Research on bus elastic departure interval based on Wavelet Neural Network

In recent years, more and more people choose to travel by bus to save time and economic costs, but the problem of inaccurate bus arrival has become increasingly prominent. The reason is the lack of scientific planning of departure time. This paper takes the passenger flow as an important basis for departure interval, proposes a passenger flow prediction method based on wavelet neural network, and uses intelligent optimization algorithm to study the bus elastic departure interval. In this paper, the wavelet neural network prediction model and the elastic departure interval optimization model are established, and then the model is solved by substituting the data, and finally the theoretical optimal departure interval is obtained

New horizons of regulatory RNA

Genetic information flows from DNA to protein through RNA in the central dogma. Different RNA species are known to accomplish essential tasks of protein encoding (mRNAs), amino acid loading (tRNAs), and translation machinery assembly (rRNAs). However, on top of these well-known roles, RNAs are central to various cellular regulatory pathways. Here we summarize newly emerging regulatory functions of RNA, specifically focusing on regulations through RNA modifications, RNP granules, and chromatin-associated regulatory RNA. In addition to being an essential building block of the central dogma, RNA can be critical to the regulation of many cellular processes

Adaptive Distribution and Vulnerability Assessment of Endangered Maple Species on the Tibetan Plateau

Climate change has had an almost irreversible impact on the distribution patterns of tree species on the Tibetan Plateau, driving some vulnerable species to the brink of extinction. Therefore, it is important to assess the vulnerability of tree species in climate-sensitive areas under the following three IPCC-CMIP6 scenarios: SSP126, SSP370, and SSP585. The MaxEnt model was used to predict adaptive distribution for one endangered (Acer wardii W. W. Smith (A. wardii)) and six vulnerable maple plants on the Tibetan Plateau under current and future conditions. We then evaluated their vulnerability using the landscape fragmentation index. Our results showed that the current adaptive areas of vulnerable maple species were mainly distributed in the southeast of the Tibetan Plateau. The dominant factors affecting adaptive areas were temperature annual range (BIO7) for Acer sikkimense Miq. and Acer sterculiaceum Wall.; annual precipitation (BIO12) for Acer cappadocicum Gled.; precipitation of driest month (BIO14) for Acer pectinatum Wall. ex G. Nicholson, Acer taronense Hand.-Mazz., and A. wardii; and subsoil clay fraction (S_CLAY) for Acer campbellii Hook.f. &amp; Thoms. ex Hiern (A. campbellii) Under the three future scenarios, the adaptive areas of maple on the Tibetan Plateau area shifted to the northwest, and habitat suitability increased in the northwestern part of the adaptive areas. In the SSP370 scenario, all seven species showed an increase in adaptive areas, while certain species decreased in some periods under the SSP126 and SSP585 scenarios. The status of the endangered maple species is likely to be even more fragile under the three future scenarios. A. wardii and A. campbellii are more vulnerable and may face extinction, requiring immediate attention and protection. In contrast, the vulnerability of the remaining five species decreased. In conclusion, this study provides recommendations for conserving vulnerable maple species on the Tibetan Plateau. Our data support understanding the distributional changes and vulnerability assessment of these tree species.</p

Evaluating Spatiotemporal Patterns and Integrated Driving Forces of Habitat Quality in the Northern Sand-Prevention Belt of China

Understanding habitat quality patterns and their drivers in arid zones is of fundamental importance to the sustainability maintenance of terrestrial ecosystems, but remains elusive. Here, we applied the InVEST model to investigate the spatiotemporal patterns of habitat quality in the northern sand-prevention belt (NSPB) across five time periods (2000, 2005, 2010, 2015, 2018), coupled with the structural equation model (SEM) and boosted regression tree (BRT) model to identify their integrated driving forces. The results exhibited that habitat quality in high-level zones expanded gradually from 2000 to 2018, while the middle- and low-level zones shrank. Climate, soil, topography, and human activities were significantly correlated with habitat quality, with mean annual temperature (MAT) and human activities being key contributing factors in the high-level and low-level zones, respectively, whereas the contribution of factors varied considerably in the middle-level zones. The interactions among climate, soil, topography, and human activities jointly drive habitat quality changes. Climate intensified the positive effects of soil on habitat quality, while the topographic and human activities mainly affected habitat quality indirectly through climate and soil. Our findings offer a scientific guidance for the restoration and sustainable management of desertification ecosystems in northern China.</p

A Hybrid Method for Short-term Freeway Travel Time Prediction Based on Wavelet Neural Network and Markov Chain

Short-term travel time prediction is an essential input to Intelligent Transportation Systems (ITS). Timely and accurate traffic forecasting is necessary for Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS). Despite several short-term travel time prediction approaches have been proposed in the past decade, especially for hybrid models which consist of machine learning models and statistical models, few studies focus on the over-fitting problem brought by hybrid models. The over-fitting problem deteriorates the prediction accuracy especially during peak hours. This paper proposes a hybrid model which embraces Wavelet Neural Network, Markov Chain and the volatility model (WNN-MAR-VOA) for short-term travel time prediction in a freeway system. The purpose of this paper is to provide deeper insights into underlining dynamic traffic patterns and to improve the prediction accuracy and robustness. This method takes periodical analysis, error correction and noise extraction into consideration and improve the forecasting performance in peak hours. The proposed methodology predicts travel time by decomposing travel time data into three components: a periodic trend presented by a modified Wavelet Neural Network (WNN), a residual part modeled by Markov Chain, and the volatility part estimated by the modified generalized autoregressive conditional heteroscedasticity (GJR-GARCH) model. Forecasting performance is investigated with freeway travel time data from Houston, Texas and examined by three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results show that the travel times predicted by the WNN-MAR-VOA method are robust and accurate. Meanwhile, the proposed method is able to capture the underlying periodic characteristics and volatility nature of travel time dataThe accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Prediction of Potential Suitable Areas and Priority Protection for Cupressus gigantea on the Tibetan Plateau

Cupressus gigantea (C. gigantea) is an endemic endangered species on the Tibetan Plateau; its potential suitable areas and priority protection in the context of global climate change remain poorly predicted. This study utilized Biomod2 and Marxan to assess the potential suitable areas and priority protection for C. gigantea. Our study revealed that the suitable areas of C. gigantea were concentrated in the southeastern Tibetan Plateau, with the center in Lang County. Temperature was identified as a crucial environmental factor influencing the distribution of C. gigantea. Over the coming decades, the suitable range of C. gigantea expanded modestly, while its overall distribution remained relatively stable. Moreover, the center of the highly suitable areas tended to migrate towards Milin County in the northeast. Presently, significant areas for improvement are needed to establish protected areas for C. gigantea. The most feasible priority protected areas were located between the Lang and Milin counties in Tibet, which have more concentrated and undisturbed habitats. These results provide scientific guidance for the conservation and planning of C. gigantea, contributing to the stability and sustainability of ecosystems.</p

Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning

Geographical origin, an important indicator of the chemical composition and quality grading, is one essential factor that should be taken into account in evaluating coal quality. However, traditional coal origin identification methods based on chemistry experiments are not only time consuming and labour intensive, but also costly. Near-Infrared (NIR) spectroscopy is an effective and efficient way to measure the chemical compositions of samples and has demonstrated excellent performance in various fields of quantitative and qualitative research. In this study, we employ NIR spectroscopy to identify coal origin. Considering the fact that the NIR spectra of coal samples always contain a large amount of redundant information and the number of samples is small, the broad learning algorithm is utilized here as the modelling system to classify the coal geographical origin. In addition, the particle swarm optimization algorithm is introduced to improve the structure of the Broad Learning (BL) model. We compare the improved model with the other five multivariate classification methods on a dataset with 243 coal samples collected from five countries. The experimental results indicate that the improved BL model can achieve the highest overall accuracy of 97.05%. The results obtained in this study suggest that the NIR technique combined with machine learning methods has significant potential for further development of coal geographical origin identification systems.Applied Science, Faculty ofNon UBCElectrical and Computer Engineering, Department ofReviewedFacult

YTHDF2 promotes mitotic entry and is regulated by cell cycle mediators.

The N6-methyladenosine (m6A) modification regulates mRNA stability and translation. Here, we show that transcriptomic m6A modification can be dynamic and the m6A reader protein YTH N6-methyladenosine RNA binding protein 2 (YTHDF2) promotes mRNA decay during cell cycle. Depletion of YTHDF2 in HeLa cells leads to the delay of mitotic entry due to overaccumulation of negative regulators of cell cycle such as Wee1-like protein kinase (WEE1). We demonstrate that WEE1 transcripts contain m6A modification, which promotes their decay through YTHDF2. Moreover, we found that YTHDF2 protein stability is dependent on cyclin-dependent kinase 1 (CDK1) activity. Thus, CDK1, YTHDF2, and WEE1 form a feedforward regulatory loop to promote mitotic entry. We further identified Cullin 1 (CUL1), Cullin 4A (CUL4A), damaged DNA-binding protein 1 (DDB1), and S-phase kinase-associated protein 2 (SKP2) as components of E3 ubiquitin ligase complexes that mediate YTHDF2 proteolysis. Our study provides insights into how cell cycle mediators modulate transcriptomic m6A modification, which in turn regulates the cell cycle

Research Progress of Aluminum Phosphate Adjuvants and Their Action Mechanisms

Although hundreds of different adjuvants have been tried, aluminum-containing adjuvants are by far the most widely used currently. It is worth mentioning that although aluminum-containing adjuvants have been commonly applied in vaccine production, their acting mechanism remains not completely clear. Thus far, researchers have proposed the following mechanisms: (1) depot effect, (2) phagocytosis, (3) activation of pro-inflammatory signaling pathway NLRP3, (4) host cell DNA release, and other mechanisms of action. Having an overview on recent studies to increase our comprehension on the mechanisms by which aluminum-containing adjuvants adsorb antigens and the effects of adsorption on antigen stability and immune response has become a mainstream research trend. Aluminum-containing adjuvants can enhance immune response through a variety of molecular pathways, but there are still significant challenges in designing effective immune-stimulating vaccine delivery systems with aluminum-containing adjuvants. At present, studies on the acting mechanism of aluminum-containing adjuvants mainly focus on aluminum hydroxide adjuvants. This review will take aluminum phosphate as a representative to discuss the immune stimulation mechanism of aluminum phosphate adjuvants and the differences between aluminum phosphate adjuvants and aluminum hydroxide adjuvants, as well as the research progress on the improvement of aluminum phosphate adjuvants (including the improvement of the adjuvant formula, nano-aluminum phosphate adjuvants and a first-grade composite adjuvant containing aluminum phosphate). Based on such related knowledge, determining optimal formulation to develop effective and safe aluminium-containing adjuvants for different vaccines will become more substantiated