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Abstract Tropical cyclones have a big impact on flood risk, and understanding how their activity interacts with population exposure under climate change is critical. Here we investigate spatiotemporal changes in flood risk using numerical models together with historical observations and future projections of tropical cyclone tracks. We find that tropical cyclone-related flood risk shifts from the Mekong Delta to the eastern lower Mekong Basin, driven by the interaction between tropical cyclones and population exposure. Historically, extreme precipitation from tropical cyclones increased flood risk in about 14% and decreased in 7% of the basin. Future tropical cyclones may increase flood risk in about 7% and reduce in nearly 18% of the basin. Moreover, population exposure growth has historically increased flood risk in 3% of the basin and is projected to result in a 1% increase. These findings highlight the complex interactions of tropical cyclone hazards and socioeconomic factors influencing flood risk.

Malignant melanoma (MM) is an extremely aggressive and fatal form of skin cancer that primarily affects the bottom layer of the epidermis and is associated with poor clinical outcomes. Early-stage MM is typically treated through surgical removal, while chemotherapy and radiotherapy are common conventional treatment options that come with harmful side effects. Emerging therapies such as immunotherapy, photodynamic therapy, biologic therapy, and photothermal therapy present hopeful options for treatment due to their effective and secure drug delivery methods. To address the limitations of current treatment options, advanced methods of drug delivery for subcutaneous MM are being developed, with hydrogels emerging as a promising alternative. To date, significant advancements have been made in the treatment of MM through the use of hydrogels-based drug delivery systems through focal plastering, injection, implantation, and microneedles. Recent research on hydrogel-based drug delivery systems that integrate multiple therapies for the treatment of subcutaneous MM is discussed in this review.

Recently, geological disasters such as land subsidence, landslides, mining-related collapse and others have been occurring more and more, posing serious threats to social and economic stability as well as human safety. InSAR (Interferometric Synthetic Aperture Radar) and advanved InSAR time-series technologies can monitor surface displacements at sub-centimeter level of accuracy, which has great and significant potential in detecting these disasters. Although several automated deformation detection methods based on CNN (Convolutional Neural Networks) have been proposed, these methods are not only time-consuming- requiring specific datasets and models- but also exhibit limited generalizability. In this study, we propose a zero-shot deformation-prompt-based SAM (Segment Anything Model) method, which does not require any pre-training operations or establishing special datasets. It initially maps the surface displacement velocity of the study area by the InSAR time-series analysis. Then the deformation-prompt-based SAM method is proposed to directly identify the exact locations and morphologies of surface deformations on the velocity maps. The method primarily extracts the rectangular boxes and points of deformation areas according to the color display characteristics of the InSAR-based land deformation map. Then these boxes and points of deformation zones serve as the prompt to the SAM, which detects deformation zones in a zero-shot manner. To validate its effectiveness, experiments on deformation extraction for various types of geohazards are conducted by utilizing Sentinel-1 and TerraSAR-X images. The results show that 137 and 93 local urban subsidence areas in Shenzhen City are identified based on different SAR images. Most of them can be automatically detected by the proposed method. Additionally, we have identified 18 landslides near Jichang Town, 36 landslides in Heifangtai and 27 major deformation zones in the Huainan mining area of Anhui Province. Statistical analysis reveals that our method can obtain high pixel accuracy in detecting land subsidence, landslides, and mining-related deformation areas with zero-shot based on different SAR images. Thus, the automatic surface deformation detection method holds considerable promise for compiling and regularly updating inventories of surface deformation data related to different types of geohazards. It has the potential to significantly enhance monitoring efficiency and provide robust technical support for disaster prevention and loss reduction.

Sarcopenia, characterized by the progressive loss of muscle mass and function, significantly affects patients with chronic obstructive pulmonary disease (COPD) and worsens their morbidity and mortality. The pathogenesis of muscle atrophy in patients with COPD involves complex mechanisms, including protein imbalance and mitochondrial dysfunction, which have been identified in the muscle tissues of patients with COPD. DKK3 (Dickkopf-3) is a secreted glycoprotein involved in the process of myogenesis. However, the role of DKK3 in the regulation of muscle mass is largely unknown. This study investigated the role of DKK3 in COPD-related sarcopenia. DKK3 was found to be overexpressed in cigarette smoking-induced muscle atrophy and in patients with COPD. Importantly, plasma DKK3 levels in COPD patients with sarcopenia were significantly higher than those without sarcopenia, and plasma DKK3 levels could effectively predict sarcopenia in patients with COPD based on two independent cohorts. Mechanistically, DKK3 is secreted by skeletal muscle cells that acts in autocrine and paracrine manners and interacts with the cell surface-activated receptor cytoskeleton-associated protein 4 (CKAP4) to induce mitochondrial dysfunction and myotube atrophy. The inhibition of DKK3 by genetic ablation prevented cigarette smoking‐induced skeletal muscle dysfunction. These results suggest that DKK3 is a potential target for the diagnosis and treatment of sarcopenia in patients with COPD.

Silicosis is the most common type of pneumoconiosis, having a high incidence in workers chronically exposed to crystalline silica (CS). No specific medication exists for this condition. GHK, a tripeptide naturally occurring in human blood and urine, has antioxidant effects. We aimed to investigate the therapeutic effect of GHK-Cu on silicosis and its potential underlying molecular mechanism. An experimental silicosis mouse model was established to observe the effects of GHK-Cu on lung inflammation and fibrosis. Moreover, the effects of GHK-Cu on the alveolar macrophages (AM) were examined using the RAW264.7 cell line. Its molecular target, peroxiredoxin 6 (PRDX6), has been identified, and GHK-Cu can bind to PRDX6, thus attenuating lung inflammation and fibrosis in silicosis mice without significant systemic toxicity. These effects were partly related to the inhibition of the CS-induced oxidative stress in AM induced by GHK-Cu. Thus, our results suggest that GHK-Cu acts as a potential drug by attenuating alveolar macrophage oxidative stress. This, in turn, attenuates the progression of pulmonary inflammation and fibrosis, which provides a reference for the treatment of silicosis.

Abstract Metal-nitrogen-carbon catalysts with hierarchically dispersed porosity are deemed as efficient geometry for oxygen reduction reaction (ORR). However, catalytic performance determined by individual and interacting sites originating from structural heterogeneity is particularly elusive and yet remains to be understood. Here, an efficient hierarchically porous Fe single atom catalyst (Fe SAs-HP) is prepared with Fe atoms densely resided at micropores and mesopores. Fe SAs-HP exhibits robust ORR performance with half-wave potential of 0.94 V and turnover frequency of 5.99 e−1s−1site−1 at 0.80 V. Theoretical simulations unravel a structural heterogeneity induced optimization, where mesoporous Fe-N4 acts as real active centers as a result of long-range electron regulation by adjacent microporous sites, facilitating O2 activation and desorption of key intermediate *OH. Multilevel operando characterization results identify active Fe sites undergo a dynamic evolution from basic Fe-N4 to active Fe-N3 under working conditions. Our findings reveal the structural origin of enhanced intrinsic activity for hierarchically porous Fe-N4 sites.

Automated guided vehicle (AGV) smart parking provides a new solution to solve the problem of urban parking difficulties. In the AGV parking lot, whether the AGV running path is reasonable affects the transportation efficiency of the entire parking lot. RRT algorithm and ant colony algorithm can achieve good path planning effect for AGV. However, the use of uniformly distributed initial pheromones can easily lead to blind search and local optimization in the early stage of the algorithm, resulting in a decrease in AGV efficiency. In this paper, an AGV based on the fast-scaling random tree-ant colony algorithm (RRT*-ACO) is proposed. The path planning method uses the fast search mechanism of RRT* algorithm and the positive feedback advantage of ant colony algorithm to combine the two algorithms and add them to the RRT* algorithm. The two-way search, heuristic dynamic sampling and dynamic step size strategies are used to accelerate the search speed of the RRT* algorithm, quickly generate an initial path, and then deploy different concentrations of initial pheromones according to the initial path to guide the ant colony algorithm search. Then, the silo excavation search strategy is added to the ant colony algorithm, which improves the pheromone update rule and reduces the number of iterations of the ant colony algorithm. Finally, several simulation experiments are carried out on the algorithm in maps of different scales, and the comparative experimental results show that the RRT*-ACO algorithm has faster convergence speed, better global search ability, and the path planning efficiency is significantly higher than that of traditional ant colony algorithm and ant colony system, and has better robustness in large-scale maps.

Chitosan, a cationic polysaccharide derived from the deacetylation of chitin, is widely distributed in nature. Its antibacterial activity, biocompatibility, biodegradability, and non-toxicity have given it extensive uses in medicine, food, and cosmetics. However, the significant impact of variations in the physicochemical properties of chitosan extracted from different sources on its application efficacy, as well as the considerable differences in its antimicrobial mechanisms under varying conditions, limit the full realization of its biological functions. Therefore, this paper provides a comprehensive review of the structural characteristics of chitosan, its preparation methods from different sources, its antimicrobial mechanisms, and the factors influencing its antimicrobial efficacy. Furthermore, we highlight the latest applications of chitosan and its derivatives across various fields. We found that the use of microbial extraction shows promise as a new method for producing high-quality chitosan. By analyzing the different physicochemical properties of chitosan from various sources and the application of chitosan-based materials (such as nanoparticles, films, sponges, and hydrogels) prepared using different methods in biomedicine, food, agriculture, and cosmetics, we expect these findings to provide theoretical support for the broader utilization of chitosan.

Abstract Background Sarcopenia and obesity are two abnormal body composition phenotypes, and sarcopenic obesity (SO) is characterized by both low skeletal muscle mass (sarcopenia) and high adiposity (obesity). SO negatively influences the clinical status of patients with chronic obstructive pulmonary disease (COPD). However, the studies exploring the prevalence and clinical effects of SO in COPD patients are limited. Our study aimed to elucidate the prevalence and impact of SO on COPD patients. Methods In this cross-sectional study, the pulmonary function, St. George’s Respiratory Questionnaire, exercise tolerance, body composition, and serum levels of resistin and TNF-α were assessed in 198 COPD patients. The clinical value of serum resistin and TNF-α for predicting SO in patients with COPD was evaluated. Results In the 198 patients with COPD, the prevalence rates of sarcopenia, obesity, and SO in COPD patients were 27.27%, 29.8%, and 9.6%, respectively. Patients with SO experienced more severe symptoms of dyspnea and worse health related quality of life. The expression of resistin increased in patients with SO compared to other patients. The AUC value of serum resistin level for predicting SO was 0.870 (95% CI: 0.799–0.940). BMI (OR: 1.474, 95% CI: 1.124–1.934) and resistin (OR: 1.001, 95% CI: 1.000-1.002) levels were independent risk factors of SO in patients with COPD in Multivariate analysis. Conclusion The prevalence rates of SO in COPD patients was 9.6%. COPD accompanied by SO is significantly associated with worse pulmonary function and poor physical performance. Serum resistin may be a potential adjunct for predicting SO in COPD patients.

Abstract Background Skeletal muscle dysfunction is an important co‐morbidity in patients with chronic obstructive pulmonary disease (COPD) and is significantly associated with increased mortality. Oxidative stress has been demonstrated an important trigger for COPD‐related skeletal muscle dysfunction. Glycine‐histidine‐lysine (GHK) is an active tripeptide, which is a normal component of human plasma, saliva, and urine; promotes tissue regeneration; and acts as an anti‐inflammatory and antioxidant properties. The purpose of this study was to determine whether GHK is involved in COPD‐related skeletal muscle dysfunction. Methods The plasma GHK level in patients with COPD (n = 9) and age‐paired healthy subjects (n = 11) were detected using reversed‐phase high‐performance liquid chromatography. The complex GHK with Cu (GHK‐Cu) was used in in vitro (C2C12 myotubes) and in vivo experiments (cigarette smoking [CS]‐exposure mouse model) to explore the involvement of GHK in CS‐induced skeletal muscle dysfunction. Results Compared with healthy control, plasma GHK levels were decreased in patients with COPD (70.27 ± 38.87 ng/mL vs. 133.0 ± 54.54 ng/mL, P = 0.009). And plasma GHK levels in patients with COPD were associated with pectoralis muscle area (R = 0.684, P = 0.042), inflammatory factor TNF‐α (R = −0.696, P = 0.037), and antioxidative stress factor SOD2 (R = 0.721, P = 0.029). GHK‐Cu was found to rescue CSE‐induced skeletal muscle dysfunction in C2C12 myotubes, as evidenced by increased expression of myosin heavy chain, reduced expression of MuRF1 and atrogin‐1, elevated mitochondrial content, and enhanced resistance to oxidative stress. In CS‐induced muscle dysfunction C57BL/6 mice, GHK‐Cu treatment (0.2 and 2 mg/kg) reduces CS‐induced muscle mass loss (skeletal muscle weight (1.19 ± 0.09% vs. 1.29 ± 0.06%, 1.40 ± 0.05%; P

Abstract With the population projections indicating continued growth during this century, construction of large dams can be considered as one of the best available options to meet the future increases in water, food, and energy demands. While there are reports that thousands of large dams will be built in the near future, a key question is: what are the appropriate conditions for selecting the sites for these dams? The site of a large dam should be carefully evaluated based on many factors, such as socioeconomic development, water resources availability, topographic characteristics, and environmental impacts. This study aims to partly address the above question through identifying the relationship between two topographic characteristics (i.e., catchment area and slope) of a river reach to build a large dam based on the 30-m-resolution global drainage networks. The information about 2815 existing large dams from the Global Reservoir and Dam (GRanD) database is collected for analysis. The confidence ellipse approach is introduced to establish the quantitative relationship between these two variables, which is then used to evaluate the site selection of a large dam from the perspective of topographic characteristics. The results show that: (1) each large dam can well correspond to the nearest river reach in the global drainage networks and (2) the logarithmic values of catchment area and slope can be well described by a confidence ellipse, which is obtained based on the means, standard deviations, and Pearson correlation coefficients of the two variables. The outcomes of this study will be of great value for policymakers to have a more comprehensive understanding of large dam development in future.

The high elasticity and excellent gas barrier properties of rubber composites make them irreplaceable in the field of sealing. Constructing a complicated barrier network to reduce free volume is crucial to improving gas barrier properties. In this research, liquid acrylonitrile-butadiene rubber/γ-Methacryloxypropyl trimethoxy silane (KH570) modified graphene oxide/butyl rubber composites (LNBR/KGO/IIR) were fabricated. A KGO lamellar network was constructed to resist gas diffusion in the IIR matrix. Meanwhile, LNBR macromolecules further occupied the free volume inside the IIR composites, thereby maximizing the retardation of the path of small molecule gas permeation. The modification of GO by KH570 was successfully demonstrated through FTIR and XRD. The grafting rate of KH570 was calculated to be approximately 71.4%. KGO was well dispersed in IIR due to emulsion compounding and the formation of lamellar networks. The 300% modulus, tensile strength and tear strength of KGO/IIR were improved by 43.5%, 39.1% and 14.8%, respectively, compared to those of the IIR composite. In addition, the introduction of LNBR resulted in a 44.2% improvement in the gas barrier performance of nitrogen permeability relative to the original IIR composite.

Abstract Airflow behavior and outdoor PM2.5 dispersion depend significantly on the building-tree layouts and orientation towards the prevailing wind conditions. To investigate this issue, the present work evaluates the aerodynamic effect of different building-tree layouts on the outdoor PM2.5 dispersions in the urban communities of Shijiazhuang City, China. The adopted numerical CFD technique was based on the standard k–ε model and the Disperse Phase Model (DPM). For this study, ten different building-tree arrangements were conceptualized and all these configurations were simulated by using Ansys Fluent software to quantify the implications on the outdoor PM2.5 dispersion due to their presence. The results have shown that: (1) a wide building interval space could benefit the air ventilation and thus decrease PM2.5 concentrations, however, this effectiveness is highly influenced by the presence of the trees; (2) the trees on the leeward side of a building tend to increase the local wind velocity and decrease the pedestrian-level PM2.5 concentrations, while those on the windward side tend to decrease the wind velocity. The small distance with trees in the central space of the community forms a wind shelter, hindering the particle dispersion; and (3) the configuration of parallel type buildings with clustered tree layouts in the narrow central space is most unfavorable to the air ventilation, leading to larger areas affected by excessive PM2.5 concentration.

BackgroundSarcopenia often occurs as a comorbidity in many diseases which ultimately affects patient prognosis. However, it has received little attention in patients with idiopathic pulmonary fibrosis (IPF). This systematic review and meta-analysis aimed at determining the prevalence and risk factors of sarcopenia in patients with IPF.MethodsEmbase, MEDLINE, Web of Science, and Cochrane databases were searched using relevant MeSH terms until December 31, 2022. The Newcastle-Ottawa Scale (NOS) was used for quality assessment and data analysis were performed using Stata MP 17.0 (Texas, USA). A random effects model was adopted to account for differences between articles, and the I2 statistic was used to describe statistical heterogeneities. Overall pooled estimates obtained from a random effects model were estimated using the metan command. Forest plots were generated to graphically represent the data of the meta-analysis. Meta-regression analysis was used for count or continuous variables. Egger test was used to evaluate publication bias and, if publication bias was observed, the trim and fill method was used.Main resultsThe search results showed 154 studies, and five studies (three cross-section and two cohort studies) with 477 participants were finally included. No significant heterogeneity was observed among studies included in the meta-analysis (I2 = 16.00%) and our study's publication bias is low (Egger test, p = 0.266). The prevalence of sarcopenia in patients with IPF was 26% (95% CI, 0.22–0.31). The risk factors for sarcopenia in patients with IPF were age (p = 0.0131), BMI (p = 0.001), FVC% (p < 0.001), FEV1% (p = 0.006), DLco% (p ≤ 0.001), and GAP score (p = 0.003).ConclusionsThe pooled prevalence of sarcopenia in patients with IPF was 26%. The risk factors for sarcopenia in IPF patients were age, BMI, FVC%, FEV1%, DLco%, and GAP score. It is important to identify these risk factors as early as possible to improve the life quality of patients with IPF.

The long-term viability of small businesses in the aftermath of multiple pandemics and consequent lockdowns has a crucial impact on the sustainable economic and social development of any region across the world. Thus, in order to investigate what has been the major impact of COVID-19 pandemic within local small businesses and to identify which main factors helped small businesses to survive none as well as multiple lockdowns, data were obtained from 382 small businesses in the main urban area of Wuhan, China, via two rounds of field investigations and surveys in July 2020 and July 2022. This paper presents the results of the field investigations and the surveys completed and describes the Bayesian methods applied to quantitatively explore the impact of different variables on the probability of each business to remain active and open even after experiencing none or multiple lockdowns. Results obtained show that the difference between survival rates associated with businesses hit by no pandemic outbreak with those hit by one or several waves is negligible. Furthermore, owners who had higher confidence in their abilities since the beginning or they implemented an accurate evaluation of their strategies to run their businesses since the start of the pandemic, demonstrated to have a higher probability to keep their business alive with none as well as additional waves of the pandemic. Reduction of employees, transition of operations and promotion activities online as well as rent subsidies and tax reduction were identified as crucial actions that enhanced the probability to maintain alive businesses that experienced at least one lockdown. Globally, there was no clear policy approach at the start of the pandemic, however this study clearly determines that in future governments should provide timely support to small businesses in regions experiencing more severe impacts of the pandemic, and this should consist of a mix of grants, loans, and temporary tax cuts since initial stages.

In Alzheimer's disease, the researchers found that if the patients were treated at the early stage of the disease, it could effectively delay the development of the disease. At present, multi-modal feature selection is widely used in the early diagnosis of Alzheimer's disease. However, existing multi-modal feature selection algorithms focus on learning the internal information of multiple modalities. They ignore the relationship between modalities, the importance of each modality and the local structure in the multi-modal data. In this paper, we propose a multi-modal feature selection algorithm with anchor graph for Alzheimer's disease. Specifically, we first use the least square loss and l2,1−norm to obtain the weight of the feature under each modality. Then we embed a modal weight factor into the objective function to obtain the importance of each modality. Finally, we use anchor graph to quickly learn the local structure information in multi-modal data. In addition, we also verify the validity of the proposed algorithm on the published ADNI dataset.

In this paper, the coupled weakly compressible (WC) and total Lagrangian (TL) smoothed particle hydrodynamics (SPH) model based on the open-source multi-physics and multi-resolution SPHinXsys library is used to simulate the structural deformation and failure problem in fluid-structure interaction (FSI). Aiming at the problem that the original TLSPH method could not simulate the cracks and their propagation, the fracture model based on TLSPH is established, and then verified by fast-loading and slow-loading cases respectively. With the fracture model in hand, the TLSPH method is coupled with the WCSPH method to simulate the breaking dam flow impacting on an elastic/elastoplastic baffle. The comparison with the literature data shows that the present coupled WC-TLSPH model can accurately simulate the FSI problem where figurative fracture process is involved, indicating the compromising potential of the established model to simulate the elastoplastic structural failure in scientific and industrial applications.

Precipitation recycling, defined as that precipitation in a region is partially contributed by evapotranspiration from the same region, is the interaction between terrestrial hydrology and atmospheric processes, and plays a crucial role in forming water resources. For agricultural lands, the evapotranspiration of irrigation water alters local climate by reprecipitation in surrounding regions, which can also be analyzed from the viewpoint of precipitation recycling. As the largest endorheic basin in China, the Tarim Basin has strong precipitation recycling contributed by the surrounding mountains, as well as large irrigation areas producing high-quality cotton, which makes it an appropriate study case for the recycling of irrigation water. In this paper, we calculate the water vapor sources of precipitation (WSoP) and the reprecipitation of evapotranspiration (RPoET) in the Tarim Basin by using the Water Accounting Model—Two Layers, analyze their spatial distributions, and find the moisture recycling ratio of the basin as 15.4%. We set up comparative scenarios of evapotranspiration increase by irrigation areas in different locations of the Tarim Basin, and study their difference in reprecipitation and moisture recycling. Results show that the evapotranspiration increase in different locations has a marginal reprecipitation ratio ranging from 8% to 24%, and further boosts the whole basin’s moisture recycling ratio by 0.11%–0.29%. Significant difference among the scenarios proves that the location of irrigation areas affects the reprecipitation of its evapotranspiration, and the optimized moisture recycling can benefit water resource and ecosystem conditions inside the basin. In summary, this work would be useful to provide a practical basis for irrigation planning by considering the land-atmosphere interaction.

Portulaca oleracea L. (purslane) is a widely distributed plant with a long history of cultivation and consumption. Notably, polysaccharides obtained from purslane exhibit surprising and satisfactory biological activities, which explain the various benefits of purslane on human health, including anti-inflammatory, antidiabetic, antitumor, antifatigue, antiviral and immunomodulatory effects. This article systematically reviews the extraction and purification methods, chemical structure, chemical modification, biological activity and other aspects of polysaccharides from purslane collected in the Chinese Pharmacopoeia, Flora of China, Web of Science, PubMed, Baidu Scholar, Google Scholar and CNKI databases in the last 14 years, using the keywords “Portulaca oleracea L. polysaccharides” and “purslane polysaccharides”. The application of purslane polysaccharides in different fields is also summarized, and its application prospects are also discussed. This paper provides an updated and deeper understanding of purslane polysaccharides, which will provide useful guidance for the further optimization of polysaccharide structures and the development of purslane polysaccharides as a novel functional material, as well as a theoretical basis for its further research and application in human health and manufacturing development.

In this study, a three-dimensional (3D) numerical model based on the smoothed particle hydrodynamics (SPH) approach was developed to simulate the near-shore current flows over a rough topographic surface in the coastal area, where the flows are shallow and demonstrate strong turbulent characteristics. The numerical program is based on the open-source code SPHysics (http://www.sphysics.org), and two major improvements are made to treat the turbulence and rough boundary effects: A modified sub-particle-scale (SPS) eddy viscosity model is developed to address the turbulence transfer of flows, and a drag force equation is included in the momentum equations to account for the influence of roughness element on the bed and lateral boundaries. The computed results of flow velocity, shear stress, and free surface characteristics are compared with the laboratory measurements for a variety of test conditions. It has shown that the present SPH model can accurately simulate 3D-free surface near-shore current flows over a realistic topography with roughness.

Facing irreversible and catastrophic changes on the earth, China has committed to peak the net carbon emission by 2030 and to achieve carbon neutrality by 2060. The pledge requires significant mitigation immediately and sustainably. Considering this background, some perspectives are given in this article based on the comprehensive use of natural resources. First, utilizing the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model and statistical data, net carbon emissions of provinces in China are anticipated, which lays a foundation for the further “three-step” carbon neutralization route. Second, a strategy of water–temperature–radiation–land coupling use is proposed, considering 1) the carbon emission cut, which relies on comparing the energy intensity and energy structure in China with those in developed countries; 2) the carbon sink increase, which depends on the evaluation of constraints of hydrometeorological factors on ecological productivity. Finally, the necessity and possibility of carbon trading and redistribution of the natural resources are discussed to ensure that China's national net carbon emission would be reduced to zero by 2060.

In this paper, we propose a new unsupervised feature selection algorithm by considering the nonlinear and similarity relationships within the data. To achieve this, we apply the kernel method and local structure learning to consider the nonlinear relationship between features and the local similarity between features. Specifically, we use a kernel function to map each feature of the data into the kernel space. In the high-dimensional kernel space, different features correspond to different weights, and zero weights are unimportant features (e.g. redundant features). Furthermore, we consider the similarity between features through local structure learning, and propose an effective optimization method to solve it. The experimental results show that the proposed algorithm achieves better performance than the comparison algorithm.

Visual question answering (VQA) is regarded as a multi-modal fine-grained feature fusion task, which requires the construction of multi-level and omnidirectional relations between nodes. One main solution is the composite attention model which is composed of co-attention (CA) and self-attention(SA). However, the existing composite models only consider the stack of single attention blocks, lack of path-wise historical memory, and overall adjustments. We propose a path attention memory network (PAM) to construct a more robust composite attention model. After each single-hop attention block (SA or CA), the importance of the cumulative nodes is used to calibrate the signal strength of nodes’ features. Four memoried single-hop attention matrices are used to obtain the path-wise co-attention matrix of path-wise attention (PA); therefore, the PA block is capable of synthesizing and strengthening the learning effect on the whole path. Moreover, we use guard gates of the target modal to check the source modal values in CA and conditioning gates of another modal to guide the query and key of the current modal in SA. The proposed PAM is beneficial to construct a robust multi-hop neighborhood relationship between visual and language and achieves excellent performance on both VQA2.0 and VQA-CP V2 datasets.