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This paper studied the individual cases of weather with and without thunderstorm gales in Guangdong Province from April to September 2010-2019. The distribution characteristics of environmental parameters related to the weather with thunderstorm gales were analyzed by using boxplots, and the three hours, six hours, and 12 hours thunderstorm gale forecast models were established. The meteorological events from April to September 2020 were tested by using the Bayesian method. The results show that the occurrence of thunderstorm gales corresponds to higher dewpoint temperature on the ground, more unstable energy, temperature difference between upper and lower layers, and total index. According to the test results, the Bayesian method may to some extent identify thunderstorm gales. The hit rate of the prediction model is above 80%, but there are some empty reports, which need to be addressed in the future. [ABSTRACT FROM AUTHOR]

Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area's precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen's Slope in the baseline and future periods with all RCPs. [ABSTRACT FROM AUTHOR]

Root zone soil moisture (RZSM) is a vital variable for agricultural production, water resource management and runoff prediction. Satellites provide large-scale and long-term near-surface soil moisture retrievals, which can be used to estimate RZSM through various methods. In this study, we tested the utility of an exponential filter (ExpF) using in situ soil moisture by optimizing the optimal characteristic time length T_opt for different soil depths. Furthermore, the parameter analysis showed that T_opt correlated negatively with precipitation and had no significant correlation with selected soil properties. Two approaches were taken to obtain T_opt: (1) optimization of the Nash–Sutcliffe efficiency coefficient (NSE); (2) calculation based on annual average precipitation. The precipitation-based T_pre outperformed the station-specific T_opt and stations-averaged T_opt. To apply the ExpF on grid scale, the precipitation-based T_pre considering spatial variability was adopted in the ExpF to obtain RZSM from a new soil moisture dataset RF_SMAP_L3_P (Random Forest Soil Moisture Active Passive_L3_Passive) continuous in time and space over Huai River Basin. Finally, the performance of RF_SMAP_L3_P RZSM (0–100 cm) was evaluated using in situ measurements and compared with mainstream products, for instance, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity Level 4 (SMOS L4) RZSM. The results indicated that RF_SMAP_L3_P RZSM could captured the temporal variation of measured RZSM best with R value of 0.586, followed by SMAP L4, which had the lowest bias value of 0.03, and SMOS L4 significantly underestimated the measured RZSM with bias value of −0.048 in the basin. Higher accuracy of RF_SMAP_L3_P RZSM was found in the flood period compared with the non-flood period, which indicates a better application for ExpF in wetter weather conditions. [ABSTRACT FROM AUTHOR]

Graph perturbation hinders graph models in real applications, and thus defense methods against graph perturbation have been attracting increasing attention. However, current defense methods limit expressiveness and demand expert knowledge. To overcome these issues, in this paper, we propose a flexible-frequency graph transformer, building on the powerful expressive ability of self-attention. Specifically, we design a frequency-extraction self-attention with three heads to extract multi-frequency representations, i.e., low-frequency representation, hybrid-frequency representation, and high-frequency representation. An adaptive fusion method is then designed to combine diverse representations to output a flexible-frequency representation. This improves the model's expressive ability and also enhances defense against graph perturbation by utilizing comprehensive information. In addition, we adaptively capture robust graph filters within self-attention, eliminating the need for expert knowledge. To boost self-attention's effectiveness, we integrate graph learning to capture graph information before conducting node representation learning within self-attention. Furthermore, we theoretically analyze the feasibility of our proposed method. Extensive experiments demonstrate that our proposed method offers a dynamic and effective defense against graph perturbation compared to existing state-of-the-art methods. • A new graph transformer is proposed to defend against graph perturbations. • It is the first Transformer with a flexible-pass filter for graph data. • Theoretical analysis reveals Transformer functions as a low-pass filter. • Contrastive learning converts the Transformer to a high-pass filter. • Empirical experiments demonstrate strong defense capabilities over GNNs. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma is one of the leading causes of cancer-related mortality globally. The emergence of immunotherapy has been shown to be a promising therapeutic approach for hepatocellular carcinoma in recent years. It has been well known that T cell plays a key role in current immunotherapy. However, sustained exposure to antigenic stimulation within the tumor microenvironment may lead to T cell exhaustion, which may cause treatment ineffectiveness. Therefore, reversing T cell exhaustion has been an important issue for the clinical application of immunotherapy, and a comprehensive understanding of the intricacies surrounding T cell exhaustion and its underlying mechanisms is imperative for devising strategies to overcome the T cell exhaustion during treatment. In this review, we summarized the reported drivers of T cell exhaustion in hepatocellular carcinoma and delineate potential ways to reverse it. Additionally, we discussed the interplay among metabolic plasticity, epigenetic regulation, and transcriptional factors in exhausted T cells in hepatocellular carcinoma, and their implication for future clinical applications. [Display omitted] [ABSTRACT FROM AUTHOR]

With the development of social media, people prefer to express views and share daily life online via visual content, which has led to widespread attention in automatic emotion analysis from images. Capturing the emotions embedded in these social images has always been important yet challenging. In this paper, we propose a visual emotion prediction method that utilizes the affective semantic concepts of an image to predict its emotion. To solve the problems of narrow semantic coverage and low discriminative power of emotions in current semantic concept sets used for visual emotion analysis, we develop a concept selection model to mine emotion-related concepts from social media. Specifically, we propose several selection strategies to build an affective semantic concept set that contains various visual concepts related to emotion conveyance. And they are discovered from affective image datasets and associated tags crawled from websites. To further leverage the discovered affective semantic concepts, we train concept classifiers to predict the concept score of each concept, which are used as the intermediate features to tackle the semantic gap problem for image emotion recognition. Extensive experimental results confirm the validity of the affective semantic concepts and show the improved performance of our method. [ABSTRACT FROM AUTHOR]

Previous methods of multi-view clustering focused on the improvement of clustering effectiveness by detecting common information of all views and individual information for every view, but they ignore the following issues, i.e., the initialization sensitivity, the cluster number determination, and the influence of outliers. However, either single-view clustering or multi-view clustering often suffers from above issues. In this paper, we propose a robust self-tuning multi-view clustering to introduce a sum-of-norm loss function to explore the issue of initialization sensitivity, design a sum-of-norm regularization to automatically determine the cluster number, and employ robust statistics techniques to reduce influence of outliers. Furthermore, we propose an effective alternating optimization method to solve the resulting objective function and then theoretically prove its convergence. Experimental results on both synthetic and real data sets demonstrated that our proposed multi-view clustering method outperformed the state-of-the-art clustering methods, in terms of four clustering evaluation metrics. [ABSTRACT FROM AUTHOR]

Spectral clustering is a popular graph-based clustering method which is widely applied to pattern recognition and image segmentation. Traditional spectral clustering usually involves two separate processes: graph learning and graph-based clustering, making such a two-step strategy easily output sub-optimal performance and moreover, noisy data and imbalanced clusters could make existing clustering methods hard to meet practical necessity. To this end, one-step spectral clustering coupled with a balanced constraint is proposed to jointly optimize the robust low-dimensional representation, the spectral rotation and the cluster indicator in a unified learning framework. Specifically, dimensionality reduction is conducted by combining subspace learning and feature selection to obtain a robust low-dimensional representation. Besides, a spectral rotation mechanism is used to produce one-step clustering for reducing clustering biases, and a balanced constraint is utilized to regularize the clustering result to generate clusters with similar sizes. Moreover, an iterative optimization method is put forward to fast solve the proposed objective function. Experimental results on ten benchmark datasets compared to state-of-the-art spectral clustering methods showed that our proposed clustering method could output the balanced clusters and the competitive clustering performance simultaneously. [ABSTRACT FROM AUTHOR]

Graph Convolutional Networks (GCNs) are widely applied in classification tasks by aggregating the neighborhood information of each sample to output robust node embedding. However, conventional GCN methods do not update the graph during the training process so that their effectiveness is always influenced by the quality of the input graph. Moreover, previous GCN methods lack the interpretability to limit their real applications. In this paper, a novel personalized diagnosis technique is proposed for early Alzheimer's Disease (AD) diagnosis via coupling interpretable feature learning with dynamic graph learning into the GCN architecture. Specifically, the module of interpretable feature learning selects informative features to provide interpretability for disease diagnosis and abandons redundant features to capture inherent correlation of data points. The module of dynamic graph learning adjusts the neighborhood relationship of every data point to output robust node embedding as well as the correlations of all data points to refine the classifier. The GCN module outputs diagnosis results based on the learned inherent graph structure. All three modules are jointly optimized to perform reliable disease diagnosis at an individual level. Experiments demonstrate that our method outputs competitive diagnosis performance as well as provide interpretability for personalized disease diagnosis. • Interpretable learning is used to output interpretable diagnosis results. • Dynamic graph learning coupled with GCN to optimize graph structure. • GCN coupled with above learning are used to output personalized diagnosis. [ABSTRACT FROM AUTHOR]

Drought is one of the significant natural disasters that has a profound impact on human societies, particularly in arid places such as Balochistan, Pakistan. Geographic information system and remote sensing has played a major role in predicting the effect of drought events and mitigate. Therefore, the purpose of this study was firstly to evaluate the spatiotemporal patterns of drought in Balochistan, Pakistan, utilizing MODIS based satellite data and validate the PMD stations data with CHIRPS data. Secondly the objective of this research to quantify the influence of drought on vegetation anomalies and comparison between droughts patterns with vegetation response. Drought conditions in Balochistan by integrating remote sensing (RS) drought indices (RSDI).RSDI was calculated through Hargreaves method using monthly data. The following remaining indices were the main focus of the study i.e., Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), Temperature Vegetation Dryness Index (TVDI), and Precipitation Condition Index (PCI). These indices offered differing perspectives, emphasizing the value of a comprehensive strategy. Approximately 60% of the area was significantly affected by drought conditions, with SPEI values for the period being less than -1.5.SPEI and TVDI performed better in identifying droughts. TVDI values ranged from 0.63 to 0.88, indicating agricultural dryness. For instance, the East experienced a severe drought between 2001 and 2022, according to SPEI. Significant drought events occurred in 2001, 2004, 2009, 2014, and 2022, allowing comparative analysis. TVDI proved more effective than VCI in predicting drought. RDI and TVDI localized drought better than PCI. SPEI, RDI, and TVDI contributed significantly to understanding drought (73.63%, 74.15%, and 72.30% respectively). Considering diverse indices is vital for long-term drought mitigation strategies. RDI, especially valuable with limited temperature data, aids in understanding drought dynamics. This analysis aids in predicting future droughts and mitigating agricultural losses in Balochistan, informing decision-making and adaptive measures. [ABSTRACT FROM AUTHOR]

Spectral clustering is widely applied in real applications, as it utilizes a graph matrix to consider the similarity relationship of subjects. The quality of graph structure is usually important to the robustness of the clustering task. However, existing spectral clustering methods consider either the local structure or the global structure, which can not provide comprehensive information for clustering tasks. Moreover, previous clustering methods only consider the simple similarity relationship, which may not output the optimal clustering performance. To solve these problems, we propose a novel clustering method considering both the local structure and the global structure for conducting nonlinear clustering. Specifically, our proposed method simultaneously considers (i) preserving the local structure and the global structure of subjects to provide comprehensive information for clustering tasks, (ii) exploring the nonlinear similarity relationship to capture the complex and inherent correlation of subjects and (iii) embedding dimensionality reduction techniques and a low-rank constraint in the framework of adaptive graph learning to reduce clustering biases. These constraints are considered in a unified optimization framework to result in one-step clustering. Experimental results on real data sets demonstrate that our method achieved competitive clustering performance in comparison with state-of-the-art clustering methods. [ABSTRACT FROM AUTHOR]

As a typical semiarid farming-pastoral ecotone sensitive to the environmennt, the Plain of West Liaohe Basin (WLBP) is currently experiencing drastic environmental changes. To identify how environmental change affect vegetation in the WLBP, we analysed spatiotemporal variation characteristics of Ecological environment factors based on monthly and annual air temperature (T), precipitation (P) and Normalized Difference Vegetation Index (NDVI) from 1982 to 2015. And the correlations between them were investigated by correlation analysis (Simple correlation, partial correlation and complex correlation) at temporal and spatial scale. The results showed that: (1) the vegetation growth of the WLBP showed ameliorated trend, with a change rate of 0.004/yr.; (2) P was more sensitive to NDVI than T; (3) and the influence of hydrothermal changes on vegetation growth was more significant than that of the change of single climate factors at time scales; (4) the effects of anthropogenic factors on vegetation change were 75.07% (1982–1993) and 98.08% (1994–2015), respectively. At the temp-special scales, P&T and land use type change (LUCC) were the main climatic and anthropogenic factors that affect vegetation changes, respectively. [ABSTRACT FROM AUTHOR]

Conventional spectral clustering is generally divided into two main steps, 1) constructing a reliable spectral representation by similarity matrix or Laplacian matrix; 2) performing K-means clustering on the spectral representation. In this paper, we propose a novel spectral clustering algorithm to improve clustering performance by separately improving these two steps in a same learning framework. Specifically, we first utilize two dimensionality reduction methods to learn the robust low-dimensional spectral representation, and a hypergraph structure to make the spectral representation keep the high-order relation of original data. Furthermore, a spectral rotation clustering method is embedded into the spectral representation learning model to conduct one-step clustering, which effectively reduces the deviation of clustering and obtains a reliable clustering performance. Besides, an effective optimization algorithm is further proposed to solve the objective problem to have a fast convergence. Experimental analysis on real data sets showed that our proposed clustering method outperformed the classical and state-of-the-art spectral clustering methods in terms of four frequently-used clustering metrics. [ABSTRACT FROM AUTHOR]

Server workload in the form of cloud-end clusters is a key factor in server maintenance and task scheduling. How to balance and optimize hardware resources and computation resources should thus receive more attention. However, we have observed that the disordered execution of running application and batching seriously cuts down the efficiency of the server. To improve the workload prediction accuracy, this paper proposes an approach using the long short-term memory (LSTM) encoder-decoder network with attention mechanism. First, the approach extracts the sequential and contextual features of the historical workload data through the encoder network. Second, the model integrates the attention mechanism into the decoder network, through which the prediction for batch workloads can be carried out. Third, experiments carried out on Alibaba and Dinda workload traces dataset demonstrate that our method achieves state-of-the-art performance in mixed workload prediction in cloud computing environment. Furthermore, we also propose a scroll prediction method, which splits a long prediction sequence into several small sequences to monitor and control prediction accuracy. This work helps to dynamically guide the configuration for workload balancing. [ABSTRACT FROM AUTHOR]

• Three suitable composite drought indices were selected for coastal drought monitoring. • The Precipitation Evaporation Runoff Composite Index has advantages in detecting short-term drought conditions. • The response of salinity to long-term droughts varies by basin. Given the complexity of coastal drought events and their unique impacts on coastal ecosystems, it is vital to develop drought indices that can reflect both freshwater availability and impacts on ecological resources to better assess drought conditions over coastal regions. Existing drought indices developed for upland areas may not be suitable to capture the timing and severity of drought in the coastal regions. To explore coastal drought issue more comprehensively, this paper constructed the Coastal Composite Drought Indices (CCDIs) based on the entropy-weighted Euclidean distance method by freely combining six variables: precipitation (P), evaporation (E), runoff (R), salinity (S), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), in the Jinjiang River Basin (JJRB) and the Jiulong River Basin (JLRB). The CCDIs were later compared to the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI) to select the Optimized Drought Indices (ODIs). The results demonstrated that the Precipitation Evaporation Runoff Composite Index (PER) is more sensitive to capturing short-term coastal drought events compared to other CCDIs. PER performed better than SPI and PDSI in detecting the onset of drought development and reflecting drought persistence. The salinity exhibited mixed responses to long-term drought events in different river basins depending on the size of each coastal system and its characteristics, thus raising the issue of regional applicability. Through a series of cross-comparisons and validations, the Runoff NDVI LST Composite Index (RNL) and the Runoff Salinity NDVI Composite Index (RSN) were chosen to monitor long-term droughts in the JJRB and JLRB, respectively. Overall, all three indices have the ability to capture the dynamics and trends of the variables and provide better information on the occurrence, persistence, and end of droughts in coastal areas. [ABSTRACT FROM AUTHOR]

A scene graph provides a powerful intermediate knowledge structure for various visual tasks, including semantic image retrieval, image captioning, and visual question answering. In this paper, the task of predicting a scene graph for an image is formulated as two connected problems, ie, recognizing the relationship triplets, structured as , and constructing the scene graph from the recognized relationship triplets. For relationship triplet recognition, we develop a novel hierarchical recurrent neural network with visual attention mechanism. This model is composed of two attention‐based recurrent neural networks in a hierarchical organization. The first network generates a topic vector for each relationship triplet, whereas the second network predicts each word in that relationship triplet given the topic vector. This approach successfully captures the compositional structure and contextual dependency of an image and the relationship triplets describing its scene. For scene graph construction, an entity localization approach to determine the graph structure is presented with the assistance of available attention information. Then, the procedures for automatically converting the generated relationship triplets into a scene graph are clarified through an algorithm. Extensive experimental results on two widely used data sets verify the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

Spectral clustering is an important component of clustering method, via tightly relying on the affinity matrix. However, conventional spectral clustering methods 1). equally treat each data point, so that easily affected by the outliers; 2). are sensitive to the initialization; 3). need to specify the number of cluster. To conquer these problems, we have proposed a novel spectral clustering algorithm, via employing an affinity matrix learning to learn an intrinsic affinity matrix, using the local PCA to resolve the intersections; and further taking advantage of a robust clustering that is insensitive to initialization to automatically generate clusters without an input of number of cluster. Experimental results on both artificial and real high-dimensional datasets have exhibited our proposed method outperforms the clustering methods under comparison in term of four clustering metrics. [ABSTRACT FROM AUTHOR]

Feature selection is an important preprocessing step for dealing with high dimensional data. In this paper, we propose a novel unsupervised feature selection method by embedding a subspace learning regularization (i.e., principal component analysis (PCA)) into the sparse feature selection framework. Specifically, we select informative features via the sparse learning framework and consider preserving the principal components (i.e., the maximal variance) of the data at the same time, such that improving the interpretable ability of the feature selection model. Furthermore, we propose an effective optimization algorithm to solve the proposed objective function which can achieve stable optimal result with fast convergence. By comparing with five state-of-the-art unsupervised feature selection methods on six benchmark and real-world datasets, our proposed method achieved the best result in terms of classification performance. [ABSTRACT FROM AUTHOR]

Previous Spectral Feature Selection (SFS) methods output promising feature selection results in many real-world applications, which deeply depend on the preservation of the local or global structures of the data via learning a graph matrix. However, current SFS methods 1) learn the graph matrix in the original data which may contain a number of noise to affect the results of feature selection, 2) conduct the learning of a low-dimensional feature space and the graph matrix individually, thus hard achieve the optimal results of feature selection even though both of these two steps achieve their individual optimization, and 3) consider either the local or global structure of data to difficult provide complementary information for feature selection. To address the above issues, this paper proposes a novel supervised feature selection algorithm to simultaneously preserve the local structure (via adaptive structure learning in a low-dimensional feature space of the original data) and the global structure (via a low-rank constraint) of the data. Moreover, we also propose a new optimization method to fast optimize the resulting objective function. We finally verify the proposed method on eight real-word and benchmark datasets, by comparing with the state-of-the-art feature selection methods, and experimental results show that our proposed method achieves competitive results in term of classification performance. [ABSTRACT FROM AUTHOR]

Agricultural drought threatens global water security, food security, and natural ecosystems. Accurate identification of agricultural drought is a crucial task to mitigate its consequences. However, it is challenging to achieve reliable and accurate regional agricultural drought assessment in both wet and dry climates at the same time. Therefore, the objective of this study is to identify a reliable and accurate agricultural drought index that performs well in both dry and wet climates. Drought indices such as the Standardized Precipitation Index (SPI), the Vegetation Condition Index (VCI), the Soil Moisture Anomaly index (SMA), and the Drought Severity Index (DSI) were calculated and compared against in situ drought information devised by official sources in China. The results showed that: (1) DSI based on the Global Land Data Assimilation System (GLDAS) products performed the best in identifying agricultural drought in both dry and wet climate regions of China. (2) Agricultural regions such as Northern arid and semiarid regions, Northeast China Plain, Huang-Huai-Hai Plain, and Loess Plateau, experienced moderate and severe agricultural droughts with a frequency of 20%. (3) The frequency of agricultural droughts observed in Northern arid and semi-arid regions and Northeast China Plain has slowed significantly over the last two decades with a significance level of 0.01. On the other hand, the number of agricultural droughts has increased in Yunnan-Guizhou Plateau since 2002. [Display omitted] • The accuracy of the five drought indices was explored. • The drought results were validated with in situ data from official sources. • Drought Severity Index based on GLDAS has better performance in China. • The agricultural drought in the north moderated and the south aggravated in China. [ABSTRACT FROM AUTHOR]

In order to select informative features from a high-dimensional multi-view dataset, we have proposed a feature selection method that simultaneously embedding the low-rank constraint, sparse representation, global and local structure learning into a unified framework. Firstly, we utilize the conventional regression function ( i.e. the least square loss function) to form a novel regression framework by introducing a low-rank constraint and a relaxation term. And then we employ an l 21 -norm regularization term to filter out the redundant and irrelative features. Furthermore, we utilize a hypergraph based regularization term rather than the simple graph to construct a Laplacian matrix that will be used in enhancing the inherent association of data. Besides, we proposed a novel optimization algorithm to solve the objective function. Finally, we feed the reduced data got by the proposed feature selection method into Support Vector Machines (SVM) in term of classification accuracy. The experimental results showed that the proposed method achieved the best classification performance, compared with the state-of-the-art feature selection methods on real multi-view dataset. [ABSTRACT FROM AUTHOR]

This paper proposed a novel feature selection method that includes a self-representation loss function, a graph regularization term and an $${l_{2,1}}$$ -norm regularization term. Different from traditional least square loss function which focuses on achieving the minimal regression error between the class labels and their corresponding predictions, the proposed self-representation loss function pushes to represent each feature with a linear combination of its relevant features, aim at effectively selecting representative features and ensuring the robustness to outliers. The graph regularization terms include two kinds of inherent information, i.e., the relationship between samples (the sample-sample relation for short) and the relationship between features (the feature-feature relation for short). The feature-feature relation reflects the similarity between two features and preserves the relation into the coefficient matrix, while the sample-sample relation reflects the similarity between two samples and preserves the relation into the coefficient matrix. The $${l_{2,1}}$$ -norm regularization term is used to conduct feature selection, aim at selecting the features, which satisfies the characteristics mentioned above. Furthermore, we put forward a new optimization method to solve our objective function. Finally, we feed reduced data into support vector machine (SVM) to conduct classification on real datasets. The experimental results showed that the proposed method has a better performance comparing with state-of-the-art methods, such as k nearest neighbor, ridge regression, SVM and so on. [ABSTRACT FROM AUTHOR]

The West Liaohe Plain (WLP) is a typical crop-pastoral ecotone of the semiarid area in Northwestern, China. Land use/cover change (LUCC) of the WLP might endanger this ecosystem, triggering long-lasting environmental concerns. LUCC data for China (1980–2020) and hydrometeorological data were analyzed to reveal factors contributing to change and explore sustainable development opportunities. The results show that characteristics of the main land-use types in the WLP have changed significantly, especially cultivated land area, which increased by 15.2% and 6.79% during the periods 1980–1995 and 2000–2020, respectively. Response relationships were observed due to natural (precipitation, temperature, and runoff) and anthropogenic (economy) factors and LUCC. Between 2000 and 2020, the impact of anthropogenic factors on cultivated land was stronger than on grassland at the class and landscape level, using the landscape indices which were selected, including percent of landscape (PLAND), number of patches (NP), largest patch index (LPI), and Shannon's evenness index (SHEI). Expansion of cultivated land from 1990 to 1995 was not only related to anthropogenic factors but also to hydrological & climatic factors. The results of this study have the potential to influence sustainable land resource development. [Display omitted] • LUCC in study area is affected by natural and anthropogenic factors. • Response between environmental factors and LUCC has periodicity. [ABSTRACT FROM AUTHOR]

Groundwater-dependent ecosystems (GDEs) in the lower reaches of arid inland river basins of Northwest China have degraded because of a decrease in water supply caused by excessive socio-economic development in the middle reaches of the river. The degraded GDEs have resulted in serious eco-environmental problems, such as drying of terminal lakes, river channel shifts, declines in riparian forest, desert activation, intensification of desert dust storms, and salinization of secondary soils. These problems have further hindered local and regional efforts at sustainable development. An ecosystem conservation and restoration project was initiated in 2000 in order to deal with the eco-environmental problems. The project has been underway for 15 years, but problems still exist. Using the Heihe River and Tarim River basins as examples, this paper explains the ecological–environmental problems and their causes, describes with the status of ecosystem restoration and conservation, analyzes the problems in the ongoing ecosystem conservation and restoration, and suggests strategies for achieving sustainable ecosystem restoration in the lower reaches of the arid inland river basins of Northwest China. [ABSTRACT FROM AUTHOR]

There can be marked variations in soil hydraulic properties in a soil vertical profile from the soil surface to the base of the root zone. Many existing two-layer soil moisture ( TLSM) models cannot well describe typical stratified soil profiles. A modified TLSM model is presented in this study. The modified model results and those from two existing models are compared with field observations. The modified TLSM model had the best agreement with the field observations. In both the surface layer and the root zone layer, the root mean square errors of soil moisture estimated by the modified model were smaller than those for the other models. The parameters in the modified TLSM model are relatively easy to determine. The modified TLSM model offers clear advantages over current TLSM models. [ABSTRACT FROM AUTHOR]

This paper proposes a graph feature selection method for dementia diagnosis, by adding the information inherent in the observations into a sparse multi-task learning framework. Specifically, this paper first defines two relations (i.e., the feature–feature relation and the sample–sample relation, respectively) based on the prior knowledge in the data. The feature–feature selection enforces the similarity relationship between features to be preserved in the coefficient matrix while the sample–sample relation is designed to preserve the relation between samples invariant in the predicted space. Then we embed these two kinds of relations into a multi-task learning framework (i.e., a least square loss function plus an l 2 -norm regularization term) to conduct feature selection. Furthermore, we feed the reduced data into Support Vector Machine (SVM) for conducting the identification of Alzheimer׳s Disease (AD). Finally, the experimental results on a subset of the Alzheimer׳s Disease Neuroimaging Initiative (ADNI) dataset showed the effectiveness of the proposed method in terms of classification accuracy, by comparing with the state-of-the-art methods, including k Nearest Neighbor ( k NN), ridge regression, SVM, and so on. [ABSTRACT FROM AUTHOR]

Six global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 under three Respectively Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5) were used to assess the impact of climate change on streamflow for the Huangnizhuang catchment (HNZ) in China. Change factor method was used for bias correction between GCM outputs and observations and the SWAT model was used to simulate the hydrological processes. The results indicated that the SWAT model performed well in the study catchment with a monthly Nash-Sutcliffe efficiency (NS) of 0.93 and 0.91 and daily NS of 0.63 and 0.68 for calibration and validation periods respectively. Their corresponding relative errors were −2.2 and 8.9, and −2.6 and 8.5 % respectively. The ensemble of multi-GCMs projected an increase of precipitation in the middle and end of twenty-first century over the HNZ, ranging from −2.4 to 9 %. However, streamflow is likely to decline in the future, ranging −6.9 to 0.8 %, mainly due to an increase of evapotranspiration in a warming world, as air temperature shows steadily increases for all the GCMs and RCPs. Average monthly streamflow from six GCMs are likely to increase in August and September but decline from October to June. The associated uncertainties of the reported results were also discussed. It includes, but is not limit to, different GCMs, emissions scenarios, downscaling techniques as well as hydrological simulations. The results of this study can inform planning of long-term basin water management strategies taking into account global change scenarios. [ABSTRACT FROM AUTHOR]

This study aims to identify the parameters that are most important in controlling the Noah land surface model (LSM), the analysis of parameter interactions, and the evaluation of the performance of parameter optimization using the parameter estimation software PEST. We found it necessary to analyze parameter sensitivity in order to properly simulate hydrological variables such as latent heat flux in the Huaihe River Basin, China. The parameters under study in the Noah LSM link thermodynamic and hydrological parts into a complete model. To our knowledge, this parameter interaction in the Noah LSM has never been studied before. There are, however, several studies concerning the influence of vegetation types and climate conditions on parameter sensitivity of the Noah LSM. Three sensitivity analysis methods, the including local sensitivity analysis method SENSAN, regional sensitivity analysis, and Sobol's method, were tested. Five experimental sites in the Huaihe River Basin were chosen to perform the simulations. The results show that the Noah LSM parameter sensitivities were impacted by the choice of the analysis method. The local method SENSAN often produced significant differences in results compared to the two global methods. The parameter interactions investigated made a significant contribution towards elucidating how one process influences another in the Noah LSM. The results show that parameters were not transferable solely based on vegetation types but also rely on climate conditions. According to the sensitivity analysis results, four sensitive parameters were chosen to be optimized using the PEST method. PEST is a widely used method for estimating parameters in models. Root-mean-square error was used to evaluate the effect of the optimization. Generally in all sites, the optimized parameters values perform better than the original parameter values. [ABSTRACT FROM AUTHOR]

Although variable refrigerant flow systems have become attractive due to good energy performance in part-load conditions, the shortcoming of the variable refrigerant flow system in outdoor air ventilation has not been well solved. A variable refrigerant flow and variable air volume combined air-conditioning system was proposed to solve this problem. The variable air volume part of the combined system consists of an outdoor air processing unit and air supply and distribution devices. The combined system was found having potential to obtain high-level energy efficiency without compromising thermal comfort by an intelligent control system. Therefore, a so-called global coordinated optimization strategy based on a local coordinated optimization strategy is proposed in this study. The local coordinated optimization strategy decreases energy consumption of the combined system through optimizing load allocation between the variable refrigerant flow unit and the outdoor air processing unit. The global coordinated optimization strategy has a much deeper coordination of the variable refrigerant flow and outdoor air processing units, as it combines the local coordinated optimization strategy with stopping and restarting of the outdoor air processing unit. Performances of the combined system in heating conditions under this strategy are evaluated in the simulation platform. Results show that the global coordinated optimization strategy can effectively reduce the energy consumption of the combined system. [ABSTRACT FROM PUBLISHER]

Abstract: An optimal control strategy for minimizing the energy consumption of variable refrigerant flow (VRF) and variable air volume (VAV) combined air conditioning systems was presented. The combined system was proposed to take advantages of VAV systems to solve the ventilation problem of VRF systems. The VAV part consists of an outdoor air processing unit and air supply and distribution devices. To determine set-point of the optimal control variable, i.e. outdoor air supply temperature, this paper proposed an optimal control strategy based on adaptive predictive model and recursive least squares estimation technique. Typical days in summer and winter were selected to test the strategy. Results indicated that the optimal control strategy reduces energy consumption of the combined system by 32.17% in summer and 2.47% in winter. The overall energy efficiency is enlarged by 12.18% in summer and 3.37% in winter, compared with the benchmark operation strategy. [Copyright &y& Elsevier]

Inhibition of Diacylglycerol O-acyltransferase 1 (DGAT1) has been a mechanism of interest for metabolic disorders. DGAT1 inhibition has been shown to be a key regulator in an array of metabolic pathways; however, based on the DGAT1 KO mouse phenotype the anticipation is that pharmacological inhibition of DGAT1 could potentially lead to skin related adverse effects. One of the aims in developing small molecule DGAT1 inhibitors that target key metabolic tissues is to avoid activity on skin-localized DGAT1 enzyme. In this report we describe a modeling-based approach to identify molecules with physical properties leading to differential exposure distribution. In addition, we demonstrate histological and RNA based biomarker approaches that can detect sebaceous gland atrophy pre-clinically that could be used as potential biomarkers in a clinical setting. [ABSTRACT FROM AUTHOR]

Highlights: [•] A VRF model for heating mode is developed and validated. [•] The model is generic and of component-number independence. [•] A VRF-ventilation combined AC system is proposed to make use of merits of both parts. [•] The combined AC system could maintain thermal comfort and IAQ simultaneously. [Copyright &y& Elsevier]

Abstract: Generic simulation models noted as AGM-I and AGM-II for performance and control analysis of variable refrigerant flow (VRF) systems were developed. Firstly, simulation models from component to whole VRF system were addressed. Then the simulation models were validated using experimental data reported in an open literature. The average error percentages to predict system cooling capacity, energy consumption and COP are 4.69%, 4.64%, 1.19%, respectively. Finally, tests were carried out. Results show that the developed models are fast computing and evaporator-number independent. From the computation speed point of view, the AGM-I is more suitable for multi-evaporator VRF system, while the AGM-II is more suitable for the one evaporator VRF system. Test results also show the system models good ability responding to varying conditions, including inlet air temperature of the evaporator, outdoor air temperature, opening of the EEVs and compressor speed, which are all very important variables for the control analysis. [Copyright &y& Elsevier]

Abstract: This paper presents a new fault diagnosis method for sensors in an air-handling unit based on neural network pre-processed by wavelet and fractal (NNPWF). Three-level wavelet analysis is applied to decompose the measurement data, and then fractal dimensions of each frequency band are extracted and used to depict the failure characteristics of the sensors. With these procedures, a signal is extracted into an eigenvector which consists of several fractal dimensions. Following, the eigenvector is introduced into a neural network developed and trained to diagnose the sensor faults. When new measurement data are obtained, similar way is applied to get the eigenvector and the prediction. By comparing the prediction with the objective vectors, the sensor faults can be diagnosed. The fault diagnosis method has been validated and the results show that the proposed method can diagnose different kinds of fault conditions efficiently. Moreover, comparing to the previous work, there is an increase in diagnosis efficiency as large as 15% for the same type of fault. [Copyright &y& Elsevier]

Abstract: In the hilly regions of China, developing sustainable agriculture requires implementing conservation management practices that prevent soil erosion and conserve soil and water resources. In the semiarid northwest Loess Plateau, the primary conservation management practice is terracing. Numerical simulation of soil water dynamics in terraces is potentially an efficient means of investigating the effects of terrace design on moisture retention, but little information is available on the accuracy of such simulations. In this work, we evaluated the accuracy of HYDRUS-2D simulations of water infiltration and redistribution in fallow, level, dryland terraces located in the Loess Plateau. The simulated soil water content distributions were in good agreement with experimental data. Modeling analyses showed that about one-third of the evaporative water losses occurred from the terrace riser surface. To prevent such losses, it is advisable to mulch the riser and minimize the riser surface area. The simulations also demonstrated that with other dimensions equal, wide terraces retain more water on a percentage basis than narrow ones due to a lower evaporating surface area are per unit volume of water storage. With other design considerations being equal, wide beds and minimal riser surface areas will likely enhance water capture and retention. Future analyses of terrace moisture dynamics may additionally include simulations of root water uptake, surface ponding, and runoff. [Copyright &y& Elsevier]

• A flash drought identification method is developed, and it includes rapid intensification and drought period and tracks flash drought paths in time and space. • This is the first specific study of the regional characteristics of flash droughts in a humid and semi-humid region. • Flash droughts in the Huaibei Plain most likely occur in May, August and October. • The main flash drought transfer pathway in the Huaibei Plain is an east to west transition, accounting for 44% of the flash droughts. Flash droughts are regional phenomena that can manifest in large areas with rapid intensification for a period of time. Few studies have considered the spatial pathways of flash droughts or their drought period. This study uses the five criteria based on the standardized evaporative stress ratio (SESR) method to identify flash droughts, and when a SESR value recovers to the 20th percentile, the flash drought is considered to terminate. To define spatially continuous flash droughts accurately, the usual order of first calculating drought patches and then identifying flash droughts is reversed to first identify flash droughts on the grid and then determine flash drought patches. In addition, this study tracks the spatial paths of flash droughts via centroid transfers of flash drought patches. Using MOD16 data, the methodology is evaluated by analyzing the regional characteristics of flash droughts in the Huaibei Plain of China from 2001 to 2019. The flash droughts in this region most frequently tracked in the northeast and west. The average flash drought duration was 31 days, of which the rapid intensification period was 18 days and drought period was 14 days. Flash drought events in this region mostly occurred in May, August and October, and east to west transition and non-transitions, which accounted for 44% and 22%, respectively, were the main spatial track paths. Only 26% of flash drought events transitioned to long term drought events. This study expands our knowledge of the evolution process of flash droughts to space-time dimensions, which is essential for flash droughts early warning and agricultural water management. [ABSTRACT FROM AUTHOR]

Abstract: Homologues of Gpi8p, Gaa1p, Gpi16p, Gpi17p, and Cdc91p are essential components of the GPI transamidase complex that adds glycosylphosphatidylinositols (GPIs 1) to newly synthesized proteins in the ER. In mammalian cells, these five subunits remain stably associated with each other in detergent. In yeast, we find no stable stoichiometric association of Gpi17p with the Gpi8p–Gpi16p–Gaa1p core in detergent extracts. Random and site-directed mutagenesis generated mutations in several highly conserved amino acids but did not yield nonfunctional alleles of Gpi17p and a saturating screen did not yield any dominant negative alleles of Gpi17p. Moreover, Gpi8p becomes unstable when any one of the other subunits is depleted, whereas Gpi17p is slightly affected only by the depletion of Gaa1p. These data suggest that yeast Gpi17p may be able to exert its GPI anchoring function without interacting in a stable and continuous manner with the other GPI-transamidase subunits. Shutting down ER-associated and vacuolar protein degradation pathways has no effect on the levels of Gpi17p or other transamidase subunits. [Copyright &y& Elsevier]

Water is a main controlling factor for economic development and eco-environmental protection in the inland river basins of arid western China. Ground-water, as a component of total water resources, plays a dominant role in the development of western China because of its stable quantity and quality. In recent years, the utilization ratio of surface water has been raised, the ground-water recharge rate from surface water has been reduced, and ground-water has been exploited on a large scale. This has led to the decline of ground-water levels and the degradation of eco-environments. Taking the Heihe river basin as an example, this paper describes ground-water characteristics in the water cycle, deals with the status of ground-water resources, analyses the problems of ground-water resource development and, finally, suggests strategies for rational use of ground-water resources in the arid inland river basins of western China. [Copyright &y& Elsevier]

The aim of this study was to identify key genes whose expression is altered by heme and heme deficiency in the human erythroleukemia K562 cells and in the NGF-induced rat pheochromocytoma neuronal PC12 cells, respectively. By quantitative RT-PCR, Northern blotting, and Western blotting analyses, we found that the expression of the CDK inhibitors p18 and p21 was upregulated at the early and late stages of heme-induced erythroid differentiation of K562 cells, respectively, while the expression of cyclin D1 was downregulated. Data from succinyl acetone and desferrioxamine treatments suggest that these effects of heme in K562 cells were specific. Further, by microarray expression analysis, we found that inhibition of heme synthesis by succinyl acetone in NGF-induced PC12 cells drastically altered the expression of several groups of important neuronal genes, including the structural genes encoding neurofilament proteins and synaptic vesicle proteins, regulatory genes encoding signaling components β-arrestin and p38 MAPK, and stress-response genes encoding hsp70. These results show that heme and heme deficiency affect the expression of diverse genes in a cell-type specific manner in mammalian cells, and that heme, although needed at different levels, is critical for both erythropoiesis and neurogenesis. These studies provide insights into how heme may act to control diverse regulatory processes in mammals. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal characteristics of hydrological components and their impacts on vegetation are critical for comprehending hydrological, climatological, and ecological processes under environmental change and solving future water management challenges. Innovative methods need to be developed in semiarid areas to analyze the special hydrological factors in the water resource systems of these areas. Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) were applied with the normalized difference vegetation index (NDVI) data in this paper to analyze spatiotemporal changes of hydrological factors in the Xiliaohe River Basin (XRB). The results showed that precipitation (P), evapotranspiration (ET) and temperature (T) had similar seasonal change patterns at rates of 0.05 cm/yr., 0.01 cm/yr. and −0.05 °C/yr., respectively. Total water storage change (TWSC) was consistent with the change trend of soil moisture change (SMC) and showed a fluctuating trend. Groundwater change (GWC) showed a decreasing trend at a rate of −0.43 cm/yr. P and ET had a greater impact on GLDAS data (R = 0.634, P < 0.05 and R = 0.686, P < 0.01, respectively) than on other factors. GWC was more sensitive to changes in T (R = 0.570, P < 0.05). Furthermore, a lag period of 0 to 1 months was observed for the effects of P and ET on TWSC and GLDAS. NDVI showed an upward trend at a rate of 0.001 yr−1 between 2002 and 2014. A spatial distribution of NDVI was heterogeneous in the study area. ET, GLDAS and GWC in growing season limited vegetation growth and were more important than other factors in XRB. The results may contribute to an understanding of the relationships between the hydrological cycle and climate change and provide scientific support for local environmental management. [ABSTRACT FROM AUTHOR]

Unsupervised spectral feature selection (USFS) methods could output interpretable and discriminative results by embedding a Laplacian regularizer in the framework of sparse feature selection to keep the local similarity of the training samples. To do this, USFS methods usually construct the Laplacian matrix using either a general-graph or a hyper-graph on the original data. Usually, a general-graph could measure the relationship between two samples while a hyper-graph could measure the relationship among no less than two samples. Obviously, the general-graph is a special case of the hyper-graph and the hyper-graph may capture more complex structure of samples than the general graph. However, in previous USFS methods, the construction of the Laplacian matrix is separated from the process of feature selection. Moreover, the original data usually contain noise. Each of them makes difficult to output reliable feature selection models. In this paper, we propose a novel feature selection method by dynamically constructing a hyper-graph based Laplacian matrix in the framework of sparse feature selection. Experimental results on real datasets showed that our proposed method outperformed the state-of-the-art methods in terms of both clustering and segmentation tasks. [ABSTRACT FROM AUTHOR]

The development of multimodal media compensates for the lack of information expression in a single modality and thus gradually becomes the main carrier of sentiment. In this situation, automatic assessment for sentiment information in multimodal contents is of increasing importance for many applications. To achieve this, we propose a joint sentiment part topic regression model (JSP) based on latent Dirichlet allocation (LDA), with a sentiment part, which effectively utilizes the complementary information between the modalities and strengthens the relationship between the sentiment layer and multimodal content. Specifically, a linear regression module is developed to share implicit variables between image–text pairs, so that one modality can predict the other. Moreover, a sentiment label layer is added to model the relationship between sentiment distribution parameters and multimodal contents. Experimental results on several datasets verify the feasibility of our proposed approach for multimodal sentiment analysis. [ABSTRACT FROM AUTHOR]

• The effect of climate change on groundwater contributions to winter wheat are investigated. • The exchanges between soil water and groundwater will increase in the future. • Climate change alters groundwater contributions to the root zone of winter wheat during each growth period, especially in the wet season. • Groundwater contributions remove the need for irrigation under RCP 4.5 in wet and normal growing seasons. Winter wheat is the predominant crop in the Huaibei Plain of Anhui Province, China. The Huaibei Plain has a transitional climate and a shallow water table. As global warming accelerates, climate change in the area may impact agricultural production. In shallow groundwater regions, capillary rise can be large, and it can act as an important water resource for crop use. However, it is not clear how climate change will impact groundwater depths and crops. Based on local weather data from 1960 to 2010, a general circulation model, HadGEM2-AO, which performed best in simulating precipitation in the study area, and three Representative Concentration Pathways (RCPs, RCP 2.6, RCP 4.5 and RCP 8.5), the validated numerical model HYDRUS-1D was used to estimate the daily groundwater contribution to the crop root zone in 2011–2100 of different hydrological growing seasons. The largest groundwater contribution and deep drainage occurred at a water table depth of 1.5 m. The groundwater contribution to crop root zones was largest for RCP 2.6, and the deep drainage was largest for RCP 8.5. The results indicated that climate change could alter the distribution of the groundwater contribution in each growing period. Meanwhile, the phenophases of winter wheat for RCP 2.6 were delayed while they were advanced for RCP 4.5 and RCP 8.5. In wet and normal seasons, there was no need for irrigation for RCP 4.5 during the whole growing season. However, in a dry season, a small amount of irrigation was still needed during the maturity stage for RCP 2.6. In general, the total irrigation amounts needed for winter wheat growth were reduced for all hydrological conditions with the three RCPs. [ABSTRACT FROM AUTHOR]

• Similarity matrix is obtained from the low-dimensional feature space of original data where both the influence of noise and the issue of high-dimensional data are considered. • Optimized K-means clustering rotates original result of K-means clustering to search optimized clustering hyperplane which partition data points into clusters. • Each of four parts (similarity matrix learning, spectral representation learning, optimized K-means clustering, and transformation matrix learning) is iteratively updated until convergence criteria is met. Previous spectral clustering methods sequentially conduct three steps, i.e., similarity matrix learning from original data, spectral representation learning, and K-means clustering on spectral representation, respectively, to difficultly output robust clustering result even though each of three steps achieves individual optimization. The reason is that each goal of former two steps is not focused on achieving optimal clustering result. Moreover, original data usually contains noise to affect the clustering result, as well as has high-dimensional representation to easily result in the curse of dimensionality. In this paper, we propose a deep spectral clustering method which embeds four parts (i.e., similarity matrix learning, spectral representation learning, optimized K-means clustering, and transformation matrix learning) in a unified framework with the following advantages: 1) similarity matrix is obtained from the low-dimensional feature space of original data where the influence of both noise and high-dimensional data are considered; 2) optimized K-means clustering rotates original result of K-means clustering to search optimized clustering hyperplane which partitions data points into clusters; and 3) each of four parts is iteratively updated so that the clustering result is obtained based on the feedback of other three parts. As a result, our proposed framework develops a two-task deep clustering model with linear activation functions to output effective clustering result. Experimental results on real data sets show the effectiveness of our method in terms of four clustering evaluation metrics, compared to state-of-the-art clustering methods. [ABSTRACT FROM AUTHOR]

The sentiment analysis of microblog text has always been a challenging research field due to the limited and complex contextual information. However, most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of emotional keywords while ignoring the transition or progressive impact of words in different positions in the Chinese syntactic structure on global sentiment, as well as the utilization of emojis. To this end, we propose the emotion-semantic-enhanced bidirectional long short-term memory (BiLSTM) network with the multi-head attention mechanism model (EBILSTM-MH) for sentiment analysis. This model uses BiLSTM to learn feature representation of input texts, given the word embedding. Subsequently, the attention mechanism is used to assign the attentive weights of each words to the sentiment analysis based on the impact of emojis. The attentive weights can be combined with the output of the hidden layer to obtain the feature representation of posts. Finally, the sentiment polarity of microblog can be obtained through the dense connection layer. The experimental results show the feasibility of our proposed model on microblog sentiment analysis when compared with other baseline models. [ABSTRACT FROM AUTHOR]

The global industrialization and modernization have witnessed a rapid progress made in agricultural production, along with the issue of soil heavy metal (HM) pollution, which has posed severe threats to soil quality, crop yield, and human health. Phytoremediation, as an alternative to physical and chemical methods, offers a more cost-effective, eco-friendly, and aesthetically appealing means for in-situ remediation. Despite its advantages, traditional phytoremediation faces challenges, including variable soil physicochemical properties, the bioavailability of HMs, and the slow growth and limited biomass of plants used for remediation. This study presents a critical overview of the predominant plant-based HM remediation strategies. It expounds upon the mechanisms of plant absorption, translocation, accumulation, and detoxification of HMs. Moreover, the advancements and practical applications of phyto-combined remediation strategies, such as the addition of exogenous substances, genetic modification of plants, enhancement by rhizosphere microorganisms, and intensification of agricultural technologies, are synthesized. In addition, this paper also emphasizes the economic and practical feasibility of some strategies, proposing solutions to extant challenges in traditional phytoremediation. It advocates for the development of cost-effective, minimally polluting, and biocompatible exogenous substances, along with the careful selection and application of hyperaccumulating plants. We further delineate specific future research avenues, such as refining genetic engineering techniques to avoid adverse impacts on plant growth and the ecosystem, and tailoring phyto-combined strategies to diverse soil types and HM pollutants. These proposed directions aim to enhance the practical application of phytoremediation and its integration into a broader remediation framework, thereby addressing the urgent need for sustainable soil decontamination and protection of ecological and human health. [ABSTRACT FROM AUTHOR]

• IMERG-E and IMERG-L is evaluated and compared with GSMaP-MVK during the record-breaking Meiyu event in 2020. • IMERG-L can generally reproduce the diurnal cycle of precipitation amount, with a two-hour earlier peak. • The bias of IMERG products in the afternoon is higher than in the morning for all precipitation intensities. • The performance of IMERG in reproducing rainstorm events fluctuates significantly throughout the day, influenced by the use of varying data sources. • IMERG-L (14 h, time lag) is recommended for relevant near-real time applications during the extrme Meiyu season, provided that the time lag is permissible. The extreme Meiyu season, characterized by continuous heavy precipitation, is often leads to numerous flood-related disasters. Accurate and timely monitoring of extreme Meiyu precipitation is crucial for the effective prevention and mitigation of these disasters. The near-real-time (NRT) products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), specifically IMERG-E and IMERG-L products, have high potential utility to acquire timely precipitation data. Therefore, it is important to quantify their errors and uncertainties during the extreme Meiyu season. In this study, we assess and compare the performance of IMERG NRT products with Global Satellite Mapping of Precipitation Motion Vector Kalman product (GSMaP-MVK) using hourly gauge observations during the recor-breaking Meiyu event in 2020. The main conclusions are as follows: all three satellite products generally capture the spatial–temporal evolution of Meiyu precipitation, with IMERG-L showing the closest agreement with gauge observations, particularly for the precipitation center. In terms of the statistical and volumetric categorical metrics, IMERG-L performs best among the three products, while IMERG-E does not exhibit superior performance compared to GSMaP-MVK. Regarding the diurnal cycle, IMERG-L generally reproduces the diurnal cycle of observed precipitation amount with a peak time occurring two hours earlier. In contrast, both IMERG-E and GSMaP-MVK exhibit notably poorer performance. When it comes to the diurnal cycle of precipitation frequency and intensity, both IMERG products display bad performance in reproducing their diurnal variations. Furthermore, all three satellite products show a wet biases for light precipitation and a dry biases for heavy precipitation, which is consistent with previous evaluation studies. However, a novel phenomenon is noticed that theses biases tend to worsen significantly in the afternoon compared to the morning. Although this study is limited to a case study, the findings can serve as a valuable reference for the NRT applications of IMERG products during the extreme Meiyu seasons and provide insights for the improvement of IMERG algorithm. [ABSTRACT FROM AUTHOR]

Support Vector Machine (SVM) has been widely applied in real application due to its efficient performance in the classification task so that a large number of SVM methods have been proposed. In this paper, we present a novel SVM method by taking the dynamic graph learning and the self-paced learning into account. To do this, we propose utilizing self-paced learning to assign important samples with large weights, learning a transformation matrix for conducting feature selection to remove redundant features, and learning a graph matrix from the low-dimensional data of original data to preserve the data structure. As a consequence, both the important samples and the useful features are used to select support vectors in the SVM framework. Experimental analysis on four synthetic and sixteen benchmark data sets demonstrated that our method outperformed state-of-the-art methods in terms of both binary classification and multi-class classification tasks. [ABSTRACT FROM AUTHOR]

The hardware-in-the-loop (HIL) employs the real controller and true signal transmission, which is efficient to test control algorithms for the situation that is inconvenient to conduct experiments. The design method of hardware-in-the-loop is presented for buildings, in which the HVAC system is simulated while the control system including controllers, A/D and D/A modules are real. The controllers embedded with control algorithms are integrated with the simulation of HVAC system. The HVAC HIL test platform is constructed so as to evaluate the candidate control strategies. The feature of HVAC HIL test platform is compared with the pure TRNSYS based simulation. The comparison tests show that pure simulation doesn't match the true control process while the HIL platform does and provides the credible control response. With real signal transmission and controllers, the control algorithms can be tested authentically. Moreover, the fault detection and diagnosis (FDD) models using basic and improved principal component analysis (PCA) are evaluated in HVAC HIL platform. Through HIL tests, the unobservable application problem of basic PCA model is discovered. An improved wavelet-PCA model is developed, which has the satisfied efficiency to detect the various biases of sensors. [ABSTRACT FROM AUTHOR]