ALBERT

Description: Aiming to solve the problem of environmental information being difficult to characterize when an intelligent service is used, knowledge graphs are used to express environmental information when performing intelligent services. Here, we specially design a kind of knowledge graph for environment expression referred to as a robot knowledge graph (R-KG). The main work of a R-KG is to integrate the diverse semantic information in the environment and pay attention to the relationship at the instance level. Also, through the efficient knowledge organization of a R-KG, robots can fully understand the environment. The R-KG firstly integrates knowledge from different sources to form a unified and standardized representation of a knowledge graph. Then, the deep logical relationship hidden in the knowledge graph is explored. To this end, a knowledge reasoning model based on a Markov logic network is proposed to realize the self-developmental ability of the knowledge graph and to further enrich it. Finally, as the strength of environment expression directly affects the efficiency of robots performing services, in order to verify the efficiency of the R-KG, it is used here as the semantic map that can be directly used by a robot for performing intelligent services. The final results prove that the R-KG can effectively express environmental information.

Description: Cyclophilin (Cyp) and Ca2+/calcineurin proteins are cellular components related to fungal morphogenesis and virulence; however, their roles in mediating the pathogenesis of Botrytis cinerea, the causative agent of gray mold on over 1000 plant species, remain largely unexplored. Here, we show that disruption of cyclophilin gene BcCYP2 did not impair the pathogen mycelial growth, osmotic and oxidative stress adaptation as well as cell wall integrity, but delayed conidial germination and germling development, altered conidial and sclerotial morphology, reduced infection cushion (IC) formation, sclerotial production and virulence. Exogenous cyclic adenosine monophosphate (cAMP) rescued the deficiency of IC formation of the ∆Bccyp2 mutants, and exogenous cyclosporine A (CsA), an inhibitor targeting cyclophilins, altered hyphal morphology and prevented host-cell penetration in the BcCYP2 harboring strains. Moreover, calcineurin-dependent (CND) genes are differentially expressed in strains losing BcCYP2 in the presence of CsA, suggesting that BcCyp2 functions in the upstream of cAMP- and Ca2+/calcineurin-dependent signaling pathways. Interestingly, during IC formation, expression of BcCYP2 is downregulated in a mutant losing BcJAR1, a gene encoding histone 3 lysine 4 (H3K4) demethylase that regulates fungal development and pathogenesis, in B. cinerea, implying that BcCyp2 functions under the control of BcJar1. Collectively, our findings provide new insights into cyclophilins mediating the pathogenesis of B. cinerea and potential targets for drug intervention for fungal diseases.

Description: In radar target detection, constant false alarm rate (CFAR), which stands for the adaptive threshold adjustment with variation of clutter to maintain the constant probability of false alarm during the detection, plays an important role. Matrix CFAR detection performed on the manifold of Hermitian positive-definite (HPD) covariance matrices is an efficient detection method that is based on information geometry. However, the HPD covariance matrix, which is constructed by a small bunch of pulses, describes the correlations among received data and suffers from severe information redundancy that limits the improvement of detection performance. This paper proposes a Principal Component Analysis (PCA) based matrix CFAR detection method for dealing with the point target detection problems in clutter. The proposed method can not only reduce dimensionality of HPD covariance matrix, but also reduce the redundant information and enhance the distinguishability between target and clutter. We first apply PCA to the cell under test, and construct a transformation matrix to map higher-dimensional matrix space to a lower-dimensional matrix space. Subsequently, the corresponding detection statistics and detection decision on matrix manifold are derived. Meanwhile, the corresponding signal-to-clutter ratio (SCR) is improved. Finally, the simulation experiment and real sea clutter data experiment show that the proposed method can achieve a better detection performance.

Description: Predicting pillar stability is a vital task in hard rock mines as pillar instability can cause large-scale collapse hazards. However, it is challenging because the pillar stability is affected by many factors. With the accumulation of pillar stability cases, machine learning (ML) has shown great potential to predict pillar stability. This study aims to predict hard rock pillar stability using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms. First, 236 cases with five indicators were collected from seven hard rock mines. Afterwards, the hyperparameters of each model were tuned using a five-fold cross validation (CV) approach. Based on the optimal hyperparameters configuration, prediction models were constructed using training set (70% of the data). Finally, the test set (30% of the data) was adopted to evaluate the performance of each model. The precision, recall, and F1 indexes were utilized to analyze prediction results of each level, and the accuracy and their macro average values were used to assess the overall prediction performance. Based on the sensitivity analysis of indicators, the relative importance of each indicator was obtained. In addition, the safety factor approach and other ML algorithms were adopted as comparisons. The results showed that GBDT, XGBoost, and LightGBM algorithms achieved a better comprehensive performance, and their prediction accuracies were 0.8310, 0.8310, and 0.8169, respectively. The average pillar stress and ratio of pillar width to pillar height had the most important influences on prediction results. The proposed methodology can provide a reliable reference for pillar design and stability risk management.

Description: Wireless sensor and robot networks (WSRNs) often work in complex and dangerous environments that are subject to many constraints. For obtaining a better monitoring performance, it is necessary to deploy different types of sensors for various complex environments and constraints. The traditional event-driven deployment algorithm is only applicable to a single type of monitoring scenario, so cannot effectively adapt to different types of monitoring scenarios at the same time. In this paper, a multi-constrained event-driven deployment model is proposed based on the maximum entropy function, which transforms the complex event-driven deployment problem into two continuously differentiable single-objective sub-problems. Then, a collaborative neural network (CONN) event-driven deployment algorithm is proposed based on neural network methods. The CONN event-driven deployment algorithm effectively solves the problem that it is difficult to obtain a large amount of sensor data and environmental information in a complex and dangerous monitoring environment. Unlike traditional deployment methods, the CONN algorithm can adaptively provide an optimal deployment solution for a variety of complex monitoring environments. This greatly reduces the time and cost involved in adapting to different monitoring environments. Finally, a large number of experiments verify the performance of the CONN algorithm, which can be adapted to a variety of complex application scenarios.

Description: Most geotextiles consist of polymers of polyolefin, polyester or polyamide family, which involve environmental problems related to soil pollution. Geotextiles can be used for at least one of the following functions: Separation, reinforcement, filtration, drainage, stabilization, barrier, and erosion protection. Due to the characteristics of high strength, low cost, and easy to use, geotextiles are widely used in geotechnical engineering such as soft foundation reinforcement, slope protection, and drainage system. This paper reviews composition and function of geotextiles in geotechnical engineering. In addition, based on literatures including the most recent data, the discussion turns to recent development of geotextiles, with emphasis on green geotextiles, intelligent geotextiles, and high-performance geotextiles. The present situation of these new geotextiles and their application in geotechnical engineering are reviewed.

Description: Hiking is a popular recreational activity and to cater to public demand, it is apt to increase the number of hiking trails. Various methodologies have been proposed to evaluate the suitability of forest trails to be constructed as hiking trails, but they can be costly and require relevant knowledge in analyzing digital information through a high-throughput dataset. Therefore, there is a need to come up with a simple method to obtain first-hand information on the trail condition, particularly considering the aspects of safety and suitability to hikers, using both on-ground and aerial observations. In this study, we introduce a new assessment approach to analyze and select old forest trails to be reconstructed as new hiking trails. This is useful for park managers who prioritize safety, comfort, and aesthetic features of the recreation site for their visitors. Trail condition assessment was carried out along the trail whereby a 2×2 m sampling plot was constructed at every 100 m. Aerial drone survey was conducted to produce an ortho-mosaic that revealed the percentage of exposed trail from above. Potential phytotourism products and scenic spots were identified and recorded for their locations along the trail to promote the aesthetic value of the recreation site. A strength distribution plot was prepared based on the trail condition, canopy coverage, and aesthetic features along the trail that were categorized using three altitude ranges (n ≤ 150 m, 150 〈 n 〈 250 m, n ≥ 250 m a.s.l.). This is to assess the trade-offs in safety, comfort, and aesthetic features along the trail. The development of this methodology offers a direct and cost-effective, yet informative approach to evaluate the quality of a potential hiking trail, thus could effectively aid in the promotion of nature-based tourism.

Description: Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data.

Description: Pattern matching is widely used in various fields such as information retrieval, natural language processing (NLP), data mining and network security. In Uyghur (a typical agglutinative, low-resource language with complex morphology, spoken by the ethnic Uyghur group in Xinjiang, China), research on pattern matching is also ongoing. Due to the language characteristics, the pattern matching using characters and words as basic units has insufficient performance. There are two problems for pattern matching: (1) vowel weakening and (2) morphological changes caused by suffixes. In view of the above problems, this paper proposes a Boyer–Moore-U (BM-U) algorithm and a retrievable syllable coding format based on the syllable features of the Uyghur language and the improvement of the Boyer–Moore (BM) algorithm. This algorithm uses syllable features to perform pattern matching, which effectively solves the problem of weakening vowels, and it can better match words with stem shape changes. Finally, in the pattern matching experiments based on character-encoded text and syllable-encoded text for vowel-weakened words, the BM-U algorithm precision, recall, F1-measure and accuracy are improved by 4%, 55%, 33%, 25% and 10%, 52%, 38%, 38% compared to the BM algorithm.

Description: During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina Weibo data from February 3rd to 12th, 2020, this paper analyzes the spatiotemporal distribution of COVID-19 cases in the main urban area of Wuhan and explores the urban spatial features of COVID-19 transmission in Wuhan. The results show that the elderly population accounts for more than half of the total number of Weibo help seekers, and a close correlation between them has also been found in terms of spatial distribution features, which confirms that the elderly population is the group of high-risk and high-prevalence in the COVID-19 outbreak, needing more attention of public health and epidemic prevention policies. On the other hand, the early transmission of COVID-19 in Wuhan could be divide into three phrases: Scattered infection, community spread, and full-scale outbreak. This paper can help to understand the spatial transmission of COVID-19 in Wuhan, so as to propose an effective public health preventive strategy for urban space optimization.