability of the sensor is found to be significantly improved. Besides, to regulate the compressibility by varying sensing layers, the performance of the proposed sensor can be further improved, and its practical application performances in healthcare pulse monitoring, tiny muscle motion, and voice recognition are demonstrated. The results confirm that PEDOT:PSS has the potential as stabilization media to AgNWs for paper-based flexible wearable electronics. [ABSTRACT FROM AUTHOR]

Purpose: To explore interpretable machine learning (ML) methods, with the hope of adding more prognosis value, for predicting survival for patients with Oropharyngeal-Cancer (OPC). Methods: A cohort of 427 OPC patients (Training 341, Test 86) from TCIA database was analyzed. Radiomic features of gross-tumor-volume (GTV) extracted from planning CT using Pyradiomics, and HPV p16 status, etc. patient characteristics were considered as potential predictors. A multi-level dimension reduction algorithm consisting of Least-Absolute-Selection-Operator (Lasso) and Sequential-Floating-Backward-Selection (SFBS) was proposed to effectively remove redundant/irrelevant features. The interpretable model was constructed by quantifying the contribution of each feature to the Extreme-Gradient-Boosting (XGBoost) decision by Shapley-Additive-exPlanations (SHAP) algorithm. Results: The Lasso-SFBS algorithm proposed in this study finally selected 14 features, and our prediction model achieved an area-under-ROC-curve (AUC) of 0.85 on the test dataset based on this feature set. The ranking of the contribution values calculated by SHAP shows that the top predictors that were most correlated with survival were ECOG performance status, wavelet-LLH_firstorder_Mean, chemotherapy, wavelet-LHL_glcm_InverseVariance, tumor size. Those patients who had chemotherapy, with positive HPV p16 status, and lower ECOG performance status, tended to have higher SHAP scores and longer survival; who had an older age at diagnosis, heavy drinking and smoking pack year history, tended to lower SHAP scores and shorter survival. Conclusion: We demonstrated predictive values of combined patient characteristics and imaging features for the overall survival of OPC patients. The multi-level dimension reduction algorithm can reliably identify the most plausible predictors that are mostly associated with overall survival. The interpretable patient-specific survival prediction model, capturing correlations of each predictor and clinical outcome, was developed to facilitate clinical decision-making for personalized treatment. [ABSTRACT FROM AUTHOR]

With the rapid development of computer vision and artificial intelligence technology, visual object detection has made unprecedented progress, and small object detection in complex scenes has attracted more and more attention. To solve the problems of ambiguity, overlap and occlusion in small object detection in complex scenes. In this paper, a multi-scale fusion feature enhanced path aggregation network MSFE-PANet is proposed. By adding attention mechanism and feature fusion, the fusion of strong positioning information of deep feature map and strong semantic information of shallow feature map is enhanced, which helps the network to find interesting areas in complex scenes and improve its sensitivity to small objects. The rejection loss function and network prediction scale are designed to solve the problems of missing detection and false detection of overlapping and blocking small objects in complex backgrounds. The proposed method achieves an accuracy of 40.7% on the VisDrone2021 dataset and 89.7% on the PASCAL VOC dataset. Comparative analysis with mainstream object detection algorithms proves the superiority of this method in detecting small objects in complex scenes. [ABSTRACT FROM AUTHOR]

Lasers have become an important technology for the medical treatment of cancer. However, the high power of visible laser knives makes conventional endoscopic imaging light pollution severe. This phenomenon can cause the physician to be unable to determine the condition of the lesion site. In this article, a colorization method for laser surgery videos is proposed. The method performs real‐time colorization of the laser surgery video under black‐and‐white imaging. The main contribution of this work is to propose a new idea for solving laser contamination in medical surgery videos, along with real‐time generative adversarial networks (RTGANs). The network takes advantage of generative adversarial networks and residual networks for fast colorization of black and white images. To evaluate the method, we conducted experiments using three datasets. The experimental results show that the RTGAN network takes only 0.026 s to colorize a single frame image. Coloring speed up to 67 times faster than advanced methods, and the Peak Signal to Noise Ratio reached a maximum of 35.736. [ABSTRACT FROM AUTHOR]

Hand pose estimation is a fundamental task in computer vision with applications in virtual reality, gesture recognition, autonomous driving, and virtual surgery. Keypoint detection often relies on deep learning methods and high-resolution feature map representations to achieve accurate detection. The HRNet framework serves as the basis, but it presents challenges in terms of extensive parameter count and demanding computational complexity due to high-resolution representations. To mitigate these challenges, we propose a lightweight keypoint detection network called LGCANet (Lightweight Ghost-Coordinate Attention Network). This network primarily consists of a lightweight feature extraction head for initial feature extraction and multiple lightweight foundational network modules called GCAblocks. GCAblocks introduce linear transformations to generate redundant feature maps while concurrently considering inter-channel relationships and long-range positional information using a coordinate attention mechanism. Validation on the RHD dataset and the COCO-WholeBody-Hand dataset shows that LGCANet reduces the number of parameters by 65.9% and GFLOPs by 72.6% while preserving the accuracy and improves the detection speed. [ABSTRACT FROM AUTHOR]

Classification of imbalanced data is an important challenge in current research. Sampling is an important way to solve the problem of imbalanced data classification, but some traditional sampling algorithms are susceptible to outliers. Therefore, an iF-ADASYN sampling algorithm is proposed in this paper. First, based on the ADASYN algorithm, we introduce the isolation Forest algorithm to overcome its vulnerability to outliers. Then, a calculation method of anomaly index which can delete outliers accurately of minority data is presented. The experimental results of four UCI public imbalanced datasets show that the algorithm can effectively improve the accuracy of the minority class, and increase the stability. In the real thrombus dataset, the AUC value of the iF-ADASYN algorithm is more significant than that of SMOTE and ADASYN algorithms, and the recognition rate of patients with thrombosis increased by 20%. The iF-ADASYN algorithm obtains better resistance to outliers than the original ADASYN algorithm. Meanwhile, it improves the accuracy of minority class decision boundary region division. [ABSTRACT FROM AUTHOR]

With the increasing popularity and widespread application of embedded devices, lightweight target tracking networks have emerged as a crucial research direction in the field of computer vision. In this paper, we propose a deformable attention object tracking network based on cross-correlation (DAM-LOTNet), which utilizes the lightweight MobileNetV3 network for efficient feature extraction. Aiming at the problem that lightweight networks cannot better capture target features and morphological changes, deformable attention modules are proposed to generate attention maps along the channel and spatial dimensions. Furthermore, a dual cross-correlation operation is proposed, aiming at fusing channel information and pixel-level information. Thus, the model obtains a more comprehensive feature representation and a more accurate similarity calculation capability while maintaining a lightweight framework. Finally, a feature enhancement network constructed by depthwise separable convolution was added to the classification and regression branches. The designed DAM-LOTNet has 0.552M parameters and 0.121G FLOPs. On the UAV123 dataset, DAM-LOTNet achieves state-of-the-art performance compared to other lightweight object tracking algorithms. Extensive experiments on visual tracking benchmarks (including VOT2018, VOT2019, OTB100, etc.) have demonstrated that DAM-LOTNet has extremely low model FLOPs and parameters compared to common deep learning-based trackers, and is essentially on par with these trackers in terms of performance. • It has excellent tracking performance under the premise of lightweight. • Deformable attention module to better focus on the content of the deformed object. • Dual cross-correlation fuses channel information and pixel information. • Improve the model's ability to perceive local and global information. [ABSTRACT FROM AUTHOR]

• Self-healing reinforced concrete beams with engineered aggregates were developed and tested. • Mechanical crack-healing is observed for the first time using capsule based healing methods. • Advanced measurement techniques: DIC and UPV were used to confirm self-healing capability. This paper presents a study on the self-healing performance of reinforced concrete (RC) beams using a new approach called engineered aggregates (EA). EA are a type of capsules that release healing agents when the concrete is cracked, without requiring any changes to the conventional concrete preparation methods. In this study, polyurethane (PU) filled EA were added to RC beams with dimensions of 127 mm × 203 mm × 1448 mm and three-point bending tests were conducted. Companion control tests were also conducted on identical beams to compare the self-healing performance of the concrete with and without EA. The development of cracks during the first (initial cracking) and second (after healing) stages was monitored using the digital image correlation (DIC) technique. The addition of EA did not have any impact on the load carrying capacity of the beams, and the DIC results showed that the cracks closed during healing reopened in subsequent testing. Ultrasonic measurements were taken before and after healing to determine the extent of crack healing. The ultrasonic pulse velocity (UPV) measurements and diffusion coefficient calculations showed that the RC beams with PU-filled EA healed the cracks. Water absorption tests were performed to determine the recovery of the original permeability after healing. The study demonstrated the effectiveness of EA in improving the self-healing performance of RC beams and its potential for practical application in the construction industry. This paper is the first member scale study for self-healing concrete with EA, which demonstrated successful filling and bonding of flexural cracks. [ABSTRACT FROM AUTHOR]

This paper presents the results from a new approach for self-healing concrete. Conventional macro-capsules such as those made of glass cannot resist the harsh mixing process of concrete and require changes to conventional preparation methods. Here, a new encapsulation method; namely, engineered aggregates (EAs) were developed and tested, which do not require any changes to concrete preparation methods. EAs were shown to resist the conventional concrete mixing methods and effectively deliver healing agents when cracks appear in concrete. The mechanical properties of the produced self-healing concrete were examined by performing uniaxial compression, split tensile, and four-point bending tests. The self-healing efficiency of the concrete with EAs containing different healing agents (polyurethane, super glue, sodium silicate, magnesium oxidate) was determined based on split tensile, water absorption and water permeability tests. It was seen that more than 50% of the original strength and stiffness could be regained after self-healing. It was also found that the water permeability could be reduced by a factor 102 to 104 due to autonomous crack healing. Although some further research is still necessary concerning functional life of encapsulated healing agents and healing of cracks less than 50 μm, self-healing EA concrete was shown to be a novel approach that could lead to changes in the concrete industry. [ABSTRACT FROM AUTHOR]

The existing self-healing concretes that rely on capsules require modifications to one or more traditional concrete mixing, placing, and consolidating methods. Additionally, concrete mixture designs need alterations to accommodate these self-healing capsules. This paper presents the development and characterization of a novel encapsulation method for self-healing concrete. This method, using engineered aggregates (EA), features macro-capsules with a cementitious coating, a brittle container, and a healing agent. Like coarse aggregates, the EA are randomly dispersed in the concrete, and they are added just like any other admixture during the mixing process. Lattice discrete particle models (LDPM) were developed employing randomly packed coarse aggregates and EA with different volume fractions, shapes, sizes, coating thicknesses, and different coating mortars. The models were calibrated using experimental results of split tensile, four-point bending, and uniaxial compression tests. The formation and propagation of cracks in the concrete matrix and EA were observed in the LDPM models. A detailed presentation of the stresses inside the EA and the concrete matrix was obtained. Stress concentration in the EA was affected by the strength of the coating mortar and the shape of EA. Parametric studies were conducted to understand the effect of volume fraction of EA, EA coating thickness, and strength on the overall mechanical behavior of concrete. The results suggest that using coating mortar with higher strength can increase the load-carrying capacity of the concrete, especially when the coating is thicker, but reduce the crack opening in the EA when the crack occurs. The concrete's strength increases with the cavity's size inside the EA at the same EA dosage. These simulations inform the EA design and develop an understanding of the effect of EA on the load-carrying capacity of concrete. [ABSTRACT FROM AUTHOR]

Aiming at accurate survival prediction of Glioblastoma (GBM) patients following radiation therapy, we developed a subregion-based survival prediction framework via a novel feature construction method on multi-sequence MRIs. The proposed method consists of two main steps: (1) a feature space optimization algorithm to determine the most appropriate matching relation derived between multi-sequence MRIs and tumor subregions, for using multimodal image data more reasonable; (2) a clustering-based feature bundling and construction algorithm to compress the high-dimensional extracted radiomic features and construct a smaller but effective set of features, for accurate prediction model construction. For each tumor subregion, a total of 680 radiomic features were extracted from one MRI sequence using Pyradiomics. Additional 71 geometric features and clinical information were collected resulting in an extreme high-dimensional feature space of 8231 to train and evaluate the survival prediction at 1 year, and the more challenging overall survival prediction. The framework was developed based on 98 GBM patients from the BraTS 2020 dataset under five-fold cross-validation, and tested on an external cohort of 19 GBM patients randomly selected from the same dataset. Finally, we identified the best matching relationship between each subregion and its corresponding MRI sequence, a subset of 235 features (out of 8231 features) were generated by the proposed feature bundling and construction framework. The subregion-based survival prediction framework achieved AUCs of 0.998 and 0.983 on the training and independent test cohort respectively for 1 year survival prediction, compared to AUCs of 0.940 and 0.923 for survival prediction using the 8231 initial extracted features for training and validation cohorts respectively. Finally, we further constructed an effective stacking structure ensemble regressor to predict the overall survival with the C-index of 0.872. The proposed subregion-based survival prediction framework allow us to better stratified patients towards personalized treatment of GBM. [ABSTRACT FROM AUTHOR]

• AgNPs/[Ru(phen) 3 ]Cl 2 /PVDF is fabricated as a thermal detector with enhanced signal-to-noise ratio. • Fluorescence enhancement (3.5 folds) and temperature sensitivity improvement (25%) are both realized. • Localized surface plasmon resonance of silver nanoparticles enhances luminescence and temperature sensitivity as well. [Display omitted] The temperature dependent photoluminescence of temperature sensitive paint (TSP) makes it an attractive candidate for infrared (IR) thermal imaging, but its low luminescence intensity and poor photostability hinder its extensive use in infrared detectors. Herein, a novel complex luminophore of AgNPs/[Ru(phen) 3 ]Cl 2 /PVDF was designed to fabricate a thermal imaging device with improved sensitivity and signal-to-noise ratio. It is found that the localized surface plasmon resonance (LSPR) of silver nanoparticles (AgNPs) could enhance the local intensity of the excitation light, consequently an increase in the luminescence intensity of neighboring luminophore can be achieved. Thus, not only the fluorescence intensity of the obtained sensor array is enhanced, but the temperature sensitivity is also improved to − 4.5%/K at 20 °C, which increased by 25% compared to that of the blank film without AgNPs addition. Furthermore, the detector array is used for IR thermal imaging, which can successfully obtain the thermal images of observation object at room temperature. The minimal detectable temperature is as small as about 313 K. [ABSTRACT FROM AUTHOR]

• Healing efficiency theory developed based on geometric probability. • New self-healing capsules, called engineered aggregates, are introduced. • Self-healing efficiency calculated for different cracking conditions. • Effect of size, shape and dosage of capsules on healing efficiency is evaluated. This paper presents optimal self-healing configurations in concrete using a new concept referred to herein as engineered aggregates (EA). EA are hollow particles designed to carry healing agents into the cracks in concrete. The concept of EA has been developed by the authors as a cost-effective method for self-healing in conventional concrete without having to modify the existing materials or mixing procedures. EA have an internal brittle capsule carrying the healing agents and they are covered with a cementitious coating. They are similar in size to a concrete coarse aggregate and they are added during the mixing process replacing a portion of the coarse aggregates. Similar to other self-healing materials with cast in capsules, one of the basic requirements of EA is that when concrete cracks, the cementitious coating and the embedded brittle capsule break to release the healing agents into the cavity of the crack. The goal of this study is to identify the optimal shapes and sizes of EA that can be effectively used in self-healing concrete while minimizing the volume fraction of the added healing agent (and thereby EA). The tools of geometric probability are used to determine the probability of intersection of cracks with EA and the probability of healing of cracks with different geometries: a single crack, parallel cracks, mapping cracks and multiple discrete penny-shape cracks. As such, the optimal size and shape of EA are selected based on the density and type of cracking that might occur in concrete. [ABSTRACT FROM AUTHOR]

• Phenological sequences of horse chestnut were examined simultaneously. • Climate warming advanced phenological sequences (leaf unfolding, flowering, fruiting, and leaf coloring). • Growing season and reproductive season prolonged with climate warming. • Significantly positive correlations existed among four phenological events. Warming-induced shifts in plant phenology have been frequently reported in recent decades. Previous studies primarily focused on isolated phenological events; however, the entire phenological sequences of a given species were rarely examined simultaneously, limiting our knowledge about aggregate life history response to climate warming. Here, using 210,000 in situ phenological observations across 2493 European sites during 1980–2015, we examined the responses of four typical phenological events (leaf unfolding, flowering, fruiting, and leaf coloring) of Aesculus hippocastanum (horse chestnut), a common temperate tree species, to climate warming. We found that all four phenophases of A. hippocastanum advanced, i.e., the entire life cycle during the year shifted earlier with climate warming. However, the early-season phenophases (leaf unfolding and flowering) advanced faster than late-season phenophases (leaf coloring and fruiting), leading to the extension of both growing season (from leaf unfolding to leaf coloring) and reproductive season (from flowering to fruiting). The extension rate of the reproductive season was slightly greater than that of the growing season. This implied that under warming climate, more time was devoted to both vegetative growth and reproduction, particularly the latter one, in A. hippocastanum. In addition, significantly positive correlations were observed among four phenological events, particularly for pairwise consecutive events, indicating that the responses of phenological sequences to warming were highly correlated. By simultaneously studying the responses of four key phenological events to climate warming, our findings demonstrate the differences in the phenological plasticity among phenological sequences in A. hippocastanum , deepening our understanding about the impact of climate warming on life history rather than examining individual phenological events in isolation. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) has become a widely used medical imaging method. Affected by imaging mechanism, magnetic field inhomogeneity and other factors, MRI images are often interfered by non-Gaussian noise such as Rician noise and non-central chi-square distribution noise. However, the existing MRI denoising methods cannot effectively remove different kinds of noise, and the image is prone to blur and detail loss, even artifacts. Thus, this paper proposes an adaptive denoising algorithm for MRI based on Nonlocal Structural Similarity and Low-Rank Sparse Representation (NSS-LRSR). Different from the existing methods, it is a new paradigm to adaptively filter non-Gaussian noise of MRI and it has a good effect on both spatially stable and spatially varying Rician or non-central chi-square distribution noise. The forward variance stable transformation is used to correct the deviation caused by non-Gaussian noise, and then the non-local information is regrouped. And considering the sparse of similar image blocks, we use improved weighted kernel norm minimization to represent the non-local image blocks based on estimation of noise's standard deviation; thereby the processed image block are aggregated and outputted. Experimental results show that compared with the currently popular algorithms, the proposed NSS-LRSR achieves better results in PSNR and SSIM quantitative indexes. [ABSTRACT FROM AUTHOR]

Simple Summary: This study recruited 3272 non-small cell lung cancer (NSCLC) cases to analyze the predictive abilities of serum tumor markers (CEA, SCC-Ag, CYFRA 21-1, NSE, ProGRP, TPSA and CA199) for metastasis and clinical stage, and found that tumor marker levels may be indicative of tumor metastasis (intrapulmonary, lymphatic and distant metastasis) and stage. Increased CEA and CA199 provided an accurate prediction of intrapulmonary and distant metastasis. Increased CEA, CYFRA 21-1 and CA199 provided an accurate prediction of lymphatic metastasis and higher tumor stage. Combined detection of serum tumor markers can indicate tumor metastasis and stage in NSCLC patients. Objective: This study aimed to explore the roles of serum tumor markers for metastasis and stage of non-small cell lung cancer (NSCLC). Methods: This study recruited 3272 NSCLC patients admitted to the Tianjin Union Medical Center and the Tianjin Medical University Cancer Institute and Hospital. The predictive abilities of some serum tumor markers (carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC-Ag), cytokeratin-19 fragment (CYFRA 21-1), neuron-specific enolase (NSE), pro-gastrin-releasing peptide (ProGRP), total prostate-specific antigen (TPSA) and carbohydrate antigen 199 (CA199)) for NSCLC metastasis (intrapulmonary, lymphatic and distant metastasis) and clinical stage were analyzed. Results: Tumor markers exhibited different numerical and proportional distributions in NSCLC patients. Elevated CEA, CYFRA 21-1 and CA199 levels were indicative of tumor metastasis and stage. Increased CEA and CA199 provided an accurate prediction of intrapulmonary and distant metastasis with the area under the receiver operator characteristic curve (AUC) of 0.69 both (p < 0.001); Increased CEA, CYFRA 21-1 and CA199 provided an accurate prediction of lymphatic metastasis with the AUC of 0.62 (p < 0.001). Conclusion: Combined detection of serum tumor markers can indicate tumor metastasis and stage in NSCLC patients. [ABSTRACT FROM AUTHOR]

Change detection determines and evaluates changes by comparing bi-temporal images, which is a challenging task in the remote-sensing field. To better exploit the high-level features, deep-learning-based change-detection methods have attracted researchers' attention. Most deep-learning-based methods only explore the spatial–spectral features simultaneously. However, we assume the key spatial-change areas should be more important, and attention should be paid to the specific bands which can best reflect the changes. To achieve this goal, we propose the spatial–spectral joint attention network (SJAN). Compared with traditional methods, SJAN introduces the spatial–spectral attention mechanism to better explore the key changed areas and the key separable bands. To be more specific, a novel spatial-attention module is designed to extract the spatially key regions first. Secondly, the spectral-attention module is developed to adaptively focus on the separable bands of land-cover materials. Finally, a novel objective function is proposed to help the model to measure the similarity of learned spatial–spectral features from both spectrum amplitude and angle perspectives. The proposed SJAN is validated on three benchmark datasets. Comprehensive experiments have been conducted to demonstrate the effectiveness of the proposed SJAN. [ABSTRACT FROM AUTHOR]

• The Ruphen/PVDF films shows high temperature-sensitivity (−4.3 %K−1) at room temperature. • We fabricated a thermal imaging device using Ruphen/PVDF films with IR absorber layer. • The low detecting threshold is found as 313 K for the device, and the response time of 1.17 s and NETD of 1.68 K were observed. The accurate temperature measurement with micrometric spatial resolution is difficult to acquire by using conventional contact thermal probes. Noncontact thermal probes possess great potential for future high resolution accurate temperature measurements. Herein, we report a thermometer based on Ruphen-based temperature sensitive paint (TSP) for high-sensitive remote temperature measurement. TSP consisting of Ruphen and PVDF with sharp emission band and high internal quantum efficiency was designed and employed to fabricate a luminescence thermometer prototype for infrared-to-visible converter in this study. The temperature dependent photoluminescence properties of the different Ruphen/PVDF films were analyzed in the range of 283 K to 363 K, and a relatively high thermal sensitivity (−4.3 %K−1 @ RT) was obtained for low Ruphen concentration sample. The as-fabricated temperature sensor prototype can detect a 623 K thermal object with a response time of 1.52 s and a noise equivalent temperature difference (NETD) of 2.40 K. Furthermore, the effect of IR absorber on the imaging performance was also investigated, and an optimized response time of 1.17 s and NETD of 1.68 K were observed. The results indicate that Ruphen/PVDF film is promising for a luminescence thermometer for room temperature mapping. [ABSTRACT FROM AUTHOR]

Excessive application of ammonium‐based N fertilizers promote nitrification, which is mainly responsible for intensive acidification in cropland soils. Thus, there is an urgent need for the development of control measures to manage soil acidification induced by N fertilization. In this study, we investigated the potential of enriching organic C bioavailability to mitigate acidification induced by N fertilization through microbial N immobilization using an indoor incubation experiment. Results showed that the sole application of ammonium sulfate [(NH4)2SO4] or urea to a cropland soil promoted nitrification of fertilizer N, and subsequently soil pH decreased by 0.48–1.00 units after 45‐d incubation. The combined application of glucose with N fertilizers induced microbial immobilization of almost all fertilizer N within 1 d and gradually induced mineralization of soil organic N after 1 wk of incubation. More than 65% of immobilized total extractable N was returned to soil solution and mainly existed as NH4+–N and extractable organic N, with the pH decrement being <0.10 units after 45‐d incubation. These results indicated that (a) enriching organic C bioavailability in cropland soils intensified microbial N immobilization, (b) intensification of microbial N immobilization induced a buffering effect to regulate soil inorganic N supply, and (c) microbial N immobilization had great potential to inhibit soil acidification and retard aluminum mobilization. The results obtained in this study provide a theoretical strategy to control soil acidification and increase N use efficiency in croplands. Core Ideas: Glucose stimulated microbial N immobilization for temporary storing fertilizer N in croplands.Glucose inhibited soil nitrification and acidification through microbial N immobilization.Intensifying microbial N immobilization had a buffering effect to regulate soil inorganic N supply.Improving organic C availability has potential to mitigate soil acidification due to N fertilization. [ABSTRACT FROM AUTHOR]

With the acceleration of the pace of work and life, people are facing more and more pressure, which increases the probability of suffering from depression. However, many patients may fail to get a timely diagnosis due to the serious imbalance in the doctor–patient ratio in the world. A promising development is that physiological and psychological studies have found some differences in speech and facial expression between patients with depression and healthy individuals. Consequently, to improve current medical care, Deep Learning (DL) has been used to extract a representation of depression cues from audio and video for automatic depression detection. To classify and summarize such research, we introduce the databases and describe objective markers for automatic depression estimation. We also review the DL methods for automatic detection of depression to extract a representation of depression from audio and video. Lastly, we discuss challenges and promising directions related to the automatic diagnoses of depression using DL. • First review on depression recognition from audiovisual cues that is dealing with both single modal and multi-modal analysis. • The review focuses on deep learning that is adopting in depression recognition task with audiovisual cues. • It covers the state-of-the-art methods for depression recognition in details. [ABSTRACT FROM AUTHOR]

Purpose: To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs. Methods: We proposed an evolutionary feature selection‐based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2‐FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Second, we employed a differential evolution algorithm to search a feature space, which composed of 416‐dimension radiomic features extracted from four sequences of MRIs and 128‐dimension high‐order features extracted by the CNN, to generate an optimal feature combination for pixel classification. Finally, we trained an EL classifier using the optimal feature combination to segment whole tumor (WT) and its subregions including nonenhancing tumor (NET), peritumoral edema (ED), and enhancing tumor (ET) in the suspected region. Experiments were carried out on 300 glioma patients from the BraTS2019 dataset using fivefold cross validation, the model was independently validated using the rest 35 patients from the same database. Results: The approach achieved a detection accuracy of 98.8% using four MRIs. The Dice coefficients (and standard deviations) were 0.852 ± 0.057, 0.844 ± 0.046, and 0.799 ± 0.053 for segmentation of WT (NET+ET+ED), tumor core (NET+ET), and ET, respectively. The sensitivities and specificities were 0.873 ± 0.074, 0.863 ± 0.072, and 0.852 ± 0.082; the specificities were 0.994 ± 0.005, 0.994 ± 0.005, and 0.995 ± 0.004 for the WT, tumor core, and ET, respectively. The performances and calculation times were compared with the state‐of‐the‐art approaches, our approach yielded a better overall performance with average processing time of 139.5 s per set of four sequence MRIs. Conclusions: We demonstrated a robust and computational cost‐effective hybrid segmentation approach for glioma and its subregions on multi‐sequence MR images. The proposed approach can be used for automated target delineation for glioma patients. [ABSTRACT FROM AUTHOR]

Purpose: To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs. Methods: We proposed an evolutionary feature selection‐based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2‐FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Second, we employed a differential evolution algorithm to search a feature space, which composed of 416‐dimensions radiomics features extracted from four sequences of MRIs and 128‐dimensions high‐order features extracted by the CNN, to generate an optimal feature combination for pixel classification. Finally, we trained an EL classifier using the optimal feature combination to segment whole tumor (WT) and its subregions including non‐enhancing tumor (NET), peritumoral edema (ED), and enhancing tumor (ET) in the suspected region. Experiments were carried out on 300 glioma patients from the BraTS2019 dataset using fivefold cross‐validation, and the model was independently validated using the rest 35 patients from the same database. Results: The approach achieved a detection accuracy of 98.8% using four MRIs. The Dice coefficients (and standard deviations) were 0.852 ± 0.057, 0.844 ± 0.046, and 0.799 ± 0.053 for segmentation of WT (NET+ET+ED), tumor core (NET+ET), and ET, respectively. The sensitivities and specificities were 0.873 ± 0.074, 0.863 ± 0.072, and 0.852 ± 0.082; the specificities were 0.994 ± 0.005, 0.994 ± 0.005, and 0.995 ± 0.004 for the WT, tumor core, and ET, respectively. The performances and calculation times were compared with the state‐of‐the‐art approaches, our approach yielded a better overall performance with average processing time of 139.5 s per set of four sequence MRIs. Conclusions: We demonstrated a robust and computational cost‐effective hybrid segmentation approach for glioma and its subregions on multi‐sequence MR images. The proposed approach can be used for automated target delineation for glioma patients. [ABSTRACT FROM AUTHOR]

Permeability is an important indicator of the durability of cement-based materials. Surface treatments are effective in reducing permeability, and thus improving durability. In this study, CO 2 surface treatment, as a new method, was investigated explore the efficiency on cement-fly ash-slag ternary concrete. The results show that the CO 2 surface treatment densified surface microstructure and reduced both air and water permeability of concrete. With increase in fly ash and slag contents, the efficiency of CO 2 surface treatment in reducing concrete permeability was enhanced. The results of thermogravimetric analysis (TGA), X-ray diffraction (XRD), environmental scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) indicated that CO 2 surface treatment consumed calcium hydroxide (CH) and formed calcium carbonate (CaCO 3). Moreover, the calcium-to-silicon ratio (Ca/Si) of calcium silicate hydrate (C–S–H) decreased after surface treatment and CaCO 3 was covered by C–S–H during further hydration. • CO 2 surface treatment reduces the air and water permeability of the concrete. • CO 2 surface treatment reduces the capillary water absorption of concrete. • Fly ash-slag combination reduces permeability better than either of them used alone. • CO 2 surface treatment reduces Ca (OH) 2 content at depth~1.5 mm. • CO 2 surface treatment reduces the Ca/Si ratio of C–S–H, CaCO 3 is covered by C–S–H after further hydration. [ABSTRACT FROM AUTHOR]