Village plus park

China’s national parks need to attract more tourists and protect existing natural habitats. Meanwhile, Chinese villages are facing the gaps between adapting to rapid modernization and protecting traditional culture. This creative project introduces ecotourism in part of Tea Valley National Park as a sustainable economic development strategy to conserve environmental heritage (including natural and cultural heritage) by engaging local communities and other stakeholders in the tourism development process. This creative project explores sustainable expansion design strategies for constructing and operating a national park, with its surrounding village. The focus area includes three sites with different existing potential and value. The project uses landscape architecture and architecture design principles to propose a master plan for developing ecotourism in Tea Valley National Park. The research uses literature review, case studies and site analysis to identify best-practice design strategies for achieving this goal. The final deliverables include a master plan, site sections, diagrams, and experiential perspectives.Thesis (M.L.A.)Department of Landscape Architectur

Multi-Task Joint Learning Model for Chinese Word Segmentation and Syndrome Differentiation in Traditional Chinese Medicine

Evidence-based treatment is the basis of traditional Chinese medicine (TCM), and the accurate differentiation of syndromes is important for treatment in this context. The automatic differentiation of syndromes of unstructured medical records requires two important steps: Chinese word segmentation and text classification. Due to the ambiguity of the Chinese language and the peculiarities of syndrome differentiation, these tasks pose a daunting challenge. We use text classification to model syndrome differentiation for TCM, and use multi-task learning (MTL) and deep learning to accomplish the two challenging tasks of Chinese word segmentation and syndrome differentiation. Two classic deep neural networks—bidirectional long short-term memory (Bi-LSTM) and text-based convolutional neural networks (TextCNN)—are fused into MTL to simultaneously carry out these two tasks. We used our proposed method to conduct a large number of comparative experiments. The experimental comparisons showed that it was superior to other methods on both tasks. Our model yielded values of accuracy, specificity, and sensitivity of 0.93, 0.94, and 0.90, and 0.80, 0.82, and 0.78 on the Chinese word segmentation task and the syndrome differentiation task, respectively. Moreover, statistical analyses showed that the accuracies of the non-joint and joint models were both within the 95% confidence interval, with pvalue < 0.05. The experimental comparison showed that our method is superior to prevalent methods on both tasks. The work here can help modernize TCM through intelligent differentiation

Selective nitric oxide electroreduction at monodispersed transition-metal sites with atomically precise coordination environment

Mitigating nitrogen oxide emissions is critical to tackling global warming and improving air quality. Electrochemically converting nitrogen oxide pollutants into value-added fuels such as ammonia (NH3) and hydroxylamine (NH2OH) is of great significance, yet the efficiency is hindered by complex reaction pathways and sluggish kinetics. Here, we demonstrated monodispersed transition-metal sites, e.g., iron (Fe) and nickel (Ni), with atomically precise coordination environments to electrocatalyze nitric oxide conversion with remarkable selectivity, activity, and durability. We observed that metal centers strongly regulate the nitrogen-product distribution. In particular, Ni preferred NH3 production with a high yield rate of 1.6 mmol mg−1 h−1, whereas Fe exhibited a superior selectivity toward NH2OH, approaching a record-high production rate of 3.1 mmol mg−1 h−1 with a selectivity of 83.5%. Operando Fourier transform infrared spectroscopy revealed different NO adsorption capabilities of single-atomic Ni and Fe, which can well explain the different reduction pathways according to the theoretical calculations.Submitted/Accepted versionThis work is funded by the National Natural Science Foundation of China (grants 21872039 22072030, and 22272029), the Science and Technology Commission of Shanghai Municipality (grants 21DZ2260400 and 22520711100), and the Fundamental Research Funds for the Central Universities (20720220008). We are thankful for financial support from the Academic Research Fund Tier 1 (no. RG10/21) and the computing resources from National Supercomputing Centre Singapore

A two-dimensional van der Waals heterostructure with isolated electron-deficient cobalt sites toward high-efficiency co2 electroreduction

Electrochemical CO2 conversion is a promising way for sustainable chemical fuel production, yet the conversion efficiency is strongly limited by the sluggish kinetics and complex reaction pathways. Here we report the ultrathin conjugated metalloporphyrin covalent organic framework epitaxially grown on graphene as a two-dimensional van der Waals heterostructure to catalyze CO2 reduction. Operando X-ray absorption and density functional theory calculations reveal the strong interlayer coupling leads to electron-deficient metal centers and speeds up electrocatalysis. The Co(III)-N4 centers exhibit a CO Faradaic efficiency of 97% at a partial current density of 8.2 mA cm-2 in an H-cell, along with a stable running over 30 h. The selectivity of CO approached 99% with a partial current density of 191 mA cm-2 in a liquid flow cell, and the turnover frequency achieved 50 400 h-1 at -1.15 V vs RHE, outperforming most reported organometallic frameworks. This work highlights the key role of strong electronic coupling between van der Waals layers for accelerating the dynamics of CO2 conversion.This work is funded by the Natural Science Foundation of China (Grants 21872039 and 22072030), Science and Technology Commission of Shanghai Municipality (Grants 18JC1411700, 19DZ2270100, and 22520711100), and the Fundamental Research Funds for the Central Universities (20720220008)

Graphdiyne/Graphene Heterostructure: A Universal 2D Scaffold Anchoring Monodispersed Transition-Metal Phthalocyanines for Selective and Durable CO2 Electroreduction

Electrochemical CO2 reduction (CO2R) is a sustainable way of producing carbon-neutral fuels, yet the efficiency is limited by its sluggish kinetics and complex reaction pathways. Developing active, selective, and stable CO2R electrocatalysts is challenging and entails intelligent material structure design and tailoring. Here we show a graphdiyne/graphene (GDY/G) heterostructure as a 2D conductive scaffold to anchor monodispersed cobalt phthalocyanine (CoPc) and reduce CO2 with an appreciable activity, selectivity, and durability. Advanced characterizations, e.g., synchrotron-based X-ray absorption spectroscopy (XAS), and density functional theory (DFT) calculation disclose that the strong electronic coupling between GDY and CoPc, together with the high surface area, abundant reactive centers, and electron conductivity provided by graphene, synergistically contribute to this distinguished electrocatalytic performance. Electrochemical measurements revealed a high FECO of 96% at a partial current density of 12 mA cm-2 in a H-cell and an FECO of 97% at 100 mA cm-2 in a liquid flow cell, along with a durability over 24 h. The per-site turnover frequency of CoPc reaches 37 s-1 at -1.0 V vs RHE, outperforming most of the reported phthalocyanine- and porphyrin-based electrocatalysts. The usage of the GDY/G heterostructure as a scaffold can be further extended to other organometallic complexes beyond CoPc. Our findings lend credence to the prospect of the GDY/G hybrid contributing to the design of single-molecule dispersed CO2R catalysts for sustainable energy conversion.National Supercomputing Centre (NSCC) SingaporeThis work is funded by the Natural Science Foundation of China (Grants 21872039 and 22072030), Ministry of Science and Technology of China (2018YFA0703502), Science and Technology Commission of Shanghai Municipality (Grants 18JC1411700 and 19DZ2270100), and Beijing National Laboratory for Molecular Sciences (BNLMS-CXTD-202001). We also thank the Academic Research Fund Tier 1 (No. RG104/18) for financial support and the computing resources from National Supercomputing Centre Singapore

Comparison of miRNAs levels in localized and metastatic PCa.

<p>Expression of miR-29b-1 (A), miR-200a (B), miR-370 (C) and miR-31 (D) in localized and metastatic PCa, fold changes of miRNAs levels in prostatic adenocarcinoma versus matched normal glands were log2 transformed on Y axis.</p

Correlations between Dicer mRNA and the miRNA expression levels in PCa.

<p>Correlations between Dicer mRNA and the miRNA expression levels in PCa.</p

The comparison between miRNA array data and Real-Time qPCR results.

<p>For the comparison between miRNA array data and Real-Time qPCR results, miR-29b-1, miR-200a, miR-370 and miR-31determined to be differentially expressed in prostate adenocarcinoma compared to matched histologically normal glands in four patients by miRNA array were validated using Real-Time qPCR. The lengths of the columns in the chart represent the log2-transformed median fold changes (tumor/normal) in expression across the four patients for each of the four miRNAs validated.</p

Expression of Dicer in PCa.

<p><b>A,</b> Expression of Dicer in prostate adenocarcinoma and its matched normal glands, after normalization to β-actin; <b>B,</b> Expression of Dicer in androgen dependent and androgen independent PCa, fold changes of Dicer mRNA levels in prostatic adenocarcinoma versus matched normal glands were log2 transformed on Y axis; <b>C,</b> Expression of Dicer in localized and metastatic PCa, fold changes of Dicer mRNA levels in prostatic adenocarcinoma versus matched normal glands were log2 ranked on Y axis.</p