Search

Land resources are foundational for human survival and development. In contrast, land use/land cover (LULC) dynamics drive considerable changes in ecosystem services. Recently, China witnessed a new stage of rapid urbanization. Therefore, investigating the relationships between ecosystem services value (ESV) and LULC in these areas is highly relevant. Based on the data of land use and socioeconomic development in Shanghai from 2000 to 2020, we adopted a land use/land cover dynamics analysis method and established the ESV per unit area at the city scale, discussed the impact of LULC on ESV spatially and quantitatively, and tested the research process based on the sensitivity analysis of the ESV coefficient. The results show that from 2000 to 2020, the LULC pattern in Shanghai rapidly changed. In particular, the area of cultivated land has shrunk by 123.96 thousand hm2, while the construction land has expanded by 141.26 thousand hm2, which has led to a decline in ESV of the entire city (especially regarding hydrological adjustment and biodiversity). Nevertheless, although the area of trench and lakes only occupies 1.67–3.16% of the total area of land, its ecological value accounts for an astonishing 23.80–50.70% of the total ESV. At the district level, the primary decline in eco-system services value was noted in the Chongming District in the north and Pudong New Area in the east of Shanghai. However, due to the overall planning of the city and the advantages of its resource endowment, Qingpu District and its surrounding areas in western Shanghai have witnessed improvements in terms of the values of hydrological adjustment, water supply, and environmental purification. This study presents a deeper and more comprehensive understanding of issues regarding ESV in rapidly urbanized areas, thereby providing an important reference for decision-makers regarding the rational layout of cities, sustainable use of land, and management of natural ecosystems.

Buildings, as major energy consumers, can provide great untapped demand response (DR) resources for grid services. However, their participation remains low in real-life. One major impediment for popularizing DR in buildings is the lack of cost-effective automation systems that can be widely adopted. Existing optimization-based smart building control algorithms suffer from high costs on both building-specific modeling and on-demand computing resources. To tackle these issues, this paper proposes a cost-effective edge-cloud integrated solution using reinforcement learning (RL). Beside RL’s ability to solve sequential optimal decision-making problems, its adaptability to easy-to-obtain building models and the off-line learning feature are likely to reduce the controller’s implementation cost. Using a surrogate building model learned automatically from building operation data, an RL agent learns an optimal control policy on cloud infrastructure, and the policy is then distributed to edge devices for execution. Simulation results demonstrate the control efficacy and the learning efficiency in buildings of different sizes. A preliminary cost analysis on a 4-zone commercial building shows the annual cost for optimal policy training is only 2.25% of the DR incentive received. Results of this study show a possible approach with higher return on investment for buildings to participate in DR programs.

With the popularisation of laparoscopic cholecystectomy, ligation clips have been commonly used for ligating the cystic duct and cystic artery. However, non-degradable clips remain in the body long-term, which significantly increases the risk of the clip becoming detached. Thus, magnesium alloys have attracted tremendous attention owing to their biodegradability and good biocompatibility. However, the poor corrosion resistance hinders the clinical application of magnesium alloys with microarc oxidation/phytic acid (MAO/PA) composite coatings as protective coatings. Here, these alloys were used to hinder the rapid material degradation in aqueous solution. Electrochemical tests were conducted to evaluate the in vivo degradation behaviour in simulated body fluid (SBF) for Mg–Zn–Y–Nd alloys, and scanning electron microscopy (SEM) was used to observe the micromorphology of in vivo clip degradation. Cell toxicity, cell adhesion, and flow cytometry were performed in vitro to detect cytocompatibility. Biochemical detection of serum magnesium, serum creatinine (CREA), blood urea nitrogen (BUN), alanine transaminase (ALT), and alanine aminotransferase (AST), and haematoxylin–eosin (HE) staining of the heart, liver, and kidney tissues in vivo was conducted to determine the biocompatibility properties after surgery. Electrochemical measurements and SEM images revealed that the MAO/PA-coated magnesium alloy delayed corrosion in SBF. The apoptosis rate increased slightly with increased extract concentration. Nevertheless, MAO/PA-coated magnesium alloys still exhibited good cytocompatibility. No obvious abnormality was observed in the blood biochemical test or HE staining. Thus, MAO/PA-coated magnesium alloys exhibit better corrosion than bare magnesium. In addition, Mg–Zn–Y–Nd and MAO/PA-coated magnesium alloys exhibited no cytotoxicity, good adhesion, and biosafety.

Objective To elucidate the neuroprotective function of metformin in suppressing propofol-induced apoptosis of HT-22 cells. Methods HT-22 cells were treated with 0, 10 or 100 μmol/L propofol, followed by determination of their proliferative ability. Subsequently, changes in proliferation and apoptosis of propofol-treated HT-22 cells induced with metformin were assessed. Apoptosis-associated genes in HT-22 cells were detected by Western blot. At last, regulatory effects of Cav-1 on propofol and metformin-treated HT-22 cells were examined. Results Propofol treatment dose-dependently decreased proliferative ability and increased apoptosis ability in HT-22 cells, which were partially blocked by metformin administration. Upregulated Bcl-2 and downregulated Bax were observed in propofol-treated HT-22 cells following metformin administration. In addition, Cav-1 level in HT-22 cells was regulated by metformin treatment. Notably, metformin reversed propofol-induced apoptosis stimulation and proliferation decline in HT-22 cells via downregulating Cav-1. Conclusion In our study, we found that propofol could induce apoptosis of HT-22 cells and metformin could rescue the apoptosis effect regulated by propofol. Then, we found that metformin protects propofol-induced neuronal apoptosis via downregulating Cav-1.

Urban parks positively affect the life quality and health of urban residents as well as the environment where they live. When it comes to the design of a future urban forest park, it is necessary to consider the protection of ecological environment, landscape sustainability and practicability. This study explored residents’ spatial preference for urban forest parks based on preference survey data. According to the rating scores obtained for four urban forest park routes during physical activities, this study used cognitive maps and multinomial logit models to figure out the potential influencing factors affecting residents’ spatial preference while they engage in physical activities. The results suggest that forest routes are still the primary choice for urban residents. Although familiarity with the spatial image preference for urban forest parks varied from person to person, residents’ choice of route shows certain commonalities, which was reflected in the sequential cognitive maps obtained from them. In addition, residents’ route preference is influenced by their exercise habits, environmental preference and residential location. There is also a certain correlation between residents’ preference and their characteristics. This study provides additional information for planners, developers, engineers, architects and foresters in building a more suitable environment that is aesthetically appealing and ecologically sound for physical exercising.

Load forecasting problems have traditionally been addressed using various statistical methods, among which autoregressive integrated moving average with exogenous inputs (ARIMAX) has gained the most attention as a classical time-series modeling method. Recently, the booming development of deep learning techniques make them promising alternatives to conventional data-driven approaches. While deep learning offers exceptional capability in handling complex non-linear relationships, model complexity and computation efficiency are of concern. A few papers have explored the possibility of applying deep neural networks to forecast time-series load data but only limited to system-level or single-step building-level forecasting. This study, however, aims at filling in the knowledge gap of deep learning-based techniques for day-ahead multi-step load forecasting in commercial buildings. Two classical deep neural network models, namely recurrent neural network (RNN) and convolutional neural network (CNN), have been proposed and formulated under both recursive and direct multi-step manners. Their performances are compared with the Seasonal ARIMAX model with regard to accuracy, computational efficiency, generalizability and robustness. Among all of the investigated deep learning techniques, the gated 24-h CNN model, performed in a direct multi-step manner, proves itself to have the best performance, improving the forecasting accuracy by 22.6% compared to that of the seasonal ARIMAX.

Urban parks provide their residents with a space for activities and interactions, and soundscapes play an important role in influencing the residents’ sense of experience of the park environment, with the result that the preferences of residents regarding multiple sound sources can be used as the basis of improving the soundscape quality and optimizing the function of urban parks. Using Shanghai Century Park as the research area, this study focused on the residents’ preference to multiple sound sources based on registered residence differences, and comprehensively used the semantic differential method, importance-performance analysis model, and multinomial logit model to conduct quantitative research on urban park soundscapes and combined the measurement results of the soundscape with the residents’ perception and preference. The results showed that the artificial sounds in Century Park were noisy during the morning and evening peak, while the human sounds were noisy when residents gathered in the park. There was a close relationship between the favorability and subjective loudness of urban park soundscapes, and the residents’ perceptions of urban park soundscapes substantially differed in terms of loudness, tone, and sense of belonging, whereas the differences in the perception of timbre, coverage, and sound source distance were relatively small. Furthermore, it is necessary to enhance the role of natural sounds as well as control the main noise-producing sound sources so that various sounds in the park will not interfere with each other; the optimization of soundscapes should also focus on the residents’ different soundscape preferences due to their individual characteristics.

Background: The occurrence and development of colorectal cancer is related to the compositional and functional variation of gut microbiota. Compared with healthy people, gut microbiota of patients with colorectal cancer is in disorder. Most traditional Chinese Medicine is effective by oral administration and has both anti-tumor effect and enteric microecological regulative effect. However, whether the dysbiosis of gut microbiota under tumor burden affects the serum metabolome of human body that related to traditional Chinese medicine is unclear. In this study, Fufangchangtai (FFCT) was chosen to be the model prescription to explore the correlation between gut microbiota and the serum metabolism related to FFCT in anti colorectal cancer treatment. Results: The gut microbiota between colorectal tumor-bearing mice and healthy mice were determined by 16S rRNA gene sequencing, showing quite differences between the two groups and suggesting that Firmicutes , Deferribacteres , Bacteroidetes and Proteobacteria were marked differential intestinal bacteria. The alternations in serum metabolome in the FFCT-treating tumor-bearing mice and simple FFCT-treating mice were detected using Liquid chromatogragh-mass spectrometer (LC/MS), showing significant differences between the two groups as well. Metabolites of FFCT like Citric acid, （±）12-HEPE, Cycloartanyl ferulate were much more in simple FFCT-treating mice, indicating that the present of tumor could affect the absorption and metabolism of FFCT. Additionally, these differential metabolites of FFCT involved in multiple pathways including the Alanine, aspartate and glutamate metabolism, Central carbon metabolism in cancer, Biosynthesis of amino acids. Different doses of FFCT were given to the tumor-bearing mice through oral administration, and the results of gut microbiota 16S rRNA gene sequencing showing that FFCT-treating groups has higher abundance of Firmicutes , Turicibacter and Roseburia than tumor-bearing group, moreover, the abundance of these bacteria was positively correlated to the drug concentration. Firmicutes and Bacteroidetes in FFCT-treating groups showed a similar trend with Healthy group, indicating the modulation of FFCT on gut microbiota of colorectal tumor-bearing mice.Conclusions: Collectively, we concluded that the dysbiosis of gut microbiota in tumor-bearing mice could affect the serum metabolome of human body that related to FFCT, and FFCT could correct the gut microbiota of colorectal tumor-bearing mice. It was pointed out that the GM should be concerned during the therapy of FFCT. The more healthier intestinal microenvironment was conductive to the better clinical curative effect.

Sepsis-associated encephalopathy (SAE) is a serious and diffuse cerebral dysregulation with a high morbidity and mortality caused by sepsis. Mitophagy plays an important role in SAE, and microglial phagocytosis of apoptotic cells (efferocytosis) is the core of the brain regenerative response. Voltage dependent anion channel (VDAC1) is an important regulator of mitophagy. However, it remains unknown whether VDAC1 influences SAE progression by regulating mitophagy and efferocytosis. Herein, we explored the mechanism where knockdown of VDAC1 alleviated the cognitive dysfunction caused by sepsis-associated encephalopathy and further elucidated the underlying molecular mechanisms. SAE model in mice was established through caecal ligation and puncture (CLP). The increased mitophagy and decreased efferocytosis were observed by the transmission electron microscope (TEM) in the SAE model. Besides, immunoblot tests showed an interaction between autophagy and efferocytosis. Further behavior tests and TEM results indicated that knockdown of VDAC1 alleviated the cognitive dysfunction by decreasing the autophagy and increasing the efferocytosis in a PINK1/Parkin-dependent manner. Based on these results, we conclude that knockdown of VDAC1 alleviates the cognitive dysfunction in the CLP-induced SAE mouse model.

Convolutional neural network (CNN) is widely used in Natural Language Processing (NLP) and has achieved relatively good results. Since the convolutional layer of CNN can be initialized by encoding important semantic features, CNN can extract important semantic features in the convolutional layer. The pooling layer of CNN uses the feature of max-pooling to filter the features, but it does not consider the key information in the sentence and the context semantic information in the text. Therefore, a novel neural network algorithm combining with the attention mechanism and the pooling layer is proposed in this paper to make the model pay attention to the keywords in the sentence and automatically maintain the most meaningful text message, thereby improving the performance of text classification. Our paper uses important information features to initialize the convolution filter so that the model can notice the important semantic features. The resulting feature is enhanced by combining the attention mechanism in the pooling layer, so that the model can maintain the important information features of the text. Experiments show that the proposed model achieves excellent results for multiple text classification tasks, including sentiment classification and topic classification.

DC injection is a critical issue in transformerless grid-connected inverters. DC injection control based on virtual capacitor has the advantages of low cost, low loss, high accuracy and easy implementation. In this paper, the principle of DC injection control based on virtual capacitor was analyzed. In addition, the applicable conditions, working process, steady state error and advantages were also discussed in detail. The design of the control parameters based on virtual capacitor was proposed in a grid-connected inverter with LCL filter. The robustness of the control parameters was also discussed. Simulation and experimental results verify the validity of the analysis and demonstrate that this research has a certain value in engineering applications.

As a new generation of technology cloud computing has been widely applied to various fields, and owing to the problem of the security, the security problems of the data in the cloud computing platform has been becoming the focus of attention. Therefore, it is vital of cloud computing that to put forward a security mechanism to ensure the security of data in the environment of cloud computing. The related technology of Intrusion Detection is researched, and the thoughts of combining misuse detection and anomaly detection together are adopting. Then a method of anomaly detection based on data mining is given. It is the improved decision trees algorithm, and achieves a real-time updates of invading the signature database by misuse detection. It was improved through the experiment the application based on improved decision trees can detect the attack effectively and guarantee the security of the cloud computing.

Phenylboronic acid-functionalized amphiphilic block copolymer Pluronic-PMCC-BA was synthesized via ring-opening polymerization of 5-methyl-5-benzyloxycarbonyl-1,3-dioxan-2-one (MBC) with fumaric acid as a catalyst followed by the deprotection of carboxyl groups by catalyzed hydrogenation and the condensation of 3-aminophenylboronic acid with the copolymer side groups. Pluronic-PMCC-BA can form stable micelle solution by self-assembly in water. The phenylboronic acid groups are located at the shell of micelle as proved by (1)H NMR. The diameter of drug-free micelles is approximate 60 nm. Nano-spheres with narrow size distribution could be observed in the TEM image. MTT assay results show that Pluronic-PMCC-BA exhibits slight cytotoxicity when the polymer concentration is higher than 25 μg mL(-1). The toxicities of DOX@Pluronic-PMCC and DOX@Pluronic-PMCC-BA to COS7, HeLa, and HepG2 cell lines are similar with those of free DOX. Interestingly, phenylboronic acid groups located at the surface of Pluronic-PMCC-BA micelles can recognize HepG2 cells and promote the drug uptake of the cells, which are observed by confocal laser scanning microscopy (CLSM). The results imply that Pluronic-PMCC-BA would be a promising material for targeted drug delivery to the cancer cells.

This paper presents an improved multistage variable-step incremental conductance method to achieve the maximum power point tracking (MPPT). Compared with the traditional variable-step incremental conductance method, this improved algorithm optimizes the selection of the step size so that the tracking of the maximum power point is more quickly and the steady-state performance is better. Furthermore, the algorithm can still guarantee the quickness and accuracy of MPPT when the environmental conditions change suddenly and tremendously. At last, matlab simulation was applied to compare the characteristics of multistage variable-step algorithm with that of the other. And the simulation results verified that the improved algorithm has better dynamic and steady-state performance.