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The surge in non-grain production on farmland (NGPF) poses significant threats to food security and land sustainability, particularly in hilly regions. However, there remains a lack of clarity on how to effectively balance grain and non-grain production in relation to land remediation. Using Wannian County as a case study, we investigate the evolution of this by leveraging high-precision land surveys and satellite imagery. Through the application of bootstrapped partial linear regression models, we identify key influencers behind each type of NGPF. In proposing land remediation solutions, we integrate the results of NGPF and land quality evaluations to identify mismatches between non-grain production and land attributes (i.e., topography, geology, soil, and land use). Our findings reveal a substantial growth in NGPF, expanding from 3838.72 ha to 5659.64 ha (2010–2020), and predominantly occurring on farmland with favorable natural conditions and connected locations such as proximity to roads, town centers, and industrial plants. Surprisingly, the basic farmland protection policy shows limited effectiveness in curbing NGPF, except for garden operations. We identify 1674 NGPF patches suitable for conversion to grain production and provide land remediation suggestions tailored to low-quality farmland with specific natural barriers, thus complementing the demand for regional non-grain production. This study thereby innovatively proposes nature-based land remediation strategies to address the non-grain production dilemma by tailoring NGPF and land quality, offering valuable insights for sustainable farmland management in China and beyond. [Display omitted] • Various non-grain productions compete for high-quality farmland in hilly regions. • Basic farmland policy played a weak role in curbing NGPF. • The '3-level' land classification demonstrates its suitability for NGPF remediation. • Demands for Grain and non-grain production can be mutually compensated based on land quality. [ABSTRACT FROM AUTHOR]

Cross-Domain Few-Shot Learning (CD-FSL) aims at recognizing unseen classes from target domains that vastly differ from training classes from source domains, utilizing only a few labeled samples. However, the substantial domain disparities between target and source domains pose huge challenges to few-shot generalization. To resolve domain disparities, we propose HybridPrompt, a novel architecture for Domain-Aware Prompting that integrates a variety of cross-domain learned prompts as knowledge experts for CD-FSL. The proposed method enjoys several merits. First, to encode knowledge from diverse source domains, several Domain Prompts are introduced to capture domain-specific knowledge. Subsequently, to facilitate the cross-domain transfer of valuable knowledge, a Transferred Prompt is specifically tailored for each target task by retrieving highly relevant Domain Prompts based on domain properties. Finally, to complement insufficient transferred information, an Adaptive Prompt is learned to incorporate additional target characteristics for model adaptation. Consequently, the collaboration of these three types of prompts contributes to a hybridly prompted model that achieves domain-aware encoding, transfer, and adaptation, thereby enhancing adaptability on unseen domains. Extensive experimental results on the Meta-Dataset benchmark demonstrate that our method achieves superior performance against state-of-the-art methods. The source code is available at https://github.com/Jamine-W/HybridPrompt. [ABSTRACT FROM AUTHOR]

Visible-infrared person re-identification (VI-ReID) is challenging due to the large modality discrepancy between visible and infrared images. Existing methods mainly focus on learning modality-shared representations by embedding images from different modalities into a common feature space, in which some discriminative modality information is discarded. Different from these methods, in this paper, we propose a novel Modality-Specific Memory Network (MSMNet) to complete the missing modality information and aggregate visible and infrared modality features into a unified feature space for the VI-ReID task. The proposed model enjoys several merits. First, it can exploit the missing modality information to alleviate the modality discrepancy when only the single-modality input is provided. To the best of our knowledge, this is the first work to exploit the missing modality information completion and alleviate the modality discrepancy with the memory network. Second, to guide the learning process of the memory network, we design three effective learning strategies, including feature consistency, memory representativeness and structural alignment. By incorporating these learning strategies in a unified model, the memory network can be well learned to propagate identity-related information between modalities and boost the VI-ReID performance. Extensive experimental results on two standard benchmarks (SYSU-MM01 and RegDB) demonstrate that the proposed MSMNet performs favorably against state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Weakly supervised object localization (WSOL) aims at localizing objects with only image-level labels, which has better scalability and practicability than fully supervised methods. However, without pixel-level supervision, existing methods tend to generate rough localization maps, which hinders localization performance. To alleviate this problem, we propose an adversarial transformer network (ATNet), which aims to obtain a well-learned localization model with pixel-level pseudo labels. The proposed ATNet enjoys several merits. First, we design an object transformer ($G$) that can generate localization maps and pseudo labels effectively and dynamically, and a part transformer ($D$) to accurately discriminate detailed local differences between localization maps and pseudo labels. Second, we propose to train $G$ and $D$ via an adversarial process, where $G$ can generate more accurate localization maps approaching pseudo labels to fool $D$. To the best of our knowledge, this is the first work to explore transformers with adversarial training to obtain a well-learned localization model for WSOL. Extensive experiments with four backbones on two standard benchmarks demonstrate that our ATNet achieves favorable performance against state-of-the-art WSOL methods. Besides, our adversarial training can provide higher robustness against adversarial attacks. [ABSTRACT FROM AUTHOR]

Weakly Supervised Object Localization (WSOL) aims to localize objects with only image-level labels, which has better scalability and practicability than fully supervised methods in the actual deployment. However, a common limitation for available techniques based on classification networks is that they only highlight the most discriminative part of the object, not the entire object. To alleviate this problem, we propose a novel end-to-end part discovery model (PDM) to learn multiple discriminative object parts in a unified network for accurate object localization and classification. The proposed PDM enjoys several merits. First, to the best of our knowledge, it is the first work to directly model diverse and robust object parts by exploiting part diversity, compactness, and importance jointly for WSOL. Second, three effective mechanisms including diversity, compactness, and importance learning mechanisms are designed to learn robust object parts. Therefore, our model can exploit complementary spatial information and local details from the learned object parts, which help to produce precise bounding boxes and discriminate different object categories. Extensive experiments on two standard benchmarks demonstrate that our PDM performs favorably against state-of-the-art WSOL approaches. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence (AI) techniques have been increasingly adopted to tackle networking problems. Although AI algorithms can deliver high-quality solutions, most of them are inherently intricate and erratic for human cognition. This lack of interpretability tremendously hinders the commercial success of AI-based solutions in practice. To cope with this challenge, networking researchers are starting to explore explainable AI (XAI) techniques to make AI models interpretable, manageable, and trustworthy. In this article, we overview the application of AI in networking and discuss the necessity for interpretability. Next, we review the current research on interpreting AI-based networking solutions and systems. At last, we envision future challenges and directions. The ultimate goal of this article is to present a general guideline for AI and networking practitioners and motivate the continuous advancement of AI-based solutions in modern communication networks. [ABSTRACT FROM AUTHOR]

With the explosive growth of video categories, zero-shot learning (ZSL) in video classification has become a promising research direction in pattern analysis and machine learning. Based on some auxiliary information such as word embeddings and attributes, the key to a robust ZSL method is to transfer the learned knowledge from seen classes to unseen classes, which requires relationship modeling between these concepts (e.g., categories and attributes). However, most existing approaches ignore to model the explicit relationships in an end-to-end manner, resulting in low effectiveness of knowledge transfer. To tackle this problem, we reconsider the video ZSL task as a task-driven message passing process to jointly enjoy several merits including alleviated heterogeneity gap, low domain shift, and robust temporal modeling. Specifically, we propose a prototype-sample GNN (PS-GNN) consisting of a prototype branch and a sample branch to directly and adaptively model all the relationships between category-attribute, category-category, and attribute-attribute. The prototype branch aims to learn robust representations of video categories, which takes as input a set of word-embedding vectors corresponding to the concepts. The sample branch is designed to generate features of a video sample by leveraging its object semantics. With the co-adaption and cooperation between both branches, a unified and robust ZSL framework is achieved. Extensive experiments strongly evidence that PS-GNN obtains favorable performance on five popular video benchmarks consistently. [ABSTRACT FROM AUTHOR]

Weakly supervised temporal action detection has better scalability and practicability than fully supervised action detection in reality deployment. However, it is difficult to learn a robust model without temporal action boundary annotations. In this paper, we propose an en-to-end Multi-Scale Structure-Aware Network (MSA-Net) for weakly supervised temporal action detection by exploring both the global structure information of a video and the local structure information of actions. The proposed SA-Net enjoys several merits. First, to localize actions with different durations, each video is encoded into feature representations with different temporal scales. Second, based on the multi-scale feature representation, the proposed model has designed two effective structure modeling mechanisms including global structure modeling and local structure modeling, which can effectively learn discriminative structure aware representations for robust and complete action detection. To the best of our knowledge, this is the first work to fully explore the global and local structure information in a unified deep model for weakly supervised action detection. And extensive experimental results on two benchmark datasets demonstrate that the proposed MSA-Net performs favorably against state-of-the-art methods. [ABSTRACT FROM AUTHOR]

To establish robust semantic correspondence between images covering different objects belonging to the same category, there are three important types of information including inter-image relationship, intra-image relationship and cycle consistency. Most existing methods only exploit one or two types of the above information and cannot make them enhance and complement each other. Different from existing methods, we propose a novel end-to-end Consistency Graph Modeling Network (CGMNet) for semantic correspondence by modeling inter-image relationship, intra-image relationship and cycle consistency jointly in a unified deep model. The proposed CGMNet enjoys several merits. First, to the best of our knowledge, this is the first work to jointly model the three kinds of information in a deep model for semantic correspondence. Second, our model has designed three effective modules including cross-graph module, intra-graph module and cycle consistency module, which can jointly learn more discriminative feature representations robust to local ambiguities and background clutter for semantic correspondence. Extensive experimental results show that our algorithm performs favorably against state-of-the-art methods on four challenging datasets including PF-PASCAL, PF-WILLOW, Caltech-101 and TSS. [ABSTRACT FROM AUTHOR]

Weakly supervised temporal sentence grounding has better scalability and practicability than fully supervised methods in real-world application scenarios. However, most of existing methods cannot model the fine-grained video-text local correspondences well and do not have effective supervision information for correspondence learning, thus yielding unsatisfying performance. To address the above issues, we propose an end-to-end Local Correspondence Network (LCNet) for weakly supervised temporal sentence grounding. The proposed LCNet enjoys several merits. First, we represent video and text features in a hierarchical manner to model the fine-grained video-text correspondences. Second, we design a self-supervised cycle-consistent loss as a learning guidance for video and text matching. To the best of our knowledge, this is the first work to fully explore the fine-grained correspondences between video and text for temporal sentence grounding by using self-supervised learning. Extensive experimental results on two benchmark datasets demonstrate that the proposed LCNet significantly outperforms existing weakly supervised methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • A controllable surface engineering strategy was performed on WO 3 catalysts. • The surface characteristics can be adjusted, e.g., morphology, phase, and vacancies. • The OER and HER performances of the tungsten oxide are largely enhanced. Tungsten oxide is a promising electrocatalyst for water splitting reactions, due to its flexible chemical state, strong structural malleability and good corrosion resistance. Herein, the surface engineering on WO 3 was conducted by a controllable N 2 plasma for enhancing the performance of water splitting reactions. Under the specific N 2 plasma treatment, the surface crystalline phase and the surface morphology of the tungsten oxide can be changed, as well as the creation of oxygen vacancies. These changes lead to a greatly increased electrochemical surface area, a narrowed bandgap, and a distorted crystal lattice. For oxygen evolution reaction (OER) in 1.0 M KOH, the activity of the modified tungsten oxide electrodes presents a positive correlation with the input plasma, and an optimal overpotential of 269 mV @ 10 mA cm−2 is achieved with a Tafel slope of 122 mV dec-1. With a high N 2 plasma flux, the W-N bond can be formed on the tungsten oxide surface, which contributes to the promotion of the hydrogen evolution reaction (HER) activity, and an overpotential of 93 mV @ 10 mA cm−2 in 1.0 M KOH is exhibited, and the Tafel slope largely reduced to 76 mV dec-1. Such a facile strategy provides an engineering route of preparing efficient electrodes for water splitting reactions. [ABSTRACT FROM AUTHOR]

Unsupervised person re-identification (Re-ID) has better scalability and practicability than supervised Re-ID in the actual deployment. However, it is difficult to learn a discriminative Re-ID model without annotations. To address the above issue, we propose an end-to-end Self-supervised Agent Learning (SAL) algorithm by exploiting a set of agents as a bridge to reduce domain gaps for unsupervised cross-domain person Re-ID. The proposed SAL model enjoys several merits. First, to the best of our knowledge, this is the first work to exploit self-supervised learning for unsupervised person Re-ID. Second, our model has designed three effective learning mechanisms including supervised label learning in source domain, similarity consistency learning in target domain, and self-supervised learning in cross domain, which can learn domain-invariant yet discriminative representations through the principled lens of agent learning by reducing domain discrepancy adaptively. Extensive experimental results on three standard benchmarks demonstrate that the proposed SAL performs favorably against state-of-the-art unsupervised person Re-ID methods. [ABSTRACT FROM AUTHOR]

Facial expression recognition, face synthesis, and face alignment are three coherently related tasks and can be solved in a joint framework. To achieve this goal, in this paper, we propose a novel end-to-end deep learning model by exploiting the expression code, geometry code and generated data jointly for simultaneous pose-invariant facial expression recognition, face image synthesis, and face alignment. The proposed deep model enjoys several merits. First, to the best of our knowledge, this is the first work to address these three tasks jointly in a unified deep model to complement and enhance each other. Second, the proposed model can effectively disentangle the global and local identity representation from different expression and geometry codes. As a result, it can automatically generate facial images with different expressions under arbitrary geometry codes. Third, these three tasks can further boost their performance for each other via our model. Extensive experimental results on three standard benchmarks demonstrate that the proposed deep model performs favorably against state-of-the-art methods on the three tasks. [ABSTRACT FROM AUTHOR]

Driven by recent advances in human-centered computing, Facial Expression Recognition (FER) has attracted significant attention in many applications. However, most conventional approaches either perform face frontalization on a non-frontal facial image or learn separate classifier for each pose. Different from existing methods, this paper proposes an end-to-end deep learning model that allows to simultaneous facial image synthesis and pose-invariant facial expression recognition by exploiting shape geometry of the face image. The proposed model is based on generative adversarial network (GAN) and enjoys several merits. First, given an input face and a target pose and expression designated by a set of facial landmarks, an identity-preserving face can be generated through guiding by the target pose and expression. Second, the identity representation is explicitly disentangled from both expression and pose variations through the shape geometry delivered by facial landmarks. Third, our model can automatically generate face images with different expressions and poses in a continuous way to enlarge and enrich the training set for the FER task. Our approach is demonstrated to perform well when compared with state-of-the-art algorithms on both controlled and in-the-wild benchmark datasets including Multi-PIE, BU-3DFE, and SFEW. The code is included in the supplementary material. [ABSTRACT FROM AUTHOR]

The video highlight detection task is to localize key elements (moments of user's major or special interest) in a video. Most of the existing highlight detection approaches extract features from the video segment as a whole without considering the difference of local features spatially. In spatial extent, not all regions are worth watching because some of them only contain the background of the environment without human or other moving objects, especially when there is lots of clutter in the background. To deal with this issue, we propose a novel region-based model which can automatically localize the key elements in a video without any extra supervised annotations. Specifically, the proposed model produces position-sensitive score maps for local regions in the spatial dimension of the video segment, and then aggregates all position-wise scores with position-pooling operation. The regions with higher response values will be extracted as key elements. Thus more effective features of the video segment are obtained to predict the highlight score. The proposed position-sensitive scheme can be easily integrated into an end-to-end fully convolutional network which aims to update parameters via stochastic gradient descent method in the backward propagation to improve the robustness of the model. Extensive experimental results on the YouTube and SumMe datasets demonstrate that the proposed approach achieves significant improvement over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• AESAA significantly reduces cognitive impairment in d -gal-induced aging mice. • AESAA alleviates oxidative damage in d -gal-induced aging mice. • AESAA suppresses d -gal-induced neuroinflammation by decreasing pro-inflammatory cytokines. • AESAA takes effects may through inhibiting RAGE/MAPK/NF-KB signaling pathway. Aging is a natural process that accompanied with progressive cognitive deficits and functional decline in organisms. Selenium (Se), an essential trace element, exhibits antioxidative and anti-inflammatory abilities. Here, our study aimed to investigate the protective effects of aqueous extracts of Se-enriched Auricularia auricular (AESAA) on aging mice induced by d -galactose (D -gal) and explore its potential mechanism. d -gal was administered (100 mg/kg) subcutaneously for 12 weeks to establish an aging mouse model. Morris water maze (MWM) test was conducted to assess the cognitive deficits of mice. Superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), catalase (CAT) activities and malondialdehyde (MDA) level in hippocampus were measured to evaluate oxidative stress. The contents of pro-inflammatory cytokines tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β) and interleukin-6 (IL-6) in hippocampus were determined by ELISA method. Further, hippocampal levels of RAGE, p-Erk, p-JNK, p-P38 and p-NF-κB were detected by western blot and the RAGE expression was confirmed by immunohistochemistry. We found that AESAA supplementation significantly decreased d -gal-induced cognitive deficits, as evidenced by better performance in the MWM test. Furthermore, AESAA treatment attenuated oxidative stress and decreased the contents of pro-inflammatory cytokines in hippocampus. Importantly, AESAA inhibited the up-regulation of RAGE, p-Erk, p-JNK, p-P38 in the hippocampus of d -gal treated mice. Moreover, the results also indicated that AESAA inhibited p-NF-κB and p-IκBα expression. In conclusion, our findings suggest that AESAA effectively decreases cognitive impairment, alleviates oxidative damage and neuroinflammation in mice through s RAGE/MAPK/NF-κB signaling pathway, which provides a potential therapy for delaying the aging process. [ABSTRACT FROM AUTHOR]

Sparse representations have been applied to visual tracking by finding the best candidate region with minimal reconstruction error based on a set of target templates. However, most existing sparse trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidate regions, thereby making them less effective when similar objects appear at close proximity or under occlusion. In this paper, we propose a novel structural sparse representation, which not only exploits the intrinsic relationships among target candidate regions and local patches to learn their representations jointly, but also preserves the spatial structure among the local patches inside each target candidate region. For robust visual tracking, we take outliers resulting from occlusion and noise into account when searching for the best target region. Constructed within a Bayesian filtering framework, we show that the proposed algorithm accommodates most existing sparse trackers with respective merits. The formulated problem can be efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. Qualitative and quantitative evaluations on challenging benchmark datasets demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual tracking. We first present the multi-task correlation filter (MCF) that takes the interdependencies among different object parts and features into account to learn the correlation filters jointly. Next, the proposed MCPF is introduced to exploit and complement the strength of a MCF and a particle filter. Compared with existing tracking methods based on correlation filters and particle filters, the proposed MCPF enjoys several merits. First, it exploits the interdependencies among different features to derive the correlation filters jointly, and makes the learned filters complement and enhance each other to obtain consistent responses. Second, it handles partial occlusion via a part-based representation, and exploits the intrinsic relationship among local parts via spatial constraints to preserve object structure and learn the correlation filters jointly. Third, it effectively handles large scale variation via a sampling scheme by drawing particles at different scales for target object state estimation. Fourth, it shepherds the sampled particles toward the modes of the target state distribution via the MCF, and effectively covers object states well using fewer particles than conventional particle filters, thereby resulting in robust tracking performance and low computational cost. Extensive experimental results on four challenging benchmark datasets demonstrate that the proposed MCPF tracking algorithm performs favorably against the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A bifunctional acid-base catalyst of CeIII immobilized on ethylenediamine (EDA)-grafted poly(vinyl chloride) (PVC) has been prepared by a simple approach. First, PVC was treated with EDA to afford aminated poly(vinyl chloride) (PVC-EDA). Thereafter, the PVC-EDA were used in the mobilization of CeCl3 to obtain a bifunctional catalyst (PVC-EDA-CeIII) in which a harmonious coexistence of Lewis acid (Ce3+) sites and Lewis base (amine) moieties on PVC was achieved. The obtained PVC-EDA-CeIII complex was characterized by EA, ICP-AES, FT-IR, SEM, EDS, TGA, and DTG techniques. The as-prepared catalyst can efficiently catalyze the one-pot four-component reactions of aromatic aldehydes, malononitrile, ethyl acetoacetate, and hydrazine in ethanol under mild conditions to afford polyfunctionalized 1,4-dihydropyrano[2,3-c]pyrazoles with excellent yields. The high catalytic performance of the PVC-EDA-CeIII bifunctional catalyst is attributed to a synergistic effect of Lewis acid sites (CeIII) and Lewis base sites (amino moieties). The significant features of the present protocol are environmentally benign, simple operation, short reaction time, high yields, and without chromatographic separation. Moreover, the catalyst can easily be separated by simple filtration and reused for five runs without obvious decline or losing its catalytic activity. [ABSTRACT FROM AUTHOR]

Cancer is closely associated with aging of the body. Aging of the extracellular matrix (ECM) leads to a fibrotic microenvironment and increasing stiffness of ECM. In this study, the porosity and stiffness were advantageous to the proliferation and aggregation of ovarian SKOV-3 cancer cells. Both the porosity and stiffness increase could lead to enhancement of proliferation and aggregation of ovarian SKOV-3 cancer cells. Further studies aimed at preventing tumoral invasion and metastasis should focus on drugs that can prevent age-induced degenerative changes in the ECM. [ABSTRACT FROM AUTHOR]

Cerium(IV) carboxymethylcellulose (CMC −Ce) was found to be a highly efficient, eco-friendly and recyclable heterogeneous catalyst for the synthesis of 2,4,6-triarylpyridine derivatives in excellent yields via one-pot multicomponent reaction of various benzaldehydes, acetophenones, and ammonium acetate under solvent-free condition. Moreover, the present protocol has the merits of easy work-up, short reaction times, and high yields. [ABSTRACT FROM AUTHOR]

The extracellular matrix (ECM) regulates cellular behaviours in a way that it is still far from completely understood, especially in cancer research field. Here we showed a simple system based on different glutaraldehyde (GA) cross-linked egg white (EW) that supported growth of cells on three dimensional (3D) scaffolds. The reaction between GA and EW was highly active. The pore distribution in GA cross-linked EW was more uniform. The mechanical properties of GA cross-linked EW can be tuned through changing the amount of added GA. Overall, the EW based scaffolds were promising tools that may be used as tunable 3D materials in vitro platforms for cancer tissue engineering and anticancer drugs resistance testing. [ABSTRACT FROM AUTHOR]

In surgical hernia repair, the intraabdominal adhesion formation needs to be prevented. As currently used prosthetic mesh materials, antiadhesion properties of polypropylene (PP) still be highly desirable. In the present study, we developed a new type of polypropylene (PP) hernia mesh, namely, polyvinyl alcohol (PVA) modified PP mesh (PP-g-PVA). The native PP mesh was first activated with O 2 plasma for 30 s, and then reacted with benzoyl peroxide (BPO) and maleic anhydride for 2 h, finally grafted with PVA in phosphorus pentachloride (PCl5) solution in pyridine. The chemical group changes on the surface of PP was monitored by X-ray photoelectron spectroscopy (XPS) spectra, and analysis results confirmed that the PVA was effectively grafted onto the PP. Antiadhesion efficacy and complications of PP-g-PVA mesh were evaluated through repairing rabbit abdominal wall defect at implantation time point of 7 days, 1 month and 3 months. Cross-sections of the mesh parietal peritoneum overlap were examined histologically and graded for inflammation reaction. Compared with the control groups (native PP mesh), a very strong ability to resist peritoneal cavity adhesions (P < 0.05) was observed in the experimental groups (PP-g-PVA mesh), and however, there was no significant increase in the inflammation formation (P > 0.05). This PVA-modified PP mesh displayed a rather satisfying antiadhesion property in animal abdominal wall defect repair. [ABSTRACT FROM AUTHOR]

Paeoniflorin has been demonstrated to exert anti-inflammatory and immunomodulatory effects in the animal study. In this study, we investigated immunoregulatory effects of paeoniflorin on anti-asthmatic effects and underlying mechanisms. Asthma model was established by ovalbumin-induced. A total of 50 mice were randomly assigned to five experimental groups: control, model, dexamethasone (2 mg/kg), and paeoniflorin (10 and 20 mg/kg). Airway resistance (Raw) were measured by the forced oscillation technique; histological studies were evaluated by the hematoxylin and eosin (HE) staining; Th1/Th2 cytokines were evaluated by enzyme-linked immunosorbent assay (ELISA); Th1/Th2 cells were evaluated by flow cytometry (FCM); and GATA3 and T-bet were evaluated by Western blot. Our study demonstrated that, compared with model group, paeoniflorin inhibited ovalbumin (OVA)-induced increases in Raw and eosinophil count; interleukin (IL)-4, IgE levels were recovered in bronchoalveolar lavage fluid compared; increased IFN-γ level in bronchoalveolar lavage fluid; histological studies demonstrated that paeoniflorin substantially inhibited OVA-induced eosinophilia in lung tissue and lung tissue compared with model group. Flow cytometry studies demonstrated that paeoniflorin can regulate Th1/Th2 balance. These findings suggest that paeoniflorin may effectively ameliorate the progression of asthma and could be used as a therapy for patients with allergic asthma. [ABSTRACT FROM AUTHOR]

Timosaponin B-II (TB), a main bioactive compound inAnemarrhena asphodeloidesBunge, has various kinds of pharmacological activities, the present study aimed to investigate the protective role of TB on lipopolysaccharide (LPS)-induced acute lung injury (ALI). ALI was induced in mice by intratracheal instillation of LPS, and TB (20 and 60 mg/kg) was given orally 1 h prior to LPS administration. After 6 h, bronchoalveolar lavagefluid (BALF) and lung tissue were collected. TB decreased LPS-induced evident lung histopathological changes, lung wet-to-dry weight (W/D) ratio and lung myeloperoxidase (MPO) activity. In addition, TB inhibited inflammatory cells and cytokines including tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6) in BALF. Furthermore, we demonstrated that TB inhibited the Toll-like receptor-2 (TLR2), Toll-like receptor-4 (TLR4), myeloid differentiation primary response gene-88 (MyD88), nuclear factor-κB (NF-κB) p65 in LPS-induced ALI. These results showed that administration of TB prior to LPS improves ALI, possibly mediating ALI through suppressing TLR/NF-κB pathway activation. [ABSTRACT FROM AUTHOR]

Ethnopharmacological relevance Zuojin Pill (ZJP), a traditional Chinese medicinal decoction, contains two herbal drugs: Coptis chinensis Franch. and Tetradium ruticarpum (A. Juss.) Hartley in the ratio of 6:1 (w/w). In this study, ZJP was evaluated for its gastroprotective potential against mucosal lesions induced by ethanol in mice. Materials and methods 50 mice were assigned to 5 groups: groups 1 and 2 were given distilled water orally. Group 3 was administered omeprazole 20 mg/kg, groups 4 and 5 were given ZJP (1 g/kg, 2 g/kg, respectively). After an additional hour, the mice in groups 2–5 received ethanol (0.2 ml/kg) orally while group 1 received distilled water instead. Mice were killed after 4 h and their serum and stomachs subjected to further studies. The superoxidase dismutase (SOD) activities and malondialdehyde (MDA) level in serum were assayed by SOD and MDA kits, respectively. The myeloperoxidase (MPO) activities in stomachs were assayed by MPO kit. The levels of inflammatory mediators including tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and interleukin-1β (IL-1β) in serum were assayed by enzyme-linked immunosorbent assay method (ELISA). Pathological changes were observed by hematoxylin-eosin (HE) staining. The levels of nuclear factor-кBp65 (NF-кBp65), P-NF-кBp65, P-IкBα, IкBα, P-IKKα, IKKα, P-IKKβ, IKKβ in stomachs were assayed by western blot. Results The data showed that treatment with the ZJP markedly attenuated MPO, MDA, TNF-α, IL-6, IL-1βand increased SOD; and ZJP also decreased protein levels of P-NF-кBp65, P-IкBα, P-IKKαand P-IKKβin gastric stomachs. Conclusion It was concluded that ZJP may represents a potential therapeutic option to reduce the risk of gastric ulceration and the gastroprotective activity of ZJP might contribute in adjusting the inflammatory cytokine by regulating the NF-кB signaling pathway. [ABSTRACT FROM AUTHOR]

[Display omitted] • A dry chemical route was designed to prepare CuO/Cu/WO 3−x /WO 3 /W heterostructure. • The structure distribution of the tungsten oxide was measured by Raman technique. • The H+ induced oxygen vacancies in the WO 3−x layer largely narrow the bandgap. • The oxygen vacancies and the Cu species are both beneficial for the charge transfer. Heterostructrural electrocatalysts are promising for enhancing reaction performance by increasing the carriers mobility and lowering the activation energy. Herein, an environmentally friendly dry chemical route based on ion beam technology is designed to construct the heterostructural CuO/Cu/WO 3−x /WO 3 /W electrocatalysts. Through H+ beam bombardment, the oxygen vacancies in the tungsten oxide are created, and the Cu species are deposited simultaneously on the surface oxidized W mesh. This fabrication leads to a significant elevation of HER performance for WO 3 based electrocatalysts. The self-supported CuO/Cu/WO 3−x /WO 3 /W electrode exhibits an overpotential of 127 mV at −10 mA cm−2 in 0.5 M H 2 SO 4 aqueous solution, which is much better than those of the other samples without the WO 3−x layer or the deposited Cu species. The H+ bombardment induced oxygen vacancies enhance the conductivity of the tungsten oxide via narrowing its bandgap, as well as provide large amounts of unsaturated W atoms that could serve as active sites. The Cu species play a role of electron mobility promoter for further enhancing the HER activity. This strategy offers a facile route for the construction of transition metal based heterostructural electrocatalysts. [ABSTRACT FROM AUTHOR]

In this work, the anti-asthma potential of platycodin D (PLD) was studied by investigation of its effect to suppress airway inflammation, a murine model of asthma and the possible mechanisms. A total of 50 mice were randomly assigned to five experimental groups: control, ovalbumin (OVA), OVA+ dexamethasone (2 mg/kg) and OVA + PLD (40, 80 mg/kg). Airway resistance (Raw) were measured; airway histological studies were evaluated by the hematoxylin and eosin (HE) staining; interleukin-4 (IL-4), interleukin-5(IL-5), and interleukin-13 in bronchoalveolar lavage fluid (BALF) were evaluated by enzyme-linked immunosorbent assay (ELISA); NF-κBp65, p-NF-κBp65, p-IKKα, IKKα, p-IKKβ, p-IкBα, and IкBα of airway were measured by Western blotting. Our study demonstrated that PLD inhibited OVA-induced increases in Raw and eosinophil count in airway; IL-4, IL-5, and IL-13 were recovered in BALF. Histological studies demonstrated that PLD substantially inhibited OVA-induced eosinophilia in airway tissue. Western blotting studies demonstrated that PLD substantially inhibited NF-κB pathway. These findings suggest that PLD may effectively ameliorate the progression of asthma and could be used as a therapy for patients with allergic asthma. [ABSTRACT FROM AUTHOR]

Vision-based sign language recognition has attracted more and more interest from researchers in the computer vision field. In this article, we propose a novel algorithm to model and recognize sign language performed in front of a Microsoft Kinect sensor. Under the assumption that some frames are expected to be both discriminative and representative in a sign language video, we first assign a binary latent variable to each frame in training videos for indicating its discriminative capability, then develop a latent support vector machine model to classify the signs, as well as localize the discriminative and representative frames in each video. In addition, we utilize the depth map together with the color image captured by the Kinect sensor to obtain a more effective and accurate feature to enhance the recognition accuracy. To evaluate our approach, we conducted experiments on both word-level sign language and sentence-level sign language. An American Sign Language dataset including approximately 2,000 word-level sign language phrases and 2,000 sentence-level sign language phrases was collected using the Kinect sensor, and each phrase contains color, depth, and skeleton information. Experiments on our dataset demonstrate the effectiveness of the proposed method for sign language recognition. [ABSTRACT FROM AUTHOR]

Tracking multiple objects is critical to automatic video content analysis and virtual reality. The major problem is how to solve data association problem when ambiguous measurements are caused by objects in close proximity. To tackle this problem, we propose a multiple information fusion-based multiple hypotheses tracking algorithm integrated with appearance feature, local motion pattern feature and repulsion-inertia model for multi-object tracking. Appearance model based on HSV-local binary patterns histogram and local motion pattern based on optical flow are adopted to describe objects. A likelihood calculation framework is proposed to incorporate the similarities of appearance, dynamic process and local motion pattern. To consider the changes in appearance and motion pattern over time, we make use of an effective template updating strategy for each object. In addition, a repulsion-inertia model is adopted to explore more useful information from ambiguous detections. Experimental results show that the proposed approach generates better trajectories with less missing objects and identity switches. [ABSTRACT FROM AUTHOR]

Sepsis is a cluster of heterogeneous syndromes associated with progressive endotoxemic developments, ultimately leading to damage of multiple organs, including the heart. This study is to investigate the effects of apigenin on heart injury in lipopolysaccharide-induced endotoxemic rat model. Normal Wistar rats were randomly divided into four groups: control group, LPS group (15 mg/kg), LPS plus apigenin groups with different apigenin doses (50 mg/kg, 100 mg/kg). Serum levels of creatine kinase (CK), lactate dehydrogenase (LDH), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-1β (IL-1β) were measured after the rats were sacrificed. SphK1/S1P signaling pathway proteins, cleaved caspase-3, cleaved caspase-9, Bax and Bcl-2 in heart were measured by Western blot. In vitro, we evaluated the protective effect of apigenin on rat embryonic heart-derived myogenic cell line H9c2 induced by LPS. Apigenin decreased serum levels of CK-MB, LDH, TNF-α, IL-6, IL-1β. SphK1/S1P signaling pathway proteins, cleaved caspase-3, cleaved caspase-9, Bax in heart were found inhibited and Bcl-2 increased in the apigenin groups in vivo. In addition, apigenin inhibited intracellular calcium, the MAPK pathway and SphK1/S1P signaling pathway in vitro. Apigenin exerts pronounced cardioprotection in rats subjected to LPS likely through suppressing myocardial apoptosis and inflammation by inhibiting the SphK1/S1P signaling pathway. [ABSTRACT FROM AUTHOR]

The bag-of-words (BOW) based methods are widely used in image classification. However, huge number of visual information is omitted inevitably in the quantization step of the BOW. Recently, NBNN and its improved methods like Local NBNN were proposed to solve this problem. Nevertheless, these methods do not perform better than the state-of-the-art BOW based methods. In this paper, based on the advantages of BOW and Local NBNN, we introduce a novel locality discriminative coding (LDC) method. We convert each low level local feature, such as SIFT, into code vector using the Local Feature-to-Class distance other than by k-means quantization. After coding, sum-pooling combined with SPM is used to construct a single feature representation vector for each image. Extensive experimental results on several challenging benchmark datasets show that our LDC method outperforms six state-of-the-art image classification methods. [ABSTRACT FROM AUTHOR]

Puerarin is an isoflavonoid isolated from the root of the plant Pueraria lobata and has been used as a prescribed drug in China for the treatment of many diseases in the clinical practice. The present study aimed to determine the protective effects and the underlying mechanisms of puerarin on ovalbumin (OVA)-induced allergic inflammation in a mouse model of allergic asthma. Asthma mice model was established by ovalbumin. A total of 50 mice were randomly assigned to five experimental groups: control, model, dexamethasone (2 mg/kg), and puerarin (10 mg/kg, 20 mg/kg). Airway resistance (Raw) was measured by the forced oscillation technique, differential cell count in BAL fluid (BALF) was measured by Wright–Giemsa staining, histological assessment was measured by hematoxylin and eosin (HE) staining, BALF levels of Th1/Th2 cytokines were measured by enzyme-linked immunosorbent assay, eotaxin-3 was evaluated by western blotting. Our study demonstrated that, compared with model group, puerarin inhibited OVA-induced increases in Raw and eosinophil count; interleukin (IL)-4, IL-5, IL-13 levels were recovered in bronchoalveolar lavage fluid compared; increased IFN-γ level in bronchoalveolar lavage fluid; histological studies demonstrated that puerarin substantially inhibited OVA-induced eosinophilia in lung tissue compared with model group. Western blotting studies demonstrated that puerarin substantially inhibited eotaxin-3 compared with model group. Our findings support puerarin can prevent some signs of allergic asthma in the mouse model. [ABSTRACT FROM AUTHOR]

Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against $$14$$ state-of-the-art tracking methods on a set of $$25$$ challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time. [ABSTRACT FROM AUTHOR]

[Display omitted] • Chemically and electrostatically double-cross-linked composite adhesive. • Heating-induced gradual increase in adhesion strength over time. • Higher and sustainable adhesive ability to various substrates. A hydrophobic composite underwater adhesive was constructed through simultaneously mixing liquid polydimethylsiloxane (PDMS), micro particle polytetrafluoroethylene (PTFE) and tetraethyl silicate (TEOS) together according to the different mass ratio of component and subsequently heating crosslinking reaction. The electrostatic interaction between hydrogen atoms charged partially positively in PDMS and fluorine atoms charged partially negatively in PTFE contribute the adhesive captivity of PDMS/PTFE while the crosslinking reaction of TEOS with PDMS will enhance further effectively mechanical properties of adhesives. The adhesive ability and sustainable time to various substrate were evaluated systematically. [ABSTRACT FROM AUTHOR]

Highlights: [•] Ovarian cancer cell line HO8910 can better grow on PLGA microspheres. [•] Collagen I coated PLGA microsphere promoted the adhesion and growth of HO8910 cells. [•] HO8910 cells grew on PLGA microspheres increased expression of E-cadherin. [Copyright &y& Elsevier]

Highlights: [•] PU films were modified with PEO by a facile, environment-friendly approach. [•] The hemocompatibility of the PEO-coated PU was significantly improved. [•] PEO-coated PU films were very hydrophilic. [•] The higher molecular weight of PEO shows better hemocompatibility. [ABSTRACT FROM AUTHOR]

Abstract: Automatically learning and grouping key motion patterns in a traffic scene captured by a static camera is a fundamental and challenging task for intelligent video surveillance. To learn motion patterns, trajectory obtained by object tracking is parameterized, and scene image is spatially and evenly divided into multiple regular cell blocks which potentially contain several primary motion patterns. Then, for each block, Gaussian Mixture Model (GMM) is adopted to learn its motion patterns based on the parameters of trajectories. Grouping motion pattern can be done by clustering blocks indirectly, and each cluster of blocks corresponds to a certain motion pattern. For one particular block, each of its motion pattern (Gaussian component) can be viewed as an instance, and all motion patterns (Gaussian components) constitute a bag which can correspond to multiple semantic clusters. Therefore, blocks can be grouped as a Multi-instance Multi-cluster Learning (MIMCL) problem, and a novel Maximum Margin Multi-instance Multi-cluster Learning (M4L) algorithm is proposed. To avoid processing a difficult optimization problem, M4L is further relaxed and solved by making use of a combination of the Cutting Plane method and Constrained Concave–Convex Procedure (CCCP). Extensive experiments are conducted on multiple real world video sequences containing various patterns and the results validate the effectiveness of our proposed approach. [Copyright &y& Elsevier]

Resource productivity enhancement stands at the center of tackling issues on environmental pollution and resource scarcity. Identifying influential socioeconomic factors should be the first step in establishing and improving resource management policy. This study compares and analyzes data from multiple countries to construct a resource productivity simulation model. The socioeconomic factors and their characteristic patterns are discussed in detail. The results demonstrate that the major factors influencing resource productivity include income level, population density, economic structure, energy structure, and raw material trade. Among these factors, the three most important are income level, population density, and economic structure. The influencing patterns can be summarized as follows: (1) Resource productivity increases with increasing income levels. (2) Countries with high population density are inclined to demonstrate high resource productivity. (3) The economic structure shows a biphase influence on resource productivity, that is, during industrialization, decreased agricultural activity and increased industrial activity lead to higher resource productivity. On the other hand, after industrialization, decreasing industrial activity and an expanding service sector become the major impetus of resource productivity enhancement. (4) Raw material export demonstrates a negative effect on resource productivity. Countries that depend heavily on raw material export show a unique resource productivity evolution pattern. For these countries, relatively high resource productivity in low income phases and relatively low resource productivity in high income phases show only small increases in resource efficiency and economic growth. Finally, insights from the study are transformed into suggestions for sustainable resource management and resource productivity enhancement. [ABSTRACT FROM AUTHOR]

It is vital to find reasons for differences in the results of environmental input-output (EIO), physical input-output (PIO), and hybrid input-output (HIO) models for industrial and environmental policy analysis. Using EIO, PIO, and HIO models, China's industrial metabolism is calculated. Four reasons were found to account for differences in the results of analysis using EIO, PIO, and HIO models: the manner in which they deal with residential consumption, service sectors, and waste recycling, and the assumption of unique sector prices. The HIO model, which treats residential consumption as sectors of the intermediate delivery matrix, is preferred to the EIO and PIO models for analyzing industrial and environmental policies. Moreover, waste recycling in five sectors-agriculture; the manufacture of paper, printing, and articles for culture, education, and sports activities; the manufacture of nonmetallic mineral products; smelting and pressing of metals; and construction-should be comprehensively considered when using the HIO model to study problems related to these five sectors. Improvements in the EIO, PIO, and HIO models and future work are also discussed. [ABSTRACT FROM AUTHOR]

Abstract: Circular economy (CE), with its basic principle of Reduce, Reuse, and Recycle, has been determined as the key strategy for the national development plan by the Chinese government. Given the economy-wide material flow analysis (EW-MFA) that leaves the inner flow of resource reutilisation unidentified, the reutilisation-extended EW-MFA is first introduced to evaluate and analyse the material input, solid waste generation, and reutilisation simultaneously. The total amount of comprehensive reutilisation (CR) is divided into three sub-flows, namely, reutilisation, recycle, and reuse. Thus, this model is used to investigate the resource CR in China from 2000 to 2010. China’s total amount of CR and its sub-flows, as well as the CR rate, remain to have a general upward trend. By the year 2010, about 60% of the overall solid waste generation had already been reutilised, and more than 20% of the total resource requirement was reutilised resource. Moreover, the growth patterns of the CR sub flows show different characteristics. Interpretations of resource reutilisation-related laws and regulations of CE and the corresponding policy suggestions are proposed based on the results. [Copyright &y& Elsevier]

Abstract: In this work, three kinds of poly(ethylene glycol)-cross linked poly(methyl vinyl ether-co-maleic acid) hydrogels, Gel-1, Gel-2, and Gel-3 with different elastic modulus of 105,100Pa, 12,020Pa, and 2191Pa were designed for simulating respectively the mechanical strength of stiffer metastasis bone sites, cancerous soft tissue, and BME or collagen, and were evaluated in three-dimensional (3D) culture of human ovarian cancer cell line HO8910. The proliferation, adhesion, migration, and invasion of ovarian cells HO8910 in these three kinds of hydrogels were systematically investigated and compared with two commercial hydrogels BME and collagen I. Hydrogels Gel-1 and Gel-2 with higher elastic modulus showed a better cytocompatibility and remarkably supported the proliferation of ovarian cancer cell line HO8910 and the formation of multicellular spheroids (MCS) in a 3D model. The mechanical properties of hydrogels played a crucial role in responses of HO8910 cells. The hydrogels Gel-1 and Gel-2 are also the potential 3D culture matrix materials for cancer cell culture. [Copyright &y& Elsevier]

The relative risk model (RRM) was applied to evaluate the ecological risk characterization of the freshwater ecosystems in China, from both overall and region-specific levels. Ten large-scale river basins (further broken into 15 risk regions) in China were chosen as the study objects; 10 sources, two habitats, and seven endpoints were identified as risk components. The results reveal the status of ecosystem conditions, key ecological risk issues, and the spatial heterogeneity of the freshwater ecosystems in China. The policy implications for the ecosystem-based water management contained in the results are discussed. The results obtained in this article provide a deeper understanding of the ecological risk characterization of the freshwater ecosystems in China, and aid in promoting the applications of the RRM as the tool for ecosystem-based water management. [ABSTRACT FROM PUBLISHER]

In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing $$\ell _{p,q}$$ mixed norms $$(\text{ specifically} p\in \{2,\infty \}$$ and $$q=1),$$ we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular $$L_1$$ tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259-2272, ) is a special case of our MTT formulation (denoted as the $$L_{11}$$ tracker) when $$p=q=1.$$ Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

Ecological risk assessment (ERA) is a scientific tool used to support ecosystem-based management (EBM), but most current ERA methods consider only a few indices of particular species or components. Such limitations restrict the scope of results so that they are insufficient to reflect the integrated risk characterization of an ecosystem, thereby inhibiting the application of ERA in EBM. We incorporate the concept of ecosystem services into ERA and develop an improved ERA framework to create a comprehensive risk map of an ecosystem, accounting for multiple human activities and ecosystem services. Using the Yellow River as a case study, we show how this framework enables the implementation of integrated risk characterization and prioritization of the most important ecological risk issues in the ecosystem-based river management of the Yellow River. This framework can help practitioners facilitate better implementation of ERA within EBM in rivers or any target ecosystem. [ABSTRACT FROM AUTHOR]

Abstract: Sustainable urban materials management is significant for environmental sustainability. This study identifies key economic sectors for sustainable urban materials management using urban physical input–output (PIOT) table and hierarchical cluster analysis method to mitigate urban air pollutants (comprising CO2, SO2 and NO x ) and examines uncertainties. Suzhou city in China is taken as an example. Key economic sectors in Suzhou considering direct amount are production and supply of electric and heat power, smelting and pressing of ferrous metals, services, other manufacturing, textile, non-metallic mineral products and chemistry, while key economic sectors considering accumulative amount are agriculture, construction, production and supply of electric and heat power, services and smelting and pressing of ferrous metals. Uncertainties for sustainable urban materials management are consistent with the development stage of the economy. Thus, when constructing urban PIOT for a particular city based on monetary input–output table (MIOT) of another city or province, the development stage of the city or province whose MIOT is used should be similar to that of the particular city. If not, lower and upper limits for the development stage should be set to investigate uncertainties. [Copyright &y& Elsevier]

Abstract: Waste recycling for paper production is an important component of waste management. This study constructs a physical input–output life-cycle assessment (PIO-LCA) model. The PIO-LCA model is used to investigate environmental impacts of four categories of waste recycling in China’s paper industry: crop straws, bagasse, textile wastes and scrap paper. Crop straw recycling and wood utilization for paper production have small total intensity of environmental impacts. Moreover, environmental impacts reduction of crop straw recycling and wood utilization benefits the most from technology development. Thus, using crop straws and wood (including wood wastes) for paper production should be promoted. Technology development has small effects on environmental impacts reduction of bagasse recycling, textile waste recycling and scrap paper recycling. In addition, bagasse recycling and textile waste recycling have big total intensity of environmental impacts. Thus, the development of bagasse recycling and textile waste recycling should be properly limited. Other pathways for reusing bagasse and textile wastes should be explored and evaluated. Moreover, imports of scrap paper should be encouraged to reduce large indirect impacts of scrap paper recycling on domestic environment. [Copyright &y& Elsevier]

Abstract: Investigating impacts of urban solid waste recycling on urban metabolism contributes to sustainable urban solid waste management and urban sustainability. Using a physical input–output model and scenario analysis, urban metabolism of Suzhou in 2015 is predicted and impacts of four categories of solid waste recycling on urban metabolism are illustrated: scrap tire recycling, food waste recycling, fly ash recycling and sludge recycling. Sludge recycling has positive effects on reducing all material flows. Thus, sludge recycling for biogas is regarded as an accepted method. Moreover, technical levels of scrap tire recycling and food waste recycling should be improved to produce positive effects on reducing more material flows. Fly ash recycling for cement production has negative effects on reducing all material flows except solid wastes. Thus, other fly ash utilization methods should be exploited. In addition, the utilization and treatment of secondary wastes from food waste recycling and sludge recycling should be concerned. [Copyright &y& Elsevier]

Summary The physical input-output table (PIOT) is a useful tool for analyzing the environmental sustainability of cities. Taking Chinese statistical sources as an example in this study, we discuss data acquisition methods for applying the PIOT to cities. We propose several methods and present a case study of Suzhou City to illustrate the proposed methods. These methods can provide foundations for constructing the PIOT of cities in other countries. [ABSTRACT FROM AUTHOR]

Abstract: Interactions of energy policies with water technology development in China are investigated using a hybrid input-output model and scenario analysis. The implementation of energy policies and water technology development can produce co-benefits for each other. Water saving potential of energy technology development is much larger than that of new energy exploitation. From the viewpoint of proportions of water saving co-benefits of energy policies, energy sectors benefit the most. From the viewpoint of proportions of energy saving and CO2 mitigation co-benefits of water technology development, water sector benefits the most. Moreover, economic sectors are classified into four categories concerning co-benefits on water saving, energy saving and CO2 mitigation. Sectors in categories 1 and 2 have big direct co-benefits. Thus, they can take additional responsibility for water and energy saving and CO2 mitigation. If China implements life cycle materials management, sectors in category 3 can also take additional responsibility for water and energy saving and CO2 mitigation. Sectors in category 4 have few co-benefits from both direct and accumulative perspectives. Thus, putting additional responsibility on sectors in category 4 might produce pressure for their economic development. [Copyright &y& Elsevier]