Live fuel moisture content (LFMC) is a key variable for affecting the occurrence of wildfires and a critical basis for constructing fire risk prediction systems. Meteorological indicators, commonly used in LFMC estimation, are often derived from reanalysis datasets of meteorological data. However, since air temperature is highly correlated with land surface temperature (LST), utilizing LST extracted from thermal infrared remote sensing data holds the potential to substitute air temperature in estimating LFMC and enhance accuracy. In this study, we estimated LFMC by involving both daytime and nighttime LST as well as air temperature from meteorological products, considering the lag effect of climate on LFMC. Our results indicate that model using nighttime LST yields the most accurate inversion results, followed by that utilizing daytime LST. Model using air temperature exhibits the lowest accuracy. However, employing all three variables concurrently can achieve higher precision in LFMC estimation.

Imputing the incomplete patterns in clustering tasks is a common but risky procedure, because the estimated values may affect the real distribution of the data and deteriorate the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) with uncertainty and imprecision reasoning is proposed in this paper. First, the complete patterns are grouped into a few clusters to obtain the corresponding reliable centers, and thereby are divided into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classify unreliable patterns and incomplete patterns edited by the neighbors. Finally, some imprecise patterns are carefully reassigned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. The simulation results show that the BPC has the potential to deal with real datasets.

Wildfire spread is affected by various factors including weather, fuel, topography, and human intervention. Previous studies have focused on wildfire probability modeling for purposes of wildfire management, with less attention paid to potential wildfire behavior characteristics such as wildfire speed and intensity. Remote sensing technology has excellent advantages in deriving the characteristics and fuel variables. This study aimed to model these characteristics for wildfire hazard assessment in the Yunnan Province of China. The random forest (RF) model and the extreme gradient boosting (XGBoost) model, were selected to establish the potential wildfire behavior characteristics (PWBC) models based on explanatory variables. The results verified that elevation, fuel moisture content, and infrastructure variables played a more significant role in the models. The RF-based models performed better than the XGBoost-based ones with higher overall accuracy (≥0.83) and kappa coefficient (≥0.79), indicating the effectiveness of predicting potential wildfire behavior characteristics to assess wildfire hazards.

Accurate estimation of potential fire intensity (PFI) can improve wildfire management. The PFI can be simulated by fire spread models, but with immeasurable uncertainties. There are also some difficulties in estimating PFI with multi-source drivers, since the fire spread is limited by fire suppression. This study aimed to estimate the probability density of PFI over southwestern China, using time-series fuel and weather data as well as topographic data. The Quantile Regression and Bi-directional Long Short-Term Memory were selected to establish the prediction model of PFI. The results showed that the QR-BiLSTM performed best at the 90% confidence level. The modal PFI values extracted from the estimated probability density were closer to the observed values. This study suggests the potential of probability density estimation of PFI with artificial intelligence, for which improves wildfire risk assessment.

Dead fuel moisture content (DFMC) was usually involved and being an important part in predicting ignition potential, fireline intensity, flame length, and rate of spread. Previous studies focused on model development and paid little attention to large-scale DFMC forecasting using these models, especially process-based models. In this study, we forecast spatial 1-h (fuel with a diameter less than 0.635 cm) and 10-h (fuel with a diameter between 0.635 cm and 2.54 cm) DFMC in 16 days with meteorological variables interpolated from Global Forecast System (GFS). First, we interpolated meteorological variables including air temperature (Tair), relative humidity (RH), wind speed (Ws) and precipitation (P) to 2 km from GFS data (0.25°) for each site with DFMC measurement in Liangshan Yi Autonomous Prefecture. Then, we forecasted DFMC hourly for 96 sites with three process-based models (Simard, Nelson and fuel stick moisture model (FSMM)). Our results show that the FSMM forecasted more accurate DFMC values (1-h: R2=0.54, RMSE=6.58%, MAE=5.63%; 10-h: R2=0.73, RMSE=3.7%, MAE=2.7%) compared to the Nelson and Simard model. Our results suggest that accurate DFMC forecasts from GFS data based on our methods can be used for fire risk assessment and fire behavior prediction.

Understanding the extent to which climate affects interannual wildfire variability is key to project and mitigate wildfire. However, obtaining a robust quantification of the climate-wildfire relationship at large spatial scales remains challenging. This study employed hierarchical Bayesian framework to estimate the effect of drought on forest wildfire frequency in subtropical China. We quantified the drought-wildfire relationship across the subtropical China that the probability of excess wildfire (i.e., above normal wildfire activity level) shown disproportionate growth from 2.4% to 76.7% when vapor pressure deficit (VPD) increased from -3 to 3 (z-score). The extreme wildfires only occurred when VPD exceeds 0.5 (z-score). Our results suggest that Bayesian hierarchical model performs better in quantifying the impact of drought on wildfire by taking into account spatiotemporal heterogeneity of climate-wildfire relationship.

The clustering of incomplete patterns is a very challenging task because the estimations may negatively affect the distribution of real centers and thus cause uncertainty and imprecision in the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) is proposed in this paper. First, the complete patterns are grouped into a few clusters by a classical soft method like fuzzy$c$-means to obtain the corresponding reliable centers and thereby are partitioned into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classifies unreliable patterns and the incomplete patterns edited by the neighbors. In this way, most of the edited incomplete patterns can be submitted to specific clusters. Finally, some ambiguous patterns will be carefully repartitioned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. By doing this, a few patterns that are very difficult to classify between different specific clusters will be reasonably submitted to meta-cluster which can characterize the uncertainty and imprecision of the clusters due to missing values. The simulation results show that the BPC has the potential to deal with real datasets.

To have students comprehend and master the course `Fundamentals of Analogue Circuits' better, the curriculum development on strategies of active learning is reported in this paper. As one strategy of active learning, previously mainly used in primary and secondary education, thinking maps, are employed in teaching Chinese college electronic engineering students by authors. Different types and application for teaching of thinking maps are discussed, designed and implemented by authors. By introducing the Classroom Observation Protocol for Undergraduate STEM (COPUS), the assessment tool to analyze the status of students in class and evaluate the teaching effects objectively is utilized by the authors. After four years' continuous modification, results to compare student performance data collected each year taught by same instructor are provided by the authors. The result that student examination and overall scores improved, whereas student score distribution did not show significant improvement are found by the authors. The study suggests that higher ratios and weights of thinking maps, better timing in introducing thinking maps, and more integrated course components in thinking map may further improve student performance.

Nucleosomes, the basic units of chromatin, are involved in transcription regulation and DNA replication. Intronless genes, which constitute 3 percent of the human genome, differ from intron-containing genes in evolution and function. Our analysis reveals that nucleosome positioning shows a distinct pattern in intronless and intron-containing genes. The nucleosome occupancy upstream of transcription start sites of intronless genes is lower than that of intron-containing genes. In contrast, high occupancy and well positioned nucleosomes are observed along the gene body of intronless genes, which is perfectly consistent with the barrier nucleosome model. Intronless genes have a significantly lower expression level than intron-containing genes and most of them are not expressed in CD4+ T cell lines and GM12878 cell lines, which results from their tissue specificity. However, the highly expressed genes are at the same expression level between the two types of genes. The highly expressed intronless genes require a higher density of RNA Pol II in an elongating state to compensate for the lack of introns. Additionally, 5' and 3' nucleosome depleted regions of highly expressed intronless genes are deeper than those of highly expressed intron-containing genes.

Facing an unknown situation, a person may not be able to firmly elicit his/her preferences over different alternatives, so he/she tends to express uncertain preferences. Given a community of different persons expressing their preferences over certain alternatives under uncertainty, to get a collective representative opinion of the whole community, a preference fusion process is required. The aim of this work is to propose a preference fusion method that copes with uncertainty and escape from the Condorcet paradox. To model preferences under uncertainty, we propose to develop a model of preferences based on belief function theory that accurately describes and captures the uncertainty associated with individual or collective preferences. This work improves and extends the previous results. This work improves and extends the contribution presented in a previous work. The benefits of our contribution are twofold. On the one hand, we propose a qualitative and expressive preference modeling strategy based on belief-function theory which scales better with the number of sources. On the other hand, we propose an incremental distance-based algorithm (using Jousselme distance) for the construction of the collective preference order to avoid the Condorcet Paradox.