Abstract:Nature-based tourism in protected areas is increasing worldwide and has strong potential to enhance biodiversity conservation, poverty alleviation, and ultimately sustainable development. Understanding the evolution of protected areas as tourism destinations and the causes and consequences of changing supply and demand elements is essential toward sustainably managing tourism in these critical ecosystems. This research applied the Tourism Area Life Cycle (TALC) model to illustrate and analyze the 30-year evolution of tourism in Wolong Nature Reserve. Being inscribed in UNESCO Biosphere Reserve and World Heritage programmes, Wolong is a flagship protected area in China. We showed that the Reserve experienced exploration, involvement, and development stages of TALC before tourism growth was completely halted by the Wenchuan Earthquake in 2008. We systematically investigated the changes related to the evolution of tourism and identified various internal and external driving forces. We examined the dynamics among politics, economy, and tourism growth that might propel the Reserve through the life cycle and identified significant tourism governance structural changes through the stages. The results have implications for sustainable tourism development in China's protected areas and also contributes to a broader and general understanding of the complex relationships among tourism, protected areas, and community development. Nature-based tourism in protected areas, which is growing worldwide, offers much potential to 3 enhance biodiversity conservation, poverty alleviation, and ultimately sustainable development. 4Understanding the evolution of protected areas as tourism destinations and the causes and 5 consequences of changing supply and demand elements is an essential step toward sustainably 6 managing tourism in these critical ecosystems. This research applied the Tourism Area Life 7Cycle (TALC) model to illustrate and analyze the 30-year evolution of tourism in Wolong Nature 8 Reserve. Being inscribed in UNESCO Biosphere Reserve and World Heritage programmes, 9Wolong is a flagship protected area in China. We showed that the Reserve experienced 10 exploration, involvement, and development stages of the TALC before tourism growth was 11 completely halted by the Wenchuan Earthquake in 2008. We systematically investigated the 12 changes related to the evolution of tourism and identified various internal and external driving 13 forces. We examined the dynamics of politics, economy and tourism growth that might propel the 14Reserve through the life cycle and identified significant tourism governance structural changes 15 through the stages. The results have implications for sustainable tourism development in China's 16 protected areas and also contributes to a broader and general understanding of the complex 17 relationships between protected areas, sustainable tourism and community development. 18

Olfactory dysfunction is an early pre-motor symptom of Parkinson’s disease (PD) but the neural mechanisms underlying this dysfunction remain largely unknown. Aggregation of α-synuclein is observed in the olfactory bulb (OB) during the early stages of PD, indicating a relationship between α-synuclein pathology and hyposmia. Here we investigate whether and how α-synuclein aggregates modulate neural activity in the OB at the single-cell and synaptic levels. We induced α-synuclein aggregation specifically in the OB via overexpression of double-mutant human α-synuclein by an adeno-associated viral (AAV) vector. We found that α-synuclein aggregation in the OB decreased the ability of mice to detect odors and to perceive attractive odors. The spontaneous activity and odor-evoked firing rates of single mitral/tufted cells (M/Ts) were increased by α-synuclein aggregates with the amplitude of odor-evoked high-gamma oscillations increased. Furthermore, the decreased activity in granule cells (GCs) and impaired inhibitory synaptic function were responsible for the observed hyperactivity of M/Ts induced by α-synuclein aggregates. These results provide direct evidences of the role of α-synuclein aggregates on PD-related olfactory dysfunction and reveal the neural circuit mechanisms by which olfaction is modulated by α-synuclein pathology.

Background:Liquid based cytology (LBC) has been reported to increase the sensitivity of cervical cytology, in comparison with conventional cytology Pap smear (CPS). Most LBC systems though require expensive automated devices.Aims:To evaluate the efficiency of a new and inexpensive LBC system - LPT cytology system.Materials and Methods:Cervical screening was performed on 31500 patients utilizing the LPT cytology system test from January 2006 to May 2007. A similar number (n = 31500) of CPS were performed from January 2004 to July 2006. All cytology positive patients underwent colposcopy and cervical biopsy with histopathology examination. Fifty cases positive both on cytology and biopsy were submitted to the high-risk human papillomavirus (HPV) L1 protein (HR-HPV L1) tests.Results:The LPT cytology system adequately preserved cellular structure for morphologic evaluation. There was a significant difference of the histology/cytology diagnosis concordant rate between that of the CPS and LPT systems [93.6 vs. 78.4%, p=0.001]. The significant higher concordant rate was also seen in the low grade intraepithelial lesion (LSIL) (95.4 vs. 78.9%, p=0.001) and in high grade intraepithelial lesion (HSIL) (90.2 vs. 76.1%, p=0.001) cytology diagnosis. There was no statistical difference in rate in atypical glandular cells (AGC) (61.5 vs. 60%) and glandular cell carcinoma (GCC) (83.3 vs. 80%). LPT resulted in a marked increased global detection over the CPS. Nuclear expression of HPV L1 was seen in 34% (17/50) of cases.Conclusions:LPT showed an increase in detection rate compared to CPS (P = 0.001) and a significantly higher histological versus cytological concordant referral rate.

Infection with HPV was prevalent among Chinese women with MPC or CC. Furthermore, the high prevalence of oncogenic genotypes observed among HPV-positive patients with MPC suggests that this group is at increased risk of developing CC.

Electricity is an important indicator for economic development, especially electricity production (EP), which is electricity industry managers making strategic decisions. There are many ways to produce electricity, which is the source of rapid growth in EP is rarely studied. Due to the nonstationary and nonlinearity of the EP time series, traditional methods are less robust to predict it. In this study, a novel combination prediction model is proposed based on wavelet transform (WT), long short-term memory (LSTM), and stacked autoencoder (SAE). Comparisons between the SAE-LSTM and the advanced prediction model. We compared SAE-LSTM and the advanced prediction model including BP (Back Propagation) etc. In addition, the performance comparison of the different wavelet layers based on SAE-LSTM and the performance comparison of the EMD and EEMD based on SAE-LSTM are also compared. At last, future average growth rates (June 2021 ! December 2022) are predicted. The empirical result shows that the combination model in view of SAE-LSTM exceeds the benchmark models. The results also imply that WT-SAE-LSTM outperforms the EMD, EEMD-SAE-LSTM, and SAE. Based on the optimal orders and layers of Coiflets combining with SAE-LSTM, natural gas is the fastest-growing source of EP in the United States.

The safety of an Internet Data Center (IDC) is directly determined by the reliability and stability of its chiller system. Thus, combined with deep learning technology, an innovative hybrid fault diagnosis approach (1D-CNN_GRU) based on the time-series sequences is proposed in this study for the chiller system using 1-Dimensional Convolutional Neural Network (1D-CNN) and Gated Recurrent Unit (GRU). Firstly, 1D-CNN is applied to automatically extract the local abstract features of the sensor sequence data. Secondly, GRU with long and short term memory characteristics is applied to capture the global features, as well as the dynamic information of the sequence. Moreover, batch normalization and dropout are introduced to accelerate network training and address the overfitting issue. The effectiveness and reliability of the proposed hybrid algorithm are assessed on the RP-1043 dataset; based on the experimental results, 1D-CNN_GRU displays the best performance compared with the other state-of-the-art algorithms. Further, the experimental results reveal that 1D-CNN_GRU has a superior identification rate for minor faults.

The difference of factor input structure determines different response to environmental regulation. This paper constructs a theoretical model including environmental regulation, factor input structure, and industrial transformation and conducts a policy simulation based on the difference of influencing mechanism of environmental regulation considering industrial heterogeneity. The findings show that the impact of environmental regulation on industrial transformation presents comparison of distortion effect of resource allocation and technology effect. Environmental regulation will promote industrial transformation when technology effect of environmental regulation is stronger than distortion effect of resource allocation. Particularly, command-control environmental regulation has a significant incentive effect and spillover effect of technological innovation on cleaning industries, but these effects do not exist in pollution-intensive industries. Command-control environmental regulation promotes industrial transformation. The result of simulation showed that environmental regulation of market incentives is similar to that of command-control.

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