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Positioning technology is an important component of environmental perception. It is also the basis for autonomous decision-making and motion control of firefighting robots. However, some issues such as positioning in indoor scenarios still remain inherent challenges. The positioning accuracy of the fire emergency reaction dispatching (FERD) system is far from adequate to support some applications for firefighting and rescue in indoor scenarios with multiple obstacles. To solve this problem, this paper proposes a fusion module based on the Blackboard architecture. This module aims to improve the positioning accuracy of a single sensor of the unmanned vehicles within the FERD system. To reduce the risk of autonomous decision-making of the unmanned vehicles, this module uses a comprehensive manner of multiple channels to complement or correct the positioning of the firefighting robots. Specifically, this module has been developed to fusion a variety of relevant processes for precise positioning. This process mainly includes six strategies. These strategies are the denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision. These strategies merge with the current scenarios-related parameter data, empirical data on sensor errors, and information to form a series of norms. This paper then proceeds to gain experience data with the confidence degree, error of different sensors, and timeliness of this module by training in an indoor scenario with multiple obstacles. This process is from data of multiple sensors (bottom-level) to control decisions knowledge-based (up-level). This process can obtain globally optimal positioning results. Finally, this paper evaluates the performance of this fusion module for the FERD system. The experimental results show that this fusion module can effectively improve positioning accuracy in an indoor scenario with multiple obstacles. Code is available at https://github.com/lvbingyu-zeze/gopath/tree/master. [ABSTRACT FROM AUTHOR]

In the prediction of time series, Empirical Mode Decomposition (EMD) generates subsequences and separates short-term tendencies from long-term ones. However, a single prediction model, including attention mechanism, has varying effects on each subsequence. To accurately capture the regularities of subsequences using an attention mechanism, we propose an integrated model for time series prediction based on signal decomposition and two attention mechanisms. This model combines the results of three networks—LSTM, LSTM-self-attention, and LSTM-temporal attention—all trained using subsequences obtained from EMD. Additionally, since previous research on EMD has been limited to single series analysis, this paper includes multiple series by employing two data pre-processing methods: 'overall normalization' and 'respective normalization'. Experimental results on various datasets demonstrate that compared to models without attention mechanisms, temporal attention improves the prediction accuracy of short- and medium-term decomposed series by 15~28% and 45~72%, respectively; furthermore, it reduces the overall prediction error by 10~17%. The integrated model with temporal attention achieves a reduction in error of approximately 0.3%, primarily when compared to models utilizing only general forms of attention mechanisms. Moreover, after normalizing multiple series separately, the predictive performance is equivalent to that achieved for individual series. [ABSTRACT FROM AUTHOR]

Effectively predicting the spatial distribution of oil and gas contributes to delineating promising target areas for further exploration. Determining the location of hydrocarbon is a complex and uncertain decision problem. This paper proposes a method for predicting the spatial distribution of oil and gas resource based on Bayesian network. In this method, qualitative dependency relationship between the hydrocarbon occurrence and key geologic factors is obtained using Bayesian network structure learning by integrating the available geoscience information and the current exploration results and then using Bayesian network topology structure to predict the probability of hydrocarbon occurrence in the undiscovered area; finally, the probability map of hydrocarbon-bearing is formed by interpolation method. The proposed method and workflow are further illustrated using an example from the Carboniferous Huanglong Formation (C2hl) in the eastern part of the Sichuan Basin in China. The prediction results show that the coincidence rate between the results of 248 known exploration wells and the predicted results reaches 89.5%, and it has been found that the gas fields are basically located in the high value area of the hydrocarbon-bearing probability map. The application results show that the Bayesian network method can effectively predict the spatial distribution of oil and gas resources, thereby reducing exploration risks, optimizing exploration targets, and improving exploration benefits. [ABSTRACT FROM AUTHOR]

This paper presents a new architecture of a fuzzy decision tree based on fuzzy rules – fuzzy rule based decision tree (FRDT) and provides a learning algorithm. In contrast with “traditional” axis-parallel decision trees in which only a single feature (variable) is taken into account at each node, the node of the proposed decision trees involves a fuzzy rule which involves multiple features. Fuzzy rules are employed to produce leaves of high purity. Using multiple features for a node helps us minimize the size of the trees. The growth of the FRDT is realized by expanding an additional node composed of a mixture of data coming from different classes, which is the only non-leaf node of each layer. This gives rise to a new geometric structure endowed with linguistic terms which are quite different from the “traditional” oblique decision trees endowed with hyperplanes as decision functions. A series of numeric studies are reported using data coming from UCI machine learning data sets. The comparison is carried out with regard to “traditional” decision trees such as C4.5, LADtree, BFTree, SimpleCart, and NBTree. The results of statistical tests have shown that the proposed FRDT exhibits the best performance in terms of both accuracy and the size of the produced trees. [ABSTRACT FROM AUTHOR]

As an effective tool for the representation and reasoning of uncertain theories, Bayesian networks are widely used in various fields of artificial intelligence. However, learning the optimal Bayesian network structure is an NP-hard problem. The score-and-search approach is a common method for solving the problems associated with Bayesian network structure learning. This paper presents a novel method for learning the structure of a Bayesian network using a discrete firefly optimization algorithm, which has been successfully applied to solve various optimization problems. In the proposed algorithm, each firefly moves in a discrete space based on a redefined movement strategy. Then, the mutation operator is employed to prevent the algorithm from stopping prematurely and falling into a local optimum. Finally, a local optimizer is used to enhance the exploitation ability of the firefly to obtain the best feasible solution. We compared the proposed algorithm with state-of-the-art algorithms on well-known benchmark networks. The experimental results show that the proposed algorithm has better convergence accuracy and higher scores in most cases, compared to other algorithms, indicating that the proposed algorithm can be used as an effective and feasible method for learning Bayesian network structures. [ABSTRACT FROM AUTHOR]

The surface chemical composition and microstructure of walnut protein obtained through aqueous buffer and bis(2-ethylhexyl) sodium sulfosuccinate ( AOT) reverse micelles were determined by X-ray photoelectron spectroscopy ( XPS) and scanning electron microscopy ( SEM). The surfaces were characterised by XPS to monitor surface composition. The different components of C 1s, N 1s and O 1s peaks were provided precisely. By comparison with walnut protein surface from aqueous buffer, XPS analysis revealed that the O atomic percentage of walnut protein powder surface from AOT reverse micelles was higher, but the C and N atomic percentages were lower. The N/C ratio of the walnut protein powders using two extraction methods was similar, while significant difference was obtained in the O/N. The obtained results indicated that the reverse micelles could affect the C, O and N components on the surface of walnut protein powder. Protein microstructure after reverse micelle treatment showed many crystals. [ABSTRACT FROM AUTHOR]

An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment. [ABSTRACT FROM AUTHOR]

Panax quinquefolius (American ginseng), a perennial plant cultivated extensively across the globe, exhibits diverse pharmacological properties and holds notable economic significance. Water stress can potentially impact the accumulation of ginsenosides, which are the primary pharmacological component of P. quinquefolium. In this study, the morphological, physiological, and biochemical responses of two-year-old seedlings of P. quinquefolius cultivar "LYS1" under different soil relative water contents (RWCs) of 30%, 50%, 70%, and 100% for 21 days were characterized. Additionally, the transcriptional responses to 30%, 70%, and 100% RWC were analyzed at 7 and 21 days. The results showed that both drought (30% and 50% RWC) and waterlogging (100% RWC) stress had a significant detrimental effect on root biomass and root activity, while simultaneously enhancing efficacy of the antioxidant protection system, which suggested that 70% RWC is optimal for the cultivation of P. quinquefolius. Drought stress induced – but waterlogging stress reduced – ginsenoside accumulation in P. quinquefolius root. In particular, drought stress changed the distribution of monomer saponins Rd, Re, Rb1, Rb2, and Rb3, while waterlogging stress altered the distribution of Rd, Re, and Rc. Drought and waterlogging specifically regulated genes involved in several pathways underlying stress tolerance, including phenylpropanoid biosynthesis, signal pathways and ginsenoside biosynthesis in roots. The integration of physiological, biochemical, and transcriptomic evidence resulted in a selection of candidate genes with potential for enhancing stress tolerance in P. quinquefolius. Meanwhile, some candidate genes were found to play potential roles in ginsenoside biosynthesis, including several CYP450s and UDP-dependent glycosyltransferase genes as well as AP2/ERF, bHLH, NAC, MYB, and WRKY transcription factor members. These findings may provide theoretical references for effectively implementing cultivation strategies by regulating soil water conditions and give valuable insights for further investigation into ginsenoside biosynthesis in P. quinquefolius. [Display omitted] • Experimental tests address the response of P. quinquefolius to soil water stress. • Drought and waterlogging stress influence ginsenoside accumulation. • Candidate genes that have the potential to enhance stress tolerance were identified. • Provides a new strategy to effectively implement cultivation for P. quinquefolius. [ABSTRACT FROM AUTHOR]

This paper proposes a novel classification technology—fuzzy rule-based oblique decision tree (FRODT). The neighborhood rough sets-based FAST feature selection (NRS_FS_FAST) is first introduced to reduce attributes. In the axiomatic fuzzy set theory framework, the fuzzy rule extraction algorithm is then proposed to dynamically extract fuzzy rules. And these rules are regarded as the decision function during the tree construction. The FRODT is developed by expanding the unique non-leaf node in each layer of the tree, which results in a new tree structure with linguistic interpretation. Moreover, the genetic algorithm is implemented on σ to obtain the balanced results between classification accuracy and tree size. A series of comparative experiments are carried out with five classical classification algorithms (C4.5, BFT, LAD, SC and NBT), and recently proposed decision tree HHCART on 20 UCI data sets. Experiment results show that the FRODT exhibits better classification performance on accuracy and tree size than those of the rival algorithms. [ABSTRACT FROM AUTHOR]

[Display omitted] • GPM is presented to measure the similarity of tiny objects. • GPM outperforms other IoU-based metrics. • The ADAS is proposed to solve the inter-sample and intra-sample imbalance. • The sufficient supervision information for network has been guaranteed by ADAS-GPM. • ADAS-GPM achieves state-of-the-art performance on AI-TODv2. Benefiting from the advanced deep learning techniques, significant achievements have been made in generic object detection. Tiny object detection (TOD) is a challenging task in computer vision due to the low resolution, insufficient geometric cues, and high noise levels. A recent trend for detectors is introducing more granular label assignment strategies to provide promising supervision information for classification and regression. However, most previous Intersection-Over-Union (IoU) based methods suffer from two main drawbacks, including (1) low tolerance of IoU for bounding box deviations in tiny objects and (2) deficient guidance for optimization caused by inter-sample and intra-sample imbalance. We propose two novel components to address these problems: the Gaussian probabilistic distribution-based fuzzy similarity metric (GPM) and the adaptive dynamic anchor mining strategy (ADAS). GPM aims to address the issue of inaccurate similarity measurement between small bounding boxes and pre-defined anchors, providing a more accurate basis for label assignment. ADAS adopts a dynamically adjusted strategy for label assignment to address the distribution bias between positive and negative samples, ensuring that the label assignment is consistent with the distribution of objects in the image. Extensive experiments are conducted on AI-TODv2 and other tiny object detection datasets to evaluate the proposed ADAS-GPM method's performance. The results demonstrate that incorporating ADAS-GPM into an anchor-based object detector yields significant outperformance over state-of-the-art methods on the challenging AI-TODv2 benchmark. The proposed ADAS-GPM method exhibits good results, clearly demonstrating its validity and potential. [ABSTRACT FROM AUTHOR]

Abstract: Corn stalk superfine powder was ground by a special designed machine. The physical–chemical properties of corn stalk powders with particle sizes of >300, 300-150, 150-74, 74-37 and <15μm were investigated. The particle size distributions of the powders were: d 90 =362, 147, 74, 40 and 12μm. The size of corn stalk powders was smaller, the surface area (from 1.188 to 2.432m2/g) and bulk density (from 0.103 to 0.1145g/ml) were greater. Light microscopy (LM) and scanning electron microscopy (SEM) observations revealed the shape and surface morphology of five types of corn stalk powders. FTIR analysis showed that some position of absorbing peaks was shifted as the powder particle size decreased. X-ray diffraction analyses for corn stalk coarse and superfine powders revealed no evident changes in X-ray pattern. However, the crystallinity, intensity of crystal peaks and crystal size of corn stalk powders with particle sizes from >300 to 300-150μm dropped, then, as the size of the powders decreased, the crystallinity, intensity of crystal peaks and crystal size increased in some degree. [Copyright &y& Elsevier]

Abstract: Fourier transform infrared (FTIR) method was used to study the conformations of soy protein obtained through aqueous solution and bis(2-ethylhexyl) sodium sulfosuccinate (AOT) reverse micelle extraction. The results showed that there were changes in signal intensity and/or position of IR bands at 4000–400 cm−1 when the soy proteins were separated by two extraction methods. The FTIR spectral changes were subsequently assessed using the second derivative spectroscopy in the amide I region (1700–1600 cm−1). The contents of α-helix, β-sheet, turn and irregular conformations for soy proteins using aqueous solution extraction were 11.6%, 32.8%, 44.3% and 11.3%, respectively; while using AOT reverse micelle extraction 13.1%, 41.6%, 32.5% and 12.9%, respectively. The amount change of these structures might affect functional properties of soy proteins. [Copyright &y& Elsevier]