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• Thermophilic and hyperthermophilic achieved similar hygienization enhancement. • Efficient inactivation of culturable rather than viable pathogens was achieved. • The diversity of pathogens before and after anaerobic digestion was analyzed. • High temperature and high VFAs concentration resulted to pathogen inactivation. • Methane production and mass balance of systems were studied. Conventional mesophilic anaerobic digestion (AD) is widely used to treat animal manure, but pathogen inactivation remains a significant challenge. Thermophilic and hyperthermophilic hydrolysis pretreatment was thus introduced as a hygienization step in a two-stage anaerobic digestion process. Results from 100-day experiments showed culturable Escherichia coli (E. coli) reduction was up to 6.9 log 10 through a hygienization step, but RT-qPCR tests showed much less reduction in viable E. coli. The difference between viable and culturable cells revealed the complexity in pathogen inactivation. High-throughput sequencing indicated that the second stage in the two-stage AD further reduced the relative abundance of pathogens, including Enterococcus , Streptococcus , and Acinetobacter , while Clostridium_sensu_stricto still exhibited high relative abundance. Thermophilic hygienization induced a 6.7% increment in methane production, while hyperthermophilic pretreatment showed minimal effect on methane production. Focusing on the energy recovery and environmental safety point of view, the introduction of an integrated system incorporating the thermophilic and two-stage anaerobic process is recommended. [ABSTRACT FROM AUTHOR]

Histopathological image segmentation based on encoder–decoder architectures has emerged as a pivotal research area in medical image analysis. However, due to the irrelevant information within multi-channel representations from the encoder, the coarse reuse of shallow features in skip connections may burden the learning and even adversely affect the decoder. While various variants have been developed to cope with this issue, the performance remains unsatisfactory. In this work, we propose a novel encoder–decoder architecture named CA-SegNet to address the above issue more effectively and achieve advanced histopathological image segmentation. Our novelty is twofold: firstly, a bottleneck-structured decoder is developed to improve the integration of multi-channel feature representations, and secondly, a sequence of channel-attention feature fusion modules (CAFFMs) are developed to adaptively guide the reuse of fine-grained shallow features in skip connections while learning the channel-wise dependencies. Experimental results on different publicly available histopathological image datasets demonstrate that our CA-SegNet outperforms existing state-of-the-art methods on both large and small-scale datasets. • A novel deep learning-based CA-SegNet model for histopathological image segmentation. • A channel-attention feature fusion module to greatly improve shallow feature reuse. • A bottleneck-structured decoder for better feature integration. • Outstanding segmentation performance on both large and small-scale datasets. [ABSTRACT FROM AUTHOR]

Crop diseases pose as a major threat to global food security. Minimizing disease-induced damage during crop growth and optimizing crop yields are vital for agricultural sustainability. Therefore, advanced disease detection and prevention of such diseases are crucial and the detection must be prompt and efficient as it is essential for the implementation of appropriate control measures. In this work, a parallel deep learning framework based on deep feature fusion is developed to precisely identify the severity of crop diseases. The framework utilizes ResNet50 and Xception as separate branches for feature extraction. Convolutional layer weights are initialized through transfer learning techniques employing models pre-trained on the ImageNet dataset. A fine-tuning strategy is employed for the optimization of convolutional layers and the design of the top layer. This framework achieves an accuracy of 88.58% on the AI Challenger 2018 dataset, marking an enhancement of 2.8% and 13% over other influential deep learning models, and it also outperforms some recent works. Likewise, the framework exhibits a recognition accuracy of 99.53% on the PlantVillage dataset. Moreover, an Android-based application is developed to diagnose the severity of crop diseases in real-time. The diagnostic system swiftly procures results and provides control recommendations through the capture and upload of images to the platform. The advanced severity detection system reduces the expertise required from users, facilitating precise prevention and control measures whilst focusing on accessibility. This work aims to provide innovative approaches and solutions for disease detection in the agricultural field, utilizing artificial intelligence to enhance agricultural informatization and creating more sustainable farming methods. • Proposed a parallel deep learning framework for identification of crop diseases. • Improved framework performance in imbalanced data with focal loss function. • Conducted extensive experiments on large datasets to validate the framework. • Developed a real-time online mobile diagnosis app using the proposed framework. [ABSTRACT FROM AUTHOR]

Several recent reviews summarize common missing value analysis methods. However, none of them provide a systematic and in-depth summary of the analytical challenges and solutions for dealing with missing values. For the purpose of guiding the handling of missing values, this review aims to consolidate current developments in novel missing-value research methodologies. In particular, we comprehensively investigated cutting-edge missing value solutions and methodically studied the main challenges associated with missing values analysis (missing mechanisms, missing patterns, and missing rates). Furthermore, we reviewed 63 publications that compare different strategies for deleting and imputing missing values. Then we investigated data characteristics, highlighted three main problems when analyzing missing values, and analyzed the performance of missing value solutions in these studied papers. Moreover, we conducted comprehensive experiments on 9 public datasets using typical missing value processing methods and provided a simple guided decision tree for handling missing values. Finally, we described current Research hotspots and open challenges, which give potential research topics. • Analyzed three major difficulties with missing value analysis. • Provided a comprehensive introduction to deletion and imputation missing approaches. • Reviewed and analyzed numerous studies and provide useful rules for processing missing values. • Conducted experiments and provided a guided decision tree for missing value processing. • Analyzed and summarized the existing research hotspots and open challenges of missing values. [ABSTRACT FROM AUTHOR]

Abstract Computer controlled ultra-precision polishing (CCUP) is widely used for high-precision surface finish processing with high form accuracy and good surface finish. CCUP is a deterministic material removal process based on the surface error-profile and the removal characteristic of the polishing tool. Material removal profile is often used to characterize the material removed in polishing process. Deviation phenomenon is a newly detected feature of the material removal profile which may contribute to the non-negligible form error of the polished surface. In this paper, the material removal model is established on the basis of Preston equation to obtain a better understanding of the characteristics of material removal in surface polishing. Firstly, contact stress model is established using Hertz contact theory. Then, relative velocity in the contact area is analyzed using geometric methods which can directly reflect the difference of the velocity of points in the contact area. Finally, single factor analysis and Taguchi method are used to investigate the influence of polishing parameters on the deviation feature of material removal profile and to provide a way for parameter optimization. The simulation and experiment results indicate that the proposed method can describe the deviation features well and parameter optimization strategies are provided in the conclusions. Highlights • The relative velocity model is established using geometric method taking the deformation of the polishing tool into consideration. • Newly defined index is used to evaluate the deviation characteristics of the material removal profile. • This paper provides a basis for parameter optimization strategies for uniform material removal in CCUP. [ABSTRACT FROM AUTHOR]

Estuarine oxygen depletion is one of the worldwide problems, which is caused by the freshwater-input-derived severe stratification and high nutrients loading. In this study we presented the horizontal and vertical distributions of dissolved oxygen (DO) in the Pearl River estuary, together with temperature, salinity, chlorophyll a concentration and heterotrophic bacteria abundance obtained from two cruises during the summer (wet) and winter (dry) periods of 2015. In surface water, the DO level in the summer period was lower and varied greater, as compared to the winter period. The DO remained unsaturated in the summer period if salinity is < 12 and saturated if salinity is > 12; while in the winter period it remained saturated throughout the estuary. In subsurface (> 5 m) water, the DO level varied from 0.71 to 6.65 mg L − 1 and from 6.58 to 8.20 mg L − 1 in the summer and winter periods, respectively. Particularly, we observed an area of ~ 1500 km 2 low DO zone in the subsurface water with a threshold of 4 mg DO L − 1 during this summer period, that located at the fresh- and saline-water intersection where is characterized with severe stratification and high heterotrophic bacteria abundance. In addition, our results indicate that spatial DO variability in surface water was contributed differently by biological and physio-chemical variables in the summer and winter periods, respectively. [ABSTRACT FROM AUTHOR]

• We proposed a multi-objective iterative model selection algorithm. • Employing a stack-based ensemble framework to predict hypertension. • We validate the efficiency of the proposed ensemble model on real-world data. • We investigate the influence of lifestyle features on hypertension prediction. Hypertension is a common health problem that is costly to treat, difficult to control, and frequently results in serious and fatal disorders like cardiovascular disease (CVD) and stroke. The main objective of this work was to design and verify a stacking ensemble framework-based model for predicting hypertension risk prospectively. Firstly, we proposed a Multi-objective Iterative Model Selection (MoItMS) strategy to maximize the accuracy of meta-learners and the diversity of the ensemble model at the same time. An effective method for classifying people for managing population health and assisting in the assessment and identification of hypertension is then provided using a stacking-based multi-objective ensemble framework that can be applied to enormous volumes of clinical data. The National Health and Nutrition Examination Survey (NHANES) collected data from 2007 to 2018. Of the 11,341 patients studied, 67.16 % were non-hypertensive and 32.84% were hypertensive, resulting in an imbalanced data set. According to the findings, the model outperformed 13 individual models and ensemble models in terms of precision (71.13 %), recall (53.76 %), accuracy (76.82 %), F1-measure (61.05 %) and AUC (area under the curve) of 0.84. Furthermore, the proposed ensemble framework produced results that were somewhat more successful (AUC = 0.788) than prior hypertension research using an artificial neural network with similar input features, which produced an AUC of 0.77. We focused on the impact of lifestyle factors on hypertension classification performance and discovered that lifestyle factors can improve the model in distinguishing hypertensive samples. Identifying people at high risk of hypertension will be easier with our method, which we hope to integrate into community health management systems in the future. [ABSTRACT FROM AUTHOR]

In crowdsourcing scenarios, each instance obtains multiple noisy labels from different crowd workers and then gets its integrated label via a label aggregation method. In spite of the effectiveness of label aggregation methods, a certain level of noise still remains in the integrated labels. To solve this problem, several noise correction methods have been proposed in recent years. However, to our best knowledge, these methods seldom take the label confidence of each instance into account. Therefore, we propose a label confidence-based noise correction (LCNC) method. At first, LCNC calculates the label confidence of each instance using its multiple noisy labels to filter all instances and obtains an original clean set and noise set. Second, LCNC builds multiple random trees on the original clean set to recalculate the label confidence of each instance using its predicted labels generated by these random trees and then refilters all instances to obtain a new clean set and noise set. Finally, LCNC builds two heterogeneous classifiers on the new clean set to correct the noise instances in the new noise set according to a consensus voting strategy. The experimental results on 34 simulated and two real-world crowdsourced datasets show that LCNC significantly outperforms all the other state-of-the-art noise correction methods used for comparison. [ABSTRACT FROM AUTHOR]

As the application of information technology in the medical field is resulting in a large amount of medical data. As early withdrawal and refusal of participants, there are a lot of missing values in medical data. Although various processing methods for missing values have been proposed, few methods for those medical data with characteristics of imbalance and mixed-type data. In this work, we proposed an adaptive Laplacian weight random forest, called ALWRF. In ALWRF, feature weights were adjusted dynamically when model constructing, which increases selection probabilities of features with low Laplacian score and high importance. Meanwhile, a random operator is introduced to increase the diversity of trees. Furthermore, we proposed an imputation method based on SMOTE-NC oversampling technology and the ALWRF method for imbalanced and mixed-type data, called SncALWRFI. Meanwhile, Bayesian optimization and cross-validation were employed to search optimal parameters. The experimental results showed that the ALWRF method outperforms random forest and Bayesian optimized random forest in terms of classification and regression accuracy. Further, in the experiment for missing values, the SncALWRFI showed the best imputation accuracy, and it performed high imputation effectiveness in public datasets with characteristics of imbalanced and mixed-type. [ABSTRACT FROM AUTHOR]

• Applied metagenomics to study the impacts of long-term thermal pollution on bacterioplankton functional assembly. • There was a functional regulation of bacterioplankton adaptation to thermal stress. • Bacterioplankton taxa with specific adaptation strategies to thermal stress dominated in the heated seawater. • Thermal pollution also caused changes in the matter and energy metabolic pathways of bacterioplankton communities. Thermal effluents from coastal nuclear power plants have led to undesirable pollution and subsequent ecological impacts on local marine ecosystems. However, despite the ecological importance, we know little about the impacts on functionality of bacterioplankton subjected in systems with long-term thermal pollution. We used metagenomic sequencing to study of the effect of thermal pollution on bacterioplankton community metagenomics in summer in a subtropical bay located on the northern coast of the South China Sea. Thermal pollution (>15 y), which resulted in an increase in the summer seawater temperature around 8°C and caused seawater temperature up to approximate 39°C, significantly decreased bacterioplankton metabolic potentials in photosynthesis, organic carbon synthesis, and energy production. The bacterioplankton community metagenomics underwent a significant change in its structure from Synechococcus -dominant autotrophy to Alteromonas, Vibrio , and Pseudoalteromonas -dominated heterotrophy, and significantly up-regulated genes involved in organic compound degradation and dissimilatory nitrate reduction for the matter and energy acquisition under thermal pollution. Moreover, the bacterioplankton community metagenomics showed an up-regulation with heating of genes involved in DNA repair systems, heat shock responsive chaperones and proteins, and proteins involved in other biological processes, such as biofilm formation and the biosynthesis of unsaturated fatty acids and glycan, to adapt to the thermal environment. Collectively, it indicates a functional regulation of bacterioplankton adaptation to high-temperature stress, which might advance the understanding of the molecular mechanisms of community adaptation to global extreme warming in aquatic ecosystems. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermophilic and hyper-thermophilic hydrolysis were compared in two stage AD. • Long-term microbial acclimation markedly changed the two temperatures' performances. • The hydrolysis and acidogenesis efficiency was comparable at both temperatures. • Hydrolysis & acidogenesis performance don't closely correlate to microbial structure. Improving hydrolysis and acidogenesis through thermophilic and hyperthermophilic temperatures is critical for enhancing the anaerobic decomposition of organic waste like pig manure. However, whether higher temperatures can provide more enhanced performance has not been elucidated experimentally. This study, therefore, conducted a 375-day continuous operation experiment at 55 and 70 °C with a 5-day hydraulic retention time. The two temperature reactors entered a stable state after about 200 days and long-term microbial acclimation markedly changed their performances. In the thermophilic and hyperthermophilic reactor, the hydrolysis efficiencies were obtained at 29.7 % and 27.3 % respectively, whereas the acidogenesis efficiency was relatively low at 1.0 % and 3.1 %. Due to the occurrence of methanogenesis, the volatile fatty acid concentration in the thermophilic reactor was only 45 % of that in the hyperthermophilic reactor. The thermophilic reactor exhibited higher bacterial diversity; however, this difference between the two reactors apparently did not correlate with hydrolysis and acidogenesis performance. [ABSTRACT FROM AUTHOR]

The effect of abrasive grain size on the material removal performances of the grinding surface was studied by theoretical modeling and grinding experiments. The results indicted that a smaller abrasive grain size of the abrasive belts led to smaller microscopic contour height and surface roughness of the ground surfaces, fewer curl chips, and more spherical chips. The smaller grain size also led to smaller macroscopic grinding depth and material removal rate. There was an approximately linear relationship between the macroscopic grinding depth and material removal rate during plane grinding. The slope was related to the abrasive grain size. Grinding force, vibration, vision, and sound signals could distinguish the macroscopic material removal changes caused by abrasive belt particle sizes. [ABSTRACT FROM AUTHOR]

Precipitation regime change presents serious ecological consequences in diverse ecosystems, particularly forests. However, little attention has been paid to soil microbial community sensitivity to precipitation changes despite the potential for significant feedbacks on the large carbon stocks in subtropical forests. Here we examined the responses of both fungal and bacterial communities in aspects of diversity and co-occurrence network to a manipulative alteration of precipitation seasonality (MAPS) by reducing dry-season rainfall, increasing wet-season rainfall, while keeping the total annual rainfall unchanged. Our results showed that MAPS significantly reduced the diversity of the fungal community but not that of the bacterial community. The richness and abundance of Sordariomycetes were reduced, while the abundance of ectomycorrhizal fungi was elevated by MAPS. The co-occurrence network of fungal OTUs (fungal network) was also changed more than the bacterial network. The fungal network under the MAPS treatment featured more links and clusters than in the control, implying the interactions among fungal OTUs could be intensified. Ectomycorrhizal fungi were suggested to affect fungal network structure under MAPS through their functions in phosphorous nutrition, as indicated by the significant correlation between fungal network structure and soil phosphorous content. The MAPS treatment also changed the correlation relationships of fungal network structure with soil enzymes, which were not found for bacterial networks. The activities of several lignocellulose degrading enzymes were positively correlated with overall fungal diversity. Our results indicate that the altered precipitation seasonality in a subtropical forest can more readily influence soil fungal than bacterial communities, with potential for serious consequences such as degradation of recalcitrant organics in soil. [ABSTRACT FROM AUTHOR]

Concerns over water conflicts between human beings and ecosystems are increasing. Also, wastewater discharged by manufacturing industries is causing multiple ecological and environmental impacts in many regions. In this research, to comprehensively assess ecological and environmental impacts of wastewater discharged from large-scale industries, a hybrid life-cycle and fuzzy-set-pair analyses (HLCA-FSPA) approach was proposed. This approach represented an integration of life cycle analysis, set pair analysis, and fuzzy sets theory. The developed method could improve previous studies in systematically reflecting impacts of industrial wastewater in terms of multiple dimensions, and considering uncertain parameters in the evaluation process. It could give a complete and robust assessment of wastewater environmental and ecological impacts based on life cycle inventory/database and uncertainty analyses. The developed HLCA-FSPA method was then applied to a pulp and paper mill in Shandong Province of China. The results indicated that the impact of wastewater at the stage of pulp production was under the limit of China's wastewater discharge standard (i.e., level III) with the connection degree of 0.47. Comparatively, the impact level of the entire life cycle of copying paper production was III with a slightly decreased connection degree (i.e., 0.38). Such a difference reflected possibility variations of the impacts at different stages. The results also indicated that the developed method can be expanded to other areas based on the corresponding LCA database. Thus, the results could provide scientific bases for supporting decision-making in industrial wastewater management to mitigate the associated ecological and environmental impacts. [ABSTRACT FROM AUTHOR]

• Applied a metacommunity framework to study reservoir bacterioplankton communities. • The bacterioplankton spatial patterns were dominantly shaped by species sorting. • Eutrophication increased heterogeneity of co-occurrence bacterioplankton networks. • Larger phytoplankton community variation contributed to deterministic processes. Understanding microbial metacommunity assembly and the underlying methanisms are fundamental objectives of aquatic ecology. However, little is known about how eutrophication, the primary water quality issue of aquatic ecosystems, regulates bacterioplankton metacommunity assembly at a regional scale in reservoirs. In this study, we applied a metacommunity framework to study bacterioplankton communities in 210 samples collected from 42 tropical coastal reservoirs in the wet summer season. We found that the spatial pattern of bacterioplankton community compositions (BCCs) at a regional scale was shaped mainly by species sorting. The reservoir trophic state index (TSI) was the key determinant of bacterioplankton metacommunity assembly. BCC turnover increased significantly with the TSI differences between sites (∆TSI) when ∆TSI was < 20, but remained at a level of about 80% when ∆TSI was > 20. Compared to oligo-mesotrophic and mesotrophic reservoirs, increased heterogeneity of co-occurrence bacterioplankton networks and bacterioplankton β-diversity were observed across eutrophic reservoirs. We propose that larger variation in phytoplankton community assembly may play directly or indirectly deterministic processes in controlling the bacterioplankton metacommunity assembly and became the potential mechanisms behind the observed higher BCC heterogeneity across the eutrophic reservoirs. Our research contributes to a broader understanding of the ecological effects of eutrophication on reservoir ecosystems and provides clues to the management of the tropical coastal reservoirs. [Display omitted] [ABSTRACT FROM AUTHOR]