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The escalating use of alicyclic compounds in modern industrial production has led to a rapid increase of these substances in the environment, posing significant health hazards. Addressing this challenge necessitates a comprehensive understanding of these compounds, which can be achieved through the deep learning approach. Graph neural networks (GNN) known for its' extraordinary ability to process graph data with rich relationships, have been employed in various molecular prediction tasks. In this study, alicyclic molecules screened from PCBA, Toxcast and Tox21 are made as general bioactivity and biological targets' activity prediction datasets. GNN-based models are trained on the two datasets, while the Attentive FP and PAGTN achieve best performance individually. In addition, alicyclic carbon atoms make the greatest contribution to biological activity, which indicate that the alicycle structures have significant impact on the carbon atoms' contribution. Moreover, there are terrific number of active molecules in other public datasets, indicates that alicyclic compounds deserve more attention in POPs control. This study uncovered deeper structural-activity relationships within these compounds, offering new perspectives and methodologies for academic research in the field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The image fusion methods based on deep learning has become a research hotspot in the field of computer vision in recent years. This paper reviews these methods from five aspects: Firstly, the principle and advantages of image fusion methods based on deep learning are expounded; Secondly, the image fusion methods are summarized in two aspects: End-to-End and Non-End-to-End, according to the different tasks of deep learning in the feature processing stage, the non-end-to-end image fusion methods are divided into two categories: deep learning for decision mapping and deep learning for feature extraction. According to the different types of the networks, the end-to-end image fusion methods are divided into three categories: image fusion methods based on Convolutional Neural Network, Generative Adversarial Network, and Encoder-Decoder Network; Thirdly, the application of the image fusion methods based on deep learning in medical image field is summarized from two aspects: method and data set; Fourthly, evaluation metrics commonly used in the field of medical image fusion are sorted out from 14 aspects; Fifthly, the main challenges faced by the medical image fusion are discussed from two aspects: data sets and fusion methods. And the future development direction is prospected. This paper systematically summarizes the image fusion methods based on the deep learning, which has a positive guiding significance for the in-depth study of multi modal medical images., Competing Interests: Declaration of competing interest The authors declare that they have no known competing fifinancial interests or personal relationships that could have appeared to inflfluence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

This is a retrospective analysis. Quantitative prediction of the children's and adolescents' spherical equivalent based on their variable-length historical vision records. From October 2019 to March 2022, we examined uncorrected visual acuity, sphere, astigmatism, axis, corneal curvature and axial length of 75,172 eyes from 37,586 children and adolescents aged 6-20 years in Chengdu, China. 80% samples consist of the training set, the 10% form the validation set and the remaining 10% form the testing set. Time-Aware Long Short-Term Memory was used to quantitatively predict the children's and adolescents' spherical equivalent within two and a half years. The mean absolute prediction error on the testing set was 0.103 ± 0.140 (D) for spherical equivalent, ranging from 0.040 ± 0.050 (D) to 0.187 ± 0.168 (D) if we consider different lengths of historical records and different prediction durations. Time-Aware Long Short-Term Memory was applied to captured the temporal features in irregularly sampled time series, which is more in line with the characteristics of real data and thus has higher applicability, and helps to identify the progression of myopia earlier. The overall error 0.103 (D) is much smaller than the criterion for clinically acceptable prediction, say 0.75 (D)., (© 2023. The Author(s).)

Fine materials (FM) from municipal solid waste (MSW) classification require disposal, and pyrolysis is a feasible method for the treatments. Hence, the behavior, kinetics, and products of FM pyrolysis were investigated in this study. A deep learning algorithm was firstly employed to predict and verify the TG data during the process of FM pyrolysis. The results showed that FM pyrolysis could be divided into drying (<138 °C), de-volatilization (138-570 °C), and decomposition stage (≥570 °C above). The de-volatilization can further be divided into stage 2 and stage 3, with values of activation energy estimated by Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose methods as 123.35 and 172.95 kJ/mol, respectively. The gas products like H 2 O, CO 2 , CH 4 , and CO, as well as functional groups like phenols and carbonyl (CO), were all detected during the process of FM pyrolysis by thermogravimetric-fourier transform infrared spectrometry at a heating rate of 10  ° C/min. The main species detected by pyrolysis-gas chromatography-mass spectrometry analyzer included acid (41.98%) and aliphatic hydrocarbon (22.44%). Finally, the 1D-CNN-LSTM algorithm demonstrated an outstanding generalization capability to predict the relationship between FM composition and temperature, with R 2 reaching 93.91%. In sum, this study provided a reference for the treatment of FM from MSW classification as well as the feasibility and practicability of deep learning applied in pyrolysis., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Objectives: To evaluate the clinical performance of a deep learning (DL)-based method for brain MRI exams with reduced gadolinium-based contrast agent (GBCA) dose to provide better understanding of the readiness and limitations of this method., Methods: Eighty-three consecutive patients (from March 2019 to August 2019) who underwent brain contrast-enhanced (CE) MRI were included. Three 3D T1-weighted images with zero-dose, low-dose (10%), and full-dose (100%) GBCA were collected. The first 30 cases were used to train a DL model to synthesize the full-dose GBCA images from the zero-dose and low-dose image pairs. The remaining 53 cases were used for testing. The enhancement pattern, number, and location of enhancing lesions were recorded. Overall image quality, image signal noise ratio (SNR), lesion conspicuity, and lesion enhancement were assessed., Results: Lesion detection from the DL-synthesized CE-MRI image accurately matched those from the true full-dose CE-MRI images in 48 of 53 cases (90.6%). The DL method identified the lesions in 34 of 36 cases (94.4%) with a single enhanced lesion and all lesions in 3 of 6 cases (50.0%) in cases with multiple enhancing lesions. The agreement between synthesized and true full-dose CE-MRI images were 0.73, 0.63, 0.89, and 0.87 for image quality, image SNR, lesion conspicuity, and lesion enhancement, respectively., Conclusions: The proposed DL method is a feasible way to minimize the dosage of GBCAs in brain MRI without sacrificing the diagnostic information. Missing enhancement of small lesions in patients with multiple lesions was observed, requiring improvements in algorithms or dosage design., Key Points: • This study evaluated the clinical performance of a DL-based reconstruction method for significant dose reduction in GBCA contrast-enhanced MRI exams. • The proposed DL method has the potential to satisfy the routine radiological diagnosis needs in certain clinical applications., (© 2021. European Society of Radiology.)

Background: Whole brain radiotherapy (WBRT) can impair patients' cognitive function. Hippocampal avoidance during WBRT can potentially prevent this side effect. However, manually delineating the target area is time-consuming and difficult. Here, we proposed a credible approach of automatic hippocampal delineation based on convolutional neural networks., Methods: Referring to the hippocampus contouring atlas proposed by RTOG 0933, we manually delineated (MD) the hippocampus on the MRI data sets (3-dimensional T1-weighted with slice thickness of 1 mm, n = 175), which were used to construct a three-dimensional convolutional neural network aiming for the hippocampus automatic delineation (AD). The performance of this AD tool was tested on three cohorts: (a) 3D T1 MRI with 1-mm slice thickness (n = 30); (b) non-3D T1-weighted MRI with 3-mm slice thickness (n = 19); (c) non-3D T1-weighted MRI with 1-mm slice thickness (n = 11). All MRIs confirmed with normal hippocampus has not been violated by any disease. Virtual radiation plans were created for AD and MD hippocampi in cohort c to evaluate the clinical feasibility of the artificial intelligence approach. Statistical analyses were performed using SPSS version 23. P < 0.05 was considered significant., Results: The Dice similarity coefficient (DSC) and Average Hausdorff Distance (AVD) between the AD and MD hippocampi are 0.86 ± 0.028 and 0.18 ± 0.050 cm in cohort a, 0.76 ± 0.035 and 0.31 ± 0.064 cm in cohort b, 0.80 ± 0.015 and 0.24 ± 0.021 cm in cohort c, respectively. The DSC and AVD in cohort a were better than those in cohorts b and c (P < 0.01). There is no significant difference between the radiotherapy plans generated using the AD and MD hippocampi., Conclusion: The AD of the hippocampus based on a deep learning algorithm showed satisfying results, which could have a positive impact on improving delineation accuracy and reducing work load.

Background: Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well-trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images., Materials and Methods: Open-source data sets and multicenter data sets have been used in this study. A three-dimensional convolutional neural network (CNN) was designed to detect pulmonary nodules and classify them into malignant or benign diseases based on pathologically and laboratory proven results., Results: The sensitivity and specificity of this well-trained model were found to be 84.4% (95% confidence interval [CI], 80.5%-88.3%) and 83.0% (95% CI, 79.5%-86.5%), respectively. Subgroup analysis of smaller nodules (<10 mm) have demonstrated remarkable sensitivity and specificity, similar to that of larger nodules (10-30 mm). Additional model validation was implemented by comparing manual assessments done by different ranks of doctors with those performed by three-dimensional CNN. The results show that the performance of the CNN model was superior to manual assessment., Conclusion: Under the companion diagnostics, the three-dimensional CNN with a deep learning algorithm may assist radiologists in the future by providing accurate and timely information for diagnosing pulmonary nodules in regular clinical practices., Implications for Practice: The three-dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment. Although it still warrants further improvement and validation in larger screening cohorts, its clinical application could definitely facilitate and assist doctors in clinical practice., Competing Interests: Disclosures of potential conflicts of interest may be found at the end of this article., (© AlphaMed Press 2019.)

In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive Impairment (MCI), from normal aging subjects. Multimodality neuroimaging data such as MRI and PET provide valuable insights into brain abnormalities, while genetic data such as single nucleotide polymorphism (SNP) provide information about a patient's AD risk factors. When these data are used together, the accuracy of AD diagnosis may be improved. However, these data are heterogeneous (e.g., with different data distributions), and have different number of samples (e.g., with far less number of PET samples than the number of MRI or SNPs). Thus, learning an effective model using these data is challenging. To this end, we present a novel three-stage deep feature learning and fusion framework, where deep neural network is trained stage-wise. Each stage of the network learns feature representations for different combinations of modalities, via effective training using the maximum number of available samples. Specifically, in the first stage, we learn latent representations (i.e., high-level features) for each modality independently, so that the heterogeneity among modalities can be partially addressed, and high-level features from different modalities can be combined in the next stage. In the second stage, we learn joint latent features for each pair of modality combination by using the high-level features learned from the first stage. In the third stage, we learn the diagnostic labels by fusing the learned joint latent features from the second stage. To further increase the number of samples during training, we also use data at multiple scanning time points for each training subject in the dataset. We evaluate the proposed framework using Alzheimer's disease neuroimaging initiative (ADNI) dataset for AD diagnosis, and the experimental results show that the proposed framework outperforms other state-of-the-art methods., (© 2018 Wiley Periodicals, Inc.)

Before 2008, China lacked high-coverage regional surface observation data, making it difficult for the China Meteorological Administration Land Data Assimilation System (CLDAS) to directly backtrack high-resolution, high-quality land assimilation products. To address this issue, this paper proposes a deep learning model named UNET_DCA, based on the UNET architecture, which incorporates a Dual Cross-Attention module (DCA) for multiscale feature fusion by introducing Channel Cross-Attention (CCA) and Spatial Cross-Attention (SCA) mechanisms. This model focuses on the near-surface 10-m wind field and achieves spatial downscaling from 6.25 km to 1 km. We conducted training and validation using data from 2020–2021, tested with data from 2019, and performed ablation experiments to validate the effectiveness of each module. We compared the results with traditional bilinear interpolation methods and the SNCA-CLDASSD model. The experimental results show that the UNET-based model outperforms SNCA-CLDASSD, indicating that the UNET-based model captures richer information in wind field downscaling compared to SNCA-CLDASSD, which relies on sequentially stacked CNN convolution modules. UNET_CCA and UNET_SCA, incorporating cross-attention mechanisms, outperform UNET without attention mechanisms. Furthermore, UNET_DCA, incorporating both Channel Cross-Attention and Spatial Cross-Attention mechanisms, outperforms UNET_CCA and UNET_SCA, which only incorporate one attention mechanism. UNET_DCA performs best on the RMSE, MAE, and COR metrics (0.40 m/s, 0.28 m/s, 0.93), while UNET_DCA_ars, incorporating more auxiliary information, performs best on the PSNR and SSIM metrics (29.006, 0.880). Evaluation across different methods indicates that the optimal model performs best in valleys, followed by mountains, and worst in plains; it performs worse during the day and better at night; and as wind speed levels increase, accuracy decreases. Overall, among various downscaling methods, UNET_DCA and UNET_DCA_ars effectively reconstruct the spatial details of wind fields, providing a deeper exploration for the inversion of high-resolution historical meteorological grid data. [ABSTRACT FROM AUTHOR]

Coherent imaging techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells. Driven by the construction of brighter sources and high-rate detectors, coherent imaging methods like ptychography are poised to revolutionize nanoscale materials characterization. However, these advancements are accompanied by significant increase in data and compute needs, which precludes real-time imaging, feedback and decision-making capabilities with conventional approaches. Here, we demonstrate a workflow that leverages artificial intelligence at the edge and high-performance computing to enable real-time inversion on X-ray ptychography data streamed directly from a detector at up to 2 kHz. The proposed AI-enabled workflow eliminates the oversampling constraints, allowing low-dose imaging using orders of magnitude less data than required by traditional methods. Next-generation light sources and fast detectors enable unparalleled materials characterization, but increased data rates and compute needs preclude real-time analysis. Here, Babu et al. leverage high-performance computing and AI@Edge to achieve real-time, low-dose imaging on streaming data at 2 KHz. [ABSTRACT FROM AUTHOR]

Orthogonal time frequency space (OTFS) is a novel modulation scheme that enables reliable communication in high-mobility environments. In this paper, we propose a Transformer-based channel estimation method for OTFS systems. Initially, the threshold method is utilized to obtain preliminary channel estimation results. To further enhance the channel estimation, we leverage the inherent temporal correlation between channels, and a new method of channel response prediction is performed. To enhance the accuracy of the preliminary results, we utilize a specialized Transformer neural network designed for processing time series data for refinement. The simulation results demonstrate that our proposed scheme outperforms the threshold method and other deep learning (DL) methods in terms of normalized mean squared error and bit error rate. Additionally, the temporal complexity and spatial complexity of different DL models are compared. The results indicate that our proposed algorithm achieves superior accuracy while maintaining an acceptable computational complexity. [ABSTRACT FROM AUTHOR]

The piston pump is the significant source of motive force in a hydraulic transmission system. Owing to the changeable working conditions and complex structural characteristics, multiple friction pairs in the piston pump are prone to wear and failure. An accurate fault diagnosis method is a crucial guarantee for system reliability. Deep learning provides a great insight into the intelligent exploration of machinery fault diagnosis. Hyperparameters are very important to construct an effective deep model with good performance. This research fully mines the feature component from vibration signals, and converts the failure recognition into a classification issue via establishing a deep model. Furthermore, Bayesian algorithm is introduced for hyperparameter optimization as it considers prior information. An adaptive convolutional neural network is established for typical failure pattern recognition of an axial piston pump. The proposed method can automatically complete fault classification and represents a higher accuracy by experimental verification. Typical failures of an axial piston pump are intelligently diagnosed with reduced subjectivity and preprocessing knowledge. The proposed method achieves an identification accuracy of more than 98% for five typical conditions of an axial piston pump. [ABSTRACT FROM AUTHOR]

Hydraulic axial piston pumps are the power source of fluid power systems and have important applications in many fields. They have a compact structure, high efficiency, large transmission power, and excellent flow variable performance. However, the crucial components of pumps easily suffer from different faults. It is therefore important to investigate a precise fault identification method to maintain reliability of the system. The use of deep models in feature learning, data mining, automatic identification, and classification has led to the development of novel fault diagnosis methods. In this research, typical faults and wears of the important friction pairs of piston pumps were analyzed. Different working conditions were considered by monitoring outlet pressure signals. To overcome the low efficiency and time-consuming nature of traditional manual parameter tuning, the Bayesian algorithm was introduced for adaptive optimization of an established deep learning model. The proposed method can explore potential fault feature information from the signals and adaptively identify the main fault types. The average diagnostic accuracy was found to reach up to 100%, indicating the ability of the method to detect typical faults of axial piston pumps with high precision. [ABSTRACT FROM AUTHOR]

COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Deep learning plays an important role in COVID-19 images diagnosis. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects; Firstly, 33 COVID-19 datasets and data enhancement methods are introduced; Secondly, COVID-19 classification methods based on supervised learning are summarized from four aspects of VGG, ResNet, DenseNet and Lightweight Networks. The COVID-19 segmentation methods based on supervised learning are summarized from four aspects of attention mechanism, multiscale mechanism, residual connectivity mechanism, and dense connectivity mechanism; Thirdly, the application of deep learning in semi-supervised COVID-19 images diagnosis in terms of consistency regularization methods and self-training methods. Fourthly, the application of deep learning in unsupervised COVID-19 diagnosis in terms of autoencoder methods and unsupervised generative adversarial methods. Moreover, the challenges and future work of COVID-19 images diagnostic methods in the field of deep learning are summarized. This paper reviews the latest research status of COVID-19 images diagnosis in deep learning, which is of positive significance to the detection of COVID-19. [ABSTRACT FROM AUTHOR]

In order to better preserve the anatomical structure information of Computed Tomography (CT) source images and highlight the metabolic information of lesion regions in Positron Emission Tomography (PET) source images, a hybrid multimodal medical image fusion method (LatLRR-GAN) based on Latent low-rank representation (LatLRR) and the dual discriminators Generative Adversarial Network (ED-D2GAN) is proposed. Firstly, considering the denoising capability of LatLRR, source images were decomposed by LatLRR. Secondly, the ED-D2GAN model was put forward as the low-rank region fusion method, which can fully extract the information contained by the low-rank region images. Among them, encoder and decoder networks were used in the generator; convolutional neural networks were also used in dual discriminators. Thirdly, a threshold adaptive weighting algorithm based on the region energy ratio is proposed as the salient region fusion rule, which can improve the overall sharpness of the fused image. The experimental results show that compared with the best methods of the other six methods, this paper is effective in multiple objective evaluation metrics, including the average gradient, edge intensity, information entropy, spatial frequency and standard deviation. The results of the two experiments are improved by 35.03%, 42.42%, 4.66%, 8.59% and 11.49% on average. [ABSTRACT FROM AUTHOR]

Accurate medical image segmentation of brain tumors is necessary for the diagnosing, monitoring, and treating disease. In recent years, with the gradual emergence of multi-sequence magnetic resonance imaging (MRI), multi-modal MRI diagnosis has played an increasingly important role in the early diagnosis of brain tumors by providing complementary information for a given lesion. Different MRI modalities vary significantly in context, as well as in coarse and fine information. As the manual identification of brain tumors is very complicated, it usually requires the lengthy consultation of multiple experts. The automatic segmentation of brain tumors from MRI images can thus greatly reduce the workload of doctors and buy more time for treating patients. In this paper, we propose a multi-modal brain tumor segmentation framework that adopts the hybrid fusion of modality-specific features using a self-supervised learning strategy. The algorithm is based on a fully convolutional neural network. Firstly, we propose a multi-input architecture that learns independent features from multi-modal data, and can be adapted to different numbers of multi-modal inputs. Compared with single-modal multi-channel networks, our model provides a better feature extractor for segmentation tasks, which learns cross-modal information from multi-modal data. Secondly, we propose a new feature fusion scheme, named hybrid attentional fusion. This scheme enables the network to learn the hybrid representation of multiple features and capture the correlation information between them through an attention mechanism. Unlike popular methods, such as feature map concatenation, this scheme focuses on the complementarity between multi-modal data, which can significantly improve the segmentation results of specific regions. Thirdly, we propose a self-supervised learning strategy for brain tumor segmentation tasks. Our experimental results demonstrate the effectiveness of the proposed model against other state-of-the-art multi-modal medical segmentation methods. [ABSTRACT FROM AUTHOR]

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network ($\text{C}^{2}\text{F}$ -Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare $\text{C}^{2}\text{F}$ -Net with state-of-the-art (SOTA) models. The results show that $\text{C}^{2}\text{F}$ -Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our $\text{C}^{2}\text{F}$ -Net in COD downstream applications. Our code is publicly available at: https://github.com/Ben57882/C2FNet-TSCVT [ABSTRACT FROM AUTHOR]

As the largest component of carbon export from terrestrial ecosystems, ecosystem respiration (RECO), together with vegetation productivity, determines the carbon stock changes in terrestrial ecosystems, so it is crucial to reveal the response of RECO to climate change. However, the simulation of RECO is usually inaccurate due to the neglect of the lagged response of respiration to changes in water conditions. In this study, we integrated meteorological data, remote sensing data, and soil data, and introduced the indicators of the previous water conditions into the deep learning model to simulate RECO. The conclusions are as follows: (a) There is a 1–2 years' lagged response of RECO to changes in water conditions. (b) It is necessary to consider the influence of previous water conditions when simulate RECO. It not only improved the simulation accuracy of RECO, but also reflected its inter‐annual fluctuations and change trends more accurately, avoiding the underestimation of RECO inter‐annual fluctuations. (c) There is an inconsistency in carbon input and carbon output change trends which impacts the carbon sink pattern and potential of terrestrial ecosystems. The growth trend of Gross Primary Production (GPP) in global terrestrial ecosystems is greater than that of RECO, with a trend of increasing carbon sinks, especially in the northern extra‐tropics; while the carbon sink capacity of tropical regions has gradually saturated, showing that the change trend of RECO is close to that of GPP, which poses a potential risk to the sustainable carbon sink capacity of global ecosystems in the future. Plain Language Summary: Ecosystem respiration (RECO) is the largest component of carbon export from terrestrial ecosystems and determines the carbon sink potential of terrestrial ecosystems. Consider from the perspective of carbon cycle mechanism, it is reasonable to believe that RECO has the lagged response to changes in water conditions. However, most studies usually consider the water conditions in growing season only, which caused the inaccurate RECO simulations and underestimation of RECO's interannual variation. In this study, we introduced the multi‐time scale standardized precipitation evapotranspiration indexes‐which can reflect the current and previous water conditions‐into deep learning models to construct an optimal model for simulating global‐scale RECO. We revealed the optimal time scales of the lagged effect that need to be considered in RECO simulations and analyzed the global pattern of RECO tendencies and its implications on terrestrial carbon sink potential. We believe this paper to be of particular interest to the readers of your journal as our method is novel that could well reveal the spatial pattern and the interannual variations of RECO at global scale and our results have important implications for the assessment of terrestrial ecosystem carbon sink. In addition, it revealed some new rules that are interesting for the academic community. Key Points: There is a 1–2 years' lagged response of ecosystem respiration (RECO) to changes in water conditionsConsidering the previous water conditions can improve the simulation accuracy of RECO and its inter‐annual fluctuationsThere is an inconsistency in carbon input and carbon output change trends which impacts the carbon sink potential of terrestrial ecosystems [ABSTRACT FROM AUTHOR]

Text classification in the long-text domain has become a development challenge due to the significant increase in text data, complexity enhancement, and feature extraction of long texts in various domains of the Internet. A long text classification model based on multi-feature weighted fusion is proposed for the problems of contextual semantic relations, long-distance global relations, and multi-sense words in long text classification tasks. The BERT model is used to obtain feature representations containing global semantic and contextual feature information of text, convolutional neural networks to obtain features at different levels and combine attention mechanisms to obtain weighted local features, fuse global contextual features with weighted local features, and obtain classification results by equal-length convolutional pooling. The experimental results show that the proposed model outperforms other models in terms of accuracy, precision, recall, F1 value, etc., under the same data set conditions compared with traditional deep learning classification models, and it can be seen that the model has more obvious advantages in long text classification. [ABSTRACT FROM AUTHOR]

Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet. [ABSTRACT FROM AUTHOR]

In recent years, marine animal study has gained increasing research attention, which raises significant demands for fine-grained marine animal segmentation (MAS) techniques. In addition, deep learning has been widely adopted for object segmentation and has achieved promising performance. However, deep-based MAS is still lack of investigation due to the shortage of a large-scale MAS dataset. To tackle this issue, we construct the first large-scale MAS dataset, called MAS3K, which consists of 3,103 images from different types, including camouflaged marine animal images, common marine animal images, and underwater images without marine animals. Furthermore, we consider different underwater conditions, such as low illumination, turbid water quality, photographic distortion, etc. Each image from MAS3K dataset has rich annotations, including an object-level mask, a category name, attributes, and a camouflage method (if applicable). Furthermore, we propose a novel MAS network, called Enhanced Cascade Decoder Network (ECD-Net), which consists of multiple Interactive Feature Enhancement Modules (IFEMs) and Cascade Decoder Modules (CDMs). In ECD-Net, the IFEMs are first utilized to extract rich multi-scale features. The resulting features are then fed to the CDMs for accurately segmenting marine animals from complex underwater environments. We perform extensive experiments to compare ECD-Net with 10 cutting-edge object segmentation models. The results demonstrate that ECD-Net is an effective MAS model and outperforms the cutting-edge models, both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Objective: This study aims to differentiate preoperative Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL) by transfer learning radiomics nomogram (TLRN) with whole slide images of GC as source domain data. Materials and Methods: This study retrospectively enrolled 438 patients with histopathologic diagnoses of Borrmann type IV GC and PGL. They received CT examinations from three hospitals. Quantitative transfer learning features were extracted by the proposed transfer learning radiopathomic network and used to construct transfer learning radiomics signatures (TLRS). A TLRN, which integrates TLRS, clinical factors, and CT subjective findings, was developed by multivariate logistic regression. The diagnostic TLRN performance was assessed by clinical usefulness in the independent validation set. Results: The TLRN was built by TLRS and a high enhanced serosa sign, which showed good agreement by the calibration curve. The TLRN performance was superior to the clinical model and TLRS. Its areas under the curve (AUC) were 0.958 (95% confidence interval [CI], 0.883–0.991), 0.867 (95% CI, 0.794–0.922), and 0.921 (95% CI, 0.860–0.960) in the internal and two external validation cohorts, respectively. Decision curve analysis (DCA) showed that the TLRN was better than any other model. TLRN has potential generalization ability, as shown in the stratification analysis. Conclusions: The proposed TLRN based on gastric WSIs may help preoperatively differentiate PGL from Borrmann type IV GC. Borrmann type IV gastric cancer, primary gastric lymphoma, transfer learning, whole slide image, deep learning. [ABSTRACT FROM AUTHOR]

Prompt and accurate traffic flow forecasting is a key foundation of urban traffic management. However, the flows in different areas and feature channels (inflow/outflow) may correspond to different degrees of importance in forecasting flows. Many forecasting models inadequately consider this heterogeneity, resulting in decreased predictive accuracy. To overcome this problem, an attention-based hybrid spatiotemporal residual model assisted by spatial and channel information is proposed in this study. By assigning different weights (attention levels) to different regions, the spatial attention module selects relatively important locations from all inputs in the modeling process. Similarly, the channel attention module selects relatively important channels from the multichannel feature map in the modeling process by assigning different weights. The proposed model provides effective selection and attention results for key areas and channels, respectively, during the forecasting process, thereby decreasing the computational overhead and increasing the accuracy. In the case involving Beijing, the proposed model exhibits a 3.7% lower prediction error, and its runtime is 60.9% less the model without attention, indicating that the spatial and channel attention modules are instrumental in increasing the forecasting efficiency. Moreover, in the case involving Shanghai, the proposed model outperforms other models in terms of generalizability and practicality. [ABSTRACT FROM AUTHOR]

The treatment of neurodegenerative diseases is expensive, and long-term treatment makes families bear a heavy burden. Accumulating evidence suggests that the high conversion rate can possibly be reduced if clinical interventions are applied at the early stage of brain diseases. Thus, a variety of deep learning methods are utilized to recognize the early stages of neurodegenerative diseases for clinical intervention and treatment. However, most existing methods have ignored the issue of sample imbalance, which often makes it difficult to train an effective model due to lack of a large number of negative samples. To address this problem, we propose a two-stage method, which is used to learn the compression and recover rules of normal subjects so that potential negative samples can be detected. The experimental results show that the proposed method can not only obtain a superb recognition result, but also give an explanation that conforms to the physiological mechanism. Most importantly, the deep learning model does not need to be retrained for each type of disease, which can be widely applied to the diagnosis of various brain diseases. Furthermore, this research could have great potential in understanding regional dysfunction of various brain diseases. [ABSTRACT FROM AUTHOR]

Deep learning (DL) based methods have been extensively studied for medical image segmentation, mostly emphasizing the design and training of DL networks. Only few attempts were made on developing methods for applying DL models in test time. In this paper, we study whether a given off-the-shelf segmentation network can be stably improved on-the-fly during test time in an online processing-and-learning fashion. We propose a new online test-time method, called TestFit , to improve results of a given off-the-shelf DL segmentation model in test time by actively fitting the test data distribution. TestFit first creates a supplementary network (SuppNet) from the given trained off-the-shelf segmentation network (this original network is referred to as OGNet) and applies SuppNet together with OGNet for test time inference. OGNet keeps its hypothesis derived from the original training set to prevent the model from collapsing, while SuppNet seeks to fit the test data distribution. Segmentation results and supervision signals (for updating SuppNet) are generated by combining the outputs of OGNet and SuppNet on the fly. TestFit needs only one pass on each test sample – the same as the traditional test model pipeline – and requires no training time preparation. Since it is challenging to look at only one test sample and no manual annotation for model update each time, we develop a series of technical treatments for improving the stability and effectiveness of our proposed online test-time training method. TestFit works in a plug-and-play fashion, requires minimal hyper-parameter tuning, and is easy to use in practice. Experiments on a large collection of 2D and 3D datasets demonstrate the capability of our TestFit method. • TestFit is a plug-and-play one-pass test-time training method for off-the-shelf segmentation networks. • The model update in TestFit is performed in an online fine-tuning fashion, which requires no extra training data, no new network design, and no offline retraining. • TestFit effectively prevents model collapse issues and achieves a good balance between the stability and effectiveness of test-time training. [ABSTRACT FROM AUTHOR]

• A SCGAC-LFC method is proposed to obtain a coordinated LFC policy. • A CMA-DMAC is proposed to achieve fast collaborative multi-task learning. • Online application of the method does not require communication between units. • The proposed method boasts lower frequency deviation and mileage payment. In a sustainable city with a large amount of renewable energy, the difficulty inherent in the coordination of the load frequency control strategies of grid units and area units lead to severe frequency fluctuations. Conventional load frequency control has difficulty overcoming the above problems due to its lack of adaptive control ability and robustness. To overcome this problem, this paper proposes a sea computing-based grid-area coordinated load frequency control (SCGAC-LFC) method, which equates each grid unit and area unit in each area as independent agents. Instead of different units relying on different strategies, all units collectively obtain the LFC policy that suits the market requirements. In its online application, no communication is needed since each unit can arrive at its own decision. In addition, this paper proposes a curriculum multiagent deep meta-actor-critic (CMA-DMAC) algorithm, which introduces meta-reinforcement learning and curriculum learning to guide the agent training to improve the robustness and quality of the obtained SCGAC-LFC strategies. Using a simulation of the four-area LFC model for the China Southern Grid (CSG), our proposed method carries significantly lower frequency error and regulation mileage payment. [ABSTRACT FROM AUTHOR]

We present a novel, biomimetic model of the eye for realistic virtual human animation. We also introduce a deep learning approach to oculomotor control that is compatible with our biomechanical eye model. Our eye model consists of the following functional components: (i) submodels of the 6 extraocular muscles that actuate realistic eye movements, (ii) an iris submodel, actuated by pupillary muscles, that accommodates to incoming light intensity, (iii) a corneal submodel and a deformable, ciliary-muscle-actuated lens submodel, which refract incoming light rays for focal accommodation, and (iv) a retina with a multitude of photoreceptors arranged in a biomimetic, foveated distribution. The light intensity captured by the photoreceptors is computed using ray tracing from the photoreceptor positions through the finite aperture pupil into the 3D virtual environment, and the visual information from the retina is output via an optic nerve vector. Our oculomotor control system includes a foveation controller implemented as a locally-connected, irregular Deep Neural Network (DNN), or "LiNet", that conforms to the nonuniform retinal photoreceptor distribution, and a neuromuscular motor controller implemented as a fully-connected DNN, plus auxiliary Shallow Neural Networks (SNNs) that control the accommodation of the pupil and lens. The DNNs are trained offline through deep learning from data synthesized by the eye model itself. Once trained, the oculomotor control system operates robustly and efficiently online. It innervates the intraocular muscles to perform illumination and focal accommodation and the extraocular muscles to produce natural eye movements in order to foveate and pursue moving visual targets. We additionally demonstrate the operation of our eye model (binocularly) within our recently introduced sensorimotor control framework involving an anatomically-accurate biomechanical human musculoskeletal model. [ABSTRACT FROM AUTHOR]

A novel CAD scheme for automated lung nodule detection is proposed to assist radiologists with the detection of lung cancer on CT scans. The proposed scheme is composed of four major steps: (1) lung volume segmentation, (2) nodule candidate extraction and grouping, (3) false positives reduction for the non-vessel tree group, and (4) classification for the vessel tree group. Lung segmentation is performed first. Then, 3D labeling technology is used to divide nodule candidates into two groups. For the non-vessel tree group, nodule candidates are classified as true nodules at the false positive reduction stage if the candidates survive the rule-based classifier and are not screened out by the dot filter. For the vessel tree group, nodule candidates are extracted using dot filter. Next, RSFS feature selection is used to select the most discriminating features for classification. Finally, WSVM with an undersampling approach is adopted to discriminate true nodules from vessel bifurcations in vessel tree group. The proposed method was evaluated on 154 thin-slice scans with 204 nodules in the LIDC database. The performance of the proposed CAD scheme yielded a high sensitivity (87.81%) while maintaining a low false rate (1.057 FPs/scan). The experimental results indicate the performance of our method may be better than the existing methods. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of active fault-tolerant coordination control (AFTCC) for proton exchange membrane fuel cells (PEMFCs), which are complex nonlinear systems with multiple inputs and outputs. Conventional fault-tolerant control methods cannot properly coordinate multiple operating variables and prevent constraint violations in PEMFCs. Our proposed AFTCC method seeks to stabilize the output performance of four operating variables and avoid PEMFC operating constraint violations during failure scenarios. Our method is supported by a curriculum-based multiagent deep meta-deterministic policy gradient (CMA-DMDPG) algorithm, which integrates meta-reinforcement learning, multiagent reinforcement learning and curriculum learning to achieve multitask collaboration of multiple agents, thereby enhancing PEMFC robustness. The algorithm consists of a meta-learner and a base learner. The base learner regards the hydrogen controller, oxygen controller, pump controller and radiator controller as four independent agents and thus achieves a cooperative control policy. The meta-learner detects PEMFC faults and selects an appropriate cooperative control policy. The performance of AFTCC under various stochastic and fault conditions is evaluated using a 75 kW PEMFC model. The results showed that the performance of AFTCC surpassed 11 other fault-tolerant control methods in terms of output voltage, oxygen excess ratio, and stack temperature, and avoided constraint violations. • An AFTCC method is proposed to realize active fault-tolerant control of the PEMFC. • A CMA-DMDPG algorithm is proposed to realize multi-task collaboration of controllers. • The AFTCC improved PEMFC control performance and prevent constraint violation. [ABSTRACT FROM AUTHOR]

We introduce a biomimetic framework for human sensorimotor control, which features a biomechanically simulated human musculoskeletal model actuated by numerous muscles, with eyes whose retinas have nonuniformly distributed photoreceptors. The virtual human's sensorimotor control system comprises 20 trained deep neural networks (DNNs), half constituting the neuromuscular motor subsystem, while the other half compose the visual sensory subsystem. Directly from the photoreceptor responses, 2 vision DNNs drive eye and head movements, while 8 vision DNNs extract visual information required to direct arm and leg actions. Ten DNNs achieve neuromuscular control—-2 DNNs control the 216 neck muscles that actuate the cervicocephalic musculoskeletal complex to produce natural head movements, and 2 DNNs control each limb; i.e., the 29 muscles of each arm and 39 muscles of each leg. By synthesizing its own training data, our virtual human automatically learns efficient, online, active visuomotor control of its eyes, head, and limbs in order to perform nontrivial tasks involving the foveation and visual pursuit of target objects coupled with visually-guided limb-reaching actions to intercept the moving targets, as well as to carry out drawing and writing tasks. [ABSTRACT FROM AUTHOR]

The sorting of Construction and Demolition (C&D) waste is a critical step to linking the recycling system and to the macro prediction, which helps to promote the development of the circular economy. Moreover, the effective classification and automated separation process will also help to stop the spreading of pathogenic organisms, such as virus and bacteria, by minimizing human intervention in the sorting process, while also helping to prevent further contamination by COVID-19 virus. This study aims to develop an efficient method to sort C&D waste through deep learning combined with knowledge transfer approach. In this paper, CVGGNet models, that is four VGG structures (VGGNet-11, VGGNet-13, VGGNet-16, and VGGNet-19), based on knowledge transfer combined with the technology of data augmentation and cyclical learning rate, are proposed to classify ten types of C&D waste images. Results show that 2.5 × 10−4, 1.8 × 10−4, 0.8 × 10−4, and 1.0 × 10−4 are the optimum learning rate for CVGGNet-11, CVGGNet-13, CVGGNet-16, and CVGGNet-19, respectively. Knowledge transfer helped shorten the training time from 1039.45 s to 991.05 s, and while it improved the performance of the CVGGNet-11 model in training, validation, and test datasets. The average training time increases as the number of the layers in the CVGGNet architecture rises: CVGGNet-11 (991.05 s) ˂ CVGGNet-13 (1025.76 s) ˂ CVGGNet-16 (1090.48 s) ˂ CVGGNet-19 (1337.81 s). Compared to other CVGGNet models, CVGGNet-16 showed an excellent performance in various C&D waste types, in terms of accuracy (76.6%), weighted average precision (76.8%), weighted average recall (76.6%), weighted average F1-score (76.6%) and micro average ROC (87.0%). In addition, the t-distributed Stochastic Neighbor Embedding (t-SNE) approach can reduce the dataset to a lower dimension and distinctly separate each type of C&D waste. This study demonstrates the good performance of CVGGNet models that can be used to automatically sort most of the C&D waste, paving the way for better C&D waste management. [Display omitted] • Dataset of construction and demolition waste classification has been collected. • Transfer learning was applied to sort construction and demolition waste. • Cyclical learning rate was adopted to quickly tune hyperparameters. [ABSTRACT FROM AUTHOR]

Bayesian computation plays an important role in modern machine learning and statistics to reason about uncertainty. A key computational challenge in Bayesian inference is to develop efficient techniques to approximate, or draw samples from posterior distributions. Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for this issue. However, the vanilla SVGD requires calculating the gradient of the target density and cannot be applied when the gradient is unavailable or too expensive to evaluate. In this paper we explore one way to address this challenge by the construction of a local surrogate for the target distribution in which the gradient can be obtained in a much more computationally feasible manner. More specifically, we approximate the forward model using a deep neural network (DNN) which is trained on a carefully chosen training set, which also determines the quality of the surrogate. To this end, we propose a general adaptation procedure to refine the local approximation online without destroying the convergence of the resulting SVGD. This significantly reduces the computational cost of SVGD and leads to a suite of algorithms that are straightforward to implement. The new algorithm is illustrated on a set of challenging Bayesian inverse problems, and numerical experiments demonstrate a clear improvement in performance and applicability of standard SVGD. [ABSTRACT FROM AUTHOR]

This study developed and evaluated nnU-Net models for three-dimensional semantic segmentation of pituitary adenomas (PAs) from contrast-enhanced T1 (T1ce) images, with aims to train a deep learning-based model cost-effectively and apply it to clinical practice. Methods: This study was conducted in two phases. In phase one, two models were trained with nnUNet using distinct PA datasets. Model 1 was trained with 208 PAs in total, and model 2 was trained with 109 primary nonfunctional pituitary adenomas (NFPA). In phase two, the performances of the two models were investigated according to the Dice similarity coefficient (DSC) in the leave-out test dataset. Results: Both models performed well (DSC > 0.8) for PAs with volumes > 1000 mm3, but unsatisfactorily (DSC < 0.5) for PAs < 1000 mm3. Conclusions: Both nnU-Net models showed good segmentation performance for PAs > 1000 mm3 (75% of the dataset) and limited performance for PAs < 1000 mm3 (25% of the dataset). Model 2 trained with fewer samples was more cost-effective. We propose to combine the use of model-based segmentation for PA > 1000 mm3 and manual segmentation for PA < 1000 mm3 in clinical practice at the current stage. [ABSTRACT FROM AUTHOR]

The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports, and public databases. The images were preprocessed to obtain 2500 high-quality images. 2500 CT images of lung tumor and 2500 from normal lung were obtained from a hospital. Transfer learning was used to initialize model parameters and pretrain three deep convolutional neural network models: AlexNet, GoogleNet, and ResNet. These models were used for feature extraction on all images. Softmax was used as the classification algorithm of the fully connected layer. The ensemble classifier EDL-COVID was obtained via relative majority voting. Finally, the ensemble classifier was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient. The results showed that the overall classification performance of the ensemble model was better than that of the component classifier. The evaluation indexes were also higher. This algorithm can better meet the rapid detection requirements of the novel coronavirus disease COVID-19. [ABSTRACT FROM AUTHOR]

Password guessing is an important research direction in password security.Password guessing based on generative adversarial network(GAN) is a new method proposed in recent years,which guides the update of the generator according to evaluation results on passwords generated by the discriminator.Consequently,password guessing sets can be generated with trained GANs.However,the existing GAN-based password guessing models have low efficiency due to inadequate guidance of the discriminator to the generator.To solve this problem,an improved GAN password guessing model AC-Pass based on reinforcement learning Actor-Critic algorithm is proposed.The AC-Pass model guides the update of the generation strategy of the Actor network at each time step through the output rewards of the discriminator and the Critic network,and realizes the reinforce guidance of password sequence generation process.The proposed AC-Pass model is implemented on RockYou,LinkedIn and CSDN data sets and compared with PCFG model and the existing GANs-based password guessing models such as PassGAN and seqGAN.Results on homologous testing sets and heterologous testing sets indicate that password cracking rate of AC-Pass model on the guessing set is higher than that of PassGAN and seqGAN.Moreover,AC-Pass shows better guessing performance than PCFG when the password spatial distribution between the testing set and the training set is significant.In addition,the AC-Pass model has a large password output space.As the size of password guessing set increases,the cracking rate continues to rise.

Due to the low spatial and temporal density of early meteorological observations in China, The China Meteorological Administration Land Data Assimilation System V3.0 (CLDAS3.0) could not obtain high-quality historical, high-resolution assimilated data. In particular, with high-resolution precipitation assimilation data, it is difficult to estimate the precipitation range accurately because of its substantial spatiotemporal heterogeneity. To solve this problem, our team proposes a two-stage downscaling framework for the precipitation field based on multi-task learning, CLDASSD-PRCP. It improves the accuracy of the downscaling task by predicting the precipitation classification mask first. We verify the algorithm using CLDAS3.0 and site observations as ground truth in Yunnan, China. The results show that CLDASSD-PRCP can accurately generate three types of precipitation masks and reduce the MAE of downscaling results by 0.32 mm/h after adding multi-classification masks (MutiClassesMask). In addition, the effects of light and extreme precipitation on precipitation spatial downscaling are also revealed. CLDASSD-PRCP is significant for establishing statistical downscaling models based on deep learning and provides a new way to deal with atmospheric elements with significant spatiotemporal heterogeneity. [Display omitted] • A two-stage precipitation downscaling framework CLDASSD-PRCP based on multi-task learning is proposed • The different effects of precipitation level on the downscaling task are revealed. • The feasibility of CLDASSD-PRCP in CLDAS-V3.0 historical precipitation data back-calculation is verified. [ABSTRACT FROM AUTHOR]

• We formulate the COVID-19 diagnosis task as a few-shot learning problem. • A self-supervised representation learning method is proposed to diagnose COVID-19 using only a limited number of samples. • Our model is pre-trained on a general chest CT image dataset, andtested on two COVID-19 benchmarks.. The current pandemic, caused by the outbreak of a novel coronavirus (COVID-19) in December 2019, has led to a global emergency that has significantly impacted economies, healthcare systems and personal wellbeing all around the world. Controlling the rapidly evolving disease requires highly sensitive and specific diagnostics. While RT-PCR is the most commonly used, it can take up to eight hours, and requires significant effort from healthcare professionals. As such, there is a critical need for a quick and automatic diagnostic system. Diagnosis from chest CT images is a promising direction. However, current studies are limited by the lack of sufficient training samples, as acquiring annotated CT images is time-consuming. To this end, we propose a new deep learning algorithm for the automated diagnosis of COVID-19, which only requires a few samples for training. Specifically, we use contrastive learning to train an encoder which can capture expressive feature representations on large and publicly available lung datasets and adopt the prototypical network for classification. We validate the efficacy of the proposed model in comparison with other competing methods on two publicly available and annotated COVID-19 CT datasets. Our results demonstrate the superior performance of our model for the accurate diagnosis of COVID-19 based on chest CT images. [ABSTRACT FROM AUTHOR]

The escalating generation of hazardous waste (HW) has become a pressing concern worldwide, straining waste management systems and posing significant health hazards. Addressing this challenge necessitates an accurate understanding of HW generation, which can be achieved through the application of advanced models. The Transformer model, known for its ability to capture complex nonlinear processes, proves invaluable in extracting essential features and making precise HW generation predictions. To enhance comprehension of the key factors influencing HW generation, visualization techniques such as SHapley Additive exPlanations (SHAP) provide insightful explanations. In this study, a novel approach combining classical deep learning algorithms with the Transformer model is proposed, yielding impressive results with an R2 value of 0.953 and an RMSE of 7.284 for HW prediction. Notably, among the five key fields considered—demographics, socio-economics, industrial production, environmental governance, and medical health—industrial production emerges as the primary contributor, accounting for over 50% of HW generation. Moreover, a high rate of industrial development is anticipated to further accelerate this process. [Display omitted] • Transformer structure is effective for hazardous waste prediction. • New hybrid Transformer architecture outperforms in waste prediction. • Industrial production contributes more than 50% to hazardous waste generation. • Varied industrial development rates lead to different hazardous waste generation. [ABSTRACT FROM AUTHOR]

We introduce a sampling-based machine learning approach, Monte Carlo fractional physics-informed neural networks (MC-fPINNs), for solving forward and inverse fractional partial differential equations (FPDEs). As a generalization of the physics-informed neural networks (PINNs), MC-fPINNs utilize a Monte Carlo approximation strategy to compute the fractional derivatives of the DNN outputs, and construct an unbiased estimation of the physical soft constraints in the loss function. Our sampling approach can yield lower overall computational cost compared to fPINNs proposed in Pang et al.(2019), hence it can solve high dimensional FPDEs at reasonable cost. We validate the performance of MC-fPINNs via several examples, including high dimensional integral fractional Laplacian equations, parametric identification of time–space fractional PDEs, and fractional diffusion equation with random inputs. The results show that MC-fPINNs are flexible and quite effective in tackling high dimensional FPDEs. [ABSTRACT FROM AUTHOR]

Increasing generation of municipal solid waste, heterogeneity of waste composition, and complex processes of waste management and recovery have limited the performance of traditional treatment approaches. It is urgent to innovate waste management toward smarter and more efficient modes and break up the bottlenecks of the current system. Recently, deep learning has emerged as a powerful method for revealing hidden patterns or deducing correlations for which traditional treatment approaches face limitations or challenges. However, deep learning concepts and practices have not been widely utilized by researches in municipal solid waste management (MSWM). Herein, this research provides a critical review for deep learning and its application in MSWM. The framework and algorithms of a variety of deep learning methods have been compared and assessed. A body of deep learning applications have been reviewed according to their engagement in waste collection, transportation, and final disposal. Application of deep learning in MSWM stays in its infancy and requires great efforts for further development. The challenges and futures opportunities in the application of deep learning in the MSWM have been discussed to highlight the potential of deep learning in this field. [ABSTRACT FROM AUTHOR]

The combustion behavior, kinetics, thermodynamics and gas products of fine screenings (FS) classified from municipal solid waste (MSW) in an air atmosphere were explored by TG-FTIR. A deep learning model was established using 1D–CNN–LSTM algorithm to predict thermogravimetric data of FS combustion, with visualization technology (TensorBoard) applied to display the weights and biases in various cells. The thermogravimetric analysis (TG) and differential thermal gravity (DTG) curves indicated that the FS combustion process can be divided into four stages. The average activation energy (E a) of FS combusted at different stages, exhibited different change tendencies with increasing levels of conversion (α). The highest enthalpy (ΔH) of 206.40 kJ/mol and free Gibbs energy (ΔG) of 55.03 kJ/mol emerged in stage Ⅳ, while the highest changes of entropy (ΔS) of 169.11 J/(mol·K) occurred in stage Ⅱ. The main gas products (CO 2 , H 2 O and CO) and functional groups (C O and phenols) were all detected. For the 1D–CNN–LSTM model, the optimal settings for the prediction of thermogravimetric data were a neuron number of 150, dropout of 0.003, epoch number of 200, and batch size of 25. The highest correlation coefficient (R2) of 94.41% was obtained using the optimum model parameters, achieving an excellent prediction performance. • Deep learning was applied to predict TG data of FS combustion. • Combustion performance, kinetics and thermodynamics of FS were discussed. • The gaseous products during the combustion of FS were detected. • 1D–CNN–LSTM demonstrated an excellent performance for the prediction of TG data. [ABSTRACT FROM AUTHOR]